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Abstract

I proposed a model relying on a typology of four types of capabilities: operations, technological, financial management, and marketing capabilities. The empirical study used a panel of data collected on 89 automotive and 117 pharmaceutical companies. The capabilities are measured with the use of the Data Envelopment Analysis (DEA) method. Results showed that in both industries, financial management capabilities need enhancement to maintain a competitive position and ensure a higher level of performance. Also, managers need to emphasize marketing capabilities and tangible investments in the automotive industry and technological capabilities and intangible investments in the pharmaceutical one. Among contributions, I proposed a design and measure of financial management capability and used the DEA method for the estimation of capabilities, as done by previous studies.

Plain language summary

The importance of financial management capability as a key success factor in two manufacturing industries.

The study is done to assess the effects of different functional capabilities on organizational performance. I proposed a model based on a typology of four types of capabilities: operations, technological, financial management, and marketing capabilities. The empirical study was carried out on a panel of data collected from 89 automotive and 117 pharmaceutical companies. The capabilities are measured with the use of the Data Envelopment Analysis (DEA) method. Results showed that in both industries, financial management capabilities need enhancement to maintain a competitive position and ensure a higher level of performance. Also, managers need to emphasize marketing capabilities and tangible investments in the automotive industry and technological capabilities and intangible investments in the pharmaceutical one.

Keywords

capabilities DEA method pharmaceutical automotive performance

Introduction

The performance of industrial companies is related to internal and external factors (Leonidou et al., 2017) and to strategies adopted to align them (Powell, 2012; Sun & Hong, 2002). The alignment is a result of capabilities of the companies and constitutes their sources of sustainable competitive advantage (Powel, 2012). Resource-based view, the most widely used strategic management perspective, indicates that a company’s resources and capabilities are the foundations of its competitive advantage. Capabilities, on the other hand, are multifaceted phenomena that require detailed examination using novel measurements, data, methods, and development approaches (Kamboj & Rahman, 2015).

Previous empirical and theoretical findings have demonstrated how specific capabilities influence performance (Nath et al., 2010; Yu et al., 2014). However, it is important to decide whether to invest in a single capability or to develop multiple ones (Symeonidou et al., 2022).

There is a lack of holistic perspectives (Rungi, 2014) and studies to fill the gap in measuring the relative contribution of a firm’s functional capabilities (George & Kerai, 2022; Kamboj & Rahman, 2015). However, these contributions have provided only fragmented evidence of some of capabilities needed to explain competitive advantage and performance.

Three facts underpin the motivation for this study. The first is that most previous theoretical and empirical RBV studies (Dutta et al., 1999; Duah et al., 2024; Narasimhan et al., 2006; Nath et al., 2010; Yu et al., 2014) have omitted the financial management capability. However, given the importance of financial resource management decisions (financing and investment) in the success of a firm, the financial management capability could be a strategic one. The finance theory cannot encompass all aspects of the financial management capability. However, it should employ a more robust theoretical tool in strategic management, in this case, RBV. The second fact arises from Hult et al. (2001) suggestion for future RBV research to be more rewarding by focusing on the interconnections (Dierick & Cool, 1989) between resources and capabilities and their impact on a business’s success. As a result, some missing enrichment works are required to better understand the nature of intra-capability interconnections as critical factors in the success of a business.

The third fact is based on recommendations to meet the challenge of assessing the relative impact of functional capabilities on performance (Kamboj & Rahman, 2015) in distinctive environmental conditions (Loureiro et al., 2021)

The purpose of this study is to delve deeper into the relative importance of ordinary capabilities and their interactions in explaining the performance. The following are our research questions: What are the critical capabilities for performance? And do financial management capabilities constitute a critical source for companies’ performances in two industrial contexts?

Most previous empirical studies that objectively measure ordinary capabilities (Dutta et al., 1999; Narasimhan et al., 2006; Nath et al., 2010; Yu et al., 2014) have focused on three types: marketing, operations, and operations, and technology. The suggestions from previous studies (Jie et al., 2023) suggest that financial capability, as a type of management capability, is still understudied compared to commonly investigated capabilities such as marketing, which is the most studied (Jie et al., 2023), and technological and operations, which are the third most researched capabilities (Jie et al., 2023).

As the first contribution of this paper, another capability was added to the analysis, the financial management capability. The second contribution of our paper is the use of sophisticated tools, such as Data Envelopment Analysis, to objectively measure capabilities. The third contribution is a comparison between the results found in automotive and pharmaceutical companies’ samples.

The preliminary estimation results have revealed the significance of the financial management capability effect, which supports our decision to include it as a key success factor. The second result emphasizes the importance of marketing and technological capabilities, both individually and collectively, to a firm’s success in the two industries.

The paper begins by presenting the theoretical context for the topic of ordinary capabilities, their coherence, and effects on performance, as well as the research hypotheses. The methodology and results are then presented, followed by a discussion and conclusions.

Theoretical Foundations

The resource-based view (RBV) is the theoretical perspective that argues that a bundle of a firm’s internal competitive capabilities, such as resource and competence possessed and managed by organizations, may create a competitive advantage, therefore improving organizational performance (Barney, 1991; Foss, 1997; Grant, 2010; Wernerfelt, 1984). The competitiveness of a corporation depends on a distinctive array of difficult-to-replicate resources, capabilities, and competencies (Barney, 1991; Grant, 1996; Wernerfelt, 1984).

Since innovation is the result of a multi-layered recombination of resources and specific capabilities (Lin et al., 2006; Xiao et al., 2022), exploiting market opportunities requires identifying and combining complementary assets needed to support the company’s innovation efforts.

Capabilities are complementary or co-specialized when their effectiveness is affected by one another’s presence, implying that the value of one asset is dependent on the level of other assets. Because their presence makes the competitors’ imitation difficult, these complementarities can improve business performance and durability (Grewal & Slotegraaf, 2007; Liao et al., 2019; Morgan et al., 2009).

To address this issue, a functional typology of four ordinary capabilities was applied. The four ordinary capabilities are following: operations capability, technological capability, financial management capability, and marketing capability.

Operations capability is the organizational ability to integrate a complex set of tasks performed by the company to ensure the production of manufacturing outputs under inefficient conditions of management of the production inputs such as materials and technological flows (Dutta et al., 1999; Nath et al., 2010). It enables the company to manage its processes through the acquisition and integration of superior knowledge (Tan et al., 2007).

The technological capability refers to the organizational ability to develop and use technological resources or the ability to deploy technology (Mikalef et al., 2020). This ability is related to the development of new products, processes, and technologies, as well as to the anticipated technological changes in the environment (Song et al., 2007).

The marketing capability is the ability of a company to utilize its tangible and intangible resources to understand the needs of its customers, differentiate its products from those of its competitors, and build a stronger brand image (Day, 1994; Dutta et al., 1999; Nath et al., 2010; Song et al., 2007).

The financial management capability is the ability to manage a company’s financial resources, particularly slack, to achieve a healthy financial situation. The financial slack can be categorized into three types: the available (Jain & Nag, 1995), the recoverable (D’aveni & Ravenscraft, 1994) and the potential slack (Harrison et al., 1993). The financial management strategies rely on deciding between long-run and short-run financing and investment decisions (Greenley & Oktemgil, 1998). Thus, the financial management capability should not be confused with the firm’s solvency, which is merely a result of the former. Furthermore, the financial management capability manifests itself through knowledge embedded in routines and processes relating to financial operations and organization.

Model Development and Hypotheses

According to Newey and Zahra (2009), operations capabilities enable an organization to carry out its primary operating activities by supporting its products and services, thereby improving its performance. The “secret” ingredient that the company uses to improve its efficiency is the operations capability (Wu et al., 2010).

Nath et al. (2010) explained the positive relationship between the operations capability and performance by focusing on the infrastructure development and the critical role of technology in improving production, such as logistics. Superior operations capability allows for more efficient resource management, lowering production and distribution costs and providing a competitive advantage (Day, 1994).

Several empirical studies in industrial strategy back up the links between the operations capability and performance (González-Benito & González-Benito, 2005; Hassan et al., 2017; Roscoe et al., 2019; Tan et al., 2007; Wang et al., 2023). Furthermore, this relationship is dependent on the unique characteristics of each company (Song et al., 2005).

H1: Operations capability influences performance positively.

Knowledge stocks are formed because of the assimilation and development of knowledge flows (De Carolis & Deeds, 1999; Yu et al., 2022). These can be acquired more quickly with an initial foundation of knowledge to guide the company’s growth strategy and support new product development activities to generate profits.

Concerning the long-term viability of the performance generated by the technological capability, it is important to note that, despite patents that are likely to provide legal protection for the position or clear advantage, it remains susceptible to imitation by competitors because of to the codified nature of the technological capability. As a result, the technological capability has a positive impact on short-term business performance (Acquaah, 2003; De Carolis, 2003), operational performance (Kafetzopoulos & Psomas, 2015), competitive performance (Mikalef et al., 2020), and innovation performance (Fakhimi & Miremadi, 2022).

H2: Technological capability influences performance positively.

The marketing capability is critical in developing the competitive advantage (Day, 1994; Morgan, 2012; Vorhies & Morgan, 2005) and performance (Pham et al., 2017). Indeed, the accumulation of successive customer relationship investments enables the company to develop a market-sensing ability (Narasimhan et al., 2006). Given its implicit nature, the marketing capability is difficult to imitate (Day, 1994) and provides a sustainable advantage.

The brand image built by the marketing capability following the sustained quality of customers’ relationships (Song et al., 2005) is a prime source of performance (Vorhies & Morgan, 2003; 2005).

Many researchers have emphasized the superiority of the marketing capability over other ordinary capabilities (Dutta et al., 1999; Krasnikov & Jayachandran, 2008; Song et al., 2005; Vorhies & Morgan, 2005) because it has been identified as a critical capability for the company. This superiority stems from the fact that operations and technological capabilities are more mobile than marketing capabilities (Mikalef et al., 2020; Nath et al., 2010), which could affect not only short-term profitability but also long term (Hirunyawipada & Xiong, 2018; Mathews et al., 2019).

Marketing capabilities are a complex phenomenon that requires further investigation, and regardless of the metrics employed, their impact on performance is primarily positive and significant (Cataltepe et al., 2022; Kamboj & Rahman, 2015; Mikalef et al., 2020).

H3: Marketing capability influences performance positively.

According to finance theories, the financial management strategies of financing or investing are critical factors in the company’s performance. The success of these strategies is dependent on the company’s financial management capability, which will allow it to better allocate its financial resources to ensure a healthy financial position and a higher level of performance (Greenley & Oktemgil, 1998; Tan & Peng, 2003).

Financial resources are multidimensional, and their availability can lead to investment orientation (Scarpellini et al., 2018). Empirically, financial resources are not directly fruitful, but admit an indirect effect on financial performance (Ramon-Jeronimo et al., 2019). This fact enhances the role of financial management capability as a source of competitive advantage and thus the firm’s performance.

H4: Financial management capability influences performance positively.

Empirical research by Song and Parry (1997) discovered that the two capabilities are correlated, and that each has a direct impact on performance. Li (1999) confirmed the existence of a positive effect of marketing/research and development integration on the performance of new products in international markets. Similarly, Vinod and Rao (2000) discovered that advertising promotion and R&D spending are both positively related, implying that the two are complementary. Because of the complexity of the interaction, this coherence between technological efforts and the marketing capability would be a source of long-term competitive advantage.

The additional contribution of the marketing/R&D interaction to performance (Griffin & Hauser, 1996) is explained by the marketing capability allowing the technological capability to receive feedback from consumers. This enables the technological capability to better adapt the product by introducing innovations that carry value in response to the expressed needs of consumers, and as a result, the marketing capability improves (Dutta et al. 1999).

Both marketing and technological capabilities are independently unique and valuable and can complement one another (Vorhies & Morgan, 2005). Ultimately, it is expected that they have a positive impact on performance (Day, 1994; Song et al., 2007).

H5: The complementarity of the marketing capability and the technological capability improves performance.

The theoretical model is summarized in Figure 1.

Figure 1.

Theoretical model.

Method

Data and Sample

Consistent growth influenced the selection of the automotive and pharmaceutical sectors; however, the pharmaceutical sector had experienced a more substantial investment in research and development, as well as a higher degree of commercial specialization in France, in contrast to the automotive sector.

The French industries classification, NAF Version 2 classification with three levels, is used in the empirical work. According to Pagell and Krause (2004), the industry identification uses the SIC code level 4. Companies, in the same class, would face similar environmental challenges. The sample consists of industrial manufacturing companies selected from the DIANE database that operate in the French automotive industry (NAF Code 29) and pharmaceutical industry (NAF Code 21).

Two major criteria were used for the selection of sampled companies:

Data completeness: All companies with significant amounts of missing data were excluded from the sample to ensure the integrity and accuracy of the analysis.

Patent publications: Only firms with patent publications in the WIPO database during the period of the study were included. This criterion imposes a drastic reduction in the size of the sample.

The sample covers the years 2002 to 2010. The information was gathered from the DIANE and WIPO databases (World Intellectual Property Organization). The latter allows for internationally submitted patent information. Data was collected on the annual number of patents registered by each company individually. The primary sample includes 1,546 automakers and 412 pharmaceutical firms. We obtained a sample of 89 automotive companies and 117 pharmaceutical companies after removing firms with missing data or outlier values. This high rate of elimination is primarily because of the low number of registered patents that will be used to assess technological capability.

Measures

The critical point on the resource-based theory as having circular reasoning was the measurement of resources and capabilities in RBV. Indeed, capability measures are our first point of focus.

Dependent Variable Measures

Two ratios will be adopted to evaluate performance: the financial profitability (ROE) (Greenley & Oktemgil, 1998) and the economic profitability (ROA) (Tan, 2003).

Independent Variables (Ordinary Capabilities) Measures

The applied methodology used in previous studies (Dutta et al., 1999; Dutta et al., 2005; Narasimhan et al., 2006; Nath et al., 2010; Yu et al., 2014) to assess the capabilities by employing an input/output approach and a nonparametric multi-criteria optimization method, specifically the DEA method (Dutta et al., 1999; 2005; Nath et al., 2010).

The following data details (Table 1) used on the measures of different types of capabilities were proposed by Dutta et al. (1999, 2005). We adapted the technological, marketing, and operations ones, and we added the financial management capability, which we designed and measured for the first time in an RBV empirical study.

Table 1.

Different Measures of Ordinary Capabilities.

Inputs/outputs	FunctionalResources/performances	Measures	References	Data sources
Functional resources	Financial resources	Financial autonomy Administratives expenditures /Turnover Net Cash-flow /Assets	(Harrison et al., 1993; D’aveni & Ravenscraft, 1994; Jain & Nag, 1995)	Diane database
	Marketing resources	Cumulated Marketing expenditure Intangibles The amount of receivablesSales growth	(Dutta et al., 1999; Nath et al., 2010; Narasimhan et al., 2006) Revised	Diane database
	Technological resources	Cumulative R&D expenses $\sum_{k = 1}^{k = t - 1} δ^{t - k} . Nber patents$ (k) The value of patents possessed	(Dutta et al., 1999 ; 2005 ; Narasimhan et al., 2006) Revised	Diane database
	Operations resources	Stock of tangible capital Flow of tangible capital Employee’s Wages and Benefits/employee number	(Dutta et al., 1999; Nath et al., 2010 ; Narasimhan et al., 2006)	Diane database
Functional Performance	Financial performance	The AFDCC Score		Diane database
	Marketing performance	TurnoverGross margin rate	(Dutta et al., 1999; Nath et al., 2010 ; Narasimhan et al., 2006) Revised	Diane database
	Technological performance	Number of patents applications tNumber of patents applications t+1 and t+2	(Dutta et al., 1999; 2005; Narasimhan et al., 2006) Revised	Wipo website
	Operations performance	Value: Economic Value added.Efficiency: Time inventories turnover	(Dutta et al., 1999; 2005; Narasimhan et al., 2006) Revised	Diane database

Note. For technological resources: I choose 0.4 $δ$ as a weight by reference to the work of Griliches (1984) who used it to appreciate temporal effect on R&D cumulated expenditure.

The AFDCC score: Good financial health score developed by the AFDCC professional association of Credit Managers in France.

The number of patents: The number of patents applications registered in year (t) and published after a period of 18 months by INPI, WIPO. To complete data, we have collected data on patents from WIPO website from 2002 to 2012.

The financial management capability enables a company to manage its potential, recoverable, and available slacks to maintain a positive financial situation. The latter is determined by the well-known French Association of Credits Managers et Consults (AFDCC) score rating.

The marketing capability is defined as the ability to manage the marketing expenditure stock, intangible resources, relationship expenditure, and an installed customer base to maximize turnover and commercial margin (Dutta et al., 1999; 2005; Nath et al., 2010; Yu et al., 2014).

The operations capability allows managing the cost of labor (Dutta et al., 1999; 2005; Nath et al., 2010), the stock of tangible capital, and the flow of tangible capital (Vincente-Lorente, 2001) to maximize the value created and flexibility.

The technological capability is the ability to manage R&D stock, prior know-how stock, and prior knowledge engaged in the R&D process to maximize current and potential technological performance (Dutta et al., 1999; 2005; Nath et al., 2010). The latter is limited to a three-year time frame because R&D efforts yield results after an average of 3 years (Artz et al., 2010).

Table 1 describes the various measures of functional capabilities, resources, and performances.

The DEA method was used to estimate each capability’s annual level for each sub-sector of the automotive and pharmaceutical industries. According to the French Classification NAF, there are three automotive sub-sectors and two pharmaceutical ones.

The multi-objective optimization method fits well with the definition of capabilities. Since capabilities enable the use of multiple resources to attain multiple outcomes at different functional levels. The DEA method has the advantage of handling multiple outputs/inputs to evaluate the efficiency of decision-making units (DMUs), which is not the case for the SFA, a parametric method. Also, DEA is a non-parametric method; thus, it doesn’t need any predefined function form for the relationship between inputs and outputs. DEA identifies best-performing firms (efficient frontier) to measure the relative efficiency of firms by comparing them to the best performers.

Financial management capability is conceptualized, like others, based on the (financial) resources itexploits to attain a level of (financial) performance.

Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. It is often applied to study scenarios with many inputs and outputs, examining and measuring the factors that affect the efficiency of decision-making units.

The DEA model is a non-parametric method that does not require any assumptions about the functional form of the production frontier. The basic DEA model can be formulated as follows:

λ, θ \max j = 1 Σ n λ j yj

subject to

j = 1 Σ n λ j xij \leq θ xi 0, i = 1, \dots, m

λ j \geq, j = 1, \dots, n

where xij is the amount of input i (resources) used by decision-making unit (DMU) j (firm), yj is the amount of output (functional performance) produced by DMU j, is the weight assigned to DMU j, and θ is the efficiency score of DMU i relative to the best performing DMU.

The choice of the input orientation, the convex performance structure, the constant return of scale, and the super-efficiency option was adopted. These options allow for the measurement of the degrees of inefficiency and the levels of efficiencies of units on the frontier, allowing for differentiation between the levels of capabilities held by the top-ranked companies.

Control Variables Measures

These variables are limited to workforce size as measured by the logarithm of the workforce (Prieto & Revilla, 2006) and age as measured by the logarithm of the number of years of activity (Zhang et al., 2009).

Model Formulation and Estimation

Besides the variables already mentioned, the formulated model includes the delayed performance and the interaction term between the marketing capability and the technological capability. The simplified theoretical model is as follows:

\begin{matrix} Y_{it} = β_{0 it} + β_{1 it} . C F_{it} + β_{2 it} . C M_{it} + β_{3 it} . C P_{it} + β_{4 it} . C T_{it} \\ + β_{5 it} . C M_{it} * C T_{it} + β_{6 it - 1} . Y_{it - 1} + β_{7 it} . T_{it} + β_{8 it} . A_{it} + ε_{it} \end{matrix}

With: $Y_{it}$ The performance measured by ROA, ROE.

$C j_{it}$ ::: Ordinary capabilities (F: Financial, M: Marketing, P: Operations, and T: Technological).

$C M_{it} * C T_{it}$ ::: Interaction term between marketing and technological capabilities (internal coherence). T: The company’s size. A: The company’s age.

We used the generalized least square (GLS) and the generalized moments method (GMM) for estimation. The former is useful for assessing the temporal and individual effects (De Carolis, 2003; Lin et al., 2006; Vincente-Lorente, 2001). The latter includes performance as an endogenous variable introduced into the model as a lagged variable. Because of the collinearity issue, we added the interaction term under the recommendations of some researchers (Russo & Fouts, 1997). The chosen estimation method has the advantage of allowing for causal effect and sustainability testing (Acquaah, 2003).

Results

The average performance of the companies in the sample, belonging to each subsector, has developed differently before and during the financial crisis of 2008.

The performance of the pharmaceutical industry isn’t affected by the financial crisis. However, the performance of the automotive industry has deteriorated significantly because of the financial crisis.

Table 2 illustrates how financial management capability, in contrast to other capabilities, influences various performance measurements. This illustrates the significance of financial management capability in explaining performance in both industries.

Table 2.

Correlations Matrix.

Pharmaceutical industry	Financial management capability	Operations capability	Marketing capability	Technological capability
ROE	0.0680*	−0.0023	0.0002	−0.2781*
ROA	0.2024*	−0.0100	−0.0014	−0.1531*
Automotive industry
ROE	0.3639*	0.0289*	0.0062	0.0071
ROA	0.3817*	−0.0238*	0.0201	0.0059

P < 5%.

Despite significant positive relationships (Table 3) between financial management-marketing capabilities, financial management-operations capabilities, and marketing-operations capabilities, all capabilities are weakly correlated, allowing all the variables to be included in a single model.

Table 3.

Correlations Matrix.

Pharmaceutical industry	Financial management capability	Operations capability	Marketing capability	Technological capability
Financial management capability	1.0000
Operations capability	0.0099	1.0000
Marketing capability	0.0403	−0.0015	1.0000
Technological capability	−0.0041	−0.0052	−0.0053	1.0000
Automotive industry
Financial management capability	1.0000
Operations capability	0.1199*	1.0000
Marketing capability	0.1247*	0.1176*	1.0000
Technological capability	0.0162	0.0190	−0.0089	0.0000

P < 5%.

The estimation results (Table 4) for the relationships between individual capabilities and performance in the automotive industry confirmed the H3 and H4 hypotheses, concerning the positive effects of the marketing capability (P 1%) and the financial management capability (P 1%). The operations capability, on the other hand, admits a significant (P 1%) negative effect. These findings invalidate hypothesis H1. However, the technological capability has no discernible impact on performance, refuting hypothesis H2.

Table 4.

Estimation Results.

	Automobile industry				Pharmaceutical industry
Performance	ROE	ROE	ROA	ROA	ROE	ROE	ROA	ROA
Models	Fixed effect interaction	Dynamic robust interaction	Fixed effect interaction	Dynamic robust interaction	Fixed effect interaction	Dynamic robust interaction	Fixed effect interaction	Dynamic robust interaction
Perf (−1)		−0.189**		0.142		0.843***		0.778***
		(0.0948)		(0.121)		(0.179)		(0.260)
Financial management capability	10.78***	12.04***	17.93***	7.216***	18.86**	14.61	27.46***	16.27***
	(1.582)	(3.951)	(2.783)	(2.328)	(9.285)	(11.48)	(4.718)	(4.319)
Operations capability	0.773	7.255	−7.209***	22.63***	−12.38***	1.317	−0.481***	−0.109
	(1.530)	(11.71)	(2.108)	(7.167)	(0.835)	(3.157)	(0.086)	(0.710)
Marketing capability	1.616**	−1.533	2.983**	−3.659	4.032	126.1	−6.357	−11.53
	(0.768)	(2.465)	(1.441)	(2.977)	(14.36)	(98.95)	(6.649)	(16.22)
Technological capability	0.140	0.845	−0.501	0.182	1.324***	−1.352	0.790***	0.697
	(0.206)	(0.687)	(0.495)	(1.216)	(0.407)	(3.342)	(0.226)	(0.495)
Marketing x tech capabilities fit	−1.123**	0.0166	0.815	−2.171	−1.326	7.197	−2.754***	−1.255
	(0.471)	(2.091)	(1.396)	(2.475)	(1.932)	(10.55)	(0.947)	(2.365)
Age	0.761	9.546	−2.425	1.496	−8.108	3.822	7.896**	3.797
	(1.959)	(13.01)	(3.371)	(2.792)	(22.66)	(55.77)	(3.207)	(11.87)
Size	3.953**	0.535	3.533	−2.392	−6.168	5.350	−1.012	−0.218
	(1.786)	(2.532)	(2.827)	(2.362)	(6.379)	(7.087)	(0.823)	(1.764)
Constant	−12.45	−22.19	9.564	3.211	41.84	−48.91	−34.91	−18.62
	(12.33)	(51.05)	(21.98)	(14.00)	(95.59)	(178.5)	(13.50)	(37.54)
R-squared	0.311		0.344		0.496		0.289
P(fisher)/chi2	0.0091	0.0012	0.0000	0.0000	0.000	0.000	0.000	0.000
Observations	306	271	307	274	384	333	384	336
Number of i	89	83	90	82	106	99	106	100
Sargan		0.2188		0.3188		0.219		0.163
Arellano-abond1		0.2559		0.2028		0.194		0.015
Arellano-abond2		0.2626		0.9709		0.182		0.886

Note. Robust standard errors in parentheses, ***p < .01, **p < .05, *p < .1. Coefficients of Significant relationships are in Bold.

In the pharmaceutical industry, however, the results confirmed the H2 and H4 hypotheses concerning the positive effects of the technological capability (P 1%) and the financial management capability (P 1%). However, significant (p<1%) negative effects are observed in the operations and marketing capabilities. These findings invalidate hypotheses H1 and H3.

In terms of the impact of the variable “internal coherence” as assessed by the “marketing-technological capabilities interaction,” the findings revealed a significant (p<5%) but negative effect on economic performance, leading to the rejection of hypothesis H5 for the two industries.

Another intriguing finding pertains to performance sustainability. The performance of the automotive industry follows a negative autoregressive model (AR1), which can be explained by the international crisis that affected the industry during the study period. However, the pharmaceutical industry’s performance follows a positive autoregressive model (AR1).

The findings also revealed the significance of the positive effect of firm size (P 1%) in the automotive industry and firm age (P 5%) in the pharmaceutical industry.

Discussion

The model estimates have revealed some exciting facts. First, the marketing capability has a positive and statistically significant effect on the automotive firm’s performance (p. 5%). The latter result supports the findings of many other researchers (Cataltepe et al., 2022; Dutta et al., 1999; Homburg & Wielgos, 2022; Kamboj & Rahman, 2015; Mikalef et al., 2020; O’Cass, & Sok, 2014; Song et al., 2007; Vorhies & Morgan, 2003; 2005). This very result supports the primacy of marketing capability over other functional capabilities, as suggested by several studies (Nath et al., 2010; Pham et al., 2017). Indeed, this result backs up Fahy’s (2002) claim that the most successful automotive companies give more value (or relational) to external expertise. Nonetheless, the marketing capability has no significant effect on the pharmaceutical firm’s performance, which confirms the findings of Morgan et al. (2009).

Secondly, technological capability has no discernible impact on the performance of automotive firms. This finding is consistent with previous research (Lin et al., 2006), which discovered no significant relationship between the technological capability and company performance (financial or market). However, the technological capability has a positive and statistically significant effect on the pharmaceutical firm’s performance (p. 1%). This result supports the findings of many other researchers (Coombs & Bierly, 2006; Dutta et al., 1999; Jaisinghani, 2016; Mikalef et al., 2020; Narasimhan et al., 2006; Nath et al., 2010; Yeoh & Roth, 1999; Yuan et al., 2016).

An explanation for this result may be the assertion that the fruit of technological capability comes after a long period of investment and has no effect on short-term performance, because learning develops over time along a technological capability trajectory (Cockburn et al., 2000; Hansen & Lema, 2019). However, for pharmaceutical companies, the outcome can be explained by the industry’s nature, which is one of the most supportive for innovations. The second explanation of the mitigated effect of technological capabilities on performance can be explained by the technological capabilities’ aspect captured by the measurement adopted (Park et al., 2021; Schoenecker & Swanson, 2002)

Third, the financial management capability has a positive and significant effect on the performance of pharmaceutical and automotive firms (p. 1%). Some researchers (Singh, 1986; Greenley & Oktemgil, 1996; 1998; Daniel et al., 2004; Carnes et al., 2019) explained this finding by studying the relationship between slack and performance and submitting excellent slack management as a source of superior performance (Lin et al., 2019). Furthermore, this result confirms the findings of Cooper et al. (1994), who discovered that financial capital contributes significantly to performance, and confirms that the financial management activity holds an unrivalled position, particularly in the pharmaceutical industry. This result confirms the indirect effect of financial resources on performance (Ramon-Jeronimo et al., 2019).

Fourth, the operations capability has no significant effect on the performance of automotive firms but a significant and negative effect on the performance of pharmaceutical firms. This finding contradicts some previous research findings (Dutta et al., 1999) and supports the ones of others (Andria et al., 2020; Yu et al., 2018). Because the operations capability is based on a codified and easily replicable process, its ability to generate value for the company and the flexibility of internal processes disintegrates, negatively affecting the company’s operating performance.

Fifth, the interaction of a company’s technological and marketing capabilities has a significant but negative impact on its performance in both industries. This finding can be explained by the moderating role of technological capability, which reduces the impact of marketing capability, contradicting some previous research findings (Nath et al., 2010; Yu et al., 2014).

The assertion made by King et al. (2008) that capability can mitigate or even eliminate the effect of another explains this result. This finding is consistent with Morbey and Reithner (1990), who discovered a significant but negative impact on performance and explained it by technological capability deficiencies. As Leenders and Wierenga (2008) discovered with pharmaceutical companies, combining marketing and technological capabilities is not always beneficial. This result confirms that it is more profitable for the firm to focus its investment on a single capability rather than multiple ones (Symeonidou et al. 2022).

Sixth, it is important to note that size is only significantly and positively related to the financial performance of automotive companies. This finding supports the findings of other researchers (Carmeli & Tishler, 2004; De Carolis, 2003; Lin, et al., 2019). This result implies that the company’s goal with tangible investments is to get closer to the optimal size corresponding to efficient production capacity meeting the needs of its market.

Seventh, it should be noted that the company’s age has a significant (p. 5%) and positive effect on the performance of pharmaceutical firms. This result corroborates previous research findings (Azimah et al., 2007; Ravichandran & Lertwongsatien, 2005; Zhang et al., 2009). This finding emphasizes the significance of cumulative experience, learning, and reputation in the pharmaceutical industry.

Eighth, the estimation of the dynamic model confirms the sustained deterioration of performance in the automobile industry during the study period. However, in the pharmaceutical industry, performance improvement is ongoing.

In summary, the results support the arguments of RBV in automobiles as previous research (Kosaka et al., 2020), as well as in the pharmaceutical industry (Andria et al., 2020; Bhaduri & Ray, 2004).

Implications

Theoretical and Methodological Implications

This study has contributed to the understanding of the relationship between ordinary capabilities and performance through the design and measurement of financial management capability. In fact, previous literature (Monteiro et al., 2019; Tan & Peng, 2003) revealed only financial slack that places no value on financial resource management.

Furthermore, this study revealed that the interaction of capabilities does not always generate value. Indeed, as well explained by King et al. (2008), the company must know when to invest in the capabilities individually (Krasnikov & Jayachandran, 2008; Symeonidou et al., 2022) and when to invest in the complementarities collectively (Grewal & Slotegraaf, 2007). In other words, the company must have a strategic organizational capability that allows it to ensure the internal coherence of its resource and capability portfolio.

Furthermore, the comparative study adds to the richness of our findings by providing additional evidence for previously established relationships with performance.

Managerial Implications

According to the findings, in both automotive and pharmaceutical industries, the financial management capability requires improvement to maintain a competitive position and ensure a higher level of performance. Also, in the automotive industry, managers must emphasize marketing capabilities and tangible investments, while, in the pharmaceutical industry, managers must emphasize technological capabilities (Jaisinghani, 2016) and intangible investments. It is worth noting that the marketing-technology interaction in both industries requires more managerial attention.

The Research Limitations and Recommendations

The paper makes use of panel data studies, but it acknowledges its limitations, as does all human work. The first limitation is the focus on two manufacturing industries, which makes it difficult to generalize the findings to the entire French industry. The second limitation is that the model must be supplemented with additional variables and capabilities that explain performance.

As a suggestion for future RBV research, the focus is on investigating several new capabilities. A qualitative study that could be conducted with business leaders in the field will help them to better understand the results of the quantitative analysis.

Conclusion

The investigation of the relationship between ordinary capabilities and performance in the automotive and pharmaceutical industries necessitates the measurement of ordinary capabilities using a multi-objective optimization method, Data Envelopment Analysis, allowing the application of an output/input approach used by some studies that objectively measured ordinary capabilities (Dutta et al., 1999; Narasimhan et al., 2006; Nath et al., 2010; Yu et al., 2014).

The findings support the hypothesis that the model must include financial management capability. This type of ordinary capability is the most important determinant of a firm’s performance in both industries.

Furthermore, as strategic success factors, the results emphasized the importance of marketing capability and tangible investments in the automotive industry, as well as technological capability and intangible investments in the pharmaceutical industry.

Theoretically and empirically, this research showed that the company must know when to invest in the capabilities individually (King et al. 2008; Kranikov & Jayachandran, 2008; Symeonidou et al., 2022) and when to invest in the complementarities collectively (Grewal & Slotegraaf 2007). Managerially, in the automotive industry, managers must emphasize marketing capabilities and tangible investments, while, in the pharmaceutical industry, managers must emphasize technological capabilities and intangible investments.

As a result, this study provides more reasons for managers to invest in the best combination of ordinary capabilities rather than in all the company’s capabilities.

Footnotes

Acknowledgements

This paper is dedicated to the memories of my thesis supervisor, P. Jacques Rojot (LARGEPA, Paris-Panthéon-Assas University, France), and my master’s degree supervisor, P. Ali Elmire (ISGT, University of Tunis, Tunisia), who have passed away. I am grateful for their support and guidance

Declaration of Conflicting Interests

The author declared no potential conflicts of interest regarding to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-23-DR-87). The author, therefore, thanks the University of Jeddah for its technical and financial support.

ORCID iD

Mehdi Younes Garrab

Data Availability Statement

Data can be shared upon request from the author.

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Financial Management Capability Matters in Capabilities—Performance Relationships: The Case of Two Industries

Abstract

Plain language summary

Keywords

Introduction

Theoretical Foundations

Model Development and Hypotheses

Method

Data and Sample

Measures

Dependent Variable Measures

Independent Variables (Ordinary Capabilities) Measures

Control Variables Measures

Model Formulation and Estimation

Results

Discussion

Implications

Theoretical and Methodological Implications

Managerial Implications

The Research Limitations and Recommendations

Conclusion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability Statement

References