Strategic Similarity and Investment Efficiency

Abstract

We examine whether sharing similar business strategies between suppliers and customers increases suppliers’ investment efficiency. Using a data set of U.S. supplier-customer dyads, we posit and find that sharing strategic objectives with major customers is associated with higher-quality supplier investment decisions. Specifically, strategic similarity between supply chain partners is associated with a lower likelihood of underinvestment and overinvestment by suppliers. We also find that strategic similarity improves supplier investment decisions when the customer’s information environment is weak. In addition, our cross-sectional analyses show that strategic similarity is associated with higher investment efficiency for suppliers in durable goods industries and for those with customers who are likely to switch to other suppliers.

Keywords

Introduction

In an era of digital revolution in which companies are becoming increasingly interconnected and interdependent, companies must develop long-term relationships with their supply chain partners to obtain competitive advantages (Hult et al., 2004, 2007). Prior literature documents that sharing similar strategic objectives (hereafter, strategic similarity) plays an important role in supply chains and is crucial to intercompany collaboration. Specifically, having similar strategic objectives helps trading partners build trust, establish long-term relationships, and resolve conflicts (Anderson & Weitz, 1989; Nielsen, 2010; Schloetzer, 2012). Collaboration and cooperative relationships further enhance efficiency and responsiveness within a supply chain (Ellram, 1990; Vachon et al., 2009) and improve suppliers’ financial performance (Das et al., 2006; Schloetzer, 2012).

Although these studies suggest that sharing similar business strategies enhances long-term commitment and instills confidence in the continuity of supply chain relationships, little is known about how sharing similar strategies affects investment decisions within a supply chain.

This question is important because business strategies establish company goals, operations, prioritize resource allocation, and investment decisions, and help to determine value creation (Hambrick, 1983). Building on prior literature that documents benefits of sharing business strategies with trading partners, we examine whether sharing similar strategies with trading partners has consequences for their investment decisions. Specifically, we examine whether strategic similarity between a supplier and its customers improves the supplier’s investment efficiency.

In a supply chain, the upstream supplier needs to acquire property, plant, and equipment to support production for its customers. When making investment decisions, the supplier must consider future customer demand and ensure that the invested capacity aligns with the expected demand. It is unfavorable for the supplier to have underutilized resources because idle capacity increases per-unit production costs (due to fixed costs being allocated to fewer units) and extends the time required to recover investment amounts (Gunasekaran et al., 2004). Conversely, insufficient preparation is also undesirable because the supplier fails to meet demand and bears the risk of losing customers (Davis, 1993). Accordingly, the quality of investment decisions depends on the efficiency of investment—whether suppliers’ investment (suppliers’ input) meets the demand of customers (suppliers’ output). Given the importance of optimal investment, we focus on investment efficiency rather than investment level.¹

Suppliers and customers often establish interactions through relationship-specific investments (Dhaliwal et al., 2016; Joskow, 1988). For example, suppliers may purchase machinery and/or equipment to support customized production for particular customers. Given that relationship-specific investments have limited value outside of the specific supplier-customer relationship (Banerjee et al., 2008), suppliers may face overinvestment problems if the committed investments cannot be fully utilized in the future because of insufficient customer demand. Under such a circumstance, the supplier may ex-ante reduce investments and thus underinvest in relationship-specific assets (Joskow, 1988). However, a supplier may overinvest because customers, not bearing the cost of idle assets, have incentives to overestimate their intended orders to suppliers to secure the supply of materials or parts from suppliers in anticipation of high demand (Lee et al., 1997). In either case, suppliers’ investments deviate from the optimal level, and these deviations are interpreted as investment inefficiency.

Investment inefficiency is mitigated when managers make more informed investment decisions. We posit that strategic similarity between a supplier and its major customer improves information-sharing within the supply chain and thus increases the supplier’s investment efficiency. When a supplier and its customer adopt similar business strategies, they align on business objectives and common interests, making effective communication more likely. This enhanced communication facilitates information sharing between the two parties (Narayanan et al., 2015; Quinn & Hilmer, 1994). Through information sharing, supplier companies can better assess customers’ ability to fulfill contracts and more accurately predict customers’ future demand. Sharing strategic objectives also fosters collaboration and facilitates joint planning in key operational areas (Simatupang & Sridharan, 2002). As a result, strategic similarity may reduce the risk of customers overestimating their intended orders to suppliers, thereby decreasing the likelihood of overinvestment by the supplier. Additionally, strategic similarity helps alleviate supplier concerns about the uncertainty of long-term relationships with its customers, thereby reducing the supplier’s underinvestment (Banerjee et al., 2008). Although we predict that sharing strategic objectives is associated with higher-quality investment decisions, it is possible that firms with more exploratory (efficiency-oriented) objectives are prone to overinvestment (underinvestment) (Navissi et al., 2017), and these investment decisions may be exacerbated by the influence of customers who pursue similar strategic goals.

Consistent with Bentley et al. (2013) and Higgins et al. (2015), we measure each firm’s business strategy based on six dimensions of the firm’s operating characteristics (i.e., research and development (R&D) intensity, labor productivity, sales growth, investment in organization capital, organizational stability, and commitment to technological efficiency). After determining each firm’s business strategy, we construct a score to quantify the degree of strategic similarity between each supplier company and its major customer. To measure investment efficiency, we follow Bae et al. (2017), Biddle et al. (2009), and García-Lara et al. (2016) and assess the extent to which a company’s investment deviates from the optimal level. Overinvesting in negative net present value (NPV) projects and forgoing positive NPV projects indicate investment inefficiency (Biddle et al., 2009).

Our empirical analyses use a sample of 13,184 firm-year observations from 1992 through 2019 to test for a relation between strategic similarity and suppliers’ investment efficiency.^2,3 We find that strategic similarity between supply chain partners is associated with a lower likelihood of underinvestment and overinvestment by supplier firms. The corresponding coefficients indicate that an interquartile increase in our strategic similarity score is associated with a 2.65% (0.69%) decrease in the probability of underinvestment (overinvestment) for an average firm. This decrease is meaningful because the estimated probability for an average company to underinvest (overinvest) in our sample is approximately 35.09% (12.12%). These results suggest that strategic similarity between supply chain partners is associated with a statistically significant and economically meaningful reduction in suppliers’ investment inefficiency.

Next, we focus on the information sharing mechanism through which strategic similarity improves suppliers’ investment efficiency by examining whether strategic similarity benefits suppliers whose customers have a weak information environment. Enhanced information-sharing is expected to improve information flows within the supply chain, which should help suppliers whose customers have an opaque information environment make investment decisions (Narayanan et al., 2015; Quinn & Hilmer, 1994). Consistent with our prediction, we find that strategic similarity benefits suppliers whose customers exhibit a weak information environment.

We also perform cross-sectional analyses to further understand how the characteristics of suppliers influence the relation between strategic similarity and investment efficiency. First, we examine how the relation varies with suppliers in durable vs. nondurable industries. Durable goods require suppliers to make more client-specific investments, and these assets are more likely to lose value for customers outside the specific supplier-customer relationship (Banerjee et al., 2008; Kale & Shahrur, 2007). Second, we examine whether strategic similarity affects investment decisions if the supplier faces a higher threat of losing customers and has difficulty in planning and undertaking investment projects due to uncertain demand. We find that strategic similarity is associated with higher investment efficiency for suppliers producing durable goods and for suppliers who are more likely to lose customers.

To evaluate the robustness of our results, we conduct several additional tests. We address concerns about reverse causality and potential functional form misspecification (Shipman et al., 2017) and find no evidence that strategic similarity between a supplier and its major customer is influenced by the supplier’s investment efficiency or by functional form misspecification. We control for features of the supplier’s corporate governance and internal control weaknesses, and we also perform a change analysis. The inferences are consistent with our main findings. Furthermore, we use an alternative measure of strategic similarity and an alternative model for predicting optimal investment (Biddle et al., 2009; Tsai et al., 2021) and our main inference remains qualitatively unchanged.

We contribute to the literature in several ways. First, we extend research by Navissi et al. (2017), who find that firms’ own business strategies are associated with investment efficiency, by examining how the strategies of customer companies also influence investment decisions. Second, we contribute to the supply chain literature by documenting the positive relation between strategic similarity of supply chain partners and suppliers’ investment efficiency, which is new to the literature. Third, Chang et al. (2022) examine how strategic similarity affects operational decisions, finding that sharing similar strategies is associated with higher performance. We extend Chang et al. (2022) and examine how strategic similarities enhance business operations, with a focus on capital planning. Finally, investments are crucial for long-term value creation and represent significant cash outflows for companies,⁴ making the improvement of investment decision quality a key concern for both companies and investors. Our study reveals that sharing strategic goals with customers helps enhance the quality of investment decisions.

Literature Review and Hypothesis Development

Types of Business Strategy

We adopt the business strategy typology developed by Miles and Snow (1978, 2003), which has been widely applied in the literature (e.g., Bentley et al., 2013; Hambrick, 1983; Higgins et al., 2015; Rajagopalan, 1997). According to Miles and Snow (1978, 2003), a company can be classified as a prospector, defender, or analyzer.⁵ Prospectors actively seek out new market opportunities for products. To exploit new market opportunities, they continuously monitor environmental conditions, innovate to create new products, maintain technological flexibility, and implement decentralized control. In contrast, defenders concentrate on a narrow segment of the market and offer a limited range of products to ensure stability. They focus on innovating their existing technology and employ centralized control to reduce costs and increase efficiency. Analyzers have the attributes of both prospectors and defenders. They defend their current market position by maintaining existing products and customers, but also simultaneously search for new market opportunities. Because analyzers need to accommodate both stable and dynamic operation domains, they have centralized control mechanisms in functional departments and decentralized mechanisms in product departments. Overall, these business strategy types fall along a continuum, with prospectors at one end and defenders at the other. Analyzers are between these two ends.

Prior literature examines whether business strategies play a role in investment decisions. Navissi et al. (2017) find that a prospector-oriented strategy is associated with overinvestment, whereas a defender-oriented strategy is associated with underinvestment. Lin et al. (2021) examine whether business strategies play a role in the positive association between corporate social responsibility (CSR) and investment inefficiency. They find that a defender strategy mitigates overinvestments associated with a high CSR ranking. However, a prospector strategy worsens the overinvestment problem. Related, Habib and Hasan (2021) examine investments in labor and find that prospectors (defenders) are associated with less (more) efficient labor investment.⁶

Hypothesis Development

In a supply chain, trading partners focus on reducing inventory costs. These costs include direct costs to stock inventory and opportunity costs, such as storage and capital costs (Gunasekaran et al., 2004). Although maintaining a large inventory allows suppliers and supply chains to respond effectively to fluctuations in customer demand, it is costly to suppliers (Gunasekaran et al., 2004). To achieve efficiency, suppliers must optimize the inventory level by considering the tradeoff between warehousing expenses and the risks of inventory stockouts, such as missed sales or production disruptions. Supply chain partners must design systems that align expected customer demand with projected inventory production to ensure and enhance collaboration among trading partners (Gunasekaran et al., 2004; McCarthy & Golicic, 2002).

The transaction cost theory (TCT) (Williamson, 1979, 1999) provides a framework for analyzing how efficiency in supply chains is achieved. The fundamental premise of TCT is that trading partners, rather than focusing on short-term gains, prioritize maintaining long-term relationships and pursuing mutual interests to enhance value creation in supply chain transactions. However, because trading partners may be driven to advance their own interests, potentially at the expense of others, they must address potential challenges that could jeopardize opportunities for mutual gain. Opportunistic behavior can be mitigated through complex contracts and governance mechanisms, but complex contracts are inherently incomplete due to limited rationality and the costs of effective monitoring (Hart & Moore, 1988; Ketokivi & Mahoney, 2020).

We posit that strategic similarity is positively associated with investment efficiency. When a supplier and its customer adopt similar business strategies, they share business objectives and common interests, making effective communication more likely (Narayanan et al., 2015; Quinn & Hilmer, 1994). Because their shared goal is to pursue mutual interests, neither party will not pursue activities that are advantageous to itself at the expense of the other party (Jap, 1999), and information is likely to be shared to achieve mutual benefits. Through more information sharing and communication, suppliers are less likely to be misled by opportunistic information that hinders investment efficiency. Furthermore, because business strategy determines a company’s goal, similar strategic objectives align trading partners’ goals. This alignment fosters a close and stable relationship and encourages supply chain partners to collaborate, helping trading partners achieve mutually beneficial outcomes, and serves as a source of competitive advantage (Dyer & Singh, 1998; Samaddar et al., 2006; Zu & Kaynak, 2012).⁷ To the extent that strategic similarity increases willingness to share information, it can reduce information asymmetry between trading parties (Inderfurth et al., 2013; Lee et al., 1997), suggesting that strategic similarity may complement contractual arrangements and governance mechanisms.

In addition, sharing strategic objectives enhances supply chain partners’ competitiveness through increased interaction among members. This helps identify and bridge gaps between market demands and current offerings (Hult et al., 2004; Ketchen & Hult, 2007). Simatupang and Sridharan (2002) highlight that a collaborative supply chain operates with integrated policies designed to maximize overall supply chain profitability. In such a system, supply chain members jointly plan and implement policies related to demand forecasting, capacity planning and utilization, inventory management, purchasing quantities, and lead time optimization. These integrated policies improve the quality of suppliers’ investment decisions, helping to reduce underutilized resources and minimize missed opportunities.

Based on the discussion above, we predict that sharing strategic objectives is positively associated with the investment efficiency of suppliers.⁸ Thus, we state our hypothesis in the alternative as follows:

Hypothesis

Strategic similarity between trading partners is positively associated with the supplier’s investment efficiency.

Research Design

Sample Construction

We construct our sample from the following data sources: (1) financial statement information from Compustat, (2) supplier-customer dyad information from the Compustat Segment file, and (3) number of analysts from IBES. Specifically, we start with 32,978 supplier firm-year observations with major customer information as required by the FASB and SEC, in our 1992–2019 sample period.⁹ Each supplier company is restricted to a single observation per year, based on its major customer with the highest revenue. We exclude 3,635 observations in regulated industries (SIC 4400-4999) and financial institutions (SIC 6000-6999) because these companies have unique operating and investing activities. We also remove 12,372 observations that do not have sufficient data to calculate the test variable, strategic similarity (STR_SIMILAR), and 3,787 observations missing dependent variable or control variables. When calculating investment efficiency and strategy scores, we include all Compustat companies. The final sample includes 13,184 observations. The Online Appendix provides an example of the disclosure of major customers.

Measure of Strategic Similarity

Following Bentley et al. (2013), Bentley-Goode et al. (2017), and Higgins et al. (2015), we use a composite measure of business strategy based on six company characteristics: (1) the ratio of R&D expense to total sales, which measures a company’s R&D intensity and its competitive strategy in innovation (Ittner et al., 1997)¹⁰; (2) the ratio of employee number to sales, which measures the labor productivity of the company (Ettlie, 1995); (3) sales growth, which measures the company’s growth and investment opportunities (Biddle et al., 2009); (4) the ratio of selling, general, and administrative (SG&A) expenses to total sales, which measures the company’s investment in organizational capital¹¹; (5) the standard deviation of employee number, which measures the company’s organizational stability (Bentley et al., 2013; Miles & Snow, 1978); and (6) net property, plant, and equipment (PP&E) scaled by total assets, which measures the company’s commitment to achieving technological efficiency (Hambrick, 1983). The ratio of net PP&E is ranked in reverse order because prospectors tend to maintain a low level of capital intensity, which ensures flexibility in their constantly changing production lines and avoids lengthy commitments to a single technological process (Bentley et al., 2013).

Following Bentley et al. (2013), we first calculate the 5-year moving averages of the six variables. Then, we group the supplier and customer firms and sort them into two-digit SIC industry and year groups based on each of the six variables. Specifically, if the value of a variable is in the highest (lowest) quintile of the industry-year group, the firm-year observation is assigned a score of 5 (1). Thus, the sum of the scores across the six variables (STRATEGY) is a composite measure of the business strategy for each firm-year observation, ranging from 6 to 30. A higher value indicates that a company is more likely to adopt a prospector strategy, and a lower value indicates that it is more likely to have a defender strategy.¹²

Next, to measure the extent to which a supplier and its customer share similar strategic objectives, we calculate the absolute difference in strategy scores between the supplier and its customer (ABS_DIFF_STRATEGY). The highest ABS_DIFF_STRATEGY between two companies is thus 24 (30 − 6). To ease interpretation, we define STR_SIMILAR as 24 minus ABS_DIFF_STRATEGY so that a higher (lower) value of STR_SIMILAR indicates that strategic similarity between a supplier and its customer is higher (lower).¹³

Measures of Investment Efficiency

Biddle et al. (2009) and Dixit and Pindyck (1994) indicate that companies invest at their optimal level if they invest in all projects with positive NPV. Under this definition, any deviation from the optimal level of investment is interpreted as investment inefficiency. Both overinvestment and underinvestment represent investment inefficiency because companies invest in projects with negative NPV (overinvestment) or forgo projects with positive NPV (underinvestment). We follow McNichols and Stubben (2008) to capture the optimal level of investment by estimating the following regression model for each industry-year group with at least 20 firm-year observations:

{I N V}_{it} = β_{0} + β_{1} {T O B I N Q}_{i t - 1} + β_{2} {C F}_{i t} + ε

(1)

where investment (INV) is a function of growth prospects (TOBINQ) and cash flows from operating activities (CF). INV is capital expenditure in year t scaled by net property, plant, and equipment in year t–1. TOBINQ is calculated as the market value of equity plus the book value of assets minus the book value of common stock, all scaled by the book value of assets. CF is cash flows from operating activities scaled by net property, plant, and equipment in year t–1. The residuals from estimating equation (1) represent the deviation from the optimal level of investment. A residual that is greater in absolute value indicates that the company deviates more from its optimal level of investment and is more likely to invest inefficiently.

Following Biddle et al. (2009) and Navissi et al. (2017), we sort the signed residuals into quartiles within each industry-year grouping. Firm-years with residuals in the lowest quartile are classified as the underinvestment group, and those in the highest quartile are classified as the overinvestment group. Firm-years with residuals in the middle two quartiles serve as the benchmark “normal investment” group. We use a multinomial logistic model (discussed below, equation (2)) to estimate the likelihood that a company is classified into the overinvestment and underinvestment groups, as opposed to the middle (i.e., benchmark) groups.

Test of the Hypothesis

To examine how strategic similarity between supply chain partners affects the supplier’s investment efficiency, we estimate the following multinomial logistic regression model:

I N V_{I N E F F}_{i t} = β_{0} + β_{1} S T R_{S I M I L A R}_{i t} + C o n t r o l s + industry and year fixed effects + ε

(2)

The dependent variable INV_INEFF is categorical, set to 1 for observations classified into the underinvestment group, 2 for those in the benchmark group, and 3 for those in the overinvestment group. Our main test variable is STR_SIMILAR, the strategic similarity between a supplier and its major customer. It is measured in year t because we expect that when making final investment decisions in year t, continual communication between trading partners (proxied by STR_SIMILAR) helps suppliers evaluate whether to postpone projects to the next period or engage in more investment in the current period. A positive (negative) coefficient on STR_SIMILAR indicates that strategic similarity is associated with a higher (lower) likelihood of underinvestment/overinvestment and thus lower (higher) investment efficiency.

Following Bae et al. (2017), Biddle et al. (2009), and McNichols and Stubben (2008), we include a battery of variables measured in year t–1 to control for company characteristics that are likely to affect investment efficiency. We include company size (SIZE) because it is widely included in prior investment efficiency literature and is correlated with various factors, including the information environment (Lang & Lundholm, 1996), growth opportunities (Evans, 1987), and dividend policy (Redding, 1997). These factors may, in turn, affect investment decisions. Financial position (LEV and LOSS), financial slack (CASH), and bankruptcy risk (ZSCORE) are included to control for financial constraints because prior studies document that the availability of financial resources for a company is associated with investments (Almeida & Campello, 2007; Kaplan & Zingales, 1997). We include the length of the operating cycle (OPCYCLE) and company age (AGE) to account for firms’ stages in the business cycle because companies may adopt different investment strategies at various business stages (Gao et al., 2021). Analyst coverage (NUM_ANALYST) is included to control for the information environment because analysts may provide information about future firm growth opportunities that can be useful to managers (Choi et al., 2020). INSTOWN is the percentage of shares owned by institutional owners and is set to zero if we do not have data. It is included to control for external monitoring (Cheng et al., 2013). Dividend payments (DIV) affect the free cash flow available to managers to spend on investments (Gugler, 2003). Tangibility (PPE) is included because asset tangibility facilitates external financing for investments (Almeida & Campello, 2007). We control for investment volatility to ensure that overinvestments and underinvestments are not driven simply by investment volatility (Biddle et al., 2009). Because sales and cash flows are related to investments, we also include sales and cash flow volatility to mitigate the potential confounding effects (Biddle et al., 2009; McNichols & Stubben, 2008). BIGN is included because using a Big N auditor is expected to improve financial statement quality. ABSDA is financial reporting quality, which is negatively associated with investment efficiency (Biddle et al., 2009). We control for the company’s own business strategy (STRATEGY) because a company’s strategic goals may be associated with investment efficiency (Lin et al., 2021; Navissi et al., 2017). Specifically, a prospector (defender) strategy is associated with overinvestment (underinvestment). We control for the length of the relationship between the supplier company and its major customer (DURATION) because as this relationship lengthens, suppliers may know customers better and improve suppliers’ investment decisions.

We also include the customer’s business strategy (C_STRATEGY) and the supplier’s strategic deviance from industry peers (STRAT_DEV). C_STRATEGY controls for the customer’s business risk which may influence the supplier’s investment decisions (Bentley et al., 2013; Higgins et al., 2015). STRAT_DEV is added because Dong, Chan, et al. (2021) and Ranasinghe and Habib (2023) find that adopting a deviant strategy is positively associated with cash holdings and investment inefficiency. We measure STRAT_DEV in year t to control for potential correlations between deviant strategy and cash holdings, which may affect investments.

We define these variables in Appendix A. We include industry- and year-fixed effects to control for systematic differences in investment efficiency across industries and years, and cluster standard errors by firm and year. All continuous variables are winsorized at the 1^st and 99^th percentiles to mitigate the effect of outliers.

Empirical Results

Descriptive Statistics

Table 1 presents descriptive statistics for the test and control variables in our regression model. The average logarithm of asset balance is 5.730 (approximately $308 million). The mean of our test variable STR_SIMILAR is 20.591. Untabulated results indicate that 22.50% and 24.06% of firm-year observations are classified as in the underinvestment group and overinvestment group, respectively.

Table 1.

Descriptive Statistics

Variables	Mean	STD	P25	Median	P75
INV_INEFF	2.016	0.682	2.000	2.000	2.000
AB_INV	−0.168	0.813	−0.270	−0.109	0.031
STR_SIMILAR	20.591	2.622	19.000	21.000	23.000
SIZE	5.730	2.158	4.132	5.680	7.323
MTB	2.602	4.147	1.047	1.838	3.220
LEV	0.219	0.223	0.019	0.178	0.339
CASH	0.190	0.194	0.033	0.123	0.290
OPCYCLE	4.836	0.536	4.484	4.844	5.192
ZSCORE	1.233	1.402	0.732	1.376	1.981
AGE	2.913	0.631	2.398	2.890	3.401
NUM_ANALYST	1.435	1.118	0.000	1.609	2.398
INSTOWN	0.351	0.373	0.000	0.206	0.713
STDSALE	0.271	0.244	0.109	0.196	0.347
STDCFO	0.080	0.070	0.036	0.060	0.099
STDINVEST	0.030	0.039	0.008	0.016	0.035
PPE	0.233	0.206	0.080	0.172	0.316
LOSS	0.347	0.476	0.000	0.000	1.000
DIV	0.287	0.453	0.000	0.000	1.000
STRATEGY	18.129	3.653	16.000	18.000	21.000
DURATION	1.347	0.923	0.693	1.386	2.079
ABSDA	0.087	0.094	0.025	0.057	0.114
BIGN	0.765	0.424	1.000	1.000	1.000
STRAT_DEV	1.971	1.423	1.131	1.629	2.312
C_STRATEGY	17.824	2.723	16.000	18.000	19.000

This table reports descriptive statistics for the variables used in the main regression model. Variable definitions appear in Appendix A.

Regression Results

Table 2 reports the regression results evaluating the relation between strategic similarity and investment efficiency. Column (1) shows results for the test examining the likelihood that suppliers underinvest. The coefficient on STR_SIMILAR is significant and negative (−0.029, t-statistic = −3.26), indicating that strategic similarity between supply chain partners is associated with a lower likelihood of underinvestment by suppliers. In terms of economic significance, the estimated probability of underinvestment is approximately 35.09% for an average firm in our sample. An interquartile increase in strategic similarity (STR_SIMILAR) is associated with a 2.65% decrease in the probability of underinvestment by an average firm.¹⁴ Column (2) presents results for overinvestment. The coefficient on STR_SIMILAR is significant and negative (−0.016, t-statistic = −1.79), indicating a negative association between strategic similarity and the likelihood of overinvestment by suppliers. Regarding economic significance, the estimated probability of overinvestment is approximately 12.12% for the average firm in our sample. An interquartile increase in strategic similarity (STR_SIMILAR) is associated with a 0.69% decrease in the probability of overinvestment by an average firm.¹⁵ The signs on the control variables are mostly consistent with prior literature. Investment efficiency is higher for companies with more assets and longer relationships with major customers. A prospector strategy is associated with overinvestment, whereas a defender strategy is associated with underinvestment. Taken together, our findings indicate that sharing similar strategies with supply chain partners is associated with a statistically significant and economically meaningful decrease in suppliers’ investment inefficiency.¹⁶

Table 2.

Strategic Similarity and Investment Efficiency Dependent Variable: INV_INEFF

	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Test variable:
STR_SIMILAR	−0.029	−3.26	0.001	−0.016	−1.79	0.073
Control variables:
SIZE	−0.160	−7.79	0.000	−0.221	−9.62	0.000
MTB	−0.002	−0.37	0.709	0.023	3.89	0.000
LEV	0.626	5.13	0.000	−0.498	−3.59	0.000
CASH	0.949	5.52	0.000	0.668	4.32	0.000
OPCYCLE	0.280	4.80	0.000	−0.042	−0.75	0.453
ZSCORE	−0.170	−6.80	0.000	0.066	2.43	0.015
AGE	0.055	1.21	0.226	−0.244	−5.25	0.000
NUM_ANALYST	−0.024	−0.73	0.468	0.214	5.90	0.000
INSTOWN	−0.247	−3.09	0.002	0.005	0.07	0.943
STDSALE	0.465	3.51	0.000	0.384	3.18	0.002
STDCFO	0.886	1.94	0.052	−0.156	−0.35	0.725
STDINVEST	−2.401	−2.65	0.008	7.792	8.51	0.000
PPE	1.713	7.48	0.000	−3.801	−13.30	0.000
LOSS	0.360	6.10	0.000	−0.310	−4.84	0.000
DIV	0.292	4.56	0.000	−0.145	−2.22	0.026
STRATEGY	−0.034	−4.03	0.000	0.035	4.29	0.000
DURATION	−0.068	−2.35	0.019	−0.069	−2.34	0.019
ABSDA	0.192	0.64	0.525	0.906	3.21	0.001
BIGN	−0.115	−1.78	0.075	−0.148	−2.23	0.026
STRAT_DEV	0.030	1.57	0.117	−0.008	−0.41	0.680
C_STRATEGY	−0.022	−2.56	0.011	0.007	0.82	0.413
Industry/year fixed effects	Yes			Yes
N	13,184			13,184
Pseudo R ²	0.1885			0.1885

This table reports the results of the relation between strategic similarity and investment efficiency. This analysis uses the multinomial logistic regression model using all observations. We sort observations into underinvestment, overinvestment, and normal (i.e., benchmark) groups. Columns (1) and (2) present the results for a model predicting the likelihood that a company is classified into the underinvestment and overinvestment groups, respectively. Standard errors are clustered by company and year. Variable definitions appear in Appendix A.

Effect of Customer’s Information Environment

In developing our research hypothesis, we posit that information sharing is a possible mechanism for the positive association between strategic similarity and suppliers’ investment efficiency. In this section, we evaluate this mechanism. When a supplier needs information to estimate customer demand and assess receivables, an opaque customer information environment increases the need for information sharing to support the supplier’s investment decisions. Because strategic similarity between supply chain dyads fosters information sharing, when the customer’s information environment is weaker, the supplier can better benefit from information sharing, leading to a positive association between strategic similarity and investment efficiency.

We first follow Elyasiani and Jia (2011) and Vincent et al. (2021) and use the bid-ask spread, SPREAD, to proxy for information asymmetry. SPREAD is calculated as the mean of (ASKHI − BIDLO)/(ASKHI + BIDLO) over the previous 12 months. ASKHI is the highest price in the month, and BIDLO is the lowest price in the month. We sort observations into two groups based on the major customers’ SPREAD by sample median in each industry-year grouping and then assign observations in the top (bottom) group into the “higher information asymmetry” (“lower information asymmetry”) subsamples. Panel A of Table 3 indicates that the coefficients on STR_SIMILAR are insignificant for companies whose customers have low bid-ask spread; however, Panel B shows that the coefficients on STR_SIMILAR are significant and negative in both the underinvestment and overinvestment analyses for companies whose customers have high bid-ask spread. These results suggest that strategic similarity is associated with higher investment efficiency for companies whose customers have higher information asymmetry.

Table 3.

Strategic Similarity and Investment Efficiency: The Information-Sharing Mechanism

Dependent variable: INV_INEFF	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
Dependent variable: INV_INEFF	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel A: Customer Companies with Low Information Asymmetry
Test variable:
STR_SIMILAR	−0.016	−1.14	0.255	0.007	0.47	0.637
Controls in Table 2	Yes			Yes
Industry/year fixed effects	Yes			Yes
N	6,107			6,107
Pseudo R ²	0.2037			0.2037

Dependent variable: INV_INEFF	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
Dependent variable: INV_INEFF	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel B: Customer Companies with High Information Asymmetry
Test variable:
STR_SIMILAR	−0.046	−3.54	0.000	−0.033	−2.54	0.011
Controls in Table 2	Yes			Yes
Industry/year fixed effects	Yes			Yes
N	6,485			6,485
Pseudo R ²	0.2011			0.2011

This table presents the results of the relation between strategic similarity and investment efficiency, focusing on the information-sharing mechanism. This analysis uses the multinomial logistic regression model. We sort observations into underinvestment, overinvestment, and normal (i.e., benchmark) groups. In all panels, Columns (1) and (2) present the results for a model predicting the likelihood that a company is classified into the underinvestment and overinvestment groups, respectively. In Panels A and B, we report the results for observations classified into customer companies with low and high Information asymmetry, respectively. We calculate SPREAD as the mean of (ASKHI − BIDLO)/(ASKHI + BIDLO) over the previous 12 months. ASKHI is the highest price in the month, and BIDLO is the lowest price in the month. We sort observations into two groups based on SPREAD by sample median in each industry-year grouping and then assign observations in the top (bottom) group into the “higher information asymmetry” (“lower information asymmetry”) subsamples. Standard errors are clustered by company and year. Variable definitions appear in Appendix A.

Given that the bid-ask spread primarily reflects investors’ perspectives, we incorporate an indicator for whether customers provide management guidance, offering a supplier-focused proxy for the information environment. Because suppliers can leverage customers’ forward-looking information to refine their investment decisions, we use the presence of management earnings forecasts to proxy for the opacity of customers’ information environments. We obtain management guidance data from the IBES Guidance database. Untabulated results reveal that the association between strategic similarity and investment efficiency is not evident for suppliers whose customers provide earnings forecasts. However, the coefficients on STR_SIMILAR are significant and negative in both the underinvestment and overinvestment analyses for suppliers whose customers do not provide earnings forecasts in year t–1 through year t. Overall, these results are consistent with our prediction that strategic similarity improves supplier investment decisions when the customer’s information environment is weak.

Cross-Sectional Analyses of the Suppliers’ Characteristics

We also perform cross-sectional tests to enhance our understanding of how suppliers’ characteristics affect the association between strategic similarity and suppliers’ investment efficiency. First, we focus on suppliers in durable vs. nondurable goods industries. Prior studies indicate that companies producing durable goods should make relationship-specific investments to provide more unique products (Banerjee et al., 2008; Kale & Shahrur, 2007). To the extent that producing durable goods requires costly relationship-specific investments, higher strategic similarity should help these suppliers make better investment decisions. Our durable goods industries include companies with 4-digit SIC codes from 1000 through 4783, consistent with Chiu et al. (2019). We separately estimate equation (2) using the two subsamples of suppliers in durable goods and nondurable goods industries and report the results in Panels A and B of Table 4. Panel A reveals that both coefficients on STR_SIMILAR are significant and negative, indicating that strategic similarity is associated with a lower likelihood of underinvestment and overinvestment by suppliers in durable goods industries. However, in Panel B, the coefficients on STR_SIMILAR are insignificant for suppliers in nondurable goods industries. These results suggest that the strategic similarity between supply chain partners is beneficial for companies mainly in durable goods industries.

Table 4.

Strategic Similarity and Investment Efficiency: Suppliers’ Characteristics

	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
	Coef.	t	P>\|t\|	Coef.	T	P>\|t\|
Panel A: Durable Goods Industries (durables: SIC 1000–4783)
STR_SIMILAR	−0.035	−3.53	0.000	−0.017	−1.70	0.088
N	10,920			10,920
Pseudo R ²	0.1838			0.1838

	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel B: Nondurable Goods Industries
STR_SIMILAR	0.001	0.06	0.949	−0.017	−0.72	0.470
N	2,264			2,264
Pseudo R ²	0.2428			0.2428

	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel C: Customers with SWITCH Below Industry-Year Medians
STR_SIMILAR	−0.044	−3.63	0.000	−0.024	−1.93	0.054
N	6,356			6,356
Pseudo R ²	0.1880			0.1880

	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel D: Customers with SWITCH Above Industry-Year Medians
STR_SIMILAR	−0.005	−0.32	0.752	−0.015	−1.09	0.276
N	6,828			6,828
Pseudo R ²	0.1609			0.1609

This table presents the relation between strategic similarity and investment efficiency, focusing on suppliers’ characteristics. We use a multinomial logistic regression model, classifying observations into underinvestment, overinvestment, and benchmark groups. Columns (1) and (2) report the likelihood of being in the underinvestment and overinvestment groups, respectively. The dependent variable is INV_INEFF. Control variables follow Table 2, with industry and year fixed effects included. Panels A and B report results for durable and nondurable goods industries. Panels C and D report results for companies whose customers have low and high switching costs, respectively. Standard errors are clustered by company and year. Variable definitions appear in Appendix A.

The cost of replacing existing suppliers (i.e., switching cost) plays a crucial role in the supplier-customer relationship (Crawford et al., 2020; Dhaliwal et al., 2016; Dong, Li, & Li, 2021). When a customer company bears lower costs of replacing its supplier, it is more likely to switch to an alternative supplier. This increases the supplier’s risk of losing the customer and creates greater uncertainty in product demand, complicating investment decisions. Although low switching costs might encourage customers to explore alternative suppliers, strategic similarities between trading partners may discourage switching. In this way, strategic similarities should enable suppliers to make more accurate forecasts and invest closer to the optimal level.

We calculate the sales-based industry market share (SWITCH) to proxy for switching costs (Crawford et al., 2020; Dhaliwal et al., 2016; Dong, Li, & Li, 2021). A higher supplier market share indicates that its customers have higher switching costs. We sort firm-year observations into two groups based on SWITCH by the sample median in each industry-year grouping and then assign observations in the top (bottom) group into the “higher switching costs” (“lower switching costs”) subsamples. Panels C and D of Table 4 present the results for suppliers whose customers bear low and high switching costs, respectively. Panel C shows that the coefficients on STR_SIMILAR are significant and negative in both underinvestment and overinvestment analyses, but we do not find an association between STR_SIMILAR and investment efficiency in Panel D.¹⁷ Collectively, we find that strategic similarity is beneficial for suppliers in durable goods industries and for those with customers who have low switching costs.

Robustness Checks

Noncapital Investments

In this section, we examine noncapital investments because prior research suggests that the nature of capital investment differs from non-capital investments (Bae et al., 2017; McNichols & Stubben, 2008). We re-estimate equation (1) using the sum of R&D expenditures and acquisition costs scaled by lagged total assets as the dependent variable and repeat the analyses in Table 2.¹⁸ Table 5 reveals that STR_SIMILAR is significantly associated with a lower likelihood of underinvestment. However, we do not find evidence of overinvestment. This finding is consistent with the notion that because noncapital investments are generally incorporated immediately into reported earnings, managers are more likely to underinvest than overinvest in noncapital assets. Moreover, these acquisitions occur less frequently than capital expenditures, decreasing the power of the test (Bae et al., 2017; McNichols & Stubben, 2008).

Table 5.

Strategic Similarity and Noncapital Investment Efficiency

Dependent variable: INV_INEFF	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
Dependent variable: INV_INEFF	Coef.	T	P>\|t\|	Coef.	t	P>\|t\|
Test variable:
STR_SIMILAR	−0.038	−4.07	0.000	0.006	0.62	0.534
Controls in Table 2	Yes			Yes
Industry/year fixed effects	Yes			Yes
N	13,184			13,184
Pseudo R ²	0.1471			0.1471

This table reports the results of the relation between strategic similarity and noncapital investment efficiency. This analysis uses the multinomial logistic regression model. We re-estimate the optimal level of investment using the sum of R&D expenditures and acquisition costs scaled by lagged total assets as the dependent variable. Columns (1) and (2) present the results for a model predicting the likelihood that a company is classified into the underinvestment and overinvestment groups, respectively. Standard errors are clustered by company and year. Variable definitions appear in Appendix A.

Tests for Reverse Causality, Functional Form Misspecification, Controlling for Additional Variables, and Change Analysis

To address the concern that strategic similarity between a supplier and its major customer may be determined by the supplier’s investment efficiency, we regress STR_SIMILAR_it on lagged investment efficiency (AB_INV_it–1, i.e., residuals obtained from equation (1)). If a supplier’s investment efficiency affects the strategic similarity, there should be a significant relation between lagged abnormal investments and strategic similarity. Panel A of Table 6 presents the results. The coefficient on AB_INV is not significant, alleviating reverse causality concerns.

Table 6.

Tests for Reverse Causality, Potential Functional Form Misspecification, Controlling for Additional Variables, and Change Analysis

Variables	Dependent variable: STR_SIMILAR_it
Variables	Coef.	p-value
Panel A: Reverse Causality
AB_INV _it–1	−0.027	0.330
Controls in Table 2
Industry/year fixed effects
N	13,099
R ²	0.0526

Variables	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
Variables	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel B: Propensity Score Matching for Functional Form Misspecification
STR_SIMILAR	−0.033	−2.79	0.005	−0.020	−1.66	0.098
Controls in Table 2	Yes			Yes
Industry/year fixed effects	Yes			Yes
N	7,788			7,788
Pseudo R ²	0.1899			0.1899

Variables	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
Variables	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel C: Controlling for Additional Variables
Test variable:
STR_SIMILAR	−0.044	−2.98	0.003	−0.027	−1.87	0.062
Control variables:
ICW	0.095	0.67	0.505	−0.073	−0.49	0.627
BOARD_SIZE	−0.074	−3.99	0.000	−0.006	−0.30	0.765
NONIND	0.242	1.90	0.057	0.278	2.23	0.026
Controls in Table 2	Yes			Yes
Industry/year fixed effects	Yes			Yes
N	5,600			5,600
Pseudo R ²	0.1865			0.1865

Variables	(1) Change from year t–1 to year t			(2) Change from year t–2 to year t			(3) Change from year t–3 to year t
Variables	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel D: Change Analysis
Test variable:
CH_STR_SIMILAR	−0.011	−2.07	0.039	−0.011	−2.13	0.033	−0.034	−2.48	0.013
Control variables:
CH_SIZE	−0.152	−3.51	0.000	−0.142	−4.38	0.000	−0.274	−5.26	0.000
CH_MTB	0.000	−2.31	0.021	0.000	−1.23	0.219	0.000	0.55	0.580
CH_LEV	0.209	2.53	0.011	0.170	2.15	0.031	0.027	0.25	0.805
CH_CASH	−0.048	−0.35	0.724	−0.017	−0.14	0.888	0.300	0.79	0.430
CH_OPCYCLE	0.012	0.28	0.780	0.026	0.56	0.574	0.300	1.20	0.231
CH_ZSCORE	−0.007	−1.50	0.134	−0.002	−0.43	0.669	−0.011	−1.80	0.072
CH_NUM_ANALYST	0.063	2.07	0.038	0.044	1.66	0.096	0.113	2.57	0.010
CH_INSTOWN	−0.022	−0.98	0.328	−0.071	−1.19	0.233	−0.279	−1.38	0.167
CH_STDSALE	−0.029	−0.39	0.699	−0.086	−1.38	0.168	−0.133	−1.14	0.254
CH_STDCFO	−0.051	−0.19	0.846	0.068	0.28	0.778	−0.151	−0.14	0.889
CH_STDINVEST	−2.980	−5.33	0.000	−3.015	−4.97	0.000	−1.096	−0.91	0.360
CH_PPE	−0.796	−3.23	0.001	−0.674	−3.32	0.001	−0.836	−2.22	0.027
CH_STRATEGY	0.017	2.15	0.032	0.012	1.62	0.105	0.023	1.48	0.140
CH_ABSDA	0.134	1.53	0.126	0.055	0.49	0.623	0.249	0.99	0.324
CH_STRAT_DEV	0.028	3.32	0.001	0.021	2.06	0.039	0.013	1.09	0.274
CH_C_STRATEGY	0.010	1.78	0.076	0.012	2.04	0.041	−0.006	−0.46	0.644
Industry/year fixed effects	Yes			Yes			Yes
N	9,592			7,265			5,595
R ²	0.1188			0.0945			0.0727

This table presents additional analysis examining the relation between strategic similarity and investment efficiency. In Panel A, we regress STR_SIMILAR_it on lagged investment efficiency (AB_INV_it–1, residuals obtained from the optimal investment prediction model). In Panel B, we report the results based on a propensity score–matched sample. In Panel C, we include additional control variables. In Panels B and C, we sort observations into underinvestment, overinvestment, and normal (i.e., benchmark) groups. Columns (1) and (2) present the results for a model predicting the likelihood that a company is classified into the underinvestment and overinvestment groups, respectively. In Panel D, the dependent variable is CH_ ABSXINV is the change in the absolute value of the residuals from equation (1). The test variable CH_STR_SIMILAR is the change in strategic similarity STR_SIMILAR. Control variables are the change in the continuous variables in equation (2). The sample period for the observations used in Panels A and B spans from 1992 through 2019, in Panel C starts from 2006 onwards, and in Panel D spans from 1993 through 2019. Standard errors are clustered by company and year. Variable definitions appear in Appendix A.

Next, we use propensity score matching (PSM) to alleviate the concerns about functional form misspecification (Shipman et al., 2017). We construct an indicator variable, STR_SIMILAR_HIGH, which equals one if STR_SIMILAR is above the median of the sample, and zero otherwise. Next, we estimate a logistic regression that includes all control variables in equation (2). To construct the matched sample, we perform one-to-one matching without replacement with a caliper distance of 0.01. The means of the company characteristics between the treatment and control groups are not significantly different for all covariates in the model (untabulated). We re-estimate equation (2) and find that the coefficients on STR_SIMILAR are significant and negative in both underinvestment and overinvestment columns (Panel B). Thus, our inferences remain unchanged.

Prior research indicates that corporate governance measures and internal control weakness disclosures are associated with financial reporting quality and investment efficiency. To address the concern that the improvement in investment efficiency may be driven by corporate governance measures rather than strategic similarity, we follow prior literature and control for board size (BOARD_SIZE), board independence (NONIND), and whether the company reports internal control weaknesses in the previous year (ICW) (Cheng et al., 2013; Christensen et al., 2024). The sample for this analysis starts in 2006 because Section 404 of the Sarbanes-Oxley Act was effective after November 15, 2004, and we require internal control information in the prior year.¹⁹ We repeat the analysis reported in Table 2 and find that the coefficient on STR_SIMILAR (Panel C of Table 6) remains significant and negative, suggesting that our findings are driven by strategic similarities rather than by board characteristics or internal control weakness reports.

Finally, we perform a change analysis to mitigate the concern that our results are affected by unobservable firm-specific characteristics. In this analysis, the dependent variable (CH_ ABSXINV) is the change in the absolute value of the residuals from equation (1). The test variable is CH_STR_SIMILAR, which is the change in strategic similarity STR_SIMILAR. We predict that an increase in strategic similarity is associated with a decrease in the absolute value of residuals. Thus, the predicted sign of CH_STR_SIMILAR is negative. Columns (1), (2), and (3) of Panel D of Table 6 report the results for changes from year t–1, t–2, and t–3 to year t, respectively. Consistent with our prediction, the coefficient on CH_STR_SIMILAR is significant and negative in all three columns.

Alternative Construction of Strategic Similarity and Optimal Investment Prediction Model

Our main test variable is constructed based on six company operating characteristics, including net property, plant, and equipment scaled by total assets (PP&E intensity). A potential concern is that the negative relation we observe between strategic similarity (STR_SIMILAR) and capital investment efficiency may be driven by a mechanical association between the test and dependent variables. Although it is unlikely that there is a mechanical relation between STR_SIMILAR and capital investment efficiency because STR_SIMILAR is jointly determined by a supply chain dyad rather than the supplier alone, we perform additional analyses and exclude PP&E from the calculation of STR_SIMILAR. The results in Panel A of Table 7 indicate that our main inferences from Table 2 hold even after we re-construct STR_SIMILAR using the five remaining company characteristics.

Table 7.

Alternative Construction of Strategic Similarity and Optimal Investment Prediction Model

	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel A: Alternative Construction of Strategic Similarity
Test variable:
STR_SIMILAR	−0.029	−2.88	0.004	−0.030	−2.94	0.003
Controls in Table 2	Yes			Yes
Industry/year fixed effects	Yes			Yes
N	13,184			13,184
Pseudo R ²	0.1863			0.1863

	(1) Underinvestment vs. normal investment			(2) Overinvestment vs. normal investment
	Coef.	t	P>\|t\|	Coef.	t	P>\|t\|
Panel B: Alternative Optimal Investment Prediction Model
Test variable:
STR_SIMILAR	−0.022	−2.40	0.017	−0.019	−2.10	0.036
Controls in Table 2	Yes			Yes
Industry/year fixed effects	Yes			Yes
N	13,184			13,184
Pseudo R ²	0.2093			0.2093

This table reports the results of the relation between strategic similarity and investment efficiency, using alternative definition of strategic similarity and alternative optimal investment prediction model. In Panel A, STR_SIMILAR encompasses R&D intensity, labor productivity, sales growth, investment in organization capital, and organizational stability (i.e., excluding commitment to technological efficiency). In Panel B, we re-estimate the optimal level of investment using an alternative optimal investment prediction model (Biddle et al., 2009; Tsai et al., 2021). Specifically, we regress investment on sales growth to obtain the normal level of investment. Next, we sort observations into underinvestment, overinvestment, and normal (i.e., benchmark) groups based on the residuals. This analysis uses the multinomial logistic regression model. We sort observations into underinvestment, overinvestment, and normal (i.e., benchmark) groups. Columns (1) and (2) present the results for a model predicting the likelihood that a company is classified into the underinvestment and overinvestment groups, respectively. In both panels, the dependent variable is INV_INEFF. Standard errors are clustered by company and year. Variable definitions appear in Appendix A.

We also examine whether our results are robust to an alternative optimal investment prediction model. We follow Biddle et al. (2009) and Tsai et al. (2021) and estimate the following model for each industry-year group with at least 20 observations:

{I N V}_{it} = β_{0} + β_{1} S A L E_{G R O W T H}_{i t - 1} + ε

(3)

where SALE_GROWTH is the difference between sales in year t−1 and year t−2, scaled by total sales in year t−2, and the residual is the alternative measure of the supplier’s investment efficiency. The results in Panel B of Table 7 indicate that the coefficient on STR_SIMILAR continues to be significant and negative, confirming the inference that strategic similarity is related to higher investment efficiency.

Concluding Remarks

This study examines whether strategic similarity between a supplier and its major customer improves the supplier’s investment efficiency. Analyzing a data set of supplier-customer dyads from 1992 through 2019, we find that the strategic similarity between suppliers and their major customers is associated with a lower likelihood of both underinvestment and overinvestment by suppliers. This finding is consistent with our hypothesis that sharing strategic objectives with major customers enables suppliers to make better investment decisions.

We also examine the information-sharing mechanism underlying the association between strategic similarity and suppliers’ investment efficiency and find that strategic similarity is associated with higher investment efficiency for supplier companies whose customer companies have an opaque information environment. In cross-sectional analyses, we find that strategic similarity is associated with higher investment efficiency for suppliers in durable goods industries and for suppliers whose customers have low supplier switching costs.

Because the SEC and FASB require companies to disclose only the identities of customers accounting for more than 10% of sales, we focus on suppliers with major customers. If data on firms’ major suppliers becomes publicly available, future research can examine issues related to supplier selection. Second, we use Bentley et al.’s (2013) approach to measuring business strategy. Although this is consistent with Miles and Snow (1978, 2003) and is widely applied in the accounting literature, companies with similar overall strategy scores can have varying scores on the individual components of their business strategies. Finally, although our analyses provide indirect evidence that information sharing serves as one mechanism through which strategic similarity improves investment efficiency, we cannot completely rule out the alternative explanation that when a supplier’s business strategy closely aligns with that of its customers, the supplier may gain a deeper understanding of customer demand through their operational similarities, thereby facilitating more informed investment decisions. Regardless, our study documents that suppliers are able to improve their investment efficiency when their business strategies align with those of their customers.

Footnotes

Acknowledgements

We thank Linda Myers (Editor), Raj Mashruwala (Discussant), three anonymous referees, and participants of the 2024 Journal of Accounting, Auditing and Finance Conference in Naples, Italy, and the 2024 European Accounting Association Annual Congress in Bucharest, Romania, for their helpful comments.

ORCID iDs

Hsihui Chang

Rong-Ruey Duh

Lin-Hui Yu

Yaou Zhou

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Lin-Hui Yu gratefully acknowledges research support from the National Science and Technology Council.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All data used in the study are available from publicly available sources cited in the text*.

Notes

Author Biographies

Hsihui Chang is the KPMG Endowed Professor of Accounting at Drexel University. Prior to joining Drexel, he taught at the University of Texas at Dallas and National Cheng Chi University in Taiwan. Chang obtained his Ph.D. from the University of Minnesota. His research interests include managerial accounting, auditing, and information technology. He has published articles in high quality journals including The Accounting Review, Contemporary Accounting Research, Journal of Accounting & Economics, Management Science, MIS Quarterly, Production and Operations Management, and Strategic Management Journal.

Rong-Ruey Duh is Professor Emeritus at the Department of Accounting, National Taiwan University as well as Chair Professor at the Department of Accounting, Soochow Univerisity, Taiwan. He earned his Ph. D. from the University of Minnesota, Minneapolis. Professor Duh’s research interest includes issues related to auditing and management control, and he has published papers in academic journals such as The Accounting Review, Auditing: A Journal of Practice & Theory, Journal of Management Accounting Research, Accounting Horizons, and European Accounting Review.

Lin-Hui Yu is an Associate Professor of Accounting at National Taiwan University. Her primary research areas include auditing, corporate governance, and financial accounting. She has published articles in Accounting Horizons, Asia-Pacific Journal of Accounting & Economics, AUDITING: A Journal of Practice & Theory, Corporate Governance: An International Review, Journal of Business Finance & Accounting, Management Review, Review of Quantitative Finance and Accounting, Review of Accounting and Finance, and Review of Accounting and Auditing Studies.

Yaou Zhou is an Assistant Professor of Accounting at Montclair State University. She also worked as an Assistant Professor at Rutgers University-Camden from 2018 to 2023. Her experience with public accounting firms inspires her research interests in auditing-related issues such as auditor quality, auditor switch, and auditor expertise. She also conducts research on management accounting with an emphasis on supply chains. Dr. Zhou has published articles in high quality journals including the Journal of Accounting, Auditing and Finance, and Journal of Business Ethics.

Appendix

APPENDIX A.

Variable Definitions

Variable	Definition
Dependent variable:
INV_INEFF	We obtain the residuals from the following model: INV_it = β₀ + β₁TOBINQ_it-1 + β₂CF_it + ε where INV is capital expenditure in year t scaled by net property, plant, and equipment in year t–1. TOBINQ is calculated as the market value of equity plus the book value of assets minus the book value of common stock, scaled by the book value of assets. CF is cash flows from operating activities, scaled by net property, plant, and equipment in year t–1. We sort residuals into three groups: (1) underinvestment group: If company i’s residual from the investment prediction model is in the smallest quartile; (2) overinvestment group: If company i’s residual is in the largest quartile, and (3) benchmark group: If company i’s residual is in the middle quartiles. INV_INEFF is set to 1 for observations classified into the underinvestment group, 2 for the benchmark group, and 3 for the overinvesting group.
Test variable:
STR_SIMILAR	Strategic similarity, measured as the absolute difference in strategy score between company i and its major customer. Strategy score is measured following Bentley et al. (2013) and encompasses R&D intensity, labor productivity, sales growth, investment in organization capital, organizational stability, and commitment to technological efficiency.
Control variables:
SIZE	Logarithm of total assets.
MTB	Market-to-book ratio, measured as the market value of common equity divided by the book value of common equity.
LEV	Leverage, measured as total debt scaled by total assets.
CASH	Cash scaled by total assets.
OPCYCLE	Operating cycle, measured as the logarithm of receivables scaled by sales plus inventory scaled by cost of goods sold.
ZSCORE	Bankruptcy risk, measured by the Altman Z-score.
AGE	Logarithm of the number of years company i has been on Compustat.
NUM_ANALYST	Logarithm of 1 plus the number of analysts. Source: IBES.
INSTOWN	Percentage of shares owned by institutional investors. Source: Thomson Reuters Institutional (13F) holdings.
STDSALE	Standard deviation of sales scaled by average total assets over the past 5 years.
STDCFO	Standard deviation of cash flows from operating activities scaled by average total assets over the past 5 years.
STDINV	Standard deviation of capital expenditures scaled by average total assets over the past 5 years.
PPE	PPE scaled by total assets
LOSS	= 1 if company i experiences a loss (IB, income before extraordinary items, is negative), and 0 otherwise.
DIV	= 1 if company i pays dividends, and 0 otherwise.
STRATEGY	Business strategy score focuses on six dimensions: R&D intensity, labor productivity, sales growth, investment in organization capital, organizational stability, and commitment to technological efficiency.
DURATION	Logarithm of the number of years the link between the supplier company and its major customer has existed.
ABSDA	The absolute values of discretionary accruals estimated from a modified cross-sectional Jones (1991) model controlling for return on assets (ROA) (Kothari et al., 2005) and estimated for each industry-year group with more than 20 companies.
BIGN	= 1 if company i is audited by a Big N auditor, and 0 otherwise.
STRAT_DEV	Strategic deviance from industry peers. It is measured following Ranasinghe and Habib (2023), encompassing advertising intensity, R&D intensity, plant and equipment newness, nonproduction overhead and inventory level, and financial leverage.
C_STRATEGY	Business strategy score of the major customer.
SPREAD	The mean of (ASKHI − BIDLO)/(ASKHI + BIDLO) over the previous 12 months. ASKHI is the highest price in the month, and BIDLO is the lowest price in the month. Source: CRSP.
SWITCH	Sales-based industry market share, calculated as sales scaled by total sales in a two-digit SIC industry.
ICW	= 1 if a company reports internal control weaknesses in year t–1, and 0 otherwise. Source: Audit analytics
AB_INV	Residuals obtained from estimating equation (1), which predicts the optimal investment level.
STR_SIMILAR_HIGH	= 1 if STR_SIMILAR is above the median of the sample, and zero otherwise.
BOARD_SIZE	The number of board members. Source: BoardEx.
NONIND	= 1 if at least 50% of the board members are classified as nonindependent. Source: BoardEx.
CH_VARIABLE	The change in the respective variable during the period specified in the table headings.
SALE_GROWTH	The difference between sales in year t−1 and year t−2, scaled by total sales in year t−2.

All variables are from Compustat except where specified.

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