Differing paths to organizational performance: strategic implications of resource transformation and capability reinforcement

Abstract

Globally, organizations have little insight into mechanisms that enable them to leverage their resources and capabilities successfully. In that endeavor, this study demonstrates that organizations can achieve competitive advantages through resource transformation and capability reinforcement. Using a conceptual framework grounded in the resource-based view and the dynamic capabilities theory in combination with Miles and Snow typology, we show how different types of organizations can succeed in the currently evolving competitive landscape by developing mechanisms that match the strategic performance measures of the organizations, such as return on assets or Tobin's Q. Notably, analyzing data obtained from 114 firms with seemingly unrelated regression, the findings reveal central roles of alternating mechanisms that drive differential organizational performance and enable the organizations to successfully deploy resources and capabilities.

Introduction

In a globally competitive business environment, every advantage counts for an organization in its endeavor to establish itself at the top of its industry. In dynamic markets and changing technologies, organizations achieve competitive advantage only temporarily as their market positions are extremely vulnerable to competition (Helfat & Martin, 2015; Porter, 1996). This has rendered organizations vulnerable with little insight into the path to achieving superior performance, particularly when competitive advantages associated with a bundle of idiosyncratic resources and deployment capabilities are at risk of erosion (Banerjee, Farooq, & Upadhyaya, 2018; Khan, Atlas, Ghani, Akhtar, & Khan, 2020; Matysiak, Rugman, & Bausch, 2018). Although relaxing regulations and globalizing markets lead to more entrants, however, failing to accurately identify sources of competitive advantage constrains efforts to achieve superior organizational performance (Day, 2011). The recent recognition of a growing gap between the fast-changing competitive landscape and the capacity of most market organizations to deal with it (Day, 2011; Hydle & Brock, 2020) has created a need to develop and empirically validate those factors that enable to close this wide gap and support effective organizational alignment with its environment.

Recently, several research scholars have pointed out the need to empirically explicate specific mechanisms that enable organizations to perform competitively (Cappa, Oriani, Pinelli, & De Massis, 2019; Day, 2011; Doyle & Armenakyan, 2014). In particular, the evolution of the digital technology has significantly revolutionized the current competitive landscape necessitating the development of new capabilities (Caputo, Fiorentino, & Garzella, 2018b). Recent findings emphasize the importance of digital-era capabilities in order to influence business model innovation in organizations (Garzella, Fiorentino, Caputo, & Lardo, 2021). Further, dynamic capabilities (DC) facilitate digital transformation and create value for customers as well as organizations (Matarazzo, Penco, Profumo, & Quaglia, 2021). Specifically, DC enable explorative and exploitative innovation that lead to strategic flexibility of the organizations (Rialti, Marzi, Caputo, & Mayah, 2020). Although prior research in resource-based view (RBV) and its DC theory extensions advance our understanding of how resources and capabilities lead to differences in organizational performance (Barney & Arikan, 2001; Teece, Pisano, & Shuen, 1997), there is limited theoretical and empirical work to demonstrate the key roles of mechanisms, such as resource transformation and capability reinforcement, in explaining differential organizational adaptation and performance in the face of rapidly changing market environment. Therefore, in this study, we develop a conceptual framework proposing that resource transformation and capability reinforcement underlie the ability of an organization in achieving a sustainable competitive advantage and superior performance in a complex and dynamic market environment.

Furthermore, organizational strategic types adapt to changes in the market environment in a way that is distinctive to their type (Miles, Snow, Meyer, & Coleman, 1978; Zahra & Pearce, 1990). A strategic type (prospector, analyzer, or defender) differs in its approach to the changes in the market environment, thus, we examine the role of organizational strategic type in determining the mechanism apt for an organization to achieve superior performance. The process of organizational performance begins with how organizational resources and capabilities are deployed based on an organization's strategic intent. Hence, we examine how different strategic types deploy distinctive resources and capabilities for divergent processes of resource transformation and capability reinforcement and improve performance.

Although existing literature on RBV and DC theory has enriched our understanding of firm performance, it has yet to examine fully the important aspects of the mediating mechanisms involved in explaining firm performance (Teece, 2007; Vorhies, Orr, & Bush, 2011). Previous research argues that the contribution of DC remains ambiguous and widely debated though supports the indirect effects of DC (Baía & Ferreira, 2019). Some studies find the direct effects of resources and capabilities on performance while other empirical studies report indirect and interaction effects, which have led to ambivalent findings (Morgan, Vorhies, & Mason, 2009; Vorhies, Orr, & Bush, 2011). Our first contribution in this study focuses on mediating processes of resource transformation and capability reinforcement to explain firm performance. This study demonstrates that resource transformation and capability reinforcement operate as key mediating variables that are important sources of competitive advantage in conferring superior performance.

Our second contribution is that by integrating Miles and Snow's strategic typology, we advance the understanding of the relative importance of resource transformation and capability reinforcement to different strategic types. Thus, this study further advances the understanding of RBV and DC theory by integrating Miles and Snow's strategic types. Each strategic type is distinct in terms of strategic decision-making for maximizing the outcomes of the potential opportunities available in the marketplace. Hence, our study advances the theoretical tenets and illustrates how an organization can deploy its assets in achieving superior performance.

Prior studies report that each strategic type is idiosyncratic in its measure of performance to remain consistent with its strategic behavior. In view of that, our third contribution is that our study explores the impact on two different measures of performance. Thus, by examining performance via two different measures, return on assets (ROA) and Tobin's Q, we identify performance measures relevant to a particular strategic type and the distinct drivers of performance. Given its relevance from both the theoretical and managerial standpoint, our research would help to better understand the performance measure most relevant to a particular strategic type. By examining unique resources and capabilities for different strategic types with two measures of performance: ROA and Tobin's Q, our study provides practical guidance to managers on ways to improve organizational performance. The findings of this study will provide important directions to managers on developing resource transformation or capability reinforcement based on different performance evaluation measures and the strategic intent of an organization.

Conceptual framework and hypotheses

Synthesizing RBV and DC theory perspectives in conjunction with Miles and Snow's theoretical framework, we develop a conceptual model to demonstrate how organizations gain competitive advantage and superior performance. Specifically, we examine the heterogeneous set of organizational resources and capabilities and the ability of the organizations to transform their resources and reinforce capabilities to increase organizational performance. The conceptual model is presented in Figure 1. In the model, we propose that firm resources influence resource transformation, which in turn influences organizational performance. Also, we propose that firm resources influence firm capabilities leading to capability reinforcement, which in turn influences firm performance. In addition, this study examines and empirically investigates these relationships in the context of Miles et al.'s (1978) adaptive strategies that organizations follow and are classified as prospectors, analyzers, or defenders. Hence, our study identifies the mediating processes, specifically, resource transformation and capability reinforcement that are unique to a strategic type.

Figure 1. Conceptual model.

Furthermore, we examine organizational performance in terms of ROA to determine how an organization can improve its profits and also investigate performance in terms of Tobin's Q to highlight how an organization can enhance its intangible assets.

Consistent with the RBV and DC theory, resources refer to stocks of available factors possessed and controlled by the firm, and capabilities represent the capacity of the firms to deploy these firm resources (Morgan, Vorhies, & Mason, 2009). Resource transformation is the extent to which resource configurations change to match the changing competitive landscape and it involves additions or deletions of specific resources from the idiosyncratic combination of resources, an organization possesses (Capron, Dussauge, & Mitchell, 1998; Eisenhardt & Galunic, 2000). Capability reinforcement represents the ability of a firm to change and evolve its capabilities as resource-deploying mechanisms for achieving sustainable competitive advantage in response to the changing needs of the marketplace (Helfat & Peteraf, 2003; Teece, 2007).

Resources and resource transformation

One of the roles of organizational processes is reconfiguration (Eisenhardt & Martin, 2000), which is considered to be a transformational concept (Karim & Mitchell, 2000). In rapidly changing market environments where it is difficult to determine the nature of future competition and markets, it is crucial for firms to recognize the need to reconfigure the firm's asset structure, and accomplish necessary internal and external resource transformation consistent with changes in the marketplace. It is important for organizations to transform their internal and external competencies to address changing market requirements. Intel, for example, developed key corporate competencies in DRAM business that were appropriately managed for about 10 years and redeployed them in the microprocessor business promptly in response to the shifting market environment.

The RBV and its DC theory extensions emphasize the critical aspect of building, integrating, and reconfiguring resources for adapting to environmental change (Teece, 2007). Resource transformation involves either addition or deletion of specific resources from the idiosyncratic combination of resources an organization possesses via mergers, acquisitions, divestment, or coevolving synergistically with other organizations (Capron, Dussauge, & Mitchell, 1998; Eisenhardt & Galunic, 2000). The capacity to reconfigure and transform resources is a learned organizational skill (Teece, Pisano, & Shuen, 1997), thus frequent practice enables firms to easily and successfully reconfigure resources consistent with market requirements to survive competitive pressures. Further, resource transformations are necessary for organizational adaptation in congruence with the strategic intent of the organization (Eisenhardt & Martin, 2000; Helfat & Peteraf, 2003; Miles et al., 1978). Hence, reconfiguring and orchestrating the resources and capabilities allow organizations to effectively address environmental shifts (Durán & Aguado, 2022).

Each strategic type exhibits its own strategy to align with the environmental change and complexity as indicated in Miles and Snow's organizational strategic typology (Ingram, Kraśnicka, Wronka-Pośpiech, Głód, & Głód, 2016; Miles et al., 1978). Consistent with each distinctive strategy, an organization has a specific combination of technology, structure, and process. The extent to which organizations engage in resource transformation should vary across Miles et al.'s (1978) categories, such as prospectors, analyzers, and defenders as each strategic type has a unique strategy to relate to its selected market and levels of resource transformation that complements their specific strategy.

The outcomes of resource deployment processes, known as organizational capabilities, have garnered greater attention (Kale & Singh, 2007, 2017). While resources refer to stocks of available factors that are possessed and controlled by the firm, capabilities represent the capacity of the firm to deploy these firm resources (Morgan, Vorhies, & Mason, 2009). Firms have several distinct types of resources available to support their business activities, such as financial, technological, knowledge, reputation, relationships, physical resources, and so on. Firms organize their resources and get things done through capabilities, such as brand management capabilities and customer relationship management (CRM) capabilities, which are deeply rooted within many different organizational processes. These organizational processes that embed the essence of capabilities are shaped significantly by resources. Organizational resources not only shape the content of these organizational processes, but also the opportunities they allow for developing competitive advantage at any point in time (Teece, 2007). Prospectors, analyzers, and defenders respond differently to changing environmental conditions by formulating a distinctive strategy and a particular configuration of the structure, process, and technology, which require different levels of resources (Kwak, Anderson, Leigh, & Bonifield, 2019; Miles et al., 1978). Hence, we argue that the strength of the relationship between resources and capabilities in the context of a firm's strategic category based on Miles et al.'s (1978) strategic typology will differ among the prospectors, analyzers, and defenders.

To address their competition, organizations need to continually transform resource configurations. While prospectors seek a potential product or market opportunities, defenders, on the other hand, reap the rewards of their success in mature markets which offer little chance to innovate (Ingram et al., 2016). The analyzers are found to be big organizations, which blend their ability to innovate as well as to defend their market position. Hence, prospectors, defenders, and analyzers engage in resource transformation but vary in levels of resource transformation.

The strategic types differ in their strategic orientations that vary based on the level of focus on seeking new opportunities and innovations. Hence, the extent of resource transformations depends on strategic orientations. We examine the relationship between resources and resource transformation in the context of a firm's strategic category based on Miles et al.'s (1978) strategic typology. Specifically, we argue that the strength of the relationship between resources and resource transformation varies among the prospectors, analyzers, and defenders. Prior literature reports that a prospector organization is entrepreneurial in nature while an analyzer organization maintains an entrepreneurial and planning strategy (Ingram et al., 2016; Kwak et al., 2019). Defender organizations are found in mature markets with fewer opportunities for innovation. Prospector strategic types commit more resources to entrepreneurial activities as they seek new market opportunities and actively respond to emerging environmental trends through different novel innovations. The allocation of resources is highest for prospectors, followed by analyzers, and is lowest for defenders.

Thus, we hypothesize that:

Hypothesis 1: Prospectors, as compared to analyzers and defenders, are more inclined to engage in resource transformation.

Hypothesis 2: The positive association between resources and capabilities will be stronger for (a) prospectors as compared to (b) analyzers and (c) defenders.

Capabilities and capability reinforcement

The research in RBV provides an important theoretical foundation for understanding how resource heterogeneity drives firm performance, however, research in DC explains how the ability to deploy resources through organizational capabilities plays a key role in driving superior performance (Teece, 2007). Drawing from these perspectives, we argue that capability reinforcement is crucial for driving performance since in a highly competitive business world especially in technology industries, sustaining competitive advantage is challenging for managers (Day, 2011). Capabilities are defined as the outcomes of resource deployment processes and have garnered greater attention recently as they are the building blocks of achieving business strategy (Slater, Olson, & Hult, 2006).

According to DC theoretical perspectives, capability reinforcement refers to the extent to which firms build, extend, or modify their capabilities or skills in response to marketplace requirements (Eisenhardt & Martin, 2000; Kale & Singh, 2007). As capabilities are the resource deploying mechanisms, capabilities are key drivers of sustainable competitive advantage and are more important than resource heterogeneity (Slater, Olson, & Hult, 2006). Thus, a capability is an important antecedent to consider for firm performance (Teece, 2007). A capability may change and evolve with time in ways that are important and long-lasting for an organization (Helfat & Peteraf, 2009), and when firms engage in capability reinforcement, they can enhance firm performance. Since strategic categories defined by Miles et al.'s (1978) strategic typology identify the distinctive ways in which firms strategize, the need for capability reinforcement varies. Hence, prospectors, analyzers, and defenders differ in their emphasis on capability reinforcement. The extent to which organizations invest in capability reinforcement depends on the characteristics of the market environment as the goal is to effectively align with the organizational environment. As a result, we argue that the strategic types of prospectors, analyzers, and defenders moderate the positive association between capability and capability reinforcement in different levels.

Prior research has suggested that the strategic alternatives available to organizations, their previous investments, and their repertoire of routines and resources constrain future behavior and shape their capabilities (Teece, 2007). Hence, the variability in the strength of the relationship between capability and capability enhancement can be explained by examining the moderating effects of strategic categories. Furthermore, capability reinforcements in congruence with strategic intent are critical to align with the needs of the market environment. Consequently, enhancing capabilities in terms of capability reinforcement is instrumental in effectively adjusting organizations to their environments and making organizations more responsive to fast-changing market requirements. Accordingly, capability enhancement occurs through different viable alternatives in conjunction with experience accumulated over time (Nguyen, Calantone, & Krishnan, 2020). According to Miles et al. (1978), appropriate implementation of strategies pertaining to each strategic type can lead to superior performance (Ingram et al., 2016). Thus, organizations that are effective are better able to maintain a promising market for their offerings and are successful in aligning with their environmental conditions (Matsuno & Mentzer, 2000). Effective organizational adaptation requires consistent modification and refinement of mechanisms such that they complement organizational strategy and allow successful alignment with the environment (Day, 2011). Thus, consistent with prior literature, it follows that capability reinforcement is crucial for firms that seek to leverage capabilities to enhance firm performance.

The levels of capability reinforcement are contingent upon the strategic type. Prospectors require R&D capability and marketing capabilities such as market-sensing and customer-linking to successfully launch new offerings in their environment. Consequently, expanding the scope of their capabilities beyond the existing marketing mix should enable prospectors to effectively adjust their organizations to their fast-changing environments. The relationship between capability and capability reinforcement is expected to be amplified for prospectors. Defenders, on the other hand, require operations capability to achieve efficiencies in their operations. Accordingly, enhancing operations and IT capabilities should enable defenders to effectively cut their costs of operations, and gain technological efficiency. This results in their organizational strategy fitting with their environment. Analyzers emphasize new products and market development of those products whose viability is established in changing environments and improve operational efficiency to successfully compete in stable markets. Therefore, analyzers' adaptability to the organizational environment should be most effective when they enhance their R&D capability and operations capability simultaneously. Thus we hypothesize that:

Hypothesis 3: The positive association between capabilities and capability reinforcement will be stronger for (a) prospectors as compared to (b) analyzers and (c) defenders.

Resource transformation and organizational performance

To build and maintain a competitive advantage, it is necessary for firms to alter and modify firm resources through resource transformation. Consistent with DC theory, improving performance is dependent on the ability to transform existing resources. Organizations that engage in resource transformation are successful in addressing the constant shifts in their competitive landscape and in creating new markets for their offerings (Karim & Mitchell, 2000). Organizations may have superior capabilities, but if they fail to transform resources, then organizational performance will be adversely affected. Thus, we argue that resource transformation accounts for the resources and performance link. This is because resource transformation allows an organization to implement initiatives by seeking, experimenting, and leveraging a set of resource combinations to obtain sustained competitive advantage (Helfat et al., 2007).

Organization processes can be easily duplicated by competing firms (Eisenhardt & Martin, 2000), and resources on their own do not provide a significant and enduring source of competitive advantage (Teece, 2007). The value lies in the resource transformations that organizations create. Consequently, retaining, and adding those resources whose productivity can be enhanced is at the core of an organization's ability to enhance its performance. Therefore, improving performance is dependent on the organization's ability to reconfigure and transform its existing resources. Thus we hypothesize that:

Hypothesis 4: An organization's resource transformation is positively associated with its performance.

Capability reinforcement and organizational performance

The DC, according to the DC theory, allow organizations to adapt and change (Eisenhardt & Martin, 2000; Helfat & Peteraf, 2009; Teece, 2007). Capability reinforcement is the ability of firms to change and evolve their capabilities in response to the changing needs of the marketplace. Hence, drawing on the DC theoretical perspectives, firms that engage in capability reinforcement are successful in appropriately adapting to match the needs of a changing environment. Thus, organizations augment their extant capabilities and leverage reinforced capabilities in their efforts to provide superior value to customers within a market segment better than their rivals. Consequently, firms are able to stay ahead of the competition and succeed. Thus, we contend that capability reinforcement and firm performance are positively associated with each other.

Prior studies assert that DC include the ability of an organization to sense and seize opportunities and to establish its competitiveness via the capability reinforcement of intangible capabilities (Ingram et al., 2016; Teece, 2007). Further, the development and use of internal DC lie at the heart of an organization's success or failure (Teece, 2007). The management practice of capability reinforcement involves enhanced exploitation of existing skills and enhanced exploration of new skills. When knowledge assets are integrated with existing knowledge and are embedded within the fabric of an organization, the organization sees capability reinforcement which leads to superior firm performance. With a focus on capability reinforcement, organizations succeed at consolidating their competitive advantage and, therefore, position themselves advantageously to achieve improved performance. Thus we hypothesize that:

Hypothesis 5: An organization's capability reinforcement is positively associated with its performance.

Research methodology

To test the hypotheses, we conducted a study in which we collected two types of data. First, we collected primary survey data from top marketing managers. We focus on marketing capabilities as a methodological choice because of our access to marketing managers. We expect the same effect independent of the type of capability we measure. Organizational capabilities enable organizations to obtain, integrate, and modify resources to adapt to environmental changes, and marketing capabilities are critical organizational capabilities that enable the integration and deployment of an organization's resources (Morgan, Vorhies, & Mason, 2009; Teece, 2018; Teece, Pisano, & Shuen, 1997). Second, we collected objective financial data on measures such as Tobin's Q, and ROA from annual reports of those organizations whose primary survey data were collected. The sample comprised of publicly traded, single-business dominant US organizations operating in industries, such as consumer package goods, business services, specialty retail, pharmaceuticals, electronic equipment, and computer hardware and software industries.

To determine our sample of firms for administering primary surveys, we examined records of firms that are publicly available and communicated with them to ascertain their key informants, whom we then contacted to obtain their consent and informed them about our research objectives. Consequently, we obtained contact information for 507 marketing executives of firms from our sample. Then, we collected secondary data concerning financial performance and firm size from Compustat database.

Common method bias

Common method bias is a common problem encountered in survey research as method biases are one of the major sources of measurement error (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Our collection of data using two different approaches, primary survey, and secondary data, allows us to limit common method bias. Out of a total of 121 surveys received from main contact informants, we dropped seven surveys as they did not have sufficient data to be considered for analysis. Thus, we had 114 surveys that we used for further analysis. The response rate was found to be 23.5%. Next, we performed a test for non-response bias.

Non-response bias

Using the extrapolation approach suggested by Armstrong and Overton (1977), we conducted the test for non-response bias. Non-response may be a problem when conducting mail surveys as marketing managers who respond may have experiences that are different from those who chose not to respond. The extent to which non-response bias exists in our sample was assessed by separating responses received before reminders and after reminders were sent out and performing t-tests to assess differences between these groups (Armstrong & Overton, 1977; Gunasekaran et al., 2017). The results of the tests suggested that there were no significant differences between respondents who responded late and those who responded before reminders were sent out. In addition, we performed t-tests on non-respondents and respondent firms using secondary data on firm size and revenue. Test results suggested no significant differences between the two groups. Thus, it is highly unlikely that the results of our study are affected by non-response bias.

Measures

We use multi-item scales to measure the constructs of this study. For financial performance, we collected data on Tobin's Q and ROA using the Compustat database. To measure the level of resources, we included measures that assess the level of an organization's financial, relational, reputational, and knowledge resources using a 24-item scale. We measured capabilities (Vorhies & Morgan, 2005) by adapting the scale to the context of this study. The specific measures for resource transformation (6-item scale) were constructed to encompass the extent to which firms transform their resources to match up with changes in their marketplace. The Appendix describes and outlines the scale development process for resource transformation and capability reinforcement. To measure a firm's extent of capability reinforcement, we developed a 7-item scale to encompass the extent to which firms improve and adjust their capabilities in response to the changing marketplace. The measures used in this study are provided in Table 1.

Table 1. Construct label and items

a Item dropped from the scale during measure purification.

All factor loadings were at least .60 or above.

To evaluate the overall firm profitability, the most popular financial measure in marketing and finance literature is ROA. Another popular measure is Tobin's Q, which also accounts for firm risk. We use two performance measures such as ROA, and Tobin's Q for assessing organizational performance in our study. The objective financial data for Tobin's Q and ROA were obtained from the Compustat database. Tobin's Q is used to quantify an organization's intangible asset value (Simon & Sullivan, 1993), and is defined as the ratio of the market value of the firm to the replacement cost of its intangible assets. Tobin's Q calculation is given below:

$${\rm Tobin^{\prime}s}\;Q = \displaystyle{{( {\rm Equity\;Market\;Value}\,{\rm} + {\rm Liabilities\;Book\;Value}) } \over {( {\rm Equity\;Book\;Value}\,{\rm} + {\rm Liabilities\;Book\;Value}) }}$$

A value of Tobin's Q greater than 1.0 indicates that the firm possesses intangible assets such as brand equity, relationships, or reputation. ROA is a measure of firm profits per dollar of assets, and is given as:

$${\rm ROA} = \displaystyle{{{\rm Net\;Income}} \over {{\rm Total\;Assets}}}$$

A value of ROA greater than 1.0 indicates that the firm is profitable given the level of assets that a firm owns.

We chose ROA and Tobin's Q as two different performance measures as we expect the effects to be different depending on whether the set of equations pertains to prospectors, analyzers, or defenders. The theoretical rationale for the effects to be different is due to the distinct strategic focus and behavior of each strategic type characterized in Miles and Snow's typology. While the ROA measures an organization's profits, Tobin's Q quantifies an organization's intangible asset value. Specifically, ROA is a measure of accounting that measures the overall effectiveness of management in producing returns on existing firm assets. Thus, ROA indicates the extent to which an asset is utilized to yield profit. A positive ROA suggests that the firm generated profits while a negative ROA indicates that the firm is not using its assets efficiently to produce a profit. Therefore, we found that ROA is an appropriate measure for assessing firm performance for defenders since they pay more attention to enhancing the efficiency of their current operations. Tobin's Q depicts the market value of a firm and a positive value indicates that the firm possesses intangible assets, such as brand equity. Tobin's Q, in contrast to ROA, is a forward-looking measure that captures the future growth potential in addition to the available assets of a firm and serves as a common innovation standard for a firm (Potepa & Welch, 2018). Thus, we considered Tobin's Q as an appropriate measure for assessing the performance of prospector strategic types. Since the analyzer strategy focuses on maintaining existing businesses as well as seeking newer opportunities, both ROA and Tobin's Q are considered to be appropriate measures for firm performance in this study.

Descriptive statistics and correlations among key measures are provided in Tables 2 and 3.

Table 2. Descriptive statistics

Note: Descriptive statistics represent unstandardized variables.

Table 3. Inter-factor correlations

Note: Correlations above .15 are significant at the .05 level (two-tailed).

There is a likelihood that in our study, some organization-specific and industry-specific factors could influence our results, thus, we controlled for their effects. We controlled for firm-specific factors such as firm size, previous year cash flows, advertising expenditure, and R&D expenditure. Firm size was measured as the number of employees in the business, and to normalize data on employees we log-transformed this variable (Moorman & Slotegraaf, 1999). In order to control for industry-specific factors, we controlled for environmental munificence and dynamism using Keats and Hitt (1988) approach. Environmental munificence supports growth, and diversification, which in turn enhances an organization's performance (Keats & Hitt, 1988). We controlled for industry growth rate as environmental munificence may influence the profit growth of an organization. We first estimated industry sales performance for 5 years using the below equation:

$$y_t = \lambda _0 + \lambda _1t + u_t, \;$$

In the equation, y_t represents a linear transformation, λ₁ is the regression coefficient, and u is the residual term. Using the regression slope coefficient from the above equation for each of the 5 years, we estimated the industry growth rate by calculating ln(λ₁) to determine industry growth at time t. Dynamism reflects an unstable or volatile or turbulent environment (Keats & Hitt, 1988). Following Keats and Hitt (1988) approach, we measured it as the volatility of sales in a certain industry. It is operationalized as the antilog of the standard error of the coefficient in the above equation. Descriptive statistics and correlations among key measures are provided in Tables 2 and 3.

Analysis and results

We evaluate the psychometric properties of measures in this study via reliability analyses and confirmatory factor analysis (CFA). Following the validation of measures, we use the seemingly unrelated regression (SUR) technique to investigate the hypothesized relationships.

Measure reliability and validity

Prior to testing the hypotheses, the psychometric properties of constructs in this study were conducted through reliability analysis and CFA. Consistent with prior studies we divided our measures into groups of variables that are theoretically associated with each other (Bentler & Chou, 1987). The first set included the scales for five kinds of resources (market knowledge, technical knowledge, financial, reputation, and relationships), and the second set was composed of three types of marketing capabilities (market-sensing, CRM, and brand management). The third set included resource transformation and capability reinforcement scales. Conducting CFA on separate sets of constructs enables us to assess construct convergence using groups of variables that are conceptually similar. Also, by separating the constructs into groups, we were able to adhere to the required sample size to parameter ratios (Cadogan, Paul, Salminen, Puumalainen, & Sundqvist, 2001). The measurement items of each construct were allowed to load on the corresponding latent variables, and all latent variables were allowed to correlate. For all the constructs in our study, items of each construct loaded strongly onto the corresponding latent variables they were designed to measure, and they did not cross-load on other latent variables. The computations and results are presented in Table 4.

Table 4. CR and convergent validity

In the first set of CFA of resources, we obtained three items each reflecting five resources, market knowledge, technical knowledge, financial, reputation, and relationships (χ² = 119.84, df = 80, p = .0026; NNFI = .96; CFI = .97; NNFI = .92; RMSEA = .07), with items loadings strongly (range from to .70 to .95) on their corresponding constructs. There was an insignificant indication of cross-loadings. The second set of CFA on capabilities yielded three items each of capabilities, market-sensing, CRM, and brand management (χ² = 41.32, df = 24, p = .0154; NNFI = .95; CFI = .97; RMSEA = .08), and items loaded strongly (range from .59 to .90). In the third set of CFA with resource transformation and capability reinforcement, the analysis led to three items each of these (χ² = 10.19, df = 8.00, p = .25; NNFI = .98; CFI = .99; RMSEA = .05), and items loaded strongly (range from .60 to .90).

We estimated construct reliability (CR) and average variance extracted (AVE) for each measure to evaluate convergent validity and reliability. The results given in Table 4 suggest that all measures of our constructs are reliable as the estimated CR for all measures exceeds the threshold criterion of .6 (Bagozzi & Yi, 1988), and the AVE values exceed the .50 benchmark (Fornell & Larcker, 1981). Two methods were used to assess discriminant validity. First, discriminant validity was assessed by comparing the AVE to the squared inter-factor correlations, and the results suggest that all measures show discriminant validity. Second, discriminant validity was assessed using the χ² difference test (Anderson & Gerbing, 1988), by fixing the interfactor correlation at 1 and comparing this result with that of the unconstrained model to assess the significance of the χ² difference. The results provided in Table 5 suggest that all measures exhibit discriminant validity.

Table 5. Discriminant validity

Note: All χ² differences are statistically significant.

Model estimation

Subsequent to determining the measures as psychometrically strong, we use SUR to simultaneously model the relationships in our conceptual model. This modeling approach has a number of advantages (Zellner, 1962). It helps us to simultaneously model the impact of resources and capabilities through resource transformation and capability reinforcement, respectively, on organizational performance. Another benefit of using SUR is to model a system of equations that generates more efficient estimates when the error terms of the regressions are correlated, which is found in our study.

We estimate a 6-equation SUR model each for three strategic types, prospectors, defenders, and analyzers. The resources included market knowledge (MKTKNOW), technical knowledge (TECHKNOW), financial resources (FIN), reputation (REPU), and relationships (REL). The capabilities included market-sensing capabilities (MKTSENSING), customer relationship management capabilities (CRMCAP), and brand management capabilities (BRANDMGMT). The control variables such as organization size (SIZE), munificence (MUNIF), and dynamism (DYNA) were also included. Each model was estimated twice, for ROA and Tobin's Q. SUR equations for each strategic type are given as follows:

$$\eqalign{& {\rm RETRANS} = \beta _0 + \beta _1\ast {\rm MKTKNOW} + \beta _2\ast {\rm TECHKNOW} + \beta _3\ast {\rm FIN} \cr & \quad \quad \quad \quad \quad + \beta _5\ast {\rm REPU} + \beta _5\ast {\rm REL} + \beta _6\ast {\rm SIZE} + \beta _7\ast {\rm MUNIF} + \beta _8\ast {\rm DYNA} + e_{RETRANS} \cr & {\rm MKTSENSING} = \beta _0 + \beta _1\ast {\rm MKTKNOW} + \beta _2\ast {\rm TECHKNOW} + \beta _3\ast {\rm FIN} \cr & \quad \quad \quad \quad \quad \quad \quad + \beta _5\ast {\rm REPU} + \beta _5\ast {\rm REL} + \beta _6\ast {\rm SIZE} + \beta _7\ast {\rm MUNIF} + \beta _8\ast {\rm DYNA} + e_{MKTSENSING} \cr & {\rm CRMCAP} = \beta _0 + \beta _1\ast {\rm MKTKNOW} + \beta _2\ast {\rm TECHKNOW} + \beta _3\ast {\rm FIN\ } \cr & \quad \quad \quad \quad \quad + \beta _5\ast {\rm REPU} + \beta _5\ast {\rm REL} + \beta _6\ast {\rm SIZE} + \beta _7\ast {\rm MUNIF} + \beta _8\ast {\rm DYNA} + e_{CRMCAP} \cr & {\rm BRANDMGMT} = \beta _0 + \beta _1\ast {\rm MKTKNOW} + \beta _2\ast {\rm TECHKNOW} + \beta _3\ast {\rm FIN\ } \cr & \quad \quad \quad \quad \quad \quad \quad + \beta _5\ast {\rm REPU} + \beta _5\ast {\rm REL} + \beta _6\ast {\rm SIZE} + \beta _7\ast {\rm MUNIF} + \beta _8\ast {\rm DYNA\ }e_{BRANDMGMT} \cr & {\rm CAPREIN} = \beta _0 + \beta _1\ast {\rm MKTSENSING} + \beta _2\ast {\rm CRMCAP} + \beta _3\ast {\rm BRANDMGMT} \cr & \quad \quad \quad \quad \quad + \beta _4\ast {\rm SIZE} + \beta _5\ast {\rm MUNIF} + \beta _6\ast {\rm DYNA} + e_{CAPREIN} \cr & {\rm PERF} = \beta _0 + \beta _1\ast {\rm CAPREIN} + \beta _2\ast {\rm RETRANS} + \beta _3\ast {\rm SIZE\ } \cr & \quad \quad \quad + \beta _5\ast {\rm MUNIF} + \beta _5\ast {\rm DYNA} + e_{PERF}} $$

Hypothesis testing results

Two models each for ROA and Tobin's Q were analyzed for all three strategic types. We tested model 1 (ROA) and model 2 (Tobin's Q) using six simultaneous equations each for the prospector, defender, and analyzer strategic types. The results, given in Tables 6 (model 1) and 7 (model 2), indicate strong overall support for the hypotheses and provide managerial implications. In testing for hypothesis 1 (model 1), we evaluated the positive association between resources and resource transformation, considering ROA as the measure for performance. The findings indicate that resources such as relationships (β = .40, p < .01) and technical knowledge (β  = .26, p < .05) significantly impact resource transformation for prospector strategic types. For analyzer strategic types, we found that resources such as financial (β = .89, p < .01) and reputation (β = .38, p < .05) play a significant role in influencing resource transformation. Resources such as reputation (β = .66, p < .01) significantly affect resource transformation for defender strategic types. In contrast, we found that only relationships (β = .42, p < .01) for prospectors, financial (β = .89, p < .01), reputation (β = .36, p < .05), and technical knowledge (β = .24, p < .05) for analyzers, and reputation (β = .66, p < .01) for defenders were significant in influencing resource transformation when Tobin's Q was considered as the measure of firm performance. The results, therefore, indicate that prospectors, analyzers, and defenders differ in terms of the types of resources they leverage to engage in resource transformation.

Table 6. SUR results for model 1 (ROA)

S.W. R ² refers to system weighted R ²; Q refers to Tobin's Q.

Note: All values given are standardized estimates.

*p < .10, **p < .05, ***p < .01.

Table 7. SUR results for model 2 (Tobin's Q)

S.W. R ² refers to system weighted R ²; Q refers to Tobin's Q.

Note: All values given are standardized estimates.

*p < .10, **p < .05, ***p < .01.

Next, hypothesis 2 examines the positive association between organizational resources and capabilities for each strategic type. The results, presented in Tables 6 and 7, indicate the differential influence of a set of resources on each strategic type's market-sensing capabilities, CRM capabilities, and brand management capabilities. Specifically, we found that while resources, such as market knowledge (model 1: β = .51, p < .01; model 2: β = .50, p < .01), technical knowledge (model 1: β = .36, p < .01; model 2: β = .35, p < .01), financial (model 1: β = .40, p < .01; model 2: β = .42, p < .01), and reputation (model 1: β = .40, p < .01; model 2: β = .44, p < .01) significantly impact market-sensing capabilities across two models. Investigating the effect of resources on CRM capabilities across two models, we find that financial (model 1: β = .41, p < .01; model 2: β = .42, p < .01), reputation (model 1: β = .29, p < .05; model 2: β = .27, p < .05), and relationship (model 1: β = .27, p < .05; model 2: β = .27, p < .05) resources impact CRM capabilities significantly. Upon examining the effect of resources on brand management capabilities, results suggest that reputation (model 1: β = .49, p < .01; model 2: β = .43, p < .05) significantly impacts brand management capabilities. Thus, these findings provide strong support for the positive association between resources and capabilities for both ROA and Tobin's Q as measures of performance. Hence, hypothesis 2 is supported.

Upon examining the effect of capabilities on capability reinforcement (hypothesis 3), it was found that strategic types differed in the capabilities that affect capability reinforcement. Specifically, results indicate that CRM capabilities (model 1: β = .42, p < .01; model 2: β = .42, p < .01) influenced capability reinforcement for prospectors. However, in addition, market-sensing capabilities (model 2: β = .29, p < .05) influenced capability reinforcement for prospectors when considering Tobin's Q as a measure of performance. Next, upon examining analyzers, we found that market-sensing capability (model 1: β = .33, p < .05; model 2: β = .51, p < .05) was positively associated with capability reinforcement across the models. In addition, brand management capability (model 1: β = .29, p < .05) was statistically significant for ROA and, on the other hand, CRM capability was significant (model 2: β = .47, p < .05) for Tobin's Q. Thus, results suggest that upon measuring performance in terms of ROA, both market-sensing and brand management capabilities are critical for capability reinforcement and on the contrary, market sensing and CRM capability are key to capability reinforcement when Tobin's Q is used to measure firm performance. Since ROA and Tobin's Q differ in terms of their emphasis, different capabilities are relevant for firms.

Most noteworthy results were found upon examining the roles of resource transformation and capability reinforcement in influencing organizational performance. The results revealed that for defenders, resource transformation (model 1: β = .37, p < .01) and capability reinforcement (model 1: β = .28, p < .01) are both determinants of ROA but are not statistically significant in influencing Tobin's Q. Similar contrasting results were obtained for prospectors and analyzers. While resource transformation (model 2: β = .35, p < .01) and capability reinforcement (model 2: β = .33, p < .01) are critical determinants of Tobin's Q, they do not influence ROA for prospectors. However, for analyzers, resource transformation (model 2: β = .54, p < .01) is related to Tobin's Q, and capability reinforcement (model 1: β = .31, p < .01) is related to ROA. Thus, findings do not provide support for hypotheses 4 and 5 for prospectors with ROA as the performance measure and for defenders with Tobin's Q as a performance measure. However, hypotheses 4 and 5 found significant support for defenders with ROA as the performance measure. Furthermore, for analyzers, resource transformation influenced Tobin's Q and capability reinforcement influenced ROA. To test our hypotheses, we used SUR in which we had five variables of interest in each of the six equations, and the number of observations was greater than the number of coefficients tested. If the number of the coefficients is larger than the number of observations then multiple testing adjustment for a high-dimensional linear model is very useful (Bühlmann, 2013). Therefore, consistent with the literature, instead of using Bonferroni adjustment, we used the standard false-discovery rate procedure (Benjamini & Hochberg, 1995) to correct for multiple statistical tests made across all tested elements (Jahanshad et al., 2013). The false-discovery rate for the study was .03 which is less than .05 and considered acceptable (Storey & Tibshirani, 2003). The results have important and interesting implications for managers who need to demonstrate their strategic management of resources and capabilities. The differential impact of resources, capabilities, resource transformation, and capability reinforcement on different measures of organizational performance is key to understanding their circuitous impact and indirect improvement of performance. This comprehensive understanding of improving the performance of an organization provides direction and focus to organizational strategic management in developing strategic direction to succeed in a competitive and ever-changing marketplace.

Discussion

Our findings support the key roles of mediating mechanisms of resource transformation and capability reinforcement in explaining firm performance. Our results indicate that depending upon the type of strategy pursued, the firms can take advantage of their resources and capabilities by using resource transformation and capability reinforcement. Our results indicate that the extent to which organizations engage in resource transformation and capabilities reinforcement varies across three categories of organizations: prospectors, analyzers, and defenders. This is because an organization in each category of strategic type (prospector, analyzer, and defender) has a unique strategy to relate to its selected market and establish levels of resource transformation and capabilities that complement its specific strategy. For defenders, our results reveal a significant direct relationship between resource transformation and ROA and between capability reinforcement and ROA as opposed to Tobin's Q. In contrast, for prospectors, our findings provide support for the effects of resource transformation and capability reinforcement on Tobin's Q. Further, for analyzers, our results indicate that resource transformation is significantly related to Tobin's Q and capability reinforcement is positively related to ROA.

Prior studies have examined RBV and DC theory to better understand the role of organizational capabilities in increasing firm performance (Morgan, Vorhies, & Mason, 2009); our results further advance the management literature by empirically supporting RBV and DC theory in terms of Miles et al.'s (1978) strategic typology. Previously, studies examined the effects of resources and capabilities on firm performance (Cacciolatti & Lee, 2016; Hooley, Greenley, Cadogan, & Fahy, 2005). In contrast, our study investigates mediating mechanisms that leverage firm resources and capabilities and account for firm performance. Some studies reported mediating mechanisms pertaining to firm capabilities (Chang, Park, & Chaiy, 2010; Nath, Nachiappan, & Ramanathan, 2010). Other studies examined the implementation of strategy, innovation capability, CRM capabilities, and DC as mediating mechanisms (Correia, Dias, & Teixeira, 2020; Foltean, Trif, & Tuleu, 2019; Morgan, Katsikeas, & Vorhies, 2012; Ngo & O'Cass, 2012), however, existing literature has not considered mediators, such as resource transformation and capability reinforcement. Previous research examined the relationship between negotiation and strategy (Caputo, Borbély, & Dabic, 2018a), in our study we identified the role of strategy in determining resource transformation and capability reinforcement. Our findings further demonstrate the relative importance of resource transformation and capability reinforcement to positively impact either ROA or Tobin's Q depending upon the type of strategy a firm adopts. Moreover, depending upon the performance measures, such as ROA and Tobin's Q, firms can increase their performance consistent with their strategic behavior emphasizing resource transformation or capability reinforcement.

Since the study combines both survey and objectively measured data, it significantly reduces the potential for common method bias in affecting research outcomes and leads to a high degree of confidence in the results of our study. Additionally, an analysis of non-response bias further increases the confidence in the research findings as the statistical analysis revealed that the possibility of non-response bias in influencing the outcomes of the study is highly unlikely. The findings reveal the different types of resources and capabilities useful for prospectors, defenders, and analyzers in their efforts to remain competitive and increase firm performance. The empirical results suggest that each strategic type utilizes a specific set of resources rather than all the types of resources. Specifically, our findings indicate that prospectors more prominently leverage resources such as reputational, relational, and financial resources, to enhance their capabilities such as CRM capabilities. Previous studies examine the link between resources and capabilities (Shan, Cai, Hatfield, & Tang, 2014), and our findings also extend the previous studies by empirically examining specific resources in improving market-sensing, CRM, and brand management capabilities. This study examines the relative importance of each of the capabilities for resource transformation and capability reinforcement. In particular, our research outcomes highlight the significant roles of market-sensing and CRM capabilities as compared to brand management capabilities in capability reinforcement for generating high Tobin's Q. On the contrary, brand management capabilities play a greater role in capability reinforcement for driving the ROA of a firm.

As organizations continue their efforts to better understand the circuitous route to performance, the results of this study offer specific ways to improve ROA and Tobin's Q values. Resource transformations and capability reinforcement are two different routes that explain superior performance in terms of ROA and Tobin's Q. First, we empirically demonstrate that organizations can achieve superior performance by leveraging and transforming their resources in terms of resource transformation. Next, we identify and empirically validate the critical role of capability reinforcement in leveraging capabilities to achieve superior performance. Notably, this study demonstrates novel pathways including resource transformation and capability reinforcement in differentially improving organizational performance and emphasizes the role of strategic management in focusing on those pathways that are meaningful to the organization's performance metrics.

This study demonstrates that each strategic type distinctively differs in its adopted path of either resource transformation or capability reinforcement to achieve superior performance. Furthermore, our study demonstrates how resource transformation significantly influences Tobin's Q measure as compared to the ROA measure of organizational performance. Capability reinforcement, on the other hand, significantly influences ROA measures as compared to Tobin's Q measure of organizational performance. The results of this study provide interesting insights and guidance for managers in selecting appropriate performance measures for assessing the effectiveness of different organizational activities and processes. Although capability reinforcement is found to be significantly associated with organizational performance differently in ROA and Tobin's Q for prospectors, analyzers, and defenders, important theoretical and managerial implications emerge from these results.

Theoretical implications

The main contribution of this research is the insight gained on RBV and DC theory predictions in combination with Miles et al.'s (1978) strategic types regarding the ability to realize distinct strategies of prospectors, defenders, and analyzers that yield increased firm performance. Prior research empirically integrating and theoretically examining the underpinnings of resource transformation and capability reinforcement remains limited. In examining the results of this study, we find strong evidence for resource transformation and capability reinforcement as important mechanisms for understanding how and why resources and capabilities influence firm performance. In addition, our results indicate that firms can increase the effectiveness of their resources and capabilities by leveraging resource transformation and capability reinforcement. This conclusion is supported by strong empirical evidence that suggests for firms in our sample both resource transformation and capability reinforcement are influenced by strategies pursued by firms and that these key mediators are significant drivers of firm performance.

The current literature recognizes the need for further exposition on DC despite the attention given to the RBV and DC theory in the literature in the past two decades (Arndt, 2019). Prior studies offer contradictory findings that led to debates about its definition, operationalization, and its value to organizations (Teece, 2018; Zhou, Zhou, Feng, & Jiang, 2019). This study contributes to progressing the DC research and offers foundational empirical work on DC continuing the state-of-the-art discourse on DC perspectives. Our study finds evidence supporting the previous theoretical assertions suggesting that resources and capabilities alone may not adequately explain performance differences across firms (Vorhies & Morgan, 2005). In view of that, our study shows that resource deployments in terms of capabilities serve as important predictors of performance outcomes. Thus, the results support the theoretical predictions concerning the resources, resource deployments, and capabilities as sources of sustainable competitive advantage. Additionally, our study extends the current theoretical predictions to provide important insights into the mechanisms that leverage firm resources and capabilities and lead to increased firm performance. Although previous studies explore the mediating mechanisms, particularly, in terms of capabilities (Hooley et al., 2005) and innovation (Zhou et al., 2019), current management literature lags in exploring additional unique factors that increase the understanding of the observed heterogeneity in firm performance among firms in an industry. Examining unique theoretical perspectives on strategies, resources, and capabilities in the management literature, we further advance the theoretical understanding of how firms can successfully thrive in a competitive and dynamic environment, which is increasingly characterizing the current business landscape.

Managerial implications

Our study also has important implications for managers. Our study highlights the role of capability reinforcement in improving organizational ROA and demonstrates that resource transformation improves an organization's Tobin's Q. Since organizations differ in their strategic focus, managers need to identify performance measures that aid in appropriately quantifying the value of their organization's strategy. By characterizing strategy in terms of Miles and Snow's strategic typology, our study demonstrates that ROA is an appropriate measure of performance for defenders than prospectors and Tobin's Q is appropriate to assess performance for prospectors versus defenders. Managers can establish appropriate assessments of performance to effectively execute their strategies and quantify their value to the top management. Managers practicing analyzer strategy should concentrate on capability reinforcement rather than resource transformation to increase the firm's ROA. In contrast, our results indicate that in their efforts to improve the firm's Tobin's Q, managers should focus on both resource transformation and capability reinforcement as they significantly influence firm performance for prospectors.

Our study also encourages managers to pay more attention to deploying their bundle of resources into capabilities, such as market-sensing, CRM, or brand management capabilities leading to resource transformation and capability reinforcement, which are key to firm performance. Hence, the findings direct the attention of managers to become adept at utilizing, assimilating, and configuring appropriate resources and capabilities to better match the needs of the changing business environment and increased performance. Our results indicate that brand management capabilities are more important than market-sensing or CRM capabilities in capability reinforcement to improve a firm's ROA for defenders as compared to prospectors. On the contrary, managers seeking to pursue prospector strategy are better able to improve the firm's Tobin's Q when they focus on developing market-sensing and CRM capabilities rather than brand management capabilities.

The positive association between resource transformation and forward-looking measure of organizational value (Tobin's Q) suggests that managerial efforts and actions pertaining to modifying their resources are reflected in subsequent year performance assessments. Thus, managers can improve their firm's performance using resource transformation that will be reflected in subsequent years' performance and obtain an advantage over competitors in future years. As ROA is a ratio, it helps to assess organizational profitability relative to organizations across industries and relative to competitors within an industry. In our study, we use ROA to assess the extent to which an organization is able to generate income in relation to resources possessed by an organization. Hence, this study offers specific and practical guidance to managers in determining how they can enhance performance given their strategic type and performance measures in their organization.

Limitations and directions for future research

There are certain limitations of our study that offer further avenues for research. Because we test our hypotheses using the US data, we need to interpret the findings with caution when extrapolating the results to other nations. Hence, it is important to test our findings using data from different national contexts to establish global generalizability. Although this study investigates a comprehensive conceptual framework, there is scope for further advancing the understanding of heterogeneity in firm performance. In particular, it will be useful to test our results by examining the role of organizational culture in strengthening or weakening the relationships investigated in this study, which can further enhance the explanatory power of our conceptual framework. This is because previous research identifies organizational culture as an antecedent of firm performance. In addition to further research needed to overcome these limitations, our study also implies additional opportunities for further examination. Future research could investigate the generalizability of relationships we report across different strategy types (e.g., cost-leadership vs. differentiation) and performance consequences (e.g., margin growth rate and cash flow ROA). Such investigations may provide theoretically and managerially relevant new research and advance the current management literature.