1

Absorptive Capacity and Firm Performance: The Moderating

Impact of Modes of Diversification.1

Pavlos C. Symeou

School of Management and Economics Cyprus University of Technology Email: pavlos.symeou@cut.ac.cy

Tobias Kretschmer

Institute for Strategy, Technology and Organization Munich School of Management

Ludwig-Maximilians-Universität München Email: t.kretschmer@lmu.de

Abstract

The entrenched use of absorptive capacity as a silver bullet to solve an increasing

number of organizational problems has detached the concept from its original

assumptions. Consequently, we are deprived of knowledge regarding the impact of

absorptive capacity’s underlying abilities to acquire, assimilate and exploit external

knowledge on firm performance and whether it can be modified by boundary

conditions. In this paper, we delve into the acquisition, assimilation, and exploitation

abilities of the firm and put forward the different moderating impact that modes of

diversification can have on their effect on performance. We test our hypotheses on a

sample of 150 large firms traded in the United States operating in the information,

communication and technology (ICT) industries for the period 1975-2010.

1 Acknowledgements to follow on final version of the paper

2

Introduction

Cohen & Levinthal (1990)’s seminal study on the concept of absorptive capacity

sparked a conversation about the firm’s ability to exploit external knowledge, which it

characterized as a critical component of innovative capabilities. Absorptive capacity’s

importance lies in that it reflects the firm’s abilities to recognize the value of new

external information; assimilate it; and apply it to new commercial ends (Lane &

Lubatkin, 1998). Active engagement in R&D activities builds the ability of the firm to

identify and value external knowledge as it enables it to grow organizational

knowledge about specific areas of technology and how these areas relate to its

products and markets. The ensuing development of processes, procedures and policies

that facilitate knowledge sharing across organizational units enable the firm to build

its ability to assimilate external knowledge. Contingent on the effectiveness of the

firm’s knowledge acquisition and assimilation, the firm can build skills of employing

this knowledge to create new knowledge, products, and markets and to anticipate

future technological trends (Zahra & George, 2002).

Several studies find that absorptive capacity exhibits important effects on

various dimensions of firm performance (e.g. Rothaermel & Alexandre, 2009). For

instance, Narasimhan, Rajiv, and Dutta (2006) find a positive link between absorptive

capacity and firm performance that becomes stronger in more dynamic environments.

In the context of international joint ventures, Lane, Salk and Lyles (2001) find that as

partners acquire and assimilate new external knowledge, performance increases.

Moreover, Fernhaber and Patel (2012) conclude that absorptive capacity can have a

positive moderating impact on the relationship between product portfolio complexity

and a young firm’s performance. They suggest that absorptive capacity allows young

firms to better integrate essential external knowledge into their management teams

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that culminates in the alleviation of potential strain on product development

processes.

Though absorptive capacity receives escalating attention by the literature, it

increasingly becomes reified since “researchers have ceased to specify the

assumptions that underlie the concept and treat it like a general-purpose solution to

an increasing number of problems” (Lane, Koka, & Pathak, 2006: 835). The

repercussions of reification can be substantial, particularly when firms misconceive

the development of distinct underlying dimensions for the development of absorptive

capacity per se (Jansen, Van Den Bosch, & Volberda, 2005). This is manifest in cases

where firms that develop their acquisition and assimilation abilities in order to benefit

from knowledge stock renewal, are left with the costs of acquisition and unexploited

knowledge. Similarly, firms that concentrate on knowledge exploitation may be able

to capture short-term profits, but lay a competence trap (Leonard-Barton, 1992;

Prahalad & Hamel, 1990) that can restrain their responses to environmental changes.

Moreover, firms which possess a strong ingenuity to understand complex technical

problems may not be as effective in translating such knowledge into product

innovation strategies (Baker, Miner, & Eesley, 2003). Therefore, new knowledge

about the effects of absorptive capacity on firm performance requires first, strategy

research to refocus on the underlying abilities of absorptive capacity and second, to

identify contextual conditions that influence the abilities’ underpinning mechanisms.

Herein lies the contribution of the current study that delves into the underlying

abilities of absorptive capacity, identifies major supporting mechanisms and

recognizes a set of boundary conditions that moderate the abilities’ impact on firm

performance.

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A major mechanism underpinning all components of absorptive capacity is

knowledge diversity that exhibits the degree of relatedness in existing knowledge and

between existing and newly acquired external knowledge. Related and therefore less

diverse knowledge, contrary to unrelated knowledge, allows the firm to sense

valuable external resources (Vasudeva & Anand, 2011), to understand them, as well

as to transfer them from other organizations (Lane et al., 2006). It enables the sharing

and transferring of externally acquired knowledge across business units and

organizational departments and therefore contributes to the development of the

assimilation ability. Moreover, it determines the promptness and ease of knowledge

retrieval (Zahra & George, 2002), the extent of the need for establishing common

interfaces between diverse knowledge vectors (Garud & Nayyar, 1994), and

knowledge transferability (Kogut & Zander, 1992), which are important prerequisites

for effective knowledge exploitation. An equally important mechanism pertains to the

organization’s communication structure. Communication structure reflects the

coordination capabilities that a firm possesses, which enable teams belonging to

different divisions to collaborate combining their varied skills, backgrounds and

knowledge to learn, assimilate and share newly acquired knowledge (Barkema &

Vermeulen, 1998; Helfat & Raubitschek, 2000).

We argue that knowledge diversity and communication structure are strongly

influenced by the diversification mode of the firm. Firms diversify in order to induce

growth, reduce the total risk of earnings variability, accelerate adaptation to

environmental change, and overall, realize positive synergies between internal

businesses (Tanriverdi & Venkatraman, 2005). From the very early studies on the

matter, diversification’s impact on performance was found to be contingent on the

mode of diversification (Rumelt, 1974). The two main modes, related and unrelated

5

diversification, differed on their underlying capacity to enable super-additive value

synergies to be captured through resource combinations (Tanriverdi & Venkatraman,

2005). Whilst the predominance of related diversification has largely rendered it the

prevailing mode (Wan, Hoskisson, Short, & Yiu, 2010) empirical studies suggest that

organizations diversify more broadly than envisaged by the relatedness logic

(Argyres, 1996; Mayer & Whittington, 2003). In effect, either mode of diversification

can modify the organization’s knowledge diversity and communication structure,

which underpin the constituent abilities of absorptive capacity. Therefore,

diversification is expected to play an important moderating role in the relationship

between absorptive capacity and firm performance.

We examined this moderating effect on a sample of 150 large firms traded in

the United States operating in the information, communication and technology (ICT)

industries for the period 1975-2010. Our base results support existing theoretical

expectations for the positive impact of the three organizational abilities underlying

absorptive capacity on performance. More importantly, our main results suggest this

impact is moderated by the firm’s diversification mode in distinct fashion.

Theory and Hypotheses

The firm’s abilities to acquire, assimilate, and exploit new external knowledge are

interrelated and jointly can produce a dynamic capability that impacts firm

performance and competitive advantage (Narasimhan et al., 2006; Teece, 2007). Their

underpinning mechanisms depend on knowledge diversity and the organization’s

communication structure that are influenced by the scope of the knowledge that the

firm desires to absorb, which varies with the mode of the diversification strategy of

the firm. Thus, understanding how diversification modes can modify the effectiveness

6

of absorptive capacity and its impact on performance is paramount. Following, we

unravel the underpinning mechanisms of the three underlying abilities of absorptive

capacity and suggest how their impact on firm performance is influenced by related

and unrelated diversification.

Acquisition of external knowledge

The firm’s ability to utilize externally held knowledge is bound up with its ability to

recognize, value, and understand potentially valuable new knowledge outside the firm

through exploratory learning (Lane et al., 2006). Superior profitability is increasingly

interlinked with technology and knowledge resources and capability-based advantages

rather than with positioning advantages (Grant, 1996). That is particularly important

in contemporary competitive conditions for many product and resource markets that

often take the form of dynamic competition or hypercompetition (D’Aveni, 1994).

Consequently, the speed and effectiveness with which positions of competitive

advantage in product and resource markets are established or undermined, are critical

and are contingent on the ability of the firm to identify, value and acquire external

resources needed to initiate competitive actions (Grant, 1996).

The acquisition ability of the firm depends on the degree of diversity in

existing knowledge and diversity between existing and newly acquired external

knowledge. The firm’s prior knowledge, basic skills, and technological developments

shape a firm’s learning about its environment (Vasudeva & Anand, 2011) that is

important to sensing valuable external intangible assets (Teece, 2007). Prior scientific

or technological knowledge must be relevant to the new knowledge to facilitate

understanding and valuing the latter (Cohen & Levinthal, 1990; Phene, Tallman, &

Almeida, 2012). For example, knowledge relatedness mitigates the degree of

7

difficulty in acquiring external knowledge, parts of which may exhibit tacitness in

complex processes and routines (Simonin, 1999; Van Wijk, Jansen, & Lyles, 2008).

Van Wijk, Jansen, and Lyles (2008) lend support to the relatedness argument as they

find that related knowledge and prior experience facilitate knowledge transfer

between different organizations. In the context of international joint ventures, Lane et

al. (2001) found that the relatedness of partners’ businesses and the similarity of the

problems in which they were involved were most important for the recognition of new

knowledge. Moreover, relatedness reduces knowledge complexity arising from the

interdependency of technologies, routines, individuals, and resources. Knowledge

complexity poses stress on the ability of the organization to evaluate and absorb

knowledge and understand all possible interlinkages between the different content

areas (Lane et al., 2006).

On the other hand, the breadth of categories into which prior knowledge is

organized, the differentiation of those categories, and the linkages across them must

increase over time so that they can permit firms to identify and acquire new and fairly

diverse knowledge (Volberda, Foss, & Lyles, 2010). Diversity in existing knowledge

will enable the firm to effectively identify and acquire diverse knowledge (Cohen &

Levinthal, 1990). In turn, diversity in newly acquired knowledge will enable further

expansion of the breadth, differentiation and linkages between categories of existing

knowledge. Schildt, Keil, and Maula (2012) lend empirical support to this logic in a

study of the effectiveness of collaboration between alliance members. They posit that

at the beginning of an alliance, experience with diverse knowledge prepares

companies to understand knowledge from external partners as do similarities between

the technological domains within which the companies in an alliance work. In

addition, knowledge and technological diversity can prepare companies for

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collaboration by increasing their long-term ability to identify and acquire valuable

resources from collaborators more thoroughly. These conclusions are congruent with

Cohen & Levinthal (1990) and Zahra and George (2002) who stress that

organizations without diverse foundations of technological knowledge cannot acquire

one readily and may overlook emergent developments in areas they do not invest.

The relationship of interdependence between external knowledge and the

hitherto and future ability of the firm to value and acquire external knowledge is

conditioned by the mode and scale of firm diversification. The relationship cannot be

sustained if the level of overlap between existing and new knowledge is marginal

(Lane & Lubatkin, 1998). That is because diversification into related technological

and knowledge domains impels the organization to expand its current acquisition

ability and induces its predictive power about emerging consumer needs. Therefore,

organizations which diversify in related areas are expected to reinforce the firm’s

acquisition ability and its positive impact on firm performance.

On the other hand, firms diversifying in unrelated areas may impair

performance as they exert mounting pressures to the effectiveness of their current

ability to recognize, value and absorb unrelated external knowledge. As diversity in

unrelated areas increases, managers experience increased coordination and

communication costs (Fernhaber & Patel, 2012) that mitigate their ability to make

optimum decisions about knowledge acquisition. Empirical evidence shows that firms

that search for external knowledge beyond a small number of sources augment their

innovative performance, but as the number of external sources of new knowledge

increases this relationship exhibits decreasing returns (Laursen & Salter, 2006). We

can therefore expect that the two modes of diversification moderate the effect of the

acquisition ability of the firm on its performance as follows:

9

H1a: Related diversification has a positive moderating effect on the relationship

between the firm’s ability to acquire new external knowledge and performance.

H1b: Unrelated diversification has a negative moderating effect on the relationship

between the firm’s ability to acquire new external knowledge and performance.

Assimilation of external knowledge

The development of processes, policies, and procedures that facilitate sharing and

transferring externally acquired knowledge within the organization manifest the

organization’s assimilation ability (Cohen & Levinthal, 1990; Lane et al., 2006). The

assimilation of external knowledge can have important implications for both

organizational performance and innovativeness (Gupta & Govindarajan, 2000; Lyles

& Salk, 2006; Van Wijk et al., 2008). On the one hand, research has shown that

effective knowledge learning and transfer contribute to the development of difficult to

imitate organizational capabilities, which subsequently enhance performance

(Szulanski, 1996). Lane et al. (2001) cast support with a study of partner knowledge

sharing in international joint ventures where they find that the more the partners

acquired and assimilated new external knowledge the higher was the joint venture’s

performance. On the other hand, existing research has shown that organizational

knowledge learning and transfer relates to innovativeness. According to Cohen and

Levinthal (1990), knowledge transfer and accumulation facilitate the efficient

utilization of knowledge and enable organizations to better understand and assess the

nature and commercial value of knowledge and technological advances. In addition,

knowledge assimilation stimulates the blending of existing and newly acquired

knowledge and reinforces the organization’s capacity for making fresh associations

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and linkages (Jansen et al., 2005). In turn, knowledge assimilation promotes the

generation of novel ideas and the development of new products (Tsai, 2001).

Because learning is a cumulative process shaped by pre-existing knowledge,

organizations can achieve better learning performance when the new knowledge

domain objective is related to what is already known (Zahra & George, 2002). Hence,

to assimilate new and externally acquired knowledge, some degree of overlap is

required with an organization’s previous knowledge (Cohen & Levinthal, 1990; Zahra

& George, 2002). However, Lane and Lubatkin (1998) and Schildt et al. (2012) find

that knowledge and technological similarity between firms in joint ventures increases

the ability of firms to rapidly transfer knowledge from one partner to another at best

moderately, while it keeps the technological domain of the partnership quite narrow

reducing opportunities to learn. This can render a firm that invariably concentrates on

related knowledge acquisition unable to successfully explore, learn, share, and

integrate innovative knowledge (Phene et al., 2012).

The above arguments suggest that the ability of assimilation of acquired

knowledge as a cognitive process is bound up with the knowledge and technological

diversity of the firm (Schildt et al., 2012). Diversity in prior related knowledge can

play a critical role as it can increase the possibility that newly acquired and inherently

uncertain knowledge will overlap with the existing domains of firm knowledge. That

is in accord with Cohen and Levinthal (1990), who draw particular attention in

contexts where there exists high uncertainty about the knowledge fields from which

potentially useful information may emerge. They suggest that firms that diversify in

related product markets and resources will expand their assimilation’s effectiveness.

However, the impact of the firm’s unrelated diversification on the power of its

assimilation ability is more complicated. Business units that typically operate as part

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of a unified firm, are more likely to learn, share, and transfer knowledge that is

reasonably related to what they already know (Van Wijk et al., 2008). Hence, the

existing structure of communication of knowledge across and within business units

underpins the organization’s ability to assimilate external knowledge (Volberda et al.,

2010). Diversifying in highly unrelated areas can cause complications in the structure

of communication since unrelated diversification affects the business units’ velocity

to assimilate the newly acquired knowledge and the effectiveness of ensuing intra-

organizational transfer of knowledge that are important for organizational

performance. This is consistent with Lei and Hitt (1995) who suggest that high levels

of merger and acquisition activity in unrelated knowledge domains are expected to

produce a diminished resource base for organizational learning and technology

development. Unlike situations in which information flow is definite and is clear

where in the firm or subunit a piece of information can be applied, the effectiveness of

knowledge transfer is challenged (Cohen & Levinthal, 1990). Under such

circumstances, the ideal knowledge structure for the subunit and the organization as a

whole should reflect a good balance between overlapping and non-overlapping,

though related knowledge (Leiblein, 2011). This balance notwithstanding, is upset

with diversification in unrelated product and resource markets.

Moreover, diversification strategy is interlinked with organizational structure

(Chandler, 1962; Hall & Saias, 1980; Hill & Hoskisson, 1987; Rumelt, 1974) that

greatly influences the organization’s communication structure (Lane et al., 2006;

Protogerou, Caloghirou, & Lioukas, 2011; Van Den Bosch, Volberda, & De Boer,

1999). Chandler (1962) and Rumelt (1974) emphasized that no two firms possess the

same organizational structure that comprises systems of control, planning,

information flow, methods of reward and techniques of coordination. Their shared

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thesis was that diversification strategy places great strains on organizational structure

that commands the refocus of R&D and performance criteria, the restructuring of

reward systems, and re-evaluation of decisions about resource development and

allocation2

Hall & Saias, 1980

. Several cases of M&As which intended to exploit promising

combinations of resources or attack new markets and technologies failed because of

the clash of cultures and the incapacity of the corporate structure to integrate them

( ). Therefore, organizational structure has a strong impact on the

effectiveness of communication structure as it determines the strength of coordination

capabilities related to knowledge sharing according to which teams belonging to

different firm divisions and departments work together combining their varied skills,

backgrounds and knowledge to learn, assimilate and share newly acquired knowledge

(Barkema & Vermeulen, 1998; Helfat & Raubitschek, 2000).

The impact of organizational structure on communication structure should

vary among the two forms of corporate diversification. Several investigators (e.g. Hill

& Hoskisson, 1987; Michel & Hambrick, 1992) argue that unrelated diversifiers

rarely promote synergies or interrelationships among divisions, but instead emphasize

financial controls that facilitate capital allocation based on relative yields. Hill and

Hoskisson (1987) posit that excessive integrative effort in an unrelated firm would

compromise autonomy and accountability, thereby making the realization of financial

economies difficult and low performance likely. Therefore, corporate managers

generally would refrain from direct intervention in divisional strategy and would not

seek synergistic relations between unrelated divisions (Michel & Hambrick, 1992).

On the other hand, related diversifiers emphasize strategic control systems designed to

encourage resource- and information-sharing among divisions that often include

2 Ensuing studies stress a reciprocal relationship between strategy and structure (e.g. Hall & Saias, 1980; Hill & Hoskisson, 1987).

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monitoring operational and product-market information to enable coordination among

divisions and identify possible synergies. We therefore expect the two forms of

diversification to moderate the positive relationship between the firm’s ability to

assimilate new external knowledge and performance as follows:

H2a: Related diversification has a positive moderating effect on the relationship

between the firm’s ability to assimilate new external knowledge and performance.

H2b: Unrelated diversification has a negative moderating effect on the relationship

between the firm’s ability to assimilate new external knowledge and performance.

Exploitation of external knowledge

Absorptive capacity extends to the organization’s ability to exploit external

knowledge for the creation of new commercial ends and new knowledge and to

predict future technological developments and opportunities ahead of its competitors

(Lane et al., 2006; Volberda et al., 2010). As an organizational capability,

exploitation can increase economic performance by refining, extending, and

leveraging existing competencies or creating new ones by incorporating acquired and

assimilated knowledge into its operations. Whereas exploitation may occur

serendipitously, it is the presence of systematic and organized routines that can allow

the organization to continuously exploit knowledge over prolonged periods for the

creation of new innovative goods, knowledge and markets (Zahra & George, 2002).

The ability of the firm to exploit the specialized knowledge stored within

individual organizational members, such as employees and business units, is primarily

determined by knowledge diversity (Grant, 1996). Particularly for the exploitation

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ability, knowledge diversity is manifest in the speed and difficulty of knowledge

retrieval (Zahra & George, 2002), the demand for common interfaces between

available knowledge vectors (Garud & Nayyar, 1994), and the transferability of

knowledge (Kogut & Zander, 1992). Diversification strategy, by influencing these

dimensions, alters the organization’s knowledge diversity and therefore, its

exploitation ability.

The swift retrieval of assimilated knowledge and technologies from the firm’s

depositories underlies the timely recognition and exploitation of emergent market

opportunities (Zahra & George, 2002). Opportunities that fall within diverse still

related regions of the current knowledge of the firm should place it in a more

responsive position than opportunities arising from unrelated territories. The

overarching logic is that retrieval speed depends on the intensity of using existing and

new knowledge for problem solving, e.g. for new product development (Cohen &

Levinthal, 1990), the recentness of knowledge use, and the proximity of new

knowledge to the organization (Garud & Nayyar, 1994). Therefore, new knowledge

acquired by organizations which diversify into related areas is more readily integrable

into existing knowledge. In addition, related diversity in a newly acquired knowledge,

product or capability can allow the organization to gain competitive advantages

through difficult to imitate combinative schemes of complementary resources.

Moreover, firms have difficulty retrieving old unused knowledge or skills and

only part of a firm’s memory is likely to be evoked at a particular time (Garud &

Nayyar, 1994). Hence, firms must be able to combine newly acquired and assimilated

knowledge with existing knowledge promptly following acquisition, since shortened

product life-cycles and increasing rivalry exert mounting pressures on market

opportunities. Early combination weakens the prerequisite for adaptation of retrieved

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knowledge to new knowledge and changed consumer needs, social and economic

circumstances. This suggests smaller lapses between product inception and

commercialization, greater responsiveness to market opportunities and superior

performance for firms that diversify in related knowledge with a given level of

exploitation ability.

The combination of different resources and knowledge from different units

within the firm requires establishing common interfaces among knowledge vectors.

That implies that the organizational design must facilitate information processing

among diverse groups within the firm, such as through lateral information processing

mechanisms. To deal with knowledge ambiguity, facilitate choice of constituent

knowledge, and accelerate retrieval, firms employ rich media to exchange views and

opinions among managers about the combinative future of technologies. They may

use a mixture of formal planning mechanisms, personal meetings and job rotation

systems to draw out, contemplate and share their understanding about the prospects of

technologies before choosing the combination of available knowledge vectors (Garud

& Nayyar, 1994). Since unrelated diversifiers are inclined to adopt variations of M-

form designs with autonomous business units (Chandler, 1962; Rumelt, 1974), they

inherently elevate the degree of difficulty to develop such mechanisms. This suggests

that unrelated diversifications are negatively associated with the effectiveness of rich

media and coordination and constrain the exploitation ability of the firm.

A similar logic underlies the transferability of “know-how knowledge” (Kogut

& Zander, 1992) across business units. Know-how knowledge refers to hitherto

practice inside firms and business units on matters such as the transformation of

knowledge inputs into outputs, the commercialization and management of new

product portfolios. Its transferability underpins the regeneration and synthesis of

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assimilated knowledge and attainment of new knowledge combinations. Know-how

knowledge is contingent on individuals’ skills and tacit knowledge that can be

extremely difficult to pass on, especially if human intelligence is dedicated on solving

differentiated problems specific to the business unit of origin (Kogut & Zander,

1992). The teaching of know-how requires frequent interaction within small groups,

often through the development of a unique language or code that is less attainable

with the establishment of increasing numbers of autonomous business units in the

unrelated firm. Therefore, diversification in unrelated areas impairs the transferability

of know-how within the multi-business firm reducing the potential to effectively

exploit emerging market opportunities. Moreover, a consequence of engaging in the

development of a vast array of unrelated products is an increased state of complexity

and information processing difficulty associated with product portfolio management

(Fernhaber & Patel, 2012; Vasudeva & Anand, 2011). The more diverse the product

portfolio the higher the coordination and communication costs that managers incur

that may overwhelm the positive implications emanating from innovativeness and

access to diverse markets. Therefore we can expect that:

H3a: Related diversification has a positive moderating effect on the relationship

between the firm’s ability to exploit new external knowledge and performance.

H3b: Unrelated diversification has a negative moderating effect on the relationship

between the firm’s ability to exploit new external knowledge and performance.

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Methods

Performance Measure

Performance was measured using a firm’s return on assets (ROA) (Miller, 2004;

Robins & Wiersema, 1995). ROA has been shown to be related to a variety of other

indicators of firm financial performance and has been widely employed in the

strategy-performance literature (Hoskisson & Hitt, 1990). Despite the existing debate

over the use of accounting versus market performance measures, Robins & Wiersema

(1995) report a number of advantages for the former, including their close connection

to the decision variables controlled by managers and their enabling direct comparison

with a substantial body of research on diversification and performance in strategic

management.

Acquisition Ability

Several studies have operationalized the unified construct of absorptive capacity as

R&D intensity (R&D expenditure to sales) (e.g. Meeus, Oerlemans, & Hage, 2001;

Rothaermel & Alexandre, 2009). However, R&D spending, as a non-core skill

(Teece, 2007), cannot reflect or give evidence of the quality of the firm’s ability to

integrate and combine assets including knowledge, which pertain to core skills (Grant,

1996; Kogut & Zander, 1992). Moreover, knowledge depositories and diversity can

differ greatly among firms despite similarities in their R&D spending (Schildt et al.,

2012). Therefore, Cohen & Levinthal (1990)’s original use of R&D intensity as a

proxy for technological diversity, which is a central determinant of absorptive

capacity, is unsound because the two are conceptually distinct (Schildt et al., 2012).

The empirical limitations of employing R&D intensity as an all-encompassing

measure of absorptive capacity is evident in the low explanatory power of R&D

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spending on firm performance (Lane et al., 2006). This calls into question the

usefulness of this unrefined, absolute measure of absorptive capacity (Lane &

Lubatkin, 1998).

Instead, in this study, we consider R&D intensity as an appropriate measure of

the acquisition ability of the firm. According to Cohen and Levinthal (1990), a firm’s

ability to acquire knowledge from its external environment is a byproduct of its own

R&D. R&D efforts provide an in-house technical capability that can keep firms

abreast of the latest technological developments and facilitate the identification,

valuation, and acquisition of new technology developed elsewhere (Lane et al., 2006).

Moreover, R&D activity can be thought of as a form of search for new knowledge and

products that allows the firm to sense what is going on in its business ecosystem

(Teece, 2007).

Assimilation Ability

The assimilation ability refers to the organization’s ability to understand technological

advances and make fresh associations and linkages between existing and newly

acquired knowledge. We therefore expect the assimilation ability of the firm to be

reflected in the diversity of knowledge resources it utilizes to produce new knowledge

and commercial ends. We operationalize the assimilation ability as the diversity in

citations the firm capitalizes on to produce its own patents. To measure diversity in

technological resources, we calculated a concentric measure of diversification

originally used by Caves, Porter, and Spence (1980) using firm patents and patent

citations that we translated both to four-digit SIC codes using the concordance index

developed by Silverman (1999). The index is given by:

19

i ij ji j

Assimilation Ability p d p=∑ ∑ (1)

where ip is the proportion of patent applications in 4-digit SIC i; jp is the proportion

of patent citations in 4-digit SIC j; and ijd equals 1,2,3,4 if i and j are in the same

4,3,2,1-digit SIC, respectively. The index ranges from 0 to 2 and is increasing in

diversity. The index compares each patent with its citations measuring the “distance”

between patents and citations concentrically (Argyres, 1996).

Exploitation ability

The exploitation ability refers to the organization’s ability to exploit external

knowledge for the creation of new knowledge and product outputs. We expect this

ability to be reflected in the diversity in the firm’s technological outputs. To

operationalize exploitation ability we calculated a concentric measure of

diversification using patents that we translated to four-digit SIC codes using the

concordance index developed by Silverman (1999). The index is given by:

t i ij ji j

Exploitation Ability p d p=∑ ∑ (2)

where ip is the proportion of patent applications in 4-digit SIC i in year t; jp is the

proportion of patent applications in 4-digit SIC j; and ijd equals 1,2,3,4 if i and j are in

the same 4,3,2,1-digit SIC, respectively. The index ranges from 0 to 2 and is

increasing in diversity. Patents assigned to more than one SICs were treated as

different applications in order to better capture firm-level technological diversity. The

20

index compares each patent with every other patent in the firm’s portfolio measuring

the “distance” between patents concentrically (Argyres, 1996).

Modes of diversification

Existing operationalizations of modes of diversification center on certain functional

resources such as product relatedness (Rumelt, 1974), technological relatedness

(Robins & Wiersema, 1995; Silverman, 1999), managerial relatedness (Prahalad &

Bettis, 1986), human resources relatedness (Farjoun, 1998), or a combination

(Tanriverdi & Venkatraman, 2005). Researchers resort to indirect measures that

capture the industry participation profiles of firms and the resource similarities of

industries (Tanriverdi & Venkatraman, 2005). In this study, we use such an indirect

measure for corporate diversification by adopting a widely used entropy measure of

diversification developed by Jacquemin & Berry (1979), which allows for the

calculation of a firm’s total, related, and unrelated diversification. Total

diversification (DT) is computed as follows:

1lnN

ii j i

DT PP=

=

∑ (3)

where N is the number of industry segments a firm operates in at the 4-digit SIC level

and Pi is the share of the ith segment in the total sales of the firm. If we let the N

number of industry segments at the 4-digit SIC level aggregate into M industry groups

at the 2-digit SIC level, related diversification (DR) can be computed as follows:

1lnji j

i j i

DR PPε

=

∑ (4)

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where jiP is defined as the share of segment i of group j in the total sales of the group.

Unrelated diversification (DU) derives from the difference between equations (3) and

(4). According to Palepu (1985) the entropy measure considers three important

elements of diversification: the number of segments in which a firm operates, their

degree of relatedness, and their relative importance for the firm’s total sales.

Controls

Firm size

Firm size has been considered as an indicator of market power and scale economies.

Empirical evidence exists linking size to profitability (Bettis, 1981; Robins &

Wiersema, 1995). Market power may allow control over pricing and economies of

scale can allow cost reductions. Combined they can enable large firms to achieve high

levels of profitability. We control for the firm’s size with the log of the number of

firm employees and expect it to have a positive relationship with performance.

Industry concentration

From the early works in industrial organization (Bain, 1956) industry concentration

has been considered as a strong indicator of barriers to entry. In highly concentrated

industries, market power enjoyed by firms may allow them to sustain high levels of

profitability. This measure reflects a firm’s relative sales in different industries by

multiplying the proportion of firm sales in a focal industry with the concentration

ratio of the industry and aggregating as follows:

4t i iIndustry Concentration CR P=∑ (5)

22

where CR4i is the four-firm concentration ratio for the 2-digit SIC industry i and Pi is

the proportion of a firm’s sales in the 2-digit SIC industry i. Prior empirical evidence

in strategy (Markides, 1995) suggests a positive relationship between industry

concentration and firm profitability.

Industry profitability

We account for the profitability in a firm’s industries to control for any industry effect

not captured by industry concentration. According to Robins & Wiersema (1995) the

interrelationships between the firm’s businesses may have an impact on performance.

A weighted measure of industry profitability can be estimated by computing the

average profitability of each 4-digit SIC industry in which a focal firm operates,

multiply it by the proportion of firm sales in the industry and aggregate for the firm as

follows:

i iIndustry Profitability ROA P=∑ (6)

where ROAi is the average return on assets for industry i and Pi is the proportion of a

firm’s sales in SIC i. Industry profitability is expected to have a positive relationship

with firm profitability.

Debt burden

Managerial discretion in the allocation of organizational resources across the

organization’s operations can be reduced in the face of high debt level. In effect, the

firm’s debt burden compels management to invest wisely and be more efficient

(George, 2005). We measure debt burden as the firm’s debt to shareholder equity ratio

(Markides, 1995).

23

Capital investments

We control for the firm’s capital investments, an indicator of the firm’s tangible

nature of assets used in firm growth, which may result in higher total factor

productivity and higher performance not attributable to absorptive capacity or

diversification (Miller, 2006; Palich, Cardinal, & Miller, 2000). Capital investments is

measured as the firm’s capital expenditures as a percent of sales. We expect it to have

a positive relationship with firm performance.

Technological output

Prior research demonstrates a positive relationship between technological output and

measures of firm performance (Miller, 2006). We control for the effect of

technological output by incorporating in the analysis the firm’s number of patents to

its sales.

Labor Productivity

Changes in labor productivity attributed to renegotiated labor contracts, new

investments in technology, and improvements in the monitoring from firm managers

during the study period can have an important effect on firm performance (Markides,

1995). We control for labor productivity using the ratio of the number of employees

to firm sales, with higher productivity expected to have a positive relationship with

performance.

Foreign sales

The literature on international diversification suggests a positive relationship between

foreign operations and profitability (Capar & Kotabe, 2003; Kotabe, Srinivasan, &

24

Aulakh, 2002; Lu & Beamish, 2004; Wan & Hoskisson, 2003). We include in the

analysis the firm’s foreign to domestic sales ratio to account for the part of variation

in firm performance attributed to variations in the extent of the firm’s international

diversification.

Advertising Intensity

Organizations that spend more resources to interact with customers are more likely to

understand complex consumer needs, achieve product differentiation relative to

competition, attain superior brand equity and in effect financial performance (Day,

1994; Dutta, Narasimhan, & Rajiv, 1999; Narasimhan et al., 2006; Song, Droge,

Hanvanich, & Calantone, 2005). We control for the effect of the firm’s marketing

capability on performance by including in the analysis the firm’s ratio of advertising

expenses to sales.

Complexity of Organizational Structure

Organizational structure determines the coordination of knowledge sharing between

business units, divisions, and operational departments and consequently impacts

organizational performance (Chandler, 1962; Hill, Hitt, & Hoskisson, 1992; Rumelt,

1974). We account for this relationship by including in the analysis a variable that

looks into the degree of organizational structure and knowledge relatedness between a

firm and its subsidiaries. The variable is calculated as follows:

t j jtj

Complextity of Organizational Structure d p=∑ (7)

25

where jtp is the proportion of subsidiaries in the 4-digit SIC j in year t; and jd takes

the value 1,2,or 3 if j and the firm’s SIC are in the same 3,2,1-digit SIC, respectively.

The index ranges from 0 to 3 and is increasing in complexity as it considers a firm

whose subsidiaries are unrelated to its core industry as one requiring more complex

organizational structure. The variable measures the “distance” between the firm’s core

industry classification and its subsidiaries’ classifications. Greater distance suggests

additional strains on the firm’s organizational structure to adjust existing systems of

control, planning, information flow, methods of reward and techniques of

coordination.

Time Effects

Because our study examines performance effects over a large number of years, we

incorporated in the analysis year dummies to control for possible unobserved time-

specific effects and the effects of serial correlation. We followed Phene et al. (2012)

and created dummies based on blocks of five years: 2005-2010 (the base), 2000-2005,

1995-2000, 1990-1995, 1985-1990, 1980-1985, and 1975-1980.

Industry Effects

According to Hoskisson and Hitt (1990) and Palich et al. (2000), one important

limitation of previous studies of the relationship between strategy and performance is

that they do not control for industry effects. In the present study, accounting for

industry effects may allow unique variance explained by the dimensions of absorptive

capacity and its interactions with diversification to be unmasked. To control for

performance variations between firms due to industry structure effects we include in

the analysis 2-digit SIC industry dummies.

26

Sample selection

The sample consists of 150 large firms traded in the United States operating in the

information, communication and technology (ICT) industries. Following the

recommendation of previous empirical studies we examine the relationship between

strategy-performance in a fine-grained study within a focused set of industries as

opposed to across multiple and diverse industries (Palich et al., 2000). Therefore,

along with the explicit control for industry effects and the incorporation of industry

and concentration effects that can have a strong influence on firm performance, our

sample implicitly renders our research findings robust to industry structure. Sample

firms were randomly selected based on a minimum of $1billion sales for 2010.

The ICT industries are characterized by a dynamic environment with rapid

technological change and intense restructuring activity. They exhibit extensive

consolidation through industry alliances and mergers, the combination of technology

and network platforms, and the integration between services and markets (Bum Soo,

Choi, Barnett, Danowski, & Sung-Hee, 2003; Greenstein, 2000; Wirtz, 2001). The

integration of technologies creates conditions of market failure (Gambardella &

Torrisi, 1998) in which organizations have increasing incentives to expand

diversification on the expectation they will benefit from an “internal capital market”

that could be more efficient for trading and sharing resources, knowledge and

technology (Chatterjee & Wernerfelt, 1991; Hill et al., 1992).

Data were collected from multiple sources. Thomson Reuters’ Derwent

database, one of the world’s most comprehensive databases of patent documents, was

used for the collection of patent data. Since large multi-business firms frequently

assign patents to subsidiaries, we used the Bureau Van Dijk’s Orbis database to

identify every subsidiary – domestic and foreign – of each firm in the sample. We

27

were thus able to search the Derwent database for patents assigned to any of these

parent or subsidiary names, and aggregate all patents at the parent level. We collected

a total of 1,914,597 patents assigned to the sample firms and their subsidiaries

between 1966 and 2010. Each patent and its cited patents are identified by

International Patent Class (IPC). We translated all patents and cited patents to the

distribution of their application across industries using the concordance index

developed by Silverman (1999), which assigns each patent to four-digit SIC codes.

Compustat was the source for the financial3

1992

, industry, and segment data. Davis

and Duhaime ( ) note that the use of Compustat for the study of diversification

offers some advantages. The assignment of business activities to Compustat segments

is conducted by respondents in firms and the data are thus expected to involve some

information about managers’ views of relationships among businesses. This additional

information can be valuable in research that employs the entropy index of

diversification (Robins & Wiersema, 1995). Data from Compustat span from 1975 to

2010. Sample firms fall in seven 2-digit industries: 35 - Industrial And Commercial

Machinery And Computer Equipment; 36 - Electronic And Other Electrical

Equipment And Components, Except Computer Equipment; 37 - Transportation

Equipment; 38 - Measuring, Analyzing, And Controlling Instruments; Photographic,

Medical And Optical Goods; Watches And Clocks; 48 - Communications; 50 -

Wholesale Trade-durable Goods; and 73 - Business Services.

The complete dataset used in the analysis comprises an unbalanced dataset of

2,344 firm-year observations with 141 firms as 9 firms were dropped from the sample

due to missing data. Table 1 presents summary statistics for and pairwise correlations

between our dependent, independent, and control variables. Interestingly, the

3 Financial data were deflated and converted to constant US$ of 2005 using the CPI.

28

correlation coefficient between the assimilation and exploitation abilities of the firm is

positive and high suggesting that firms with higher assimilation ability tend to also

have higher exploitation ability. Based on the underlying variables, the positive

correlation implies that the sample firms expand the breadth of knowledge they use in

the production of new technological output over time and also increase the range of

applications of each patent produced. Moreover, their low correlations between the

assimilation and exploitation abilities with the acquisition ability suggest that the

operationalization of an absolute measure of absorptive capacity as R&D intensity by

existing studies is unsound.

- Insert Table 1 about here -

Results

Table 2 presents the results from our analysis. The models were estimated with

generalized least-squares regression that accounts for the problem of

heteroskedasticity we diagnosed in our data. Otherwise, the estimation of our models

with ordinary least-squares would result in inefficient estimates (Cameron & Trivedi,

2009). Previous studies of strategy’s effects on performance recommend the use of

lagged effects in empirical analyses. In particular, changes in industry structure,

industry concentration, and also firm diversification strategy are not fully realized

until a number of years has elapsed (Markides, 1995). We account for lagged effects

by lagging all control variables, apart from industry and time dummies, for one year.

The first model on Table 2 presents the output when ROA is regressed on only the

control variables.

29

In the next two models, we introduce the effects of the three dimensions of

absorptive capacity and the two modes of diversification. With regard to the size of

lags for the main independent variables, we follow a more systematic approach, which

represents a balance between the rationale used for the controls, the contribution of

the lagged effects to the explanatory power of the model, and the ensuing loss of

observations. In particular, we allow for longer lags for the effects of absorptive

capacity on firm performance than for the effects of diversification on performance.

This is because we expect that changes in firm resources require time before they can

influence hitherto abilities to acquire, assimilate, and exploit external knowledge.

Moreover, following existing studies of the effect of firm diversification on

performance (Markides, 1995) and studies of the transformative capacity of the firm

(Garud & Nayyar, 1994), we tested alternate models that involved the 4- to 5-year

lagged effects of diversification and the 2- to 3-year lagged effects of absorptive

capacity. For the evaluation of alternative model specifications we capitalized on the

Akaike and Bayesian Information Criteria. The model specification that resulted in

the loss of the fewest data points and yielded the lowest AIC and BIC values involved

two-year and four-year lags for the effects of absorptive capacity and diversification,

respectively.

The last model on Table 2 incorporates the interaction effects between the

acquisition, assimilation, and exploitation abilities of the firm and related and

unrelated diversification. The incorporation of variables that suffered from several

missing values along with the use of long lags, culminated in the drop of 17 firms and

323 firm-year observations from our sample. To ensure that our results would not

suffer from a possible selection bias attributed to the smaller sample size, we tested

whether the firms dropped from the model and those preserved for the remaining of

30

the analysis differed in size or industry membership. Our t-tests rejected the

hypotheses of differences across the two groups of firms.

- Insert Table 2 about here -

With regard to the control variables, sample firms that improve their

productivity, expand international sales, and invest more in advertising exhibit higher

performance on average. On the other hand, firms with more complex organizational

structures experience lower performance. Contrary to our expectations, the firm’s

size, technological output, capital investments, and debt burden have negative and

statistically significant effects on firm performance. Moreover, industry profitability

does not have a statistically significant effect.

Our results confirm existing theoretical expectations about the positive effect

of absorptive capacity on firm performance and the opposite effects for the two forms

of diversification on performance. Specifically, the Base and Diversification models

on Table 2 suggest that the firm’s acquisition, assimilation, and exploitation abilities

induce performance. Moreover, related diversification has a positive effect whereas

unrelated diversification has a negative effect. The Interactions model, based on

which we test this study’s hypotheses, suggests that the main effects remain stable at

large, apart from the effect of unrelated diversification which changes sign and

becomes positive and statistically significant.

The majority of the interaction effects are statistically significant lending

support to four of our hypotheses whilst they reject the remaining two. In particular,

the interaction effect between the acquisition ability of the firm and related

diversification is positive and statistically significant, supporting H1a and suggesting

31

that related diversification reinforces the acquisition ability’s underlying mechanisms

that contribute to firm performance. This relationship is illustrated in Figure 1 where

Graph (a) exhibits how the marginal effect of the firm’s acquisition ability on

performance is modified with higher values of related diversification. Graph (a)

shows a linear moderating effect for related diversification on the impact of the

acquisition ability on performance that is positive and gradually increasing. Contrary

to our expectations, H2a that assumed a positive moderating effect for related

diversification on the impact of the assimilation ability of the firm is rejected. Not

only is the interaction effect negative, but it is also statistically significant, suggesting

that the expansion of related diversification undermines the mechanisms that underpin

the firm’s ability to assimilate and share existing and newly acquired knowledge. This

relationship is illustrated in Graph (b) that allows us to make additional important

observations. For instance, low levels of related diversification moderate the impact

of the assimilation ability on performance positively. This result partially supports

H2a, however, as related diversification increases and passes the sample firms’

average related diversification, the moderating effect becomes negative. This suggests

a non-linear moderating effect for related diversification on the assimilation ability,

since the effect becomes increasingly negative as the firm increasingly exceeds the

sample’s mean related diversification. As regards H3a, the coefficient of the

interaction variable between related diversification and the exploitation ability is

positive and statistically significant. This supports our hypothesis that related

diversification has a positive moderating effect on the relationship between the

exploitation ability of the firm and performance. The moderating effect is illustrated

in Graph (c) where related diversification shows a positive but decreasing effect on

the relationship between the exploitation ability of the firm and performance.

32

The results also support H1b and H3b and reject H2b, which pertain to the

moderating effects of unrelated diversification on the three dimensions of absorptive

capacity. The coefficients for the interaction variables between unrelated

diversification and the acquisition and exploitation abilities of the firm are expectedly

negative and statistically significant. These findings are illustrated in Graphs (d) and

(e). Interestingly, contrary to the moderating effect of unrelated diversification on the

relationship between the exploitation ability and performance, the moderating effect

on the relationship between the acquisition ability and performance is non-linear.

Accordingly, firms which preserve unrelated diversification lower than the sample’s

mean, reinforce the impact of their acquisition ability on performance. It is not after

they exceed the sample’s mean unrelated diversification that firms exhibit a

decreasing effect for the acquisition ability on performance.

- Insert Figure 1 about here -

Discussion

In this paper, we argue that due to the reification of absorptive capacity (Lane et al.,

2006) existing empirical research has shed limited light on its underlying abilities that

drive its impact on firm performance. As this calls for an expansive view that

accounts for contextual conditions that may influence absorptive capacity, we delved

into its constituent dimensions, which Cohen and Levinthal (1990) put forward as the

firm’s acquisition, assimilation, and exploitation abilities, and identified important

boundary conditions that can moderate their impact on firm performance. We

postulated that the main mechanisms underpinning the dimensions of absorptive

capacity relate to knowledge diversity and the organization’s communication

33

structure. The former is reflected in the speed and difficulty of knowledge retrieval,

the demand for common interfaces between available knowledge vectors, and the

transferability of knowledge, whereas the latter determines the effectiveness of

internal knowledge transfer and sharing. We then examined how related and unrelated

diversification can influence these mechanisms and consequently, moderate the

impact that absorptive capacity has on firm performance. Our results support existing

theoretical expectations that suggest the positive impact of the three dimensions of

absorptive capacity on performance and the opposite effect of the two modes of

diversification. More importantly, our results illustrate that the effects of the

constituent abilities of absorptive capacity are conditioned by the firm’s mode of

diversification strategy in different fashions.

Our study allows us to make several important contributions to the literature.

First, by disassembling the construct of absorptive capacity, we empirically find that

each dimension has a distinct positive effect on firm performance. This finding

suggests that the misconception of absorptive capacity as a unified construct detached

from its underlying abilities may render this capability of strategic value largely

unexploited. The overarching logic is that firms that invest in developing any of the

constituent abilities may attain performance improvements but fall into the fallacy of

building absorptive capacity. In effect, firms may focus on the valuation and

assimilation of external knowledge and thereby renew their knowledge stock and gain

access to unique resources, but remain with the costs of acquisition if they do not

convert resources into innovative outputs. Moreover, the invariant focus on the

resource exploitation and production of new commercial ends may limit the firm’s

resilience and responsiveness to environmental and technological shifts.

34

Second, by looking into the underlying abilities of absorptive capacity and

identifying their supporting mechanisms, we recognized important boundary

conditions whose effect appears to be critical. Absorptive capacity depends on

diversity among internal and external knowledge and the communication structure of

the firm, which jointly determine the development of its underlying abilities. This

dependence renders absorptive capacity vulnerable to the firm’s actions that can alter

the qualities of underpinning mechanisms and ultimately moderate its effect on

performance. Specifically, we posited and examined the modifying effect that modes

of diversification strategy can have on the relationship between absorptive capacity

and firm performance by influencing the underpinning mechanisms of its constituent

abilities. Our expectations suggested that related and unrelated diversification

strategies can alter the nature of knowledge diversity and exert differential pressures

on the firm’s communication structure. Correspondingly, our empirical findings

report that these effects have important and different moderating influence on firm

performance.

Third, the moderating impact of diversification strategy unearths a deeper

insight. Namely, absorptive capacity and the diversification mode are strategic tools

that cannot be designed and implemented in isolation, for their interdependence

requires the formulation and implementation of an orchestrated strategy. On the one

hand, absorptive capacity reflects the scale of the ability of the firm to acquire,

assimilate, and exploit externally acquired knowledge. On the other hand, our findings

suggest that related diversification reinforces the positive impact of the acquisition

and exploitation abilities of the firm on its performance. Moreover, unrelated

diversification moderates these effects negatively, though sufficiently low levels of

unrelated diversification reinforce the acquisition ability of the firm. The functions of

35

absorptive capacity along with our empirical findings jointly suggest that

organizations cannot diversify unreservedly, because hitherto amounts of absorptive

capacity are decisive for the effective implementation of diversification strategy and

vice versa. Therefore, according to this reciprocal relationship the existing absorptive

capacity of the firm determines the feasible type and magnitude of newly acquired

knowledge and resources it can absorb. Moreover, unless the organization pushes the

existing frontiers of absorptive capacity through a carefully designed diversification

mode the firm risks to fail to recognize emerging opportunities to create competitive

advantages (Zahra & George, 2002).

Fourth, our study additionally sheds some light on the ongoing debate in

strategy research over the effects of related and unrelated diversification on firm

performance. The prevailing resource-based view (RBV) of diversification postulates

that resource relatedness can allow the production of super-additive value and sub-

additive costs that improve firm performance (Farjoun, 1998; Markides &

Williamson, 1994; Robins & Wiersema, 1995). This reasoning is consistent with our

absorptive capacity perspective, which emphasizes the role of related knowledge in

building the acquisition, assimilation, and exploitation abilities as well as in

mitigating the pressure exerted on the organization’s communication structure.

Despite that our empirical findings suggest that the assimilation ability of the firm is

impaired when related diversification exceeds the sample’s average, overall, the

interplay between absorptive capacity and related diversification is reinforcing and

yields positive returns. On the other hand, the logic of synergies and path-dependence

of the RBV is too narrowly defined to account for the firm’s efforts to alleviate the

risk attached to resource allocation in conditions of market failure, which lead to

unrelated diversifications (Ng, 2007; Villalonga, 2004). Our empirical examination

36

that looks into a sample of firms from markets that exhibit imperfections

(Gambardella & Torrisi, 1998) shows that unrelated diversification does contribute to

performance. The base effect of unrelated diversification was negative and

statistically significant, but it reversed when its interaction effects were incorporated

in the model. Moreover, despite that unrelated diversification weakened the

exploitation ability of the firm, as we hypothesized, it only impaired the acquisition

ability after it exceeded the sample’s average. Instead, it reinforced the acquisition

ability at lower levels. Therefore, our study is in congruence with the RBV’s support

of related diversification in that firms can exercise greater discretion in diversifying in

related than in unrelated knowledge areas.

37

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Table 1: Summary statistics and correlation matrix

Variable Mean SD Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1. ROA 0.05 0.10 -0.85 0.48 1

2. DR 0.56 0.54 0.00 2.34 0.12 1 3. DU 0.52 0.70 0.00 3.91 -0.18 0.12 1

4. Acquisition 0.06 0.06 0.00 0.44 0.09 -0.11 -0.12 1 5. Assimilation 0.97 0.61 0.00 1.68 -0.02 0.04 -0.10 0.17 1

6. Exploitation 0.87 0.55 0.00 1.71 -0.01 0.10 0.02 0.02 0.78 1 7. Advertising intensity 0.02 0.02 0.00 0.12 0.01 0.15 0.06 -0.01 -0.06 -0.01 1

8. Complexity of organizational

2.08 0.70 0.00 3.00 -0.04 -0.18 -0.04 0.04 0.24 0.33 -0.11 1 9. Technological output 0.04 0.08 0.00 0.57 -0.11 -0.13 0.27 0.08 0.11 0.15 -0.22 0.23 1

10. Firm size 3.41 1.45 -1.93 6.06 -0.12 0.24 0.45 -0.25 0.09 0.21 -0.01 0.01 0.25 1 11. Industry Concentration 0.14 0.17 0.00 0.81 -0.04 -0.10 0.37 0.03 0.02 0.07 -0.13 0.19 0.37 0.49 1

12. Industry Profitability 0.36 3.45 0.00 70.23 -0.03 -0.02 0.10 0.00 -0.06 -0.05 -0.05 0.02 0.13 0.10 0.13 1 13. Capital investments 0.09 0.08 0.01 0.88 -0.03 0.04 -0.12 -0.36 -0.18 -0.12 0.02 -0.13 -0.08 -0.03 -0.23 -0.04 1

14. Debt burden 0.50 7.47 6.53 108.33 0.00 0.03 0.01 0.00 0.04 0.06 -0.08 0.02 0.03 0.07 0.03 0.01 0.01 1 15. Labor productivity 379.46 297.79 6.02 2281.21 0.11 0.03 -0.16 0.02 0.04 -0.02 0.03 0.04 -0.11 -0.16 -0.09 -0.03 -0.02 -0.01 1

16. Foreign to Domestic Sales Ratio 0.41 0.28 0.00 0.93 0.20 0.11 -0.03 0.36 0.07 0.07 0.10 0.00 -0.04 0.07 0.08 -0.02 -0.26 -0.01 0.12 1 17. Time effects 7.65 1.40 1.00 7.00 0.03 0.31 0.02 0.04 0.00 -0.02 0.02 -0.18 -0.28 0.14 -0.22 -0.02 -0.09 0.01 0.33 0.34 1

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Table 2: Regression Outputs: The moderating impact of modes of diversification on

the effects of acquisition, assimilation, and exploitation abilities on firm performance

Dependent variable: ROA Controls Base Diversification Interactions

Coef./SE Coef./SE Coef./SE Coef./SE Acquisition

0.099* 0.098* 0.091†

(0.04) (0.05) (0.05)

Assimilation

0.006* 0.006* 0.006†

(0.00) (0.00) (0.00)

Exploitation

0.006* 0.007* 0.010**

(0.00) (0.00) (0.00)

DR

0.007* 0.009**

(0.00) (0.00)

DU

-0.004* 0.009*

(0.00) (0.00)

DR x Acquisition

0.129†

(0.07)

DR x Assimilation

-0.016**

(0.01)

DR x Exploitation

0.014*

(0.01)

DU x Acquisition

-0.208***

(0.06)

DU x Assimilation

0.010

(0.01)

DU x Exploitation

-0.012*

(0.01)

Firm size -0.000 -0.006*** -0.006** -0.008**

(0.00) (0.00) (0.00) (0.00)

Technological output -0.061** -0.057** -0.059** -0.094***

(0.02) (0.02) (0.02) (0.02)

Capital investments -0.024* -0.064* -0.054† -0.018

(0.01) (0.03) (0.03) (0.04)

Debt burden -0.000 -0.001** -0.001** -0.001**

(0.00) (0.00) (0.00) (0.00)

Industry concentration -0.051*** -0.044*** -0.046*** -0.056***

(0.01) (0.01) (0.01) (0.01)

Industry profitability -0.000 -0.000 -0.000 -0.000

(0.00) (0.00) (0.00) (0.00)

Labor productivity 0.000** 0.000* 0.000* 0.000*

(0.00) (0.00) (0.00) (0.00)

Complexity of organizational structure -0.009† -0.026*** -0.026*** -0.021***

(0.00) (0.01) (0.01) (0.01)

Advertising intensity 0.440** 1.039*** 1.050*** 0.499*

(0.14) (0.21) (0.21) (0.23)

Foreign to domestic sales ratio 0.006 0.013* 0.014* 0.020*

(0.01) (0.01) (0.01) (0.01)

Industry effects: SIC 35 0.019*** -0.001 0.004 0.014 (0.01) (0.01) (0.01) (0.01) 36 0.007 -0.012† -0.009 0.011

(0.00) (0.01) (0.01) (0.01)

37 0.022† 0.020 0.027†

43

(0.01) (0.01) (0.01)

38 -0.002 -0.006 0.002 0.016

(0.01) (0.01) (0.01) (0.02)

50 -0.030** -0.044** -0.040** (0.01) (0.02) (0.01) 73 0.012** 0.017* 0.019* 0.020†

(0.00) (0.01) (0.01) (0.01)

Time effects: 1975-1980 0.021** 0.029*** 0.030***

(0.01) (0.01) (0.01)

1980-1985 0.006 0.013* 0.015* 0.021**

(0.01) (0.01) (0.01) (0.01)

1985-1990 0.003 0.006 0.008 0.014†

(0.00) (0.01) (0.01) (0.01)

1990-1995 -0.005 -0.003 -0.002 0.002

(0.00) (0.01) (0.01) (0.01)

1995-2000 -0.004 -0.006 -0.004 -0.002

(0.00) (0.00) (0.00) (0.01)

2000-2005 -0.006* -0.007* -0.005† -0.002

(0.00) (0.00) (0.00) (0.00)

Constant 0.047*** 0.069*** 0.061*** 0.052***

(0.01) (0.01) (0.01) (0.01)

Observations 2344 2021 2021 2021 Firms 141 124 124 124 Chi_sq 137.68 195.57 213.56 224.70 Notes:

1. Financial figures were converted to constant USDs of 2000. 2. Models are estimated using feasible generalized least squares that yields robust estimates to autocorrelation within panels, cross-sectional

correlation and heteroskedasticity across panels. 3. All control variables except “Period” and “Industry” dummies are lagged for one year 4. The base Time effect dummy refers to the period 2005-2010 5. The base “Industry Effect” is the 2-digit SIC 48 (Communications) 6. Absorptive capacity variables are lagged for two years 7. Diversification variables are lagged for four years

† p<0.07, * p<0.05, ** p<0.01, *** p<0.001

44

Figure 1: Graphical presentation of the interaction effects

.0

5.1

.15

.2

Mar

gina

l Eff

ect

0 .1 .2 .3 .4

Acquisition Ability

DR = Mean - 0.5SD DR = Mean DR

DR = Mean + 1SD DR = Mean + 2SD

DR = Mean + 3SD

Graph (a)The Marginal Effect of the Acquisition Ability:

The Moderating Effect of Related Diversification

.06

.07

.08

.09

.1

Mar

gina

l Eff

ect

0 .3 .6 .9 1.2

Assimilation Ability

DR = Mean - 0.5SD DR = Mean DR

DR = Mean + 1SD DR = Mean + 2SD

DR = Mean + 3SD

Graph (b)The Marginal Effect of the Assimilation Ability:The Moderating Effect of Related Diversification

.04

.05

.06

.07

.08

.09

Mar

gina

l Eff

ect

0 .4 .8 1.2 1.6

Exploitation Ability

DR = Mean - 0.5SD DR = Mean DR

DR = Mean + 1SD DR = Mean + 2SD

DR = Mean + 3SD

Graph (c)The Marginal Effect of the Exploitation Ability:

The Moderating Effect of Related Diversification

-.2-.1

0.1

Mar

gina

l Eff

ect

0 .1 .2 .3 .4 .5

Acquisition Ability

DU = Mean - 0.5SD DU = Mean DU

DU = Mean + 1SD DU = Mean + 2SD

DU = Mean + 3SD DU = Mean + 4SD

Graph (d)The Marginal Effect of the Acquisition Ability:

The Moderating Effect of Unrelated Diversification

-.02

0.0

2.0

4.0

6

Mar

gina

l Eff

ect

0 .4 .8 1.2 1.6

Exploitation Ability

DU = Mean - 0.5SD DU = Mean DU

DU = Mean + 1SD DU = Mean + 2SD

DU = Mean + 3SD DU = Mean + 4SD

Graph (e)The Marginal Effect of the Exploitation Ability:

The Moderating Effect of Unrelated Diversification