Resources and resource transformation

One of the roles of organizational processes is reconfiguration (Eisenhardt & Martin, Reference Eisenhardt and Martin2000), which is considered to be a transformational concept (Karim & Mitchell, Reference Karim and Mitchell2000). 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, Reference Teece2007). 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, Reference Capron, Dussauge and Mitchell1998; Eisenhardt & Galunic, Reference Eisenhardt and Galunic2000). The capacity to reconfigure and transform resources is a learned organizational skill (Teece, Pisano, & Shuen, Reference Teece, Pisano and Shuen1997), 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, Reference Eisenhardt and Martin2000; Helfat & Peteraf, Reference Helfat and Peteraf2003; Miles et al., Reference Miles, Snow, Meyer and Coleman1978). Hence, reconfiguring and orchestrating the resources and capabilities allow organizations to effectively address environmental shifts (Durán & Aguado, Reference Durán and Aguado2022).

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, Reference Ingram, Kraśnicka, Wronka-Pośpiech, Głód and Głód2016; Miles et al., Reference Miles, Snow, Meyer and Coleman1978). 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 (Reference Miles, Snow, Meyer and Coleman1978) 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, Reference Kale and Singh2007, Reference Kale, Singh, Mesquita, Ragozzino and Reuer2017). 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, Reference Morgan, Vorhies and Mason2009). 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, Reference Teece2007). 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, Reference Kwak, Anderson, Leigh and Bonifield2019; Miles et al., Reference Miles, Snow, Meyer and Coleman1978). 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 (Reference Miles, Snow, Meyer and Coleman1978) 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., Reference Ingram, Kraśnicka, Wronka-Pośpiech, Głód and Głód2016). 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 (Reference Miles, Snow, Meyer and Coleman1978) 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., Reference Ingram, Kraśnicka, Wronka-Pośpiech, Głód and Głód2016; Kwak et al., Reference Kwak, Anderson, Leigh and Bonifield2019). 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:

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, Reference Teece2007). 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, Reference Day2011). 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, Reference Slater, Olson and Hult2006).

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, Reference Eisenhardt and Martin2000; Kale & Singh, Reference Kale and Singh2007). 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, Reference Slater, Olson and Hult2006). Thus, a capability is an important antecedent to consider for firm performance (Teece, Reference Teece2007). A capability may change and evolve with time in ways that are important and long-lasting for an organization (Helfat & Peteraf, Reference Helfat and Peteraf2009), and when firms engage in capability reinforcement, they can enhance firm performance. Since strategic categories defined by Miles et al.'s (Reference Miles, Snow, Meyer and Coleman1978) 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, Reference Teece2007). 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, Reference Nguyen, Calantone and Krishnan2020). According to Miles et al. (Reference Miles, Snow, Meyer and Coleman1978), appropriate implementation of strategies pertaining to each strategic type can lead to superior performance (Ingram et al., Reference Ingram, Kraśnicka, Wronka-Pośpiech, Głód and Głód2016). 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, Reference Matsuno and Mentzer2000). Effective organizational adaptation requires consistent modification and refinement of mechanisms such that they complement organizational strategy and allow successful alignment with the environment (Day, Reference Day2011). 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:

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, Reference Karim and Mitchell2000). 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., Reference Helfat, Finkelstein, Mitchell, Peteraf, Singh, Teece, Winter, Helfat, Finkelstein, Mitchell, Peteraf, Singh, Teece and Winter2007).

Organization processes can be easily duplicated by competing firms (Eisenhardt & Martin, Reference Eisenhardt and Martin2000), and resources on their own do not provide a significant and enduring source of competitive advantage (Teece, Reference Teece2007). 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:

Capability reinforcement and organizational performance

The DC, according to the DC theory, allow organizations to adapt and change (Eisenhardt & Martin, Reference Eisenhardt and Martin2000; Helfat & Peteraf, Reference Helfat and Peteraf2009; Teece, Reference Teece2007). 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., Reference Ingram, Kraśnicka, Wronka-Pośpiech, Głód and Głód2016; Teece, Reference Teece2007). Further, the development and use of internal DC lie at the heart of an organization's success or failure (Teece, Reference Teece2007). 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:

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, Reference Podsakoff, MacKenzie, Lee and Podsakoff2003). 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 (Reference Armstrong and Overton1977), 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, Reference Armstrong and Overton1977; Gunasekaran et al., Reference Gunasekaran, Papadopoulos, Dubey, Wamba, Childe, Hazen and Akter2017). 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, Reference Vorhies and Morgan2005) 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, Reference Simon and Sullivan1993), 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, Reference Potepa and Welch2018). 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, Reference Moorman and Slotegraaf1999). In order to control for industry-specific factors, we controlled for environmental munificence and dynamism using Keats and Hitt (Reference Keats and Hitt1988) approach. Environmental munificence supports growth, and diversification, which in turn enhances an organization's performance (Keats & Hitt, Reference Keats and Hitt1988). 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, Reference Keats and Hitt1988). Following Keats and Hitt (Reference Keats and Hitt1988) 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.

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, Reference Bentler and Chou1987). 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, Reference Cadogan, Paul, Salminen, Puumalainen and Sundqvist2001). 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, Reference Bagozzi and Yi1988), and the AVE values exceed the .50 benchmark (Fornell & Larcker, Reference Fornell and Larcker1981). 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, Reference Anderson and Gerbing1988), 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, Reference Zellner1962). 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, Reference Bühlmann2013). Therefore, consistent with the literature, instead of using Bonferroni adjustment, we used the standard false-discovery rate procedure (Benjamini & Hochberg, Reference Benjamini and Hochberg1995) to correct for multiple statistical tests made across all tested elements (Jahanshad et al., Reference Jahanshad, Rajagopalan, Hua, Hibar, Nir, Toga and Weiner2013). The false-discovery rate for the study was .03 which is less than .05 and considered acceptable (Storey & Tibshirani, Reference Storey and Tibshirani2003). 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.