External financing, channel power structure and product green R&D decisions in supply chains

Fan, Jianchang; Li, Zhun; Ye, Fei; Li, Yuhui; Wan, Nana Article External financing, channel power structure and product green R&D decisions in supply chains Modern Supply Chain Research and Applications Provided in Cooperation with: Emerald Publishing Limited Suggested Citation: Fan, Jianchang; Li, Zhun; Ye, Fei; Li, Yuhui; Wan, Nana (2023) : External financing, channel power structure and product green R&D decisions in supply chains, Modern Supply Chain Research and Applications, ISSN 2631-3871, Emerald, Bingley, Vol. 5, Iss. 3, pp. 176-208, https://doi.org/10.1108/MSCRA-04-2023-0013 This Version is available at: https://hdl.handle.net/10419/314912 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ External financing, channel power structure and product green R&D decisions in supply chains Jianchang Fan Nanjing University of Finance and Economics –Xianlin Campus, Nanjing, China Zhun Li Zhejiang Wanli University, Ningbo, China Fei Ye Chengdu University of Information Technology, Chengdu, China Yuhui Li Guilin University of Electronic Technology, Guilin, China, and Nana Wan Southwest University of Science and Technology, Mianyang, China Abstract Purpose –This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability. Design/methodology/approach –A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed. Findings –Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm’s profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm’s profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above. Practical implications –This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability. MSCRA 5,3 176 © Jianchang Fan, Zhun Li, Fei Ye, Yuhui Li and Nana Wan. Published in Modern Supply Chain Research and Applications. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// creativecommons.org/licences/by/4.0/legalcode The authors wish to express their appreciation to the editors and the anonymous reviewers for their constructive comments and suggestions that significantly improve the quality and presentation of the paper. This research is supported by the National Natural Sciences Foundation of China [grant numbers 72002094, 71802168], the Humanities and Social Science Foundation of Ministry of Education of China [grant numbers 20YJC630020, 22XJA630003], the Humanities and Social Sciences Project of the Training Plan for Thousand Young Backbone Teachers in Guangxi Universities [grant number 2021QGRW039], Special Project of Modern Think Tanks Innovative Research Team of Ningbo Maritime Silk Road Institute [grant number HSY2022ZK04], Yinzhou Social Science Project [grant number Y23YJ-26] and Talent programs of Chengdu University of Information Technology [grant number KYTZ202240]. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2631-3871.htm Received 1 April 2023 Revised 6 July 2023 Accepted 30 August 2023 Modern Supply Chain Research and Applications Vol. 5 No. 3, 2023 pp. 176-208 Emerald Publishing Limited 2631-3871 DOI 10.1108/MSCRA-04-2023-0013 Originality/value –The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data. Keywords Capital constraint, External financing, Channel power structure, Green supply chain Paper type Research paper 1. Introduction Product green R&D is a critical strategic issue that has sparked growing concern among supply chain firms. In general, supply chain firms can participate in product green R&D in a variety of ways, including raising the environmentally friendless level of the product (Ghosh and Shah, 2012), improving resources utilization efficiency (Song and Gao, 2018;Das et al., 2022), optimizing manufacturing processes (Ge et al., 2014;Sana, 2022a) and lowing carbon emissions (Chen and Akmalul’Ulya, 2019), among others. For example, Adidas developed environmentally friendly technologies such as MMVEA and Eco-Grip to lessen the environmental impact of their manufacturing items (Ghosh and Shah, 2012). Other wellknown worldwide firms, such as Canon, Acer, Dell and Xerox, have also invested in green R&D to increase green operation efficiency and reduce carbon emissions (Chen and Akmalul’Ulya, 2019). However, it should be noted that apart from the external forces derived from laws and regulations, the demand expansion effect and the cost reduction effect are two primary incentives that drive firms to invest in product green R&D activities (Ranjan and Jha, 2019;Hong et al., 2023). Some practice surveys have found that green R&D plays an important influence in stimulating customer demand, such as Accenture’s report, which demonstrated that more than 80% of customers will evaluate the green characteristics of items in their purchasing process (Hong and Guo, 2019). Concerning the cost reduction effect of green R&D, Pepsi Cola’s green R&D investment in the use of reusable plastic shipping containers saves 196 million dollars (Ranjan and Jha, 2019). Additionally, Xerox’s green operations have effectively reduced energy consumption by 31%, assisting in the reduction of operating and production costs (Chen and Akmalul’Ulya, 2019). Without a doubt, an increase in consumer demand and a decrease in operating (production) costs both contribute to increased revenue or profit, motivating supply chain firms to concentrate more on green R&D. However, it can be observed that there are also a large number of firms that put in less effort to engage in product green R&D (Ghosh and Shah, 2015). The primary reason is that investing in product green R&D is not free and instead incurs a substantial cost for firms (Yang and Chen, 2018). Especially, when these firms face capital constraints, undertaking the effort for green R&D of the product is a significant barrier for them even while it is a tremendous potential to gain market share and enhance operational performance. Therefore, seeking financing to ease firms’capital pressures may be an effective strategy to incentivize them to enhance the green R&D of the product. For the present period, external financing, such as bank credit financing and bond financing, is a major and prevalent channel that supply chain firms have prioritized for investments in product green R&D activities. For example, the China Green Finance Development Research Report 2021 revealed that China’s green credit balance in 2021 exceeded 15.9 trillion yuan. In 2022, Baosteel Group Corporation issued 500 million yuan of low-carbon transformation green corporate bonds to support its construction of a hydrogen-based vertical furnace system project. It is simple to comprehend that the capital constraints and the related financing cost will have a substantial impact on the green R&D decisions of firms facing capital constraints, changing the contractual connection among supply chain firms and their corresponding performance. The primary goal of this research is to investigate how capital constraints impact supply chain firms’investing incentives in green R&D and corresponding performance, as well as Green R&D decisions in supply chains 177 whether supply chain firms make different green R&D decisions when the channel power structure changes. More specifically, based on the situation that product green R&D can stimulate increased consumer demand and lower production costs, this study mainly focuses on the following questions. (1) What are the optimal green R&D decisions, optimal price decisions, optimal consumer demand and corresponding optimal performance of supply chain firms under different channel power structures when the upstream firm is capital constrained or not? (2) Under what condition is the upstream firm motivated to seek financing from external channels? And how do capital constraints, financing costs and cost reduction effects of green R&D affect the upstream firm’s green R&D decision and supply chain firm performance? (3) How do channel power structures influence the upstream firm’s green R&D decisions? (4) Can the above-mentioned relevant results be supported by practice data? To address the aforementioned concerns, this study considers a two-echelon supply chain with an upstream firm and a downstream firm linked by a wholesale price contract. The upstream firm faces capital constraints and invests in improving the product’s green R&D level, which increases consumer demand while decreasing production costs. The upstream firm chooses green R&D and wholesale price, whereas the downstream firm decides on market price (or retail margin). This study constructs four supply chain game models by considering whether the upstream firm dominates the supply chain or not and whether capital is constrained or not. With the equilibriums, this study first characterizes the upper and lower limits of the upstream firm’s initial capital under different models, revealing the conditions under which the upstream firm needs to seek financing when facing capital constraints. Following that, this study compares the levels of green R&D and profitability in four models to investigate the effects of capital constraints and channel power structures. Then, the comparative static analysis is used to examine the impacts of financing cost and cost reduction efficiency on the manufacturer’s green R&D decision and supply chain performance. Finally, this study presents the empirical analysis based on a sample of Chinese listed firms obtained from the China Stock Market Accounting Research (CSMAR) database to validate the relevant conclusions about the effects of capital constraints, channel power structures and financing costs on equilibrium solutions. This study mainly contributes to three aspects listed below. First, driven by the benefits of product green R&D on raising consumer demand and lowering production cost, this study designs supply chain models by taking capital constraints into account under different channel power structures. A lot of studies have discussed the green R&D investment strategies in the supply chain under capital constraints, however, few have considered the cost reduction effect of green R&D and capital constraints in combination. Second, this study examines the impact of financing costs and channel power structures on supply chain green R&D decisions and performance. The results reveal that a greater financing cost results in lower product green R&D level and downstream firm profitability, whereas the upstream firm’s profit will increase if the initial capital is high enough in the downstream firm-led Stackelberg model. Additionally, in the downstream firm-led Stackelberg model, both the product green R&D level and the downstream firm’s profitability are higher than in the upstream firm-led Stackelberg model, while the comparison of the upstream firm’s profitability depends on the green R&D investment efficiency. Finally, and most importantly, the influences of finance costs and channel power structures are studied using practice data, and the majority of the aforementioned theoretical results are confirmed by practice. The remainder of this study is organized as follows. In Section 2, we summarized the literature related to our research setting and contributions. Section 3 describes the conceptual MSCRA 5,3 178 framework and development of the model. Section 4 characterizes the optimal decisions for different supply chain structures and whether or not capital is constrained. Section 5 delves into comparative statics. In Section 6, we employ practice data to validate the results presented in Section 5. The managerial insights and conclusion of our work are highlighted in Section 7 and Section 8, respectively. All proofs of the propositions are given in Appendix. 2. Literature review This study focuses on the capital-constrained upstream firm’s green R&D decisions in the supply chain under different channel power structures, as well as how financing costs, channel power structures and cost-reducing efficiency, among other things, affect the supply chain’s interplay and corresponding profits. This study is closely related to four streams of literature: (1) green R&D decisions, (2) cost reduction efficiency of the product (green) R&D, (3) channel power structure and (4) capital constraint. Accordingly, a summary of the relevant literature is listed below. In addition, Table 1 also provides a summary of the relevant literature. 2.1 Supply chain (quality) R&D decisions This study contributes to the stream of literature on the supply chain green (quality) R&D decisions. For example, Xu et al. (2017) studied the green R&D and production decisions in the supply chain with a manufacturer and a retailer under the assumption that the green technology of the manufacturer is conducive to reducing carbon emissions. Yang and Chen (2018) considered the revenue-sharing and cost-sharing contracts provided by the retailer to motivate the manufacturer to invest in green R&D for reducing carbon emissions. Song and Gao (2018) explored the manufacturer’s optimal green R&D decisions in the supply chain under two types of revenue-sharing contracts, i.e. the revenue-sharing contract negotiated by the manufacturer and the retailer or determined solely by the retailer. Assuming that the demand function depends on the green R&D level, service effort level and market price, Ranjan and Jha (2019) investigated the manufacturer’s optimal green R&D decision and the retailer’s service effort decision in a dual-channel supply chain. Heydari et al. (2021) examined Paper Green R&D Green R&D sensitive demand Cost reduction Channel power structure Upstream firm capital constraint External financing Mondal and Giri (2022) √√ 33 33 Sana (2022b) √√ 33 33 Chen et al. (2023) √√ 3√33 Hong et al. (2023) √√ √ 333 Wu et al. (2019) √√ 33 3√ Yang et al. (2019) √√ 33 3√ Peng et al. (2023) √√ 33 √√ Tang and Yang (2020) √√ 3√3√ This paper √√ √ √ √ √ Source(s): Created by authors Table 1. Summary of previous literature Green R&D decisions in supply chains 179 green R&D and pricing decisions in the supply chain faced with a linear consumer demand relying on green R&D and market price. Ma et al. (2021) investigated the effect of government regulation on the manufacturer’s decision on green technology R&D for reducing carbon emissions. Mondal and Giri (2022) examined the manufacturer’s and retailer’s green R&D decisions, as well as their performance, in two types of revenue-sharing contracts that are determined entirely by the retailer and bargained by both the manufacturer and the retailer, respectively. Sana (2022b) investigated the optimal green R&D decisions of the supply chain by considering two types of demand functions. Even though the above studies have considered the optimal green R&D decisions under the assumption of green R&D sensitive demand, they all fail to consider the cost reduction effect of green R&D as well as the impact of capital constraints on green R&D decisions under different channel power structures. 2.2 Impact of (green) R& D on cost reduction The second stream of literature relevant to this study has examined the effects of (green) R&D on lowering production costs. For example, assuming upstream and downstream firms’R&D in lowering production cost, Ge et al. (2014) proved that R&D collaboration can only create a win-win scenario in the supply chain if the contribution levels are Pareto matched. By considering a supply chain with isoelastic demand, Hu et al. (2019) constructed four distinct models in which each firm can solely or cooperatively, or independently invest in R&D to lower production costs and identified the conditions under which a firm’s investment in R&D leads to a higher level of R&D and supply chain performance. Fu et al. (2021) considered a platform supply chain with a supplier, an online platform firm and a third-party logistics firm (3PL) and investigated the 3PL’s optimal equity financing for the investment in technology R&D to reduce the transportation cost. However, the studies mentioned above all fail to take into account the assumption that consumer demand is dependent on (green) R&D. Moreover, these studies do not consider the case of the supply chain firms facing capital constraint and thus neglect to examine the optimal (green) R&D choices and to compare the differences between the models with and without capital constraints. These studies have also ignored the impact of channel power structures on (green) R&D decisions. By assuming that process R&D can increase consumer demand and decrease production costs, Genc and De Giovanni (2020) investigated the optimal pricing and R&D investment decisions in a closed-loop supply chain. With a similar consumer demand assumption, Hong et al. (2023) optimized the green R&D and quality decisions made by the upstream firm concurrently or by two supply chain members separately and revealed that the upstream firm making both green R&D and quality decisions contributes to higher levels of green R&D and quality as well as supply chain performance than the separate decision case. Differing from the above two studies, this study focuses on the green R&D decisions in the supply chain with capital constraints and different channel power structures. 2.3 Impact of channel power structure on green R&D decisions Another relevant focus of research in the literature is the influence of channel power structures on green R&D initiatives in the supply chain. Ghosh and Shah (2012), for example, examined green R&D strategies in the clothing serial supply chain in the context of environmentally conscious customers and identified which channel power structure model contributes to greater green R&D level and supply chain performance. Chen et al. (2019) investigated the optimal green R&D decisions of two supply chain participants in cooperation and non-cooperation scenarios while taking the channel power relationship into account. Guan et al. (2020) examined the manufacturer’s green R&D decisions and the retailer’s advertising effort decision in supply chain models under different channel power structures while considering Nash bargaining fairness concerns and found that compared to MSCRA 5,3 180 fairness-neutral scenarios, the fairness-concerned will change the comparative relationship of the levels of green R&D and advertising effort under different channel power structures. Fan et al. (2020) investigated the impact of cost-sharing on the manufacturer’s green R&D decisions in the manufacturer-led and retailer-led Stackelberg models, finding that as costsharing increases, the manufacturer does not change its green R&D decision in the manufacturer-led Stackelberg model but increases the green R&D level in the retailer-led Stackelberg model. Chen et al. (2023) investigated the effect of channel power structures on green energy R&D strategies in the electricity supply chain with an electricity generator and an electricity retailer and reported that the Nash game model has the greatest degree of green R&D. This research differs from the aforementioned studies in three ways. First, in addition to characterizing the influence of green R&D on consumer demand, this study examines the efficacy of green R&D in lowering production costs. Second, this study models the supply chain game with and without capital constraints and compares the differences between them. Third, this study investigates the influence of financing cost, cost-reducing efficiency and channel power structures on green R&D decisions and supply chain performance. 2.4 Impact of capital constraints on green R&D decisions Finally, this study devotes to the work on the investigation of the influence of capital constraints on green R&D decisions. For example, Wu et al. (2019) examined the manufacturer’s carbon emission investment decisions under the assumption that the retailer is capital-constrained and can seek financing through bank financing and trade credit financing. Yang et al. (2019) studied the manufacturer’s optimal green R&D decisions in a supply chain with one manufacturer and two capital-constrained retailers, where retailers can conduct operations using external financing or trade credit. Cao et al. (2019) investigated the financial preferences of the supply chain when a downstream manufacturer invests in carbon abatement but lacks funds and can seek financing from an upstream supplier or an external channel. Tang and Yang (2020) investigated the impact of financing mechanisms (bank loans and early payment) and power structures on the operational decisions of a low-carbon supply chain that consisted of a capital-constrained manufacturer and a capital-abundant retailer. Peng et al. (2023) investigated the manufacturer’s optimal carbon emission reduction decisions in an e-commerce supply chain under the bank credit and cost-sharing financing schemes. In a closed-loop supply chain comprising a supplier and a capital-constrained OEM, Zhang and Chen (2022) have compared the effects of three different financing strategies on the supplier’s remanufacturing decisions. Differing from the aforementioned studies, this study assumes that green R&D is beneficial in lowering operating or manufacturing costs. More importantly, this study uses practice data to validate theoretical findings. 3. Model description Considering a two-tiered decentralized supply chain comprised of an upstream and a downstream firm. The supply chain members are linked by a wholesale price contract, through which product transactions are conducted between them. The upstream firm manufactures products and invests in product green R&D activities, such as improving product greenness, optimizing product manufacturing processes and increasing resource utilization efficiency, among others. Assume that the upstream firm is facing the capital constraint that makes it impossible to continue with normal production tasks and product green R&D investment. As a result, the upstream firm has the incentive to seek external financing to ensure that its decisions on product green R&D investment and production (or wholesale price) are optimal for profit maximization. Moreover, in terms of the supply chain focusing on product green R&D under the condition of capital constraint, we have the Green R&D decisions in supply chains 181 following necessary assumptions and settings for the model construction and equilibrium analysis. First, we assume that the upstream firm manufactures q(q> 0) units of products at a unit production cost c(c> 0) and then sells them to the downstream firm at a wholesale price w, where w>c. After purchasing, the downstream firm then serves the consumers in the final market at a sales price p, where p>w. Without loss of generality, we do not consider product inventory in our supply chain model, which means that the upstream firm’s production quantity equals the downstream firm’s ordering quantity and also equals the final consumer demand. Second, we assume that the upstream firm usually invests in product green R&D and the level of the product green R&D is assumed to be θ(θ> 0). Accordingly, to achieve the product green R&D level of θ, the upstream firm needs to perform the green R&D investment at a cost of 1 2kθ2, where kmeasures the efficiency of green R&D investment. Clearly, a higher k generally indicates a lower investment efficiency in product green R&D, implying that the upstream firm should invest more to improve one unit of product green R&D level. This type of green R&D investment cost function has been very commonly used in the literature on supply chain management, such as in studies by Fan et al. (2020),Chen et al. (2023) and Hong et al. (2023). Suppose that one of the primary objectives of product green R&D investment is to reduce production costs. Ge et al. (2014), for example, clearly show that R&D on process improvement can lower production costs. In practice, Apple invests in green R&D to use clean energy, which can significantly reduce energy consumption and thus operating costs. As a result of the investment in product green R&D, the upstream firm’s unit production cost is reduced from cto c(1 δθ). Here, δdenotes the cost-reducing efficiency, which is the same as that of Ge et al. (2014) and Hong et al. (2023). One can see that a higher δindicates that the product green R&D investment is more effective in reducing the production cost. Furthermore, as shown by Ge et al. (2014) and Hong et al. (2023) that the cost reduction would not exceed the original unit production cost, the range of cost-reducing efficiency we assume is δ∈0;1 θ  . Moreover, we set that the upstream firm has an initial capital S(>0) to organize its product production and green R&D investment. If the upstream firm’s initial capital is no less than its capital expenditure, which is the sum of product production costs and green R&D investment cost, i.e. S≥cð1−δθÞqþ1 2kθ2, the upstream firm faces no capital constraint and can continue to operate normally while determining the wholesale price and product green R&Datan optimal level to maximize profit. However, if S<cð1−δθÞqþ1 2kθ2, the upstream firm faces a capital constraint of L¼cð1−δθÞqþ1 2kθ2−Sand has no choice but to seek capital support for operations. Assume that the upstream firm has the option of obtaining external financing, such as bank loaning. Obviously, the amount of money borrowed by the upstream firm to ensure its normal product production and green R&D investment is L. However, the upstream firm must also pay the interest cost for the financing from the external channel, and we assume that the financing interest (or financing cost) is r(0 ≤r≤1). Furthermore, we assume that except for the cost-reducing effect, product green R&D also exerts a positive impact on consumer demand. In other words, if the upstream firm improves its product green R&D, consumers are willing to pay a higher price for products or consume more at a given sales price. Thus, based on the studies of Xu et al. (2017) and Hong et al. (2023), we assume that the consumer’s inverse demand function is p¼aβqþλθ;(1) MSCRA 5,3 182 where ais the initial market potential, βdenotes the price sensitivity to demand and a higher β means the consumer will change its demand less in response to a change in the sales price of the product, λmeasures the impact of the product green R&D level on the sales price and a higher λindicates a higher price that the consumers are willing to pay for per unit of green R&D improvement. Based on the assumptions stated above, we formulate the profit functions of the upstream and the downstream firms under capital constraints as follows: π u¼½wcð1δθÞq1 2kθ2rL;(2) π d¼ðpwÞq:(3) In this study, we investigate two different channel power structures: the upstream and the downstream firms act as the leader of the supply chain, respectively. Thus, in the upstream firmled model, the decision sequences of both firms in the supply chain are as follows. First, the upstream firm chooses its optimal product green R&D and wholesale price decisions to maximize profit; second, by learning the upstream firm’s decision information and applying the profit maximization principle, the downstream firm determines the optimal sales price decision. In the downstream firm-led model, the downstream firm decides the sales price first, followed by a response from the upstream firm, which determines product green R&D and wholesale price. Next, we will discuss the equilibrium results under the two different channel power structure models in the following section. However, to satisfy the non-negative optimal solution and the negative definite Hessian matrix, as well as to ensure that the optimal results are in the same range for the comparative analysis, the following two conditions should be met: λ>cδand k>ðcδþλÞ½λðaþcÞþ2λ 4β. Furthermore, the condition λ>cδobviously means that the green R&D of the product has a greater impact on the sales price rather than the cost, implying that improving the green R&D level of the product increases the willingness of consumers to pay a higher price for the green product or persuades more consumers to purchase the green product more effectively than lowering the production cost. The condition k>ðcδþλÞ½λðaþcÞþ2λ 4βshows that the efficiency of green R&D investment is insufficient, implying that it is difficult for the upstream firm to improve the product’s green R&D level. Similar assumptions can be seen in studies by Fan et al. (2017,2023),Chen et al. (2023) and Hong et al. (2023). 4. Equilibrium results 4.1 Upstream firm-led model with no capital constraint We begin by exploring the equilibrium decisions and corresponding profits in the upstream firm-led model with no capital constraint (denoted as the UN model), which serves as a benchmark model to that with capital constraint. Thus, with Equa. (2) and Equa. (3), the upstream firm’s and the downstream firm’s profit functions can be expressed as follows: π u¼½wcð1δθÞq1 2kθ2;(4) π d¼ðpwÞq:(5) This is a two-stage Stackelberg game model that can be solved by backward induction. First, substituting Equa.(1) into Equa.(5), one can see that v2 π d vp2¼−2 β<0, indicating that the downstream firm’s profit π d is strictly concave in p. Hence, by taking the derivative of π d with respective to p, we have: Green R&D decisions in supply chains 183 According to Proposition 7, a direct finding is that capital constraints do not affect the comparative results for product green R&D level and the downstream firm’s profit under different channel power structures. That is, regardless of whether the upstream firm suffers from capital constraints, the results of the above comparison between the upstream and downstream firm-led models are indifferent. However, the capital constraint has a significant impact on the comparisons of the upstream firm’s profits under different channel power structures. More specifically, a counterintuitive comparison result on product green R&D can be seen first. According to most existing studies, an upstream firm acting as a leader in the supply chain usually has a stronger incentive to improve product green R&D investment because it Figure 2. Impact of the financing cost on green R&D (a58, c52, β51, λ54, k515, S52 and δ50.1) Figure 3. Impact of the financing cost on the downstream firm’s profit (a58, c52, β51, λ54, k515, S52 and δ50.1) MSCRA 5,3 190 can capture more profit relative to the downstream follower (Guan et al., 2020;Agi and Yan, 2020). However, our finding shows that the level of product green R&D is relatively higher when the upstream firm acts as a follower rather than a leader. One can also see Figure 2 for a visual comparison. The intuitive reason for this result is that, as the supply chain leader, the downstream firm will exert more pressure on the upstream firm for improving product green R&D level due to the effectiveness of product green R&D in increasing consumer demand and lowering the wholesale price as production cost decreases. Naturally, higher product green R&D leads to higher profit for the downstream firm by increasing consumer demand. This is consistent with the observation in the existing literature that a higher channel power is generally indicative of a higher profit (Ghosh and Figure 4. Impact of the financing cost on the upstream firm’s profit (a58, c52, β51, λ54, k515, S52 and δ50.1) Figure 5. Impact of the financing cost on the upstream firm’s profit (a58, c52, β51, λ54, k510, S52 and δ50.1) Green R&D decisions in supply chains 191 Shah, 2015;Chen et al., 2023;Fan et al., 2020). The numerical result in Figure 3 also demonstrated it. However, this is not the case for the upstream firm. Moreover, a higher product green R&D level does not always imply a higher benefit for the upstream firm. One can see from Proposition 7 that whether the upstream firm’s leadership results in higher profit is determined by the efficiency of green R&D investment. Specifically, if the efficiency of the investment in improving green R&D is low (high), the profit of the upstream firm is higher (lower) when it acts as a leader in the supply chain rather than as a follower. The reason behind this is that the profit of the upstream firm is determined by two effects: the green R&D investment cost effect, which reduces profit, and the consumer demand effect, which raises profit. Clearly, when the green R&D investment efficiency is low, the cost effect of investment in green R&D outweighs the effect of consumer demand. Consequently, when acting as a leader, the upstream firm’s weaker incentive to improve product green R&D will induce it to invest less in product green R&D, which in turn will benefit its profit. Otherwise, increased consumer demand induced by higher product green R&D will result in higher profit when the upstream firm acts as a follower. Additionally, it should be pointed out that because of the financing cost, the threshold value of the efficiency of green R&D investment in the scenario with the capital constraint differs from the scenario without the capital constraint. As depicted in Figures 4 and 5, if the efficiency of green R&D investment is high enough (k515), the upstream firm’s profit is higher when acting as a leader than when acting as a follower, whereas the comparison of the upstream firm’s profits in scenarios with capital constraints under different channel power structures is determined by the financing cost if the efficiency of green R&D investment is low enough (k510). 5.4 Effects of financing costs The primary aim of this subsection is to examine the effects of financing costs (interest costs) on product green R&D decisions and profits of two supply chain members under different channel power structures when the upstream firm faces capital constraints. This investigation can assist managers in recognizing the importance of financing costs in the firm’s green R&D investment and guide them to choose an appropriate external financing channel by taking into account the effect of financing costs and the firm’s status among supply chain members. P8. With the increase in the financing cost r, under the UE model, it holds that vθ*ue vr<0; v π *ue d vr<0 and v π *ue u vr<0; under the DE model, it holds that vθ*de vr<0 and v π *de d vr<0, whereas v π *de u vr>ð≤Þ0ifS>(≤)S # . Proposition 8 demonstrates that in the UE model, where the upstream firm faces capital constraints and serves as the supply chain leader, the upstream firm’s incentive to improve product green R&D decreases with the financing cost, resulting in lower profits for two supply chain members. Higher financing cost indicates that a greater proportion of the upstream firm’s revenue procured from product green R&D improvement ought to be paid to the substance that provides the external financing, which naturally diminishes the upstream firm’s incentive to further improve product green R&D level. Also, this reduces the profits of both supply chain members. Without a doubt, in the scenario where the downstream firm acts as the leader (DE model), higher financing costs will also reduce the upstream firm’s product green R&D level as well as the downstream firm’s profit. The above results are also shown in Figures 2–5. However, in contrast to the UE model, the impact of the financing cost on the upstream firm’s profit in the DE model is dependent on the initial capital. It demonstrates that if the MSCRA 5,3 192 upstream firm’s initial capital is insufficient, implying that more financing is required, its profit decreases with the financing cost, which is the same as in the UE model. Surprisingly, if the upstream firm has sufficient initial capital, its profit will increase with the financing cost, indicating that the upstream firm can benefit from a higher financing cost at a specific condition. As displayed in Figure 2, as the financing cost increases, its effect on the product green R&D level is significantly larger in the DE model than in the UE model, prompting a faster decline in the product green R&D level as well as consumer demand. Hence, the upstream firm’s total costs, including product production and green R&D investment costs, will diminish faster than the increment in the financing cost, contributing to an increase in profit. By observing Figures 4 and 6, the numerical results also show that the upstream firm’s profit in the DE model decreases in the financing cost if S51.5 but decreases first and then increases if S52. 5.5 Effects of production cost-reducing efficiency In this subsection, we investigate the impact of the production cost-reducing efficiency on product green R&D decisions and supply chain profits and check whether the product costreducing efficiency can cause differential effects under different channel power structures. P9. With the increase in the production cost-reducing efficiency δ, under the UE model, it holds that vθ*ue vδ>0; v π *ue d vδ>0 and v π *ue u vδ>0; under the DE model, it holds that vθ*de vδ>0; v π *de d vδ>0 and v π *de u vδ>0. Proposition 9 demonstrates that regardless of channel power structures, an increase in production cost-reducing efficiency leads to an increase in product R&D, bringing about higher profits for the two supply chain members. Undoubtedly, higher efficiency in reducing production costs driven by product green R&D will urge the upstream firm to invest more in product green R&D improvement, resulting in a higher product green R&D level. Increased product green R&D level benefits both supply chain members by increasing consumer demand. Moreover, decreased production costs permit the upstream firm to lower the wholesale price, which likewise helps to increase the profits of the two supply chain members Figure 6. Impact of the financing cost on the upstream firm’s profit (a58, c52, β51, λ54, k515, S51.5 and δ50.1) Green R&D decisions in supply chains 193 because a lower product market price determined by the downstream firm increases consumer demand. In this manner, the profits of the two supply chain members are increasing with the production cost-reducing efficiency. This proposition reveals that product green R&D that enhances production efficiency can benefit each member of the supply chain. Additionally, the results in Proposition 9 are also supported by the numerical analysis in Figure 7. 6. Empirical tests 6.1 Data and sample The theoretical conclusions in Section 5 are summarized here from the perspective of the upstream firm, which serves as the main investor of green R&D. (1) Capital constraints reduce green R&D and upstream firm profitability. (2) The downstream firm’s leadership benefits green R&D, but if the green R&D investment efficiency is low (high), the upstream firm’s profitability increases (decreases). (3) Financing costs reduce green R&D and upstream firm profitability, with the exception of the downstream firm serving as the leader case, where the upstream firm’s profitability is raised if its initial capital is sufficient. This section will examine these predictions using a sample of Chinese A-share listed firms during 2007 and 2019. The sample begins in 2007 because new accounting standards for firms listed in China were implemented in that year. The initial sample is cleaned as follows: first, financial firms are excluded; second, ST and *ST firms are excluded; third, samples are excluded with data errors in which return on assets (ROA) is less than 1; and last, all financial variables are winsorized at the 1% and 99% levels to restrict the impact of outliers. The final sample consists of 5,105 firm-year observations from 1,635 firms. Green innovation and financial data are sourced from the CSMAR database. 6.2 Variable definition The green R&D level is measured by the number of green patent applications (Li et al., 2022). Upstream firm profitability is quantified by Return on Assets (ROA) and Return on Equity (ROE). Capital constraints are measured by the WW index (Whited and Wu, 2006). The firm Figure 7. Impact of the costreducing efficiency on R&D and supply chain profits (a58, c52, β51, λ54, k515, S52 and r50.2) MSCRA 5,3 194 with a higher WW index has greater capital constraints. The channel power structure is measured by customer concentration, which is determined by the ratio of a supplier’s largest customer’s sales to the supplier’s overall sales (Dhaliwal et al., 2016;Wang and Mao, 2021). A smaller value of the ratio indicates a lower level of customer concentration, implying that the upstream firm has more power to influence the supply chain. Financing cost is estimated by the debt cost, calculated as the interest expense divided by total liabilities (Pittman and Fortin, 2004). The detailed definitions are shown in Table 2. The firms are ranked each year by WW index, customer concentration and debt cost, respectively, and 30th and 70th percentiles are computed as data breakpoints. Using the 30th and 70th percentiles of WW index, customer concentration and debt cost, the firms are divided into three groups, respectively. Table 3 reports the summary statistics for the variables. The mean of number of green patent applications is about 8, which is more than the median of 3 and is much less than the maximum of 139. On average, the ratio of supplier’s largest customer’s sales to supplier’s overall sales is 13.554%. The average ROA and ROE are 0.045 and 0.074, respectively. The mean of WW Index is 1.026. In addition, the average proportion of interest expense to total liabilities is 0.017. 6.3 Impact of capital constraints on green R&D and upstream firm profitability Figure 8 examines the impact of capital constraints on the green R&D level. It shows that when the WW index rises, so do green patent applications, implying that a firm with more capital constraints will reduce its green R&D investment. This empirical result is consistent with the corresponding theoretical result in Proposition 6. Variable Mean SD Min p25 p50 p75 Max Green R&D 8.403 18.138 1 1 3 7 139 ROA 0.045 0.050 0.154 0.019 0.044 0.071 0.183 ROE 0.074 0.094 0.415 0.039 0.076 0.118 0.300 Channel power structure 13.554 12.512 0.900 5.280 9.350 17.350 64.520 WW index 1.026 0.068 1.223 1.066 1.021 0.981 0.877 Debt financing cost 0.017 0.012 0.000 0.007 0.016 0.025 0.046 Source(s): Created by authors Variable Definition Green R&D Number of green patent applications ROA Return on assets ROE Return on equity WW index WW 50.091CF 0.062DIV þ0.021TLTD 0.044SIZE þ0.102ISG 0.035SG Where CF is the ratio of cash flow to total assets, DIV is an indicator that takes the value of 1 if the firm pays cash dividends, TLTD is the ratio of the long-term debt to total assets, SIZE is the natural log of total assets, ISG and SG are the sales growth of industry and firm, respectively Channel power structure Ratio of supplier’s largest customer’s sales to supplier’s overall sales (%) Debt financing cost Ratio of interest expense to total liabilities Source(s): Created by authors Table 3. Descriptive statistics for the variables Table 2. Definitions of variables Green R&D decisions in supply chains 195 Figure 9 examines the impact of capital constraints on upstream firm profitability. It reveals that both ROA and ROE fall as WW index rises, indicating that increased capital constraints result in lower upstream firm profitability. Proposition 6’s corresponding theoretical result is consistent with this empirical evidence. 6.4 Impact of channel power structure on green R&D and upstream firm profitability Figure 10 examines how the channel power structure influences the green R&D level. As seen in Figure 10, as the upstream firm’s channel power declines, the number of green patent applications increases, implying that the upstream firm’s motivation to improve green R&D Figure 9. Impact of capital constraints on upstream firm profitability Figure 8. Impact of capital constraints on green R&D MSCRA 5,3 196 increases. Naturally, this empirical evidence fully supports the theoretical result in Proposition 7. Figure 11 investigates the influence of channel power structure on the profitability of the upstream firm. The empirical study shows that as channel power declines, both ROA and ROE fall, meaning that the upstream firm’s profitability decreases as well. When the upstream firm’s green R&D investment efficiency is low enough, this empirical result is completely consistent with the corresponding theoretical result in Proposition 7, but it Figure 11. Impact of capital constraints on upstream firm’s profitability Figure 10. Impact of channel power structure on green R&D Green R&D decisions in supply chains 197 contradicts the theoretical result when the green R&D investment efficiency is high enough. But in practice, firms’green R&D investment efficiency is typically low since they must invest more in green R&D improvement while getting less green R&D output. 6.5 Impact of financing costs on green R&D and upstream firm profitability Figure 12 depicts the empirical result of the influence of financing costs on the level of green R&D. The result suggests that green patent applications will decrease as financing costs increase. This indicates that rising financing costs lower the motivation for the upstream firm to improve its green R&D level. This empirical evidence supports the theoretical findings in Proposition 8. Figure 13 explores the effect of financing costs on the upstream firm’s profitability. It highlights that when financing costs rise, both ROA and ROE fall, showing that rising financing costs impair the upstream firm’s profitability. This empirical result is completely congruent with the corresponding theoretical result in Proposition 8 under the UE model. Additionally, if the initial capital is low enough, the theoretical result under the DE model is also corroborated by the empirical result. Even though the upstream firm’s profitability is increased if its initial capital is sufficient under the DE model, it should be noted that in practice, firms typically face severe capital constraints and are compelled to pay high interest expenditure for financing, especially as the financing cost rises. The profitability of firms with capital constraints is lowered logically. 7. Managerial implications Based on the previous analysis of equilibrium results under models with and without capital constraints, we can derive the following managerial implications. (1) Even though supply chain firms suffer capital constraints in their green R&D and product manufacturing operations, it does not guarantee that they will seek financing right now. Our findings indicate that supply chain firms seek external financing only Figure 12. Impact of debt financing cost on green R&D MSCRA 5,3 198 when their initial capital is low enough. Furthermore, even if capital constraints may be addressed through financing, the level of green R&D and supply chain operational performance cannot be achieved to the same extent as in the absence of capital constraints. These findings explain why so many firms in practice, particularly middle and small-sized firms, are reluctant to invest in green product R&D. These findings can help firms in the supply chain choose when to seek external finance if they lack the initial capital to invest in green R&D and produce green products. In addition, it is advised that government regulatory agencies formulate new green financing policies to reduce financing pressure and cost, therefore, enhancing supply chain firms’incentives to engage in the improvement of green R&D. (2) The channel power structures have a significant impact on supply chain firms’ decisions on product green R&D, and hence on their profitability. Therefore, firms should consider their status in the supply chain when seeking financing and determining their investment in green R&D. Increased cost-reducing efficiency pushes supply chain firms to improve product green R&D and profitability. This implies that supply chain firms should actively seek green R&D activities that improve operation efficiency, as this will not only reduce supply chain firms’operation (production) costs but also increase their motivations to invest in green R&D, allowing them to benefit more from green R&D. In general, lower financing costs incentivize firms in the supply chain toinvest in green R&D and so boost their revenues. This implies, on the one hand, that capital-constrained firms should select financing channels with the lowest interest rates, and on the other, that government regulators should take supportive measures to assist supply chain firms in lowering financing costs for green R&D. 8. Conclusion In this study, we consider a green supply chain system, comprising an upstream firm and a downstream firm, and explore the impact of capital constraints on the green R&D decisions of Figure 13. Impact of debt financing cost on upstream firm’s profitability Green R&D decisions in supply chains 199 which indicates that π *un u>ð<Þ π *dn uif k>ð<Þ3ðcδþλÞ2 4β. (2) Under the capital constraint models, by comparing θ* ue and θ* de , we have: θ*ue θ*de ¼−½acð1þrÞ½cδð1þrÞþλ3 22kβð1þrÞ½cδð1þrÞþλ2 no 4kβð1þrÞ½cδð1þrÞþλ2 no <0; which indicates that θ* ue <θ* de . By comparing π *ue dand π *de d, we have: π *ue d π *de d¼− kð1þrÞ½acð1þrÞ24k2β2ð1þrÞ2þ2kβð1þrÞ½cδð1þrÞþλ2 hi 2  42kβð1þrÞ½cδð1þrÞþλ2 no 4kβð1þrÞ½cδð1þrÞþλ2 no 2<0; which indicates that π *ue d< π *de d. By comparing π *ue uand π *de u, we have: π *ue u π *de u¼ kð1þrÞ½acð1þrÞ 4kβð1þrÞ3ðcδð1þrÞþλÞ2 hi 82kβð1þrÞ½cδð1þrÞþλ2 no 4kβð1þrÞ½cδð1þrÞþλ2 no ; which indicates that π *ue u>ð<Þ π *de uif k>ð<Þ3ðcδþλÞ2 4β. Proof of Proposition 8. Under the upstream firmled model with capital constraint, we can derive vθ*ue vr; v π *ue d vrand v π ue u vras follows: vθ*ue vr¼−4kaβλ þ4kδβc2ð1þrÞcðaδþλÞ½cδð1þrÞþλ2 4kβð1þrÞ½cδð1þrÞþλ2 hi 2<0; which indicates that vθvue vr<0. v π *ue d vr¼−2k2βð1þrÞðaccrÞfa½cδð1þrÞþλ½λcδð1þrÞ þ 2cð1þrÞ½2kβð1þrÞλ½cδð1þrÞþλg 4kβð1þrÞ½cδð1þrÞþλ2 no 3<0; which indicates that v π *ue d vr<0. v π *ue u vr¼S kðaccrÞa½cδð1þrÞþλ½λcδð1þrÞ þ cð1þrÞ8kð1þrÞþλ½cδð1þrÞþ2λ2 hino 4kβð1þrÞ½cδð1þrÞþλ2 no 2<0; which indicates that v π ue u vr<0. Under the downstream firm-led model with capital constraint, we can derive vθ*de vr; v π *de d vrand v π *de u vras follows: vθ*de vr¼−2kβaλþc2δð1þrÞ½þcðaδþλÞ½cδð1þrÞþλ2 22kβð1þrÞ½cδð1þrÞþλ2 no 2<0; which indicates that vθ*de vr<0. MSCRA 5,3 206 v π *de d vr¼−k½acð1þrÞ c2ð1þrÞ2½4kβcδ½cδð1þrÞþλþ½cδð1þrÞþλ½a½cδð1þrÞλþ3λcð1þrÞg n 42kβð1þrÞ½cδð1þrÞþλ2 no 2<0; which indicates that v π $de d vr<0. v π *de u vr¼S k½acð1þrÞ c2ð1þrÞ2½4kβcδ½cδð1þrÞþλþ½cδð1þrÞþλ½a½cδð1þrÞλþ3λcð1þrÞg h 82kβð1þrÞ½cδð1þrÞþλ2 no 2: From the above equation, let S#¼ k½acð1þrÞ c2ð1þrÞ2½4kβcδ½cδð1þrÞþλþ½cδð1þrÞþλ½a½cδð1þrÞλþ3λcð1þrÞg h 82kβð1þrÞ½cδð1þrÞþλ2 no 2>0: Therefore, with S<S* de , we can obtain that: S*de S#¼ckð1þrÞðaccrÞ 42kβð1þrÞ½cδð1þrÞþλ2 no >0: Thus, we have v π $de u vr>ð≤Þ0ifS>(≤)S # . Proof of Proposition 9 Under the upstream firm-led model with capital constraint, we can derive vθ$ue vδ; v π *ue d vδand v π *ue u vδas follows: vθ*ue vδ¼ cð1þrÞðaccrÞ4kβð1þrÞþ½cδð1þrÞþλ2 no 4kβð1þrÞ½cδð1þrÞþλ2 no 2>0; which indicates that vθ*ue vδ>0. v π *ue d vδ¼4k2cβð1þrÞ3ðaccrÞ2½cδð1þrÞþλ 4kβð1þrÞ½cδð1þrÞþλ2 no 3>0; which indicates that v π $ue d vδ>0. v π ue u vδ¼kcð1þrÞ2ðaccrÞ2½cδð1þrÞþλ 4kβð1þrÞ½cδð1þrÞþλ2 no 2>0; which indicates that v π *ue u vδ>0. Under the downstream firm-led model with capital constraint, we can derive vθ$de vδ; v π $de d vδand v π de u vδas follows: vθ*de vδ¼ cð1þrÞðaccrÞ2kβð1þrÞþ½cδð1þrÞþλ2 no 22kβð1þrÞ½cδð1þrÞþλ2 no 2>0; which indicates that vθ*de vδ>0. Green R&D decisions in supply chains 207 v π $de d vδ¼kcð1þrÞ2ðaccrÞ2½cδð1þrÞþλ 22kβð1þrÞ½cδð1þrÞþλ2 no 2>0; which indicates that v π $de d vδ>0. v π *de u vδ¼kcð1þrÞ2ðaccrÞ2½cδð1þrÞþλ 42kð1þrÞβ½cð1þrÞδþλ2 no 2>0; which indicates that v π de u vδ>0. Corresponding author Zhun Li can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] MSCRA 5,3 208