Abstract
We investigate the relationship between strategic alignment between suppliers and their major customers and the asymmetric behavior of costs, specifically cost of goods sold (COGS) and selling, general, and administrative (SG&A) costs. Utilizing mandated disclosures regarding major customers from 1978 to 2018, we construct supplier–customer dyads to examine this connection. Our results indicate a positive association between strategic alignment in supply chain partnerships and the stickiness of COGS and SG&A costs for suppliers. This positive association is more pronounced for suppliers in the early stages of their relationships than those with long-term partnerships. Additionally, strategic alignment increases the stickiness of suppliers’ research and development (R&D) costs. Furthermore, we observe that strategic alignment correlates with an extended duration of supplier–customer relationships and improved supplier performance. Our findings hold across alternative constructs of strategic alignment, various supplier strategy types, and when accounting for characteristics of the supply-chain relationship.
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1 Introduction
Prior research in supply chain management highlights the significance of suppliers’ customer-specific investments in fostering collaborative relationships within the supply chain (Chen et al. 2017; Chiu et al. 2019; Dekker 2004; Dyer 1997). However, due to the unpredictable outcomes of these customer-specific investments and their diminished value beyond specific customer–supplier relationships (Heide and John 1990; Trevelen 1987), suppliers sometimes hesitate to commit to such investments. We approach the examination of supplier–customer commitment from a different perspective—by assessing the supplier’s willingness to maintain expenditures on specialized resources to meet the customers’ needs during a period of sales decline (Anderson et al. 2023). Anderson et al. (2003) propose that firms’ decisions on resource adjustments during a sales decrease involve a trade-off between the costs of operating with unutilized capacity and the adjustment costs associated with reducing and subsequently restoring committed resources. This decision reflects the suppliers’ expectations about future customer demand and is affected by the level of trust between the supplier and the customer.
This paper examines the impact of strategic alignment between suppliers and customers on suppliers’ expectations about future customer demands and their cost reduction decisions when sales volume decreases. Prior research has considered how economic factors, agency issues, and product market competition affect managerial decisions on resource commitment (Anderson et al. 2003; Banker et al. 2014; Chen et al. 2019). However, we are unaware of any study examining the relationship between supplier–customer strategic alignment and suppliers’ decisions on retaining spending on excess capacity and resources when faced with a sales decrease. This study aims to address this gap.
Banker et al. (2014) document the substantial influence of managerial expectations on future demand in asymmetric cost behavior, known as cost stickiness. They posit that if managers anticipate a positive future demand during a sales downturn, their optimism prompts the retention of idle capacity and excess resources, anticipating their use in future sales growth—resulting in cost stickiness. Conversely, a pessimistic outlook leads to anti-stickiness, where managers, expecting a future sales decline, curtail current resource acquisition and even reduce existing slack capacity during a sales increase. These findings underscore that cost stickiness stems from short-term resource adjustment decisions influenced by managers’ expectations regarding future customer demand (Lee et al. 2020).
We build upon the theory of managerial expectations underlying the asymmetric cost behavior literature and hypothesize that strategic alignment between suppliers and customers influences managerial expectations. Strategic alignment, in this context, denotes similar business strategies among trading partners within the same supply chain. When trading partners share similar business strategies, their strategic objectives, competitive priorities, and actions are more likely to complement each other (Dekker et al. 2013). This alignment fosters shared goals, collaborative planning, and coordinated efforts, enhancing suppliers’ ability to meet customer needs and cultivating trust and loyalty between the partners (Chang et al. 2022). The prospects of a sustainable, long-term collaborative relationship is pivotal to the success of both customers and suppliers, thereby increasing suppliers’ inclination to retain capacity and resources during periods of decreased sales.
In addition, strategic alignment facilitates open and transparent communication between suppliers and customers. In such aligned relationships, customers are more inclined to share pertinent information with their suppliers (Stephen and Coote 2007). This increased information-sharing enhances suppliers’ comprehension of their customers’ production and demand needs, fostering collaboration and instilling confidence in the potential for a long-term business relationship. In such a scenario, suppliers are more willing to make long-term investments for their clients. Recognizing the pivotal role of trust in times of uncertainty, strategically-aligned suppliers are predisposed to retaining committed resources during sales declines compared to their counterparts.
Moreover, as relationship-specific investments hold limited value outside the specific business relationship, suppliers face the risk of exploitation by opportunistic customer behavior. The establishment of inter-firm trust through strategic alignment can mitigate the occurrence of opportunistic behavior by trading partners (Bradach and Eccles 1989). Hence, strategic alignment serves as a deterrent, discouraging customers from engaging in ex-post opportunism or leveraging their bargaining power. This reduction in operational risk enhances suppliers’ willingness to bear the costs associated with retaining committed resources for customers during sales declines. Additionally, suppliers and customers with strategic alignment are more prone to engage in long-term relationships and multi-period projects. Such projects often necessitate a sequence of expenditures and investments after the commencement of the project for ongoing maintenance and improvement (Li and Zheng 2017). Hence, cost stickiness is more prevalent when suppliers anticipate a sustained long-term business relationship rather than a short-term one. Consequently, we expect that strategic alignment among supply chain partners will increase the suppliers’ degree of cost stickiness.
We use the Customer Segment Files of Compustat dataset to construct a dataset of customer–supplier dyads from 1978 to 2018. We categorize a firm’s business strategy as Prospector, Defender, or Analyzer based on the business strategy framework of Miles and Snow (1978). After identifying the business strategy of each firm, we develop a metric to measure the similarity of strategy between supplier and customer in the same supply chain—strategic alignment. Following Bentley et al. (2013), we first create a composite measure of business strategy based on six firm-level variables. For each variable, we rank the observations into quintiles. We then sum the quintile ranks across these six variables to calculate a strategy score for each company (Bentley et al. 2013; Bentley-Goode et al. 2017; Navissi et al. 2017). Finally, we follow Chang et al. (2021) and measure the strategic alignment of supply chain participants by subtracting the absolute value of the strategy score difference in supplier–customer pairs from its largest possible value of 24.
Our empirical results indicate that strategic alignment between supply chain partners is positively associated with the stickiness of suppliers’ cost of goods sold (COGS) and selling, general, and administrative (SG&A) costs. This result suggests that having a major customer with similar strategic objectives encourages the supplier to retain relationship-specific investments during a period of sales decrease.
For cross-sectional analysis, we find that the positive association between strategic alignment and cost stickiness is more pronounced for suppliers in the early years of their supply chain relationship. It is consistent with the argument that during the early years of the supplier–customer relationship, there is a lack of trust and cooperation between the trading parties. Strategic alignment helps build trust and enhance collaboration between trading partners in these early years.
Further, we analyze the stickiness of suppliers’ R&D spending. R&D spending, characterized by its discretionary nature and extended revenue realization cycle (Chang et al. 2022), is particularly susceptible to contract imperfections. Suppliers tend to invest in R&D in anticipation of long-term relationships and associated benefits, rather than expecting immediate returns. We use R&D spending to proxy for relationship-specific investments by suppliers. We find that the long-term relationship instilled by strategic alignment increases the stickiness of R&D costs, indicating that strategic alignment between supply chain partners increases the cost stickiness of suppliers’ R&D spending. Our results are robust to alternative measures of strategic alignment and employing propensity score matching to address endogeneity. We also observe that strategic alignment correlates with an extended duration of the supplier–customer relationship and improved supplier performance.
Our study makes two contributions. First, it extends the literature on cost behavior by demonstrating that strategic alignment between supply chain partners results in increased persistence of COGS and SG&A costs for suppliers. The establishment of trust and the anticipation of a long-term relationship, stemming from strategic alignment, motivate suppliers to maintain committed resources even when faced with a decline in sales.
Second, our research aligns with an emerging research stream that evaluates the impact of strategic alignment on corporate behavior (Ashfaq and Raja 2013; Schreiner et al. 2009; Vachon et al. 2009). This study investigates the influence of strategic alignment on suppliers’ expectations regarding future customer demand and their incentives to adjust costs during periods of sales decline. We also document that strategic alignment contributes to customer retention and the establishment of longer-lasting relationships, ultimately resulting in improved supplier performance.
The rest of this paper is structured as follows: In Sect. 2, we present an overview of the related literature and outline the development of our hypothesis. Section 3 describes the sample and the empirical method employed in our study. Section 4 discusses our empirical results. Section 5 details sensitivity checks and additional analyses. Section 6 provides concluding remarks.
2 Literature review and hypothesis development
2.1 Strategic alignment in supply chain relationships
Over the past decade, there has been growing evidence that, in order to remain competitive, companies are moving away from arms-length market transactions towards establishing longer-term relationships with a select few suppliers (Chang et al. 2022). This evolution leads to a dependency between trading partners, encompassing both tangible and intangible resources and resulting in interdependence. In mutually dependent relationships, each partner contributes expertise to the collaboration and gains access to resources or competencies that they lack individually (Dyer 1996). Interdependence can manifest in various ways. For instance, suppliers of parts in the automotive industry make transaction-specific investments for particular transactions, computer manufacturers invest in new production lines for joint technological development with their customers, and chip manufacturers share information with their trading partners to control costs and ensure quality (Anderson and Dekker 2005; Dekker 2004; Dyer and Singh 1998).
However, the presence of contractual incompleteness and information asymmetry between suppliers and customers can lead to opportunistic behavior by either party. For instance, customers can opt to switch to an alternative supplier. In such scenarios, issues such as expropriation and holdup problems arising from relationship-specific investments could emerge. These problems create a situation where suppliers are hesitant to make relationship-specific investments ex-ante (Williamson 1985; Baiman and Rajan 2002a; Clemons and Row 1992; Dwyer et al. 1987).
Existing research explores various mechanisms aimed at mitigating opportunistic behavior among trading partners. For example, Titman (1984) observes that firms reduce their leverage ratio to minimize bankruptcy risk. The decreased bankruptcy risk provides reassurance to trading partners, encouraging them to invest in relationship-specific assets. Hart and Moore (1990) propose that companies can employ contractual arrangements, such as joint ventures, to increase the level of relationship-specific investments undertaken by suppliers or customers. Alternatively, Allen and Phillips (2000) suggest that businesses can derive benefits from establishing a long-term partial ownership position. They provide empirical evidence that companies with corporate block owners are more likely to invest in product market relationships or other relationship-specific assets. Fee et al. (2006) document that the duration of customer–supplier relationship tends to be longer when the customer holds equity ownership in the supplier. In addition to equity ownership, Baiman and Rajan (2002b) note that an improved channel for information exchange helps suppliers or customers assess the risk associated with investing in specialized assets, consequently reducing the risk involved in making relationship-specific investments (Dou et al. 2013).
The strategic alignment between suppliers and customers yields enduring advantages for both parties. A customer who has “a supplier that shares its strategic objectives and, as a result, may be willing to trade the exercise of bargaining power to obtain these benefits.” This alignment results in fewer coordination issues facilitated by information sharing (Zsidsin and Ellram 2003), consequently reducing contracting costs (Anderson and Weitz 1989). In addition, the strategic fit enhances the likelihood of the customer achieving its long-term goals. Consequently, the customer is willing to trade its immediate bargaining power for the prospect of these enduring benefits. This reciprocal arrangement not only aids the customer in reaching its long-term goals but also allows the supplier to maintain profitability when engaging with influential customers.
With higher margins and profits derived from strategically-aligned customers, the supplier is more inclined to invest in skilled labor, training, technology, and production facilities that better cater to the customers’ needs. Both the supplier and the customer anticipate a long-term relationship, shaping their decisions based on this sustained cooperation rather than short-term profit maximization.
2.2 Managerial resource commitment decisions
We use the concept of cost stickiness, as developed in the managerial accounting literature, to proxy for a firm’s resource commitment decisions. Anderson et al. (2003) document that Selling, General, and Administrative (SG&A) costs exhibit a smaller decrease when sales fall compared to the increase observed when sales rise by an equivalent amount. This asymmetry in cost behavior, termed “cost stickiness,” is attributed to managerial decisions on resource adjustments influenced by resource adjustment costs. According to Anderson et al. (2003), the phenomenon of sticky costs arises because “managers deliberately adjust the resources committed to activities.” Managers refrain from cutting slack resources when sales decrease due to the associated costs of reducing the resources (e.g., severance payments) and the subsequent expenses of reinstating them when sales rebound (e.g., costs of hiring and training new employees) (Abel and Eberly 1994). If managers anticipate a temporary decline in sales, they choose to retain the underutilized resources to mitigate adjustment costs.
In addition, Anderson et al. (2023) suggest that an important part of maintaining a resource-based competitive advantage involves the willingness to continue spending on specialized resources even during periods of sales and profits decline, resulting in cost stickiness. Their study find a positive association between SG&A cost stickiness and future customer satisfaction. SG&A costs encompass investments in marketing research and strategy, building customer and social relationships, developing brand equity, and human capital (Ballas et al. 2022; Enache and Srivastava 2018). The results suggest that retaining specialized resources throughout sales downturns,, as indicated by SG&A cost stickiness, helps sustaining relationships with customers and enhancing overall customer satisfaction. Customer satisfaction defined as “a function of resources acquired and developed by the firm to enhance the customer experience and represents an intangible assset” (Anderson et al. 2023; Srivastava et al. 1998), contributes to building and sustaining resource-based competitive advantages. Consistent with Anderson et al. (2023), we use cost stickiness as a proxy for a company’s resource commitment. The literature, including studies by Anderson et al. (2003), Balakrishnan et al. (2004), Balakrishnan and Gruca (2008) and Banker et al. (2011), documents that deliberate managerial choices aimed at maximizing firm value can induce cost stickiness.
Banker et al. (2011) suggest that SG&A costs encompass investment spending on various facets, such as brand development, research and development (R&D), information technology, marketing and distribution, and employee training. These investments are instrumental in building long-term relationships with customers. Additionally, the Cost of goods sold (COGS) includes investments in specific facilities and skilled labor to fulfill production requirements and manufacture goods. The decision to retain these facilities and labor during a period of sales decrease reflects the manager’s expectation that the sales decline is temporary and signifies their commitment to retaining resources to better meet customer demand when sales rebound. Hence, the cost stickiness observed in both SG&A and COGS can serve as an indicator of a manager’s resource commitment decision in the day-to-day operating environment.
Building on Anderson et al.’s (2003) seminal work, subsequent studies further establish that resource adjustment decisions are influenced by managers’ expectations regarding future product demand. For example, Banker et al. (2014) find that the direction of prior period sales changes shapes managerial expectations about future demand and sales. Their findings indicate that, following two consecutive periods of sales increases, managers tend to be optimistic and are more inclined to retain slack resources if sales decline in the current period. In contrast, after experiencing two prior sales decreases, managers become pessimistic and are more likely to dispose of slack resources when sales drop in the current period. These results underscore the significant role of managerial expectations in shaping asymmetric cost behavior. Chen et al. (2019) propose that managers’ expectations carry the most weight when adjustment costs are high. Constraints imposed by adjustment costs amplify the importance of managerial expectations regarding future demands. Additionally, using a panel of elections in 55 countries, Lee et al. (2020) find that cost stickiness tend to increase before elections, as “managers retain slack resources when political uncertainty is high but to be resolved soon.” Zhou (2024) document that cost stickiness decreases following debt covenant violations. Collectively, these studies suggest that management expectations serve as a primary determinant of cost stickiness. On the other hand, Du et al. (2024) suggest that digital innovation reduces cost stickiness by enhancing resource adjustment efficiency.
2.3 Hypothesis development
To the extent that managerial expectations about future demand shape capacity choices and resource adjustment decisions (Anderson et al. 2003; Banker et al. 2014), we propose that strategic alignment between suppliers and customers acts as an informal mechanism through which supply chain partners sustain their long-term collaborative relationships. A firm’s business strategy guides managerial activity, shaping expectations, goals, and facilitating the organization’s efforts to achieve its objectives. When suppliers and customers are strategically aligned, they are more likely to share common goals and act concertedly (Chang et al. 2022; Dekker et al. 2013). This alignment in goals and coordinated actions allows trading partners to fulfill each other’s business requirements, fostering similar long-term objectives and a willingness to share information for building more sustainable relationships. Increased information sharing enhances suppliers’ understanding of the production and demand needs of strategically-aligned customers. It also instills confidence in the prospects of a long-term business relationship with these customers. Consequently, the supplier is more likely to anticipate that a decrease in sales is temporary and that sales to strategically-aligned customers will rebound in subsequent periods.
When both the suppliers and customers pursue the prospector strategy, the supplier is more likely to continue its investments in R&D to improve the quality of products and manufacturing procedures. Conversely, when both the supplier and customers adopt the defender strategy, the supplier is less likely to reduce its expenditure to streamline production and improve cost efficiency. With the significant investment in labor, technology, and facilities, the cost of trimming these resources and then rebuilding them when sales increase can be higher than the costs of holding the slack resources. That is, the expected adjustment costs are significantly higher than the holding costs. Consequently, the supplier is more likely to retain slack resources when sales decrease. Therefore, cost stickiness is more likely to be observed in a supplier with strategically-aligned customers.
Moreover, relationship-specific investments diminish in value once outside that relationship. This renders suppliers’ investments riskier when customers switch to a different supplier. By strategically aligning with supply chain partners, the probability of customers engaging in ex-post opportunistic behavior is reduced. This alignment also allows the supplier to compete beyond pricing, offering a distinct dimension. These benefits of strategic alignment create an environment where suppliers anticipate more positive customer relationships. This optimism about future client relationships encourages them to maintain committed resources even during periods of declining sales.
In addition, suppliers and customers aligned strategically are inclined to engage in multi-period projects and agreements. The nature of these projects necessitates ongoing investments to uphold the relationship and sustain the initiatives. Even during periods of declining sales, suppliers are less prone to cease investments in these multi-period projects. In essence, we argue that supply chain relationships characterized by strategic alignment foster trust and enhance collaboration beyond the capabilities of a contractual arrangement. Therefore, we propose the following hypothesis:
Hypothesis
There is a positive association between strategic alignment among supply chain partners and the stickiness of supplier cost.
On the other hand, the enhanced exchange and sharing of information between strategically-aligned trading partners can yield contrasting effects on cost stickiness. Banker et al. (2014) posit and note that demand uncertainty amplifies cost stickiness. That is, asymmetrical cost stickiness behavior can arise from imperfect information or irrational decision-making by company executives. Information sharing among supply chain partners serves to mitigate demand uncertainty, consequently reducing cost stickiness for suppliers when strategic alignment is present among these partners. Hence, the impact of strategic alignment on cost stickiness remains an empirical question.
3 Research method
3.1 Measurement of strategic alignment
We operationalize strategic alignment by quantifying the extent of similarities between suppliers’ and their customer’s business strategies. We follow the accounting literature and adopt the business strategy typology construct developed by Bentley et al. (2013) to capture each firm’s business strategy. This construct draws upon Miles and Snow’s (1978, 2003) business strategy typology and comprises an aggregate measure derived from six key firm characteristics: (1) the ratio of R&D expenses to total sales, (2) the ratio of SG&A expenses to total sales, (3) sales growth, (4) the ratio of net PP&E to total assets, (5) the ratio of employee numbers to sales, and (6) standard deviation of the total number of employees. Each of these six characteristics is specifically designed to capture a component of the firm’s business strategy.
Some of these characteristics, initially proposed by Ittner et al. (1997), serve as reflections of a company’s strategy: (a) the ratio of research and development to sales, (b) the ratio of employees to sales, (c) the number of new product or service introduction, and (d) market-to-book ratio as a proxy for growth. In Ittner et al.’s (1997) original measure, the count of new product or service introductions relies on a proprietary dataset and is not publicly available. Bentley et al. (2013) address this data limitation by substituting this measure with the ratio of selling and administrative expenses (SG&A) to sales. Hambrick (1983) finds significant differences in this measure between prospectors and defenders. Additionally, Bentley et al. (2013) uses the one-year percentage change in sales, instead of the market-to-book ratio, as a proxy for a firm’s growth, aligning with the notion that prospectors exhibit greater growth potential than defenders (Ittner et al. 1997). The ratio of research and development expenses to sales is chosen to capture a firm’s inclination toward developing new products, with prospectors expected to allocate more to R&D than defenders (Hambrick 1983). Meanwhile, the ratio of employees to sales reflects a firm’s efficiency in producing and distributing goods and services, with defenders anticipated to hire fewer employees per dollar of sales (Ittner et al. 1997; Thomas et al. 1991).
Bentley et al. (2013) introduce two additional measures to Ittner et al.’s ratio list: (i) the standard deviation of total employees to capture organizational stability, intended to capture organizational stability; and (ii) a measure of capital intensity computed as net PPE scaled by total assets, reflecting efficiency and automation of operations. Hambrick (1983) notes that defenders tend to be more automated and efficient, or more capital intensive, while prospectors adopt “more flexible, labor intensive capacity configurations”. Together, these six measures aim to capture a firm’s business strategy.
Following Bentley et al. (2013) method, we compute all six variables using a rolling average over the prior 5 years. The five-year average aligns with capturing firms’ long-term strategic orientation and is consistent with prior research practices (Balsam et al. 2011; Bentley et al. 2013; Higgins et al. 2015; Ittner et al. 1997). We follow Bentley et al. (2013) and require only 3 years of non-missing data for each measure, provided the company has at least six consecutive years of data in Compustat.Footnote 1
We quintile-rank firms within the same two-digit SIC industry code and year for each variable. A score of 1–5 is assigned to observations from the lowest to the highest quintile rank for each variable. We then aggregate the scores of all six variables for each firm-year observation to generate the business strategy score. This score ranges from a minimum of 6 to a maximum of 30, with higher scores indicative of prospector-like characteristics and lower scores associated with defender-like features. The use of the sum of rankings, rather than ratios, is preferred to construct the strategy measure, as the six ratios cannot meaningfully be summed together. Bentley et al. (2013) document evidence that the composite measure “is greater than the sum of its parts,” justifying the use of the composite measure constructed from quintile ranks in our analyses. This approach, evaluating a firm’s ranking relative to its peers in the industry for each strategy dimension, helps minimize changes driven by macroeconomic conditions, technological advancements, and industry practices, focusing on the portion of change attributable to a company’s strategic decisions. Organizational theory also recommends that the strategic components should be evaluated relative to industry competitors (Bentley et al. 2013).
Next, we assess the strategic alignment between a supplier and its customers by comparing the proximity of their respective strategy scores. Following Chang et al. (2021), we calculate the absolute value of the difference in the strategy scores between a supplier and its customer. We then subtract this absolute value from the highest possible value of 24 to quantify the strategic alignment for each customer–supplier pair, denoted as “Align.”
where the value of strategic alignment (Align) ranges from 0 to 24. The higher the value of Align, the greater alignment between the supplier’s and its customer’s strategies. Appendix 1 provides examples of supplier–customer s strategic alignment in the prospector, defender, and analyzer categories.
3.2 Empirical models
We employ cost stickiness models developed in the managerial accounting literature to test our hypothesis. Specifically, we adopt the baseline cost behavior model introduced by Anderson et al. (2003):
where Δlog(Costi,t) ≡ log(Costi,t)–log(Costi,t − 1) is the log-change in cost of goods sold (COGS) or log-change in SG&A costs for firm i in year t. Δlog(Salesi,t) is the log-change in sales revenue approximating the firm’s activity level in year t. Deci,t is an indicator variable equal to one when year t sales are lower than year t − 1, and zero otherwise. The coefficient β2 captures the degree of cost stickiness (anti-stickiness). A negative (positive) β2 suggests cost stickiness (anti-stickiness). Additionally, the sum of coefficients (β1 + β2) captures the percentage decrease in costs for a 1% decrease in sales. Following Anderson et al. (2003), we incorporate various factors known to influence the degree of cost stickiness:
where strategic alignment, Alignit, captures the degree to which the supplier’s and its customer’s strategies align. Additionally, we control for other variables that could influence managerial expectations about future demand. GNPt is the real gross national product growth rate during year t. Managers tend to be more optimistic in a growing macroeconomic environment, viewing sales declines as transient. This optimism may lead them to retain resources, fostering cost stickiness. SucDecit is a dummy variable equal to one if sales revenue in year t − 1 is lower than sales revenue in year t − 2 and zero otherwise. Managers are more likely to adopt a negative outlook on future demand after observing two consecutive periods of sales decreases, potentially mitigating cost stickiness.
Further, we include two firm-level controls identified in the literature as determinants of cost asymmetry: AssetIntit and EmpIntit. AssetIntit is the asset intensity (i.e., assets to sales ratio) of firm i in year t. EmpIntit is the employee intensity (i.e., number of employees to sales ratio) of firm i in year t. Firms requiring more assets or employees to support their sales activity face higher adjustment costs when cutting resources. Appendix 2 provides detailed definitions for each variable.
Banker et al. (2014) and Banker et al. (2013) point out that managers exercise discretion when making decisions about resource allocation. Therefore, we specify the slope coefficient β1 for sales changes as a function of strategic alignment, GNP growth, asset intensity, and employee intensity:
Combining Eq. (1) with Eqs. (2) and (3), we obtain our main estimation model (4), shown below:
A negative coefficient for \(\alpha_{0}\) in model (4) suggests a baseline level of cost stickiness. Based on our hypothesis, the coefficient \(\alpha_{1}\) is expected to be negative, which implies that strategic alignment increases cost stickiness. To estimate the parameters, we employ pooled OLS regressions with industry-fixed effects (defined by two-digit SIC codes) and year-fixed effects to control for potential unobserved industry factors and time trends that affect changes in costs. Also, standard errors are clustered at the firm level (Petersen 2009) to account for potential correlations within firms.
3.3 Data and sample
Our initial sample consists of all publicly traded U.S. companies with available data to identify major customer–supplier relationships between 1978 and 2018. In accordance with the Financial Accounting Standards Board (FASB) and the Securities Exchange Commission (SEC) regulations, public companies are mandated to disclose customers contributing more than 10% of their total annual revenue. We extract information on each company’s major customers and the corresponding revenue generated from Compustat Segment Files. To identify customers, we follow established procedures from prior research (Chang et al. 2018, 2022; Pandit et al. 2011). This involves matching disclosed customer names with their respective Compustat identifiers (GVKEY). We retain observations only when customers can be precisely identified. After merging the Compustat Annual Files with Compustat Segment Files, our resulting sample comprises 69,583 supplier firm-year observations, each associated with identifiable major customers.
We exclude financial service firms (SIC 6000–6999) and utility firms (SIC 4900–4940) due to regulatory and operational differences. Following Banker and Byzalov (2014), we also exclude firm-year observations where COGSt > SALESt and SG&At > SALESt before log-change computations. Additionally, we exclude observations with missing customer sales data and those voluntarily reported customers that contribute less than 10% of sales revenue. For suppliers disclosing multiple customers, we retain the customer with the highest trading amount under the assumption that the largest customer holds the most significant influence.Footnote 2 Observations with incomplete financial data are also excluded from the analysis. Our final sample consists of 16,951 firm-year observations. To address potential outlier effects, we winsorize continuous variables at the 1st and 99th percentiles. Table 1 presents our sample selection procedure.
4 Empirical results
4.1 Descriptive statistics
Table 2 provides descriptive statistics for the variables used in our main analysis. The average (median) firm in our sample reports sales revenue of approximately $1861 ($187) million dollars, COGS of about $1211 ($107) million dollars, and SG&A costs of $346 ($37) million. The mean value of SG&A costs as a percentage of sales revenue is around 27%. These statistics are comparable with the value reported by Banker et al. (2014). On average, nearly 34% of our sample firms experience a decrease in sales revenue relative to the previous year. Approximately 20% of the suppliers exhibit a similar strategy to that of their major customers.
Table 3 reports the Spearman (above the diagonal) and Spearman correlations (below the diagonal) between the variables. The majority of correlations are statistically significant but exhibit small magnitudes, except for the large correlations between Δlog(Sales), Δlog(COGS), Δlog(SG&A), and Dec.
4.2 Strategic alignment and cost stickiness
Table 4 presents the results of the analysis regarding the impact of strategic alignment between supply chain participants on the stickiness of suppliers’ COGS in Columns (1) and (2) and SG&A costs in Columns (3) and (4). We use pooled cross-sectional regressions, controlling for year- and industry-fixed effects (two-digit SIC codes). The estimated coefficients and t-statistics are derived using firm-clustered standard errors, a method that addresses issues of heteroskedasticity and intrafirm error correlation inherent in panel data (Petersen 2009).
To assess the base-level cost stickiness in our sample, we examine the coefficient estimates of Dec*ΔLog(Sales). As presented in Columns (1) and (3) of Table 4, we observe a negative and significant coefficient for the interaction term Dec*ΔLog(Sales), indicating the presence of cost stickiness in our sample. However, in Column (2), when we add the three-way interaction term Align*Dec*ΔLog(Sales) into our COGS model, the coefficient of Dec*ΔLog(Sales) is no longer significant. Instead, the three-way interaction term, Align*Dec*Δlog(Sales), takes on a significantly negative coefficient of − 0.004. These results suggest that cost stickiness in COGS is predominantly observed for suppliers that exhibit strategic alignment with their customers. Specifically, for a unit increase in the strategic alignment score (Align), the incremental impact on COGS for sales decrease (computed at the mean Δlog(Sales) of 0.073) is − 1.393% (− 0.014 + 0.005*0.073–0.004*0.073). Given the mean Δlog(COGS) of 7.5%, this increase in cost stickiness is deemed economically significant.
When we analyze the stickiness of SG&A expenses, we observe once again that cost stickiness is more pronounced for suppliers strategically aligned with their customers. The coefficient of the three-way interaction term, Align*Dec*Δlog(Sales), is significantly negative at − 0.008. For a unit increase in the strategic alignment score, the incremental impact on SG&A for sales decrease (computed at the mean Δlog(Sales) of 0.073) is − 5.1% (− 0.051 + 0.006*0.073–0.008*0.073). Considering the mean Δlog(SG&A) of 7.5%, this increase in cost stickiness represents 68% of the mean. These findings highlight the role of strategic alignment in influencing the stickiness of costs in production and discretionary spending of suppliers.
Regarding the control variables, the three-way interaction terms are generally consistent with findings from prior studies (Anderson et al. 2003; Banker et al. 2014; Chen et al. 2012). In Columns (2) and (4), the estimated coefficient of GNP*Dec*Δlog(Sales) is significantly positive. Previous research has documented mixed results for GNP*Dec*Δlog(Sales). While Anderson et al. (2003) argue that managers are more optimistic in prosperous economic climates and less likely to cut costs during a sales decline, Banker et al. (2013) report both positive and negative coefficient estimates for this term. The coefficient of SucDec*Dec*Δlog(Sales) is positive and significant in both Columns (2) and (4), indicating that managers are more inclined to cut spending when facing consecutive periods of sales decline. This aligns with the notion that managers perceive a sales decline as permanent when occurring in two consecutive years. The coefficients of AssetInt*Dec*Δlog(Sales) and EmpInt*Dec*Δlog(Sales) are negative but statistically insignificant for SG&A analysis.
5 Additional tests
5.1 Sensitivity analyses
5.1.1 Moderating effect of strategy type
A firm’s business strategy can play a significant role in shaping resource adjustment decisions (Ballas et al. 2020). We investigate whether the effect of strategic alignment on cost stickiness varies with the strategy type of strategic alignment—prospector, defender, and analyzer.Footnote 3 We introduce three terms: Align_Prospector, Align_Defender, Align_Analzer, along with their interaction terms in the model. Align_Prospector captures the effect of strategic alignment when both the supplier and its largest customer pursue a prospector strategy. Prospector firms are characterized by innovation and efforts to develop and exploit new products and market opportunities (Miles and Snow 1978, 2003). Align_Prospector takes a value of 1 when both the suppler and its largest customer have a strategy score between 24 and 30 and zero otherwise. Align_Defender captures the effect when the supplier–customer pair pursues a defender strategy. Defender firms are focused on efficiency in the production and distribution of products and services (Miles and Snow 1978, 2003). Align_Defender takes a value of 1 when both the supplier and its largest customer have a strategy score between 6 and 12 and zero otherwise. Align_Analyzer captures the strategic alignment effect for the analyzer strategy. It takes a value of 1 when both the supplier and its largest customer have a strategy score between 13 and 23 and zero otherwise. Table 5 Panel A presents the results. Only the coefficients of Align_analyzer and its interaction terms Align_Analyzer*Δlog(Sales) and Align_Analyzer*Dec*Δlog(Sales) are statistically significant. The insignificance of Align_Prospector, Align_Defender, and their interaction terms could be influenced by the small number of observations in these categories.
In Panel B, we conduct a similar analysis using the quintile ranking of suppliers and customers to define their strategy. In this approach, we introduce three indicator variables to capture different strategy typologies and their interaction terms with Dec*Δlog(Sales) and Δlog(Sales) in our model. The strategy constructs in Panel B are based on the quintile ranking of the strategy scores of the supplier and its largest customers within the same two-digit SIC industry and year. Align_Prospector_Q is an indicator variable that equals one if both the supplier’s and the largest customer’s strategy scores belong to the highest quintile. Align_Defender_Q is an indicator variable that equals one if both the supplier’s and the largest customer’s strategy scores are in the lowest quintile. Align_Analyzer_Q is an indicator variable that equals one if both the supplier’s and the largest customer’s strategy scores are in the three middle quintiles. Using this strategic alignment measure, Align_Analyzer_Q*Dec*Δlog(Sales) has a significantly negative coefficient for both COGS and SG&A analyses. Align_Analyzer_Q*Δlog(Sales) is statistically significant only for the SG&A analysis. In contrast, Align_Prospector_Q and Align_Defender_Q, along with their interaction terms with Dec*Δlog(Sales) and Δlog(Sales), are mostly insignificant.
In Panel C, we follow Chang et al. (2021) by classifying our sample observations into only the Prospector and the Defender groups, using the sample median of the strategy score as a cutoff. Suppliers and customers with a strategy score above the sample median are classified as pursuing the Prospector strategy, while those with a score below the median are considered as pursuing the Defender strategy.
We then construct three indicator variables. Align_PP takes a value of 1 if both the supplier and its major customer pursue the Prospector strategy and zero otherwise. Align_DD takes a value of 1 if both parties pursue the Defender strategy and zero otherwise. Our benchmark group is Align_DIFF, which takes a value of 1 when the supplier and the customer pursue different strategies and zero otherwise. We replace Align with Align_PP and Align_DD in model (4) and re-run the analyses. The results are reported in Table 5 Panel C.
The results presented in Table 5 Panel C, reveal that the coefficients of both Align_PP*Dec*Δlog(Sales) and Align_DD*Dec*Δlog(Sales) are negative and statistically significant. These negative coefficients suggest that when both the supplier and its major customer pursue the same business strategy, either the Prospector or the Defender strategy, the supplier’s COGS and SG&A costs tend to be stickier than in cases where a supplier–customer duet pursues different strategies.
5.1.2 Alternative measures of strategic alignment
5.1.2.1 Three-year rolling average
In this section, we consider alternative constructs of strategic alignment. First, we use a three-year average of the strategy ratios, instead of the five-year average, to construct a firm’s strategy and the strategic alignment measure.Footnote 4 We mandate that the company must have at least four consecutive years of data in Compustat to be included in this sample. The results using this alignment construct are provided in Table 6. Our results are consistent with those obtained using the five-year rolling average. Therefore, our conclusion of increased cost stickiness for strategically-aligned suppliers and customers appears robust to variations in the number of years used to compute the ratios and the alignment measure.
5.1.2.2 Weighted-average strategic score of all customers
Our main analyses focused on the strategic alignment between a supplier and its largest customer. In this section, we replicate the analyses using the weighted-average strategic scores of all the suppliers’ major customers. The strategic score for each customer is computed as before. We use the portion of the supplier’s sales from a specific customer as the weight to compute a weighted-average strategic score for all its major customers. We then compute Align using the supplier’s strategic score and the weighted-average score of all its major customers.
The results using this alternative weighted-average strategic alignment measure are presented in Table 7. Our results remain robust to this alternative approach. The coefficient of Align*Dec*Δlog(Sales) continues to be significantly negative in both the analysis of COGS and SG&A.
5.1.2.3 Residual measure of strategic alignment
Following the approach of Chang et al. (2022) and Venkatraman (1989), we compute an alternative strategic fit measure in three steps. First, we conduct regressions of the supplier’s spending on the corresponding customer’s expenditures in specific areas (e.g., R&D expenses scaled by total assets). The original business strategy measure is based on the following six firm characteristics: (1) the ratio of R&D expenses to total sales, (2) the ratio of SG&A expenses to total sales, (3) sales growth, (4) the ratio of net PP&E to total assets, (5) the ratio of employee numbers to sales, and (6) standard deviation of the total number of employees. In this step, we regress each of the above supplier’s spending/values on the corresponding customer’s expenditure/values in that specific dimension. The estimation period is limited to the duration of the supplier–customer relationship, and we require at least eight observations for each regression analysis. Consequently, the number of observations is significantly reduced. Second, we calculate the absolute values of the residuals from these regressions. Third, we take the negative of these absolute values and replace our aggregate measure of strategic alignment in model (4) with each of these residual measures. The results using these alternative measures are reported as statistically insignificant.
Bentley et al. (2013) assert that the composite strategy score is a superior measure compared to its individual components for capturing the construct of a firm’s business strategy, we aggregate the six adjusted residuals to derive an alternative measure of strategic alignment. Subsequently, we re-run model (4) using this alternative measure. The results are presented in Table 8 Panel A. Our results remain robust to this alternative measure of strategic alignment.
In the second approach, we conduct factor analysis to generate a common factor from the adjusted residuals of the six dimensions. The results using the factor analysis are provided in Table 8 Panel B. However, the results obtained from the factor analysis construct of Align are weaker compared to those using the aggregate sum in Panel A. The coefficient of Align*Dec*Δlog(Sales) continues to be negative but is statistically significant for the SG&A analysis only.
For the third approach, we quintile-rank the six adjusted residuals of each firm by industry (as defined by two-digit SIC code) and year. Observations in the highest quintile are assigned a score of 5, while those in the lowest quintile receive a score of 1. We then aggregate these scores to construct Align. The results using this Align construct are presented in Table 8 Panel C, and are found to be similar to those in Panel B.
5.1.2.4 Strategic alignment measure constructed using factor analysis of the six components of business strategy
We also follow Bentley et al. (2013) and Higgins et al. (2015) in constructing a strategy measure for each firm employing factor analysis on the raw scores in all six dimensions of business strategy. All the components load on one factor. We then adopt the two-stage analysis proposed in Venkatraman (1989) to derive a strategic alignment measure. In the first stage, we regress the suppliers’ factor scores on the customers’ factor scores. The residuals from this regression represent the strategic “misfit” between the supplier and customer pair. In the second stage, we take the absolute values of the residuals and then negate them to obtain a measure of strategic alignment. We then re-run our cost stickiness analyses using this alternative measure of strategic alignment. The results of the analyses are presented in Table 9 and we continue to observe increased cost stickiness for strategically-aligned suppliers.
5.1.2.5 Other alternative strategic alignment measures
In this section, we employ three alternative measures of strategic alignment: Align_ratio, Align_diff, and Align_rank, and re-estimate Eq. (4) (Chang et al. 2021). The construct Align_ratio varies with the relative magnitude of the supplier’s and customer’s scores. When the customer’s score is lower than the supplier’s score, Align_ratio is computed as the ratio of the customer’s strategic score to the supplier’s score. Conversely, when the supplier’s score is lower, Align_ratio is calculated as the ratio of the supplier’s score to the customer’s score. It takes the value of one when a supplier has the same strategy score as its major customer. Align_diff is the difference between a supplier’s and customer’s quintile ranks of their strategy scores; we then subtract the difference from its largest possible value of 4. Align_rank is a dummy variable that equals one when the strategy scores of a supplier and its major customer belong to the same quintile rank and zero otherwise. We present the regression results using these three alternative measures of strategic alignments in Table 10. Our SG&A results are robust to these alternative measures of strategic alignment. The coefficient of the three-way-interaction terms is significantly negative in all cases. However, it is significantly negative only for Align_ratio when COGS is used as the dependent variable.
5.2 Additional analyses
5.2.1 Changing effect of strategic alignment over the customer–supplier relationship
The impact of strategic alignment can vary with the duration of the supplier–customer relationship. First, trust between suppliers and customers likely grows with the duration of their relationship. That is, the longer the relationship, the greater the trust between the two parties. The growth in trust can be particularly fast for strategically-aligned suppliers and customers. Hence, the effect of strategic alignment can increase with the duration of the supplier–customer relationship.
Second, suppliers in the early years of a customer–supplier relationship have to make considerable investments in customer-specific assets and, consequently, are more likely to suffer from a loss, especially when sales decline. However, since strategic alignment fosters trust between supply chain partners and helps sustain a long-term collaborative relationship, strategically-aligned suppliers are more willing to invest and maintain committed resources for their customers in the early stage of the business relationship than other suppliers. In contrast, suppliers in the maturity stage of their customer relationship may not need to increase or keep relationship-specific investments when sales decline. Also, trading partners have not built a strong relationship in the early stage. The relationship and trust grow with the duration of the trading. Under this argument, we anticipate the positive effect of strategic alignment on cost stickiness is stronger in the early years of the supply chain relationship than in the mature stage. Whether the first or second effect dominates remains an empirical question.
To investigate this time-series variation in the effect of strategic alignment, we rank the duration of the relationship between the supplier and its customers into five groups. If the supplier firm-year observation falls into the first and second quintiles of the duration rank, it is considered to be in the early stage of the supply-chain relationships. Conversely, those in the fourth and fifth quintiles are deemed to be in the mature stage of their relationships. Table 11 presents the results of the analysis.
As shown in Table 11, the coefficient of Align*Dec*Δlog(Sales) is negative and significant for suppliers in the early stage of the relationship (Columns (1) and (3)) but insignificant for suppliers in the mature stage of the relationship (Columns (2) and (4)). These results are consistent with our expectation that strategically-aligned suppliers are more willing to invest and retain committed resources for their customers in the early stage of the relationship, even when sales decline.
5.2.2 Controlling for the effect of product market competition and economic policy uncertainty
Li and Zhang (2017) suggest that product market competition can motivate management to hold on to slack resources when sales decline. This helps to maintain the firm’s competitive advantage. We introduce the product market competition measure (THHI) proposed by Hoberg and Phillips (2016) into model (4). Jin and Wu (2021) find that cost stickiness, in particular the stickiness of cost of goods sold and number of employees, decreases with the aggregate economic policy uncertainty. We account for the effect of economic policy uncertainty by including the EPU index (EPU), developed by Baker et al. (2016) in our model. As THHI and EPU indices are available only from 1989 and 1985, respectively, there is a drop in the number of observations for these analyses.
The results in Table 12, after accounting for the impact of these two factors, confirm the robustness of the positive association between the supplier’s cost stickiness and the strategic alignment with its customer. The first two columns present the results when we include the industry fixed effects (defined by the two-digit SIC code). The last two columns provide the results when we include the Fixed Industry Classification (FIC300Footnote 5) fixed effects.
The coefficient of EPU*Dec*Δlog(Sales) is significantly positive, consistent with the findings of Jin and Wu (2021), indicating that cost stickiness decreases with economic policy uncertainty. This implies that, in times of heightened economic uncertainty, firms may be more inclined to reduce costs in response to declining sales. On the other hand, the coefficient of THHI*Dec*Δlog(Sales) is significantly negative, aligning with Li and Zhang’s (2017) conclusion that cost stickiness increases with product market competition. This suggests that in competitive markets, firms are more likely to retain slack resources to maintain a competitive advantage, contributing to cost stickiness.
5.2.3 Controlling for the characteristics of the supply chain relationship
In this section, we address the impact of customer concentration and customer bargaining power on cost stickiness. Dhaliwal et al. (2016) find that a more concentrated customer base increases a supplier’s risk and leads to a higher cost of equity. Their findings suggest that the characteristics of the supply chain relationship can affect suppliers’ costs. We account for the effect of the concentration of customer base by dividing our sample into two sub-samples: high and low customer concentration. We then run the cost stickiness analysis separately for the two sub-samples. We use the Herfindahl–Hirschman index to measure customer concentration (CHHI), following the approach of Dahliwal et al. (2016). Observations with a CHHI value above the median are categorized as part of the high customer concentration group, while those falling below the median are deemed part of the low customer concentration group.
Dhaliwal et al. (2016) point out that a more concentrated customer base heightens a supplier’s risk. Hence, we anticipate that suppliers facing a more concentrated customer base would be inclined to retain resources during periods of sales decline to sustain long-term customer relationships. Thus, we predict that cost stickiness will be more pronounced in the high customer concentration sub-sample.
Regarding the effect of strategic alignment on cost stickiness under different customer concentrations, we refrain from providing a directional prediction. On one hand, strategic alignment could potentially mitigate the risks associated with customer concentration. On the other hand, its impact could vary based on the specific characteristics of the supply chain relationships in high versus low customer concentration scenarios.
The results are presented in the first four columns of Table 13. In the COGS analysis, we observe that Align*Dec*Δlog(Sales) takes on a negative coefficient in both the high and low customer concentration sub-samples. However, the coefficient is statistically significant solely for the low customer concentration sub-sample. Turning to the SG&A analysis, the coefficient of Align*Dec*Δlog(Sales) is negative and statistically significant in both the high and low customer concentration sub-samples. When we perform a statistical test to assess the difference in the coefficient of Align*Dec*Δlog(Sales) between the two sub-samples, we do not observe any statistically significant distinction.
In this section, we explore the influence of customer bargaining power on the relationship between supplier–customer strategic alignment and cost stickiness. Chang et al. (2022) document that strategic aligned supplier can mitigate the negative impact of customers’ bargaining power on supplier performance because the strategic fit provides the customer long-term benefits. The customers are willing to “trade the short-term benefits obtained through supplier concessions with the long-term benefits”. Nguyen et al. (2023) observes a transfer of risk taking along the supply chain. They find that when a major customer takes more risk to increase their bargaining power and rent extraction ability, suppliers also engage in more risk taking to improve their bargaining positions. Both studies suggest that customers’ bargaining power affects suppliers’ decisions and performance.
To quantify customer bargaining power (BP_MS), we compute the logarithm of the ratio of a customer’s market share to the supplier’s market share. Observations with BP_MS values above the median are considered as having strong customer bargaining power, while those with values below the median are classified as having weak customer bargaining power group. We expect observations with strong customer bargaining power will exhibit higher cost stickiness.
The results, as presented in Columns (5) to (8) of Table 13, reveal that the coefficient of Align*Dec*Δlog(Sales) is negative in both the strong and weak customer bargaining power groups for both the COGS and SG&A analyses. However, statistical significance is observed only for the strong customer bargaining power group. This aligns with the notion that suppliers are inclined to retain excess resources during sales decline to cultivate a long-term relationship with strategically-aligned customers possessing strong bargaining power. In exchange, these customers forego short-term benefits derived from supplier concessions in favor of longer-term benefits. A statistical test assessing the difference between the coefficients in the two groups is not significant for the COGS analysis but attains statistical significance at the 1% level for the SG&A analysis.
We also repeat this analysis using two alternative measures of bargaining power. Following Nguyen et al. (2023), we consider suppliers producing durable goods and differentiated products to have a more comparable bargaining power as their customers than those producing non-durable or more standardized products. The results using these two proxies are provided in Table 13 Panel B. Contrary to the results using the relative market share of suppliers and customers, coefficient of Align*Dec*Δlog(Sales) is negative and statistically significant for the durable and differentiated product group. That is, the effect of strategic alignment is most significant where the customers do not have dominant bargaining power.
One potential explanation for this result is that in addition to bargaining power, these two measures also reflect the specific investments a supplier has to make to produce differentiated/durable products. For differentiated products, the specific skills of labor and specialized machinery required are likely more significant than for standardized or non-durable products. Labor with specific skills and machinery can be difficult and expensive to acquire. Hence, suppliers with strategic alignment are less likely to fire their labor or dispose their customized machinery when sales decline.
5.2.4 The effect of strategic alignment on R&D cost stickiness
Irvine et al. (2016) note that long-term business relationships enhance customers’ willingness to share information regarding technological advancements. They argue that the firm’s patent activity experiences significant increase over the life cycle of the business relationship. Given the expected longevity of strategically-aligned partnerships between suppliers and customers, it is anticipated that they will prioritize investments in technology specific to their relationship. This, in turn, amplifies the persistence of cost stickiness in R&D. Accordingly, we re-estimate model (4) with the change in the logarithm of R&D costs as the dependent variable, and report the results in Table 14.
The coefficient estimate of Dec*ΔLog(Sales) in Column (1) of Table 14 is negative but insignificant, which does not support the base-level R&D cost stickiness. However, the coefficient of Align*Dec*ΔLog(Sales) is significantly negative (Column (2)). This implies that strategic alignment motivates suppliers to establish enduring relationships with their customers. As a result, suppliers with strategic alignment are more inclined to retain their committed R&D resources even when sales decline. This, in turn, accentuates the persistence of R&D cost stickiness among strategically-aligned suppliers.
5.2.5 The effect of strategic alignment on supplier’s performance and relationship with customers
A key motivation for a supplier to retain surplus resources amid a sales downturn is the desire to uphold its resource-based competitive advantage and preserve the strategically-aligned customer. In this section, our investigation focuses on how strategic alignment influences the supplier’s ability to sustain a long-term relationship with the customer and its subsequent impact on the supplier’s performance.
First, we examine whether the duration of the supplier–customer relationship is affected by the strategic alignment between the two parties. Following the model proposed by Raman and Shahrur (2008), we incorporate additional variables that affect the incentives of both suppliers and customers to prolong their business association.
We use the Compustat segment data to determine the duration of a supplier–customer relationship. Under SFAS 131, suppliers are obligated to disclose customers contributing over 10% of their sales. Customers falling below this threshold are not required to be disclosed. Following Raman and Shahrur (2008), we classify customers exiting the disclosure in the Compustat segment dataset as terminating the supplier–customer relationship, aligning with the concept of “customer defection” as defined in Hollmann et al. (2015).
In computing relationship duration, we assume the commencement when a supplier–customer pair initially appears in the Compustat segment dataset and conclude it when the pair is no longer present in the segment dataset. Our analysis includes supplier–customer relationships commencing post-1978, as we cannot precisely identify the initiation date for those existing in 1978 (the first year the data is available). The duration analysis extends until 2017, with predictions for relationship termination in the subsequent year. Relationships persisting until the final year of our sample are treated as right censored. Detailed results of these analyses are reported in Table 15.
We use three different models to examine the effect of strategic alignment on the duration of the relationship between a supplier and its major customer (Allison 2010). The first model uses logistic regression, where the dependent variable is an indicator variable taking a value of 1 if the supplier–customer relationship concludes in the subsequent year, and 0 otherwise. In contrast, the Cox and Weibull models gauge relationship duration as the number of years the relationship lasts, treating it as a continuous variable.
Results from the logistic analysis are reported in the first column of Table 15. The coefficient for Align is negative and statistically significant at the 5% level. This implies that suppliers with strategic alignment are less likely to terminate their relationships compared to those without such alignment. Additionally, factors contributing to a lower likelihood of termination include the customer constituting a significant portion of sales revenues, the supplier being larger and more mature. On the other hand, the probability of relationship termination increases if the supplier experiences negative free cash flows and the customer purchases a substantial portion of materials from the supplier.
Second, we estimate a Cox proportional hazards model. Introduced by Cox (1972), this model serves as a semi-parametric generalization of the Weibull model. Unlike the Weibull model, the Cox model’s baseline hazard function does not necessitate specific distributional assumptions. This makes it particularly advantageous when distributional assumptions of the Weibull model are unmet or challenging to verify, rendering the Cox model more robust (Allison 2010). In this model, the hazard of relationship termination is assumed to vary based on the same set of explanatory variables as in the logistic model. Given that the estimation of this model is grounded in the hazard function, a negative coefficient for a given explanatory variable suggests that a higher value of that variable corresponds to a longer relationship duration.
Results derived from the Cox model are reported in Columns (2) and (3) of Table 15. The coefficient of Align is negative and statistically significant at the 1% level. This suggests that a closer strategic alignment between a supplier and a customer is associated with a longer duration of their relationship. The control variables are similar to those in the logistic model.
Third, we estimate the relationship duration between suppliers and their major customers using the Weibull model and report the results in Column (4) of Table 15. In contrast to the prior two models, a positive coefficient in the Weibull model indicates that an increase in the explanatory variable is associated with a longer survival time. The coefficient of Align is positive and statistically significant at the 1% level in Column (4). This result suggests that when a supplier and a customer share a closer strategic alignment, their relationship tends to endure for a longer duration. This outcome aligns with the results reported by Chang et al. 2021.
Beyond the examination of relationship duration, we explore whether strategic alignment influences the supplier’s performance. We anticipate that suppliers benefit from extended relationships with strategically-aligned customers, as reflected in their willingness to invest and retain slack resources during sales declines. Hence, we hypothesize that strategic alignment between the supplier and the customer can enhance the supplier’s performance on average.
To assess the impact of strategic alignment on supplier performance, we incorporate additional control variables that have been identified as influential factors, following the suggestions of (Chang et al. 2022). We use model (5) to estimate the effect of strategic alignment on the supplier’s performance.
where ROA_S is the suppliers’ return on assets. It is computed as the ratio of income before extraordinary items to total assets. The key explanatory variable is Align, our measure of strategic alignment. The vector of controls includes variables correlated with supplier performance. These include lagged return on assets in year t − 1 for the supplier (ROA_St − 1), the relative market share of the customer to that of the supplier (BP/MS), supplier’s (customer’s) firm size (Size_S, Size_C), supplier’s (customer’s) firm age (Age_S, Age_C), supplier’s (customer’s) annual sales growth in percentage (Growth_S, Growth_C), an indicator variable for a multi-segment supplier firm (SEG_S), supplier’s financial leverage (FLEV_S), and the duration of the supplier–customer relationship (Duration).
Results from the performance analysis (Table 16) indicate a positive association between strategic alignment among suppliers and customers and the suppliers’ performance. Our findings suggest that suppliers derive performance benefits from strategic alignment with their customers. This finding provides insights into the supplier’s willingness to bear the cost of maintaining slack resources during a sales decline for strategically-aligned customers.
Overall, the results from the above two tests consistently support the notion that suppliers derive benefits from strategic alignment with their customers. The evidence indicates that suppliers, engaged in strategic alignment, are inclined to foster longer-term relationships and exhibit better performance. This aligns with the argument that strategic alignment within the supply chain contributes to the development of a resource-based competitive advantage. The findings underscore the significance of strategic alignment in fostering enduring and beneficial relationships along the supply chain.
5.2.6 Endogeneity of strategic alignment
In our investigation, we document a significant association between a supplier’s cost stickiness and its strategic alignment with its major customer. While we control for various factors influencing a company’s cost behavior, it remains plausible that unobserved constructs drive both cost stickiness and strategic alignment. This raises concerns about potential spurious relationships. To mitigate the impact of endogeneity, we adopt the propensity score matching (PSM) procedure. This involves identifying a set of control firms (i.e., suppliers lacking strategic alignment with major customers) whose propensity scores closely align with the treatment firm (i.e., a supplier with strategic alignment).
In executing the propensity score matching (PSM), we first quintile-rank all observations based on their strategy scores within each industry and year. We then construct an indicator variable, Align_rank, which takes a value of 1 if the strategy scores of the supplier and its major customer fall within the same quintile rank and 0 otherwise. Next, we estimate a logistic regression that includes control variables as per Chang et al. (2022) model (1) to generate propensity scores, representing the likelihood that a supplier has strategic alignment with its major customer (i.e., Align_rank = 1). The matching process involves pairing each treatment firm (Align_rank = 1) with a control firm (Align_rank = 0) possessing the closest propensity score. A one-to-one match without replacement is performed, with a caliper distance set at 0.01 to generate a matched sample (Gong and Luo 2018). This matching procedure yields 3783 treatment observations and matched control observations. Table 17 Panel A displays the covariate balance following the PSM, while Panel B presents the results derived from this PSM sample.
The absence of a significant difference in covariates after the PSM procedures indicates the effectiveness of the matching technique. When employing this PSM pre-treated sample in the cost stickiness analysis, the continued observation of a significant negative coefficient for Align*Dec*Δlog(Sales) underscores the robustness of our results to the PSM procedures.
6 Conclusion
This study examines the impact of strategic alignment on the cost stickiness of suppliers. Using a dataset of supplier–customer dyads from 1978 to 2018, we find that strategic alignment increases the stickiness of suppliers’ COGS and SG&A costs. This is attributed to the trust fostered by strategic alignment, motivating suppliers to cultivate long-term collaborative relationships with their customers. The willingness of strategically-aligned suppliers to bear the costs of holding committed resources during sales declines and their strengthened commitment to invested R&D expenditures substantiate the impact of strategic alignment on cost behavior.
This study also provides insights by revealing that the influence of strategic alignment on cost stickiness is more pronounced in the early stages of supply chain relationships, aligning with the idea that significant relationship-specific investments are made during this period. Additionally, the examination of cross-sectional variations, such as the concentration of the supplier’s customer base and customer bargaining power, enriches the understanding of how strategic alignment affects cost stickiness in diverse contexts. A consequence of strategic alignment is a longer duration of the supplier–customer relationship and enhanced performance.
Our study extends the literature on the impact of strategic alignment on corporate behavior (Ashfaq and Raja 2013; Schreiner et al. 2009; Vachon et al. 2009). By examining how strategic alignment influences suppliers’ expectations about future sales and their decisions regarding resource commitment and retention, this study provides a valuable perspective on the broader implications of strategic alignment in supply chain relationships. Future studies can further explore other potential channels that strategic alignment can influence a company’s policy and behavior.
Notes
We also perform a sensitivity test using a three-year rolling average for each ratio and require only four years of consecutive data in Compustat. Our results are robust to this alternative construct method. Results are provided in the Sensitivity Analysis section.
We perform a sensitivity analysis using the weighted-average of all major customers’ strategy scores to construct the alignment measure. Results are reported in the Sensitivity Analysis Section.
Following Bentley et al. (2013), we exclude reactors strategy from our sample as it is short-term and unstable.
For both the three-year and five-year rolling averages, we require there to be at least three observations to compute the average. Any observations with fewer than three observations in the prior three (five) years are considered as having missing values for the three-year (five-year) rolling average.
Hoberg and Phillips (2016) developed the Fixed Industry Classification (FIC) by grouping similar firms, based on product similarity scores, into fixed groups of industries and requires membership transitivity.
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Appendices
Appendix 1: Business strategy alignment examples
This appendix provides descriptive examples of supplier–customer duet having high strategic alignment scores in our example. We classify the strategies of both suppliers and customers into Prospector, Analyzer, and Defender. Prospectors are companies with a strategy score range of 24–30; Analyzers are those with a strategy score range of 13–23; and Defenders are those with a strategy score range of 6–12.
Prospector 1. Capstone (supplier) Signs Strategic Agreement with UTC Power (customer) for the Commercial Launch of a New 200KW Microturbine Product “Capstone Turbine Corporation (NASDAQ:CPST) and UTC Power, a United Technologies Corp. (NYSE:UTX) company, today announced the companies are extending their collaborative relationship to develop and launch a 200 kW microturbine product.” “We are excited to build on our past success with Capstone,” said John Fox, UTC Power on-site Product Manager. “This new C200 product will expand our offerings and allow us to bring the PureComfort on-site power system benefits to a larger number of customers.” (https://www.capstonegreenenergy.com/news/press-releases/detail/1384/) | Capstone’s strategy score: 24 UTC Power’s strategy score: 24 Strategic alignment score: 24 |
2. Finisar (supplier) Awarded Excellence in Delivery and Flexibility by Cisco (customer) “This prestigious award recognizes Finisar® for executing under tremendous delivery and flexibility pressures, while quickly adapting to Cisco’s Lean hubbing requirements and adopting the company’s cloud-based SCP material management platform.” “Our Supplier Appreciation Awards highlight exceptional performances by the very best of our extended supply chain, and gives us the opportunity to recognize all of our trusted partners’ hard work and dedication,” said John Kern, senior vice president, Supply Chain Operations, Cisco. “Our theme this year, ‘Embrace the Pace: Transforming Tomorrow Together’, emphasizes the criticality of flexibility, collaboration and accelerated innovation – all qualities shared by this year’s award recipients.” (https://www.fibre-systems.com/news/finisar-awarded-excellence-delivery-and-flexibility-cisco) | Finisar’s strategy score: 28 Cisco’s strategy score: 25 Strategic alignment score: 21 |
Defender 1. New Tengasco Line Begins Service (Knoxville-based Tengasco (supplier) last week began natural gas deliveries over its new intrastate pipeline to Eastman Chemical Co. (customer)) “Knoxville-based Tengasco last week began natural gas deliveries over its new intrastate pipeline to Eastman Chemical Co. under a 20-year sales contract that calls for a minimum of 10 MMcf/d. The contract, which will supply at least 80% of Eastman’s gas requirements in Kingsport, TN, is estimated to generate over $200 million of revenues during its term.” (https://www.naturalgasintel.com/new-tengasco-line-begins-service/) | Knoxville-based Tengasco’s strategy score: 12 Eastman Chemical Co’s strategy score: 12 Strategic alignment score: 24 |
2. ‘A new TreeHouse (supplier):’ Private label giant builds for success by thinking small, CEO says the snacks and beverages manufacturer has evolved into a more focused company that can better innovate and respond to the needs of retailers such as Walmart (customer) and Amazon “TreeHouse Foods has radically transformed itself into a leaner, more focused company that is better positioned to capitalize on the growing demand for private-label offerings from retailers.” “It’s a new TreeHouse. It’s a totally different TreeHouse,” Steve Oakland, TreeHouse’s CEO, said in an interview. “We’re easier to run, and we’re just in very, very different categories that are attractive to our customers.” “Today, TreeHouse is a major supplier of private-label offerings to Walmart, Kroger, Costco, Amazon, Target and Aldi. The company, which posted sales in the first quarter of $895 million, expects revenue to grow between 3 and 5% from 2024 to 2027. In comparison, TreeHouse’s portfolio pre-divestiture was expected to grow by 1–2%.” | TreeHouse’s strategy score: 12 Walmart’s strategy score: 12 Strategic alignment score: 24 |
Analyzer 1. Daimler Truck AG (customer) and Cummins INC. (supplier) Announce Global Plan for Medium Duty Commercial Vehicle Engines “Today Daimler Truck AG and global power leader and independent engine manufacturer Cummins Inc. (NYSE: CMI) announced that the companies have signed a memorandum of understanding establishing a global strategic partnership for medium duty engine systems. The companies added that other opportunities for collaboration are also being evaluated As part of the planned strategic partnership, Cummins will invest in the further development of medium duty engine systems for Daimler Trucks and Buses and the global production and delivery of medium duty engines by Cummins for Daimler Trucks and Buses beginning in the second half of the decade.” “We are pleased to announce this important strategic partnership with Daimler to provide the medium duty engine systems for Daimler Trucks and Buses in global markets,” said Tom Linebarger, Chairman and CEO, Cummins Inc. “Our partnership is a terrific opportunity for both companies to be more competitive, drive global innovation, expand offerings to customers and reduce emissions. We are looking forward to working with Daimler on this and exploring other potential opportunities to grow our respective companies.“ “Daimler anticipates the partnership with Cummins will enable Daimler to increase and accelerate its development efforts on alternative and emerging technologies, including non-diesel engines.“ | Daimler Truck AG’s strategy score: 18 Cummins INC.’s strategy score: 16a Strategic alignment score: 22 |
2. Dana (supplier), Paccar (customer) Collaborating on Electric Trucks “Dana Inc. is collaborating with two commercial vehicle manufacturers on electric powertrain technologies for medium-duty trucks. Both Kenworth Truck Co. and Peterbilt Motors Co. are subsidiaries of Paccar, which announced the projects at 2020 Consumer Electronics Show in Las Vegas.” https://www.americanmachinist.com/news/article/21120093/new-paccar-electric-trucks-unveiled-dana-inc | Dana’s strategy score: 15 Paccar’s strategy score: 14b Strategic alignment score: 23 |
3. Cummins (supplier) and Paccar (customer) form a joint venture to advance battery cell production in the US “Accelera™ by Cummins, the zero-emissions business unit of Cummins Inc. [NYSE: CMI], Daimler Trucks & Buses US Holding LLC (a Daimler Truck Group Company; DAX: DTR0CK; “Daimler Truck”) and PACCAR [NASDAQ: PCAR] are partnering to accelerate and localize battery cell production and the battery supply chain in the United States.” | Cummins’ strategy score: 17 Paccar’s strategy score: 15c Strategic alignment score: 22 |
Appendix 2: Variable definitions
Variables | Description |
|---|---|
Δlog(Sales) | The logarithm of changes in sales revenues (in millions) |
Δlog(COGS) | The logarithm of changes in the cost of goods sold (in millions) |
Δlog(SG&A) | The logarithm of changes in selling, general, and administrative costs (in millions) |
Dec | An indicator variable that equals one when sales revenue for period t is less than the previous period and zero otherwise |
Align | Strategic alignment measure. It is computed as the absolute value of the distance between a supplier’s strategy score and its major customer’s. This absolute distance is then subtracted from its highest possible value of 24 |
GNP | The growth rate in the U.S. gross national product |
SucDec | An indicator variable equals one when sales revenue for period t − 1 is less than period t − 2 and zero otherwise |
AssetInt | Asset intensity. It is computed as the ratio of total assets divided by sales revenue |
EmpInt | Employee intensity. It is computed as the ratio of total employees divided by sales revenue |
AT | Total assets |
EMP | The number of employees (in thousands) |
Additional control variables used in additional tests | |
Align_Prospector | An indicator variable that equals to one if both the supplier and the largest customer have a strategy score between 24 and 30, and zero otherwise |
Align_Defender | An indicator variable that equals to one if both the supplier and the largest customer have a strategy score between 6 and 12, and zero otherwise |
Align_Analyzer | An indicator variable that equals to one if both the supplier and the largest customer with a strategy score between 13 and 23, and zero otherwise |
Align_Prospector_Q | An indicator variable that equals to one if both the supplier’s and the largest customer’s strategy scores belong to the highest quintile |
Align_Defender_Q | An indicator variable that equals to one if both the supplier’s and the largest customer’s strategy scores are in the lowest quintile |
Align_Analyzer_Q | An indicator variable that equals to one if both the supplier’s and the largest customer’s strategy scores are within the second to fourth quintile |
Align_PP | An indicator variable that equals to one if both the supplier’s and the largest customer’s strategy scores are higher than the median of strategy score in the full sample, and zero otherwise |
Align_DD | An indicator variable that equals to one if both the supplier’s and the largest customer’s strategy scores are lower than the median of strategy score in the full sample, and zero otherwise |
Align_ratio | The ratio of customer strategy score to supplier strategy score if the customer’s score is lower than the supplier’s score and the ratio of supplier strategy score to customer strategy score if the supplier’s score is lower than the customer’s score. It takes a value of one when the supplier’s score equals the customer’s |
Align_diff | The difference in strategy ranking. It is computed as five minus the difference between a supplier’s and its customer’s quintile ranks of their strategy scores |
Align_rank | A dummy variable equals one when the strategy scores of a supplier and its major customer belong to the same quintile rank and zero otherwise |
THHI | The sales-based concentration based on industry classification of the Text-Based Network Industry Classification (TNIC) developed by Hoberg and Phillips (2016). We multiply the THHI by − 1 so that a higher THHI value indicates more competition. Following Li and Zheng (2017), we rank the THHI into deciles and also standardize it to be between 0 and 1 as well |
EPU | The aggregate monthly average EUP-related articles volume |
CHHI | Customer concentration, squaring the percentage of sales contributed by the supplier’s large customers, then summing |
BP_MS | The logarithm of the ratio of the customer’s market share to the supplier’s market share |
Rel_Stop_D | A dummy variable equals one when the supplier–customer relationship ends in the subsequent year, and zero otherwise |
Rel_Stop | The length (in years) of the relationship for each supplier–customer pair, starting when a supplier–customer pair first appears in our dataset (1978) and ending when the pair is no longer present in our data set (2017) |
EM_S | The absolute value of discretionary accruals of the supplier estimated using the modified Jones model (Raman and Shahrur 2008) |
EM_C | The absolute value of discretionary accruals of the largest customer estimated using the modified Jones model (Raman and Shahrur 2008) |
DEP_S | Sales to the largest major customer divided by supplier total sales |
DEP_C | Purchases from supplier divided by the largest customer total cost of goods sold |
Size_S | Natural logarithm of the supplier assets |
Size_C | Natural logarithm of the largest customer assets |
Age_S | Supplier age measured as the number of years the supplier is listed in Compustat |
Age_C | The largest customer age measured as the number of years the largest customer is listed in Compustat |
NegFCF_S | A dummy variable that equals 1 if the supplier free cash flow is negative, and 0 otherwise |
NegFCF_C | A dummy variable that equals 1 if the largest customer free cash flow is negative, and 0 otherwise |
RD_S | The ratio of the supplier R&D expense to total assets |
RD_C | The ratio of the largest customer R&D expense to total assets |
Duration | The duration of the link between the firm and its largest customer |
ROA_S | Suppliers’ return on assets measured as the ratio of income before extraordinary items to total assets |
BP/MS | Relative market shares measured as the ratio of the customer’s market share in its industry to the supplier’s market share in its industry |
Growth_S | Supplier annual sales growth percentage |
Growth_C | The largest customer annual sales growth percentage |
SEG_S | An indicator variable equal to 1 if the firm reports at least two business segments |
FLEV_S | The supplier’s financial leverage at the beginning of the year, measured as the ratio of the book value of total assets to the book value of equity |
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Chiang, CH., Choy, H., Yang, YJ. et al. Strategic alignment between supply chain partners and cost stickiness of suppliers. Rev Quant Finan Acc (2024). https://doi.org/10.1007/s11156-024-01324-x
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DOI: https://doi.org/10.1007/s11156-024-01324-x