Voola, R., & OCass, A. (2010). Implementing competitive strategie.docx

Voola, R., & O'Cass, A. (2010). Implementing competitive
strategies: The role of responsive and proactive market
orientations.European Journal of Marketing, 44(1/2), 245–266.
doi: 10.1108/03090561011008691 (ProQuest Document ID:
237032252)
Introduction
Organising marketing activities in ways that successfully enable
business strategy implementation is recognised as one of the
most difficult challenges facing managers [...] and researchers
know little about how marketing activities should be organised
to enable business strategy implementation or how this affects
performance ([85] Vorhies and Morgan, 2003, p. 100).
A fundamental objective of marketing strategy research is to
examine and better understand how firms develop and sustain
competitive advantage, and how this leads to better firm
performance outcomes ([45] McNaughton et al. , 2002).
However, as the above extract from [85] Vorhies and Morgan
(2003) indicates, implementation is a critical issue requiring
much more attention. With regard to competitive advantage and
firm performance, market orientation has been identified as a
key issue. As such, market orientation as a firm capability has
been studied extensively in the context of how firms develop
and sustain competitive advantages. However, there still exists
a research gap in understanding how market orientation fits in
the implementation of competitive strategies (e.g. [34]
Homburg et al. , 2004). In particular, because the relationship
between marketing and strategic management is axiomatic ([53]
Morgan and Strong, 1998) and the contribution of marketing to
corporate strategy is under-researched ([65] Olson et al. , 2005),
understanding the relationships between market orientation and
competitive strategies is crucial to understanding how market
orientation contributes to firm performance ([83] Vijande et al. ,

2005). Furthermore, [34] Homburg et al. (2004) argue that
although examining the role of market orientation in the
relationship between competitive strategies and firm
performance is important, understanding the role of market
orientation when a firm simultaneously attempts to achieve
differentiation and cost-leadership is vital.
Although previous studies have examined the relationships
among differentiation strategy, cost-leadership strategy, market
orientation and new product activity ([27] Frambach et al. ,
2003) and differentiation, market orientation and firm
performance ([34] Homburg et al. , 2004), existing research has
not simultaneously examined the relationships between
differentiation, cost-leadership competitive strategies,
responsive market orientation (RMO), proactive market
orientation (PMO) and firm performance. The issue of
simultaneity is particularly important because in most
circumstances, the constructs derive their essence from the
conceptual (nomological) network in which they are rooted.
Furthermore, in reality, a firm may choose one or adopt both
differentiation and cost-leadership competitive strategies ([32]
Hall, 1980). Although adopting both these strategies may seem
contradictory, because it may lead to conflicts in an
organisation's requirements, some research indicates that a
mixed strategy is not only feasible ([89] White, 1986) but
essential for organisational success ([12] Chan and Mauborgne,
2005). This argument is analogous to [30] Gibson and
Birkinshaw's (2004) notion of organisational ambidexterity, or
simultaneously achieving alignment and adaptability, and [4]
Atuahene-Gima's (2005) assertion that firms must
simultaneously implement exploitation and exploration
strategies.
For the most part, the existing literature examining market
orientation and competitive strategies has not conceptualised
market orientation within the notion of RMO and PMO. This is
important, as [57] Narver et al.(2004) argue that dominant
conceptualisations of market orientation ([40] Kohli and

Jaworski, 1990; [55] Narver and Slater, 1990) emphasise
responding to the expressed needs of the customers and
consequently do not include the proactive nature of market
orientation or understanding the latent needs of the customers.
This results in market-oriented firms ignoring or paying
insufficient attention to new markets and/or potential
competitors ([3] Argyris, 1994; [73] Slater and Narver, 1995).
Furthermore, [31] Grinstein (2008) highlights that few studies
have made the distinction between RMO and PMO, and that
research in the future should study these constructs, their
antecedents and consequences. Moreover, adding to [31]
Grinstein's (2008) view, [16] Coltman et al. (2008) indicate a
disparity between the focus on RMO versus PMO and contend
that given the growing evidence, industry and customer
foresight (PMO) are potentially the most important components
of market orientation. Importantly, as suggested earlier, the
primary criticism of the conventional treatment of market
orientation is that it has been conceptualised as responsive.
Critics of RMO argue that it hinders a firm's innovativeness ([9]
Berthon et al. , 1999), confuses a firm's processes ([46]
MacDonald, 1995), and results in narrow-minded research and
development activities ([28] Frosch, 1996). We respond to these
issues and the call for research by proposing that competitive
strategies are antecedents to RMO and PMO and firm
performance is a key consequence of RMO and PMO; we test
this contention using an empirical study. As such, we seek to
contribute to both the strategy implementation literature and the
market orientation literature by focusing on the strategy-
capability-performance connections.
This study is structured as follows. First, we present a
background discussion in relation to the strategy formulation
and strategy implementation perspectives. Within this
discourse, we adopt the RB theory to shed light on the strategy
formulation-strategy implementation perspectives. Second, in
line with the strategy implementation approach and the RB
theory, specifically focusing on market orientation as a key

variable in RB theory, we develop six hypotheses. Third, we
discuss the quantitative method and the findings. Last, we
highlight implications for research and practice and discuss
limitations and opportunities for extending this research.
Strategy formulation and strategy implementation
There has been a debate on whether organisational dimensions
affect competitive strategies (i.e. strategy formulation) or
whether competitive strategies affect organisational dimensions
(i.e. strategy implementation) (e.g. [59] Noble and Mokwa,
1999; [68] Prahalad and Bettis, 1986). Historically, the
emphasis of both marketing strategy scholars and practitioners
has been on strategy formulation, with strategy implementation
being viewed as a strategic afterthought ([59] Noble and
Mokwa, 1999). Furthermore, strategy formulation has been at
the core of strategic management for several decades (e.g. [51]
Mintzberg, 1973), and is an attempt to explain how effective
strategies are developed within a firm ([76] Slater et al. , 2006).
The strategy formulation approach views organisational
variables as antecedents to strategy on the basis of the argument
that managerial action is founded on underlying beliefs,
behaviours and cognitive maps such as dominant logics and
worldviews (e.g. [23] Foil and Huff, 1992; [34] Homburg et al. ,
2004).
The strategy implementation approach involves viewing strategy
as affecting organisational dimensions, or organisational
dimensions are adapted to strategy, which then results in higher
firm performance ([34] Homburg et al. , 2004). Although
traditionally organisational dimensions such as structure and
systems have been emphasised, with the advent of the RB
theory ([6] Barney, 1991; [18] Day, 1994), firm capabilities are
increasingly being viewed as organisational dimensions that
must be developed and deployed to implement a particular
competitive strategy effectively. Essentially, the RB theory
adopts an inside-out approach to strategy, taking the position
that internal firm factors explain more variance in firm
performance than do external industry-related factors, a view

that emphasises an outside-in approach to strategy ([6] Barney,
1991; [63] O'Cass and Ngo, 2007b; [87] Wernerfelt, 1984).
The importance of strategy and the significance of the strategy
debate can be seen in the argument over formulation versus
implementation, with various scholars such as [10] Bonoma
(1984), [13] Chebat (1999), [15] Chimhanzi (2004), [44]
McGuinness and Morgan (2005), [65] Olson et al. (2005) and
[72] Slater and Mohr (2006) raising the contention that
marketing scholars must align themselves with the strategy
implementation approach. Furthermore, the role of strategy and
its implementation can be seen in the work of [63] O'Cass and
Ngo (2007b), who show empirically that strategy drives market
orientation. Moreover, [7] Barney (2001) highlights the
importance of implementation and its connection with RB
theory. According to the original conception of RB theory,
competitive advantage results from a firm's unique capabilities
that are valuable, rare and inimitable ([6] Barney, 1991).
Therefore, market orientation positioned as a capability is
valuable because it enables firms to better serve their target
markets. However, a limitation of early RB theory is the lack of
attention to implementation issues, and as [7] Barney (2001)
notes, for simplicity's sake even he early on adopted the view
that "once a firm understands how to use its resources [...]
implementation follows, almost automatically" ([7] Barney,
2001, p. 53). This view perpetuated the notion that the actions
the firm should take to exploit these capabilities will be self-
evident and automatic.
However, the link between capabilities and actions may not be
obvious, and proper strategy implementation remains a major
challenge. For example, [7] Barney (2001) and [75] Slater and
Olson (2001) argue that there is a need for more research on
how marketing can facilitate the implementation of competitive
strategies. However, a key question relates to whether strategy
formulation or strategy implementation is more realistic to
apply, and it refers to the causality between strategy and
organisational dimensions. This study adopts a strategy

implementation approach based on the work of [34] Homburg et
al. (2004), who argue that it is more likely that strategy has a
stronger positive effect on structure than structure on strategy
([2] Amburgey and Dacin, 1994), whilst acknowledging that
strategy and structure affect each other. Furthermore, the notion
that competitive strategies influence market orientation is
consistent with the work of [66] Pelham and Wilson (1996) and
[27] Frambach et al. (2003). For example, [27] Frambach et
al. (2003) argue that behaviours inherent in market orientation,
such as responsiveness to information, are likely to be
influenced by strategic choices at the broader corporate level.
The theory development here extends [34] Homburg et al. 's
(2004) and [27] Frambach et al. 's (2003) work on the role of
market orientation in the competitive strategy-firm performance
relationship. Furthermore, by conceptualising and
operationalising market orientation as having two theoretical
components - specifically RMO as responsive and PMO as
proactive - it responds to the call for this distinction by many
researchers, including [57] Narver et al. (2004) and [31]
Grinstein (2008). The argument put forward here is that
differentiation and cost-leadership strategies drive RMO and
PMO, which then drive firm performance. The conceptual model
outlining the key constructs and hypothesised paths is presented
in Figure 1 [Figure omitted. See Article Image.]. This model
essentially shows:
- a direct positive effect for differentiation (H1 ) and cost-
leadership strategies (H2 ) on RMO;
- a direct positive effect for differentiation (H3 ) and cost-
leadership (H4 ) on PMO; and
- a positive effect for RMO (H5 ), and PMO (H6 ) on firm
performance.
Hypotheses development
Competitive strategies, responsive and proactive market
orientation
Responsive and proactive market orientation
To overcome the limitations of the responsive nature of the

market orientation conceptualisations, [56] Narveret al. (2000,
p. 7) highlight the concept of PMO and define it as "the attempt
to understand and satisfy customers' latent needs". The
development of this capability is based on [37] Jaworski and
Kohli's (1996) and [73] Slater and Narver's (1995) assertion that
there are two types of customers' needs:
expressed; and
latent.
They define expressed needs as those needs that the customers
are aware of and consequently can express; whereas latent
needs are needs that the customers are unaware of and reside in
the subconscious of the customers. The treatment of RMO and
PMO here as distinct constructs is consistent with empirical
research (i.e. [5] Atuahene-Gima et al. , 2005; [16] Coltman et
al. , 2008; [57] Narver et al. , 2004; [70] Saini and Johnson,
2005; [80] Tsai et al. , 2007) and [57] Narver et al. 's (2004)
argument that RMO and PMO are statistically related but
distinct constructs.
The effects of competitive strategies on RMO and PMO
The extent of market orientation in a firm must be congruent
with the competitive strategy adopted (e.g. [17] Conant et al. ,
1990; [86] Walker and Ruekert, 1987). In fact, the importance
of the match between business strategy and marketing strategy
has been empirically illustrated ([65] Olson et al. , 2005). The
argument that competitive strategies drive market orientation is
founded on the assertion that marketing activities are likely to
be influenced by strategic choices at the macro competitive
strategy level ([75] Slater and Olson, 2001). Furthermore,
literature suggests that top management leadership is critical in
developing market orientation ([18] Day, 1994; [55] Narver and
Slater, 1990). This point is alluded to by [79] Stoelhorst and
van Raaij (2004), who in the context of the RB theory, argue
that the role of the manager is to acquire, combine and deploy
appropriate capabilities. In this sense, such capabilities should
logically be deployed to implement the strategy of the firm
through its managers. We concur with [47] Makadok's (2001)

definition of a capability as an asset that cannot be observed
(therefore, intangible), cannot be valued and is traded only as
part of its entire unit. Typically, these capabilities are
idiosyncratic to the firm, have been built over time with heavy
reliance on tacit knowledge and skills, involve complex
interrelationships with other resources, and are by their nature
important factors in affecting firm performance outcomes ([35]
Hooley et al. , 2005). Therefore, we contend that if market
orientation is a firm capability ([5] Atuahene-Gima et al. ,
2005), then the strategy the firm develops and seeks to pursue
requires implementation. Thus, market orientation provides the
mechanism through which strategy is enacted through its
possession and appropriate deployment. This, we argue, is
fundamental, in that top management is critical for developing
strategies (i.e. differentiation and cost-leadership) and therefore
they will drive market orientation development and deployment
to implement the strategy. The connection between strategy and
market orientation is further identified by [74] Slater and
Narver (1996), who contend that successful execution of a
strategy is facilitated by market orientation. Importantly, [27]
Frambach et al. (2003) provide support for the notion that a
firm's differentiation strategy and/or cost-leadership strategy
will influence the extent of market orientation.
When a differentiation strategy leads to increased customer
benefits, it is consistent with market orientation's external focus
on customer needs as implied by RMO ([74] Slater and Narver,
1996). Furthermore, differentiation strategy is based on the
ability of a firm to balance product benefits and costs to the
customer ([75] Slater and Olson, 2001). Therefore, a
differentiation strategy affects market orientation, as it requires
an intimate examination of customer-related information.
Essentially, we argue that a firm adopting a differentiation
strategy will need to develop unique insights into customers
(RMO) for the differentiation strategy to result in higher
performance. On this issue, [34] Homburg et al. (2004) argue
that product differentiation strategy affects market orientation,

which they operationalise as RMO. Furthermore, various
empirical studies provide evidence that market orientation is
influenced by a firm pursuing a differentiation strategy ([27]
Frambach et al. , 2003; [34] Homburg et al. , 2004; [55] Narver
and Slater, 1990; [66] Pelham and Wilson, 1996; [74] Slater and
Narver, 1996). Thus we hypothesise the following:
H1. Differentiation strategy is positively related to responsive
market orientation.
Using internal efficiencies, market-oriented firms create
customer value by reducing customers' acquisition and usage
costs ([74] Slater and Narver, 1996). Firms adopting a cost-
leadership strategy must be market-oriented because they need
to understand the perceived customer value to decide whether
cost advantages should be transferred to their target market(s)
and to determine which aspects of the firm's marketing
activities can be reduced without affecting customers' perceived
value ([27] Frambach et al. , 2003). Furthermore, firms
emphasising activities that seek to understand customer needs
and satisfy those needs are more likely to manufacture products
that have lower defects, which then should result in lower costs
([66] Pelham and Wilson, 1996). Moreover, [27] Frambach et
al. (2003) found that cost-leadership strategy was positively
related to customer orientation (i.e. RMO). They contend that
understanding the customer is critical in deciding how to
decrease the marketing budget in the most cost-effective
manner. Therefore, market orientation plays an important role
in transmitting the benefits of a cost-leadership strategy to firm
performance. Several empirical studies provide evidence that
cost-leadership strategy influences market orientation (e.g. [27]
Frambach et al. , 2003; [74] Slater and Narver, 1996). Thus, we
hypothesise the following:
H2. Cost-leadership strategy is positively related to responsive
market orientation.
The existing literature relating to the competitive strategy-firm
performance relationship has conceptualised market orientation
primarily as a responsive construct. Some authors have alluded

to the proactive nature of market orientation in examining its
relationship to competitive strategies ([74] Slater and Narver,
1996; [81] Vazquez et al. , 2001). For example, [74] Slater and
Narver (1996, p. 163) conceptualise market orientation as
including both responsive and proactive elements, but
operationalise it primarily as a responsive construct. Here, we
argue that PMO is a critical mechanism by which the benefits of
differentiation strategy are transmitted to firm performance. The
contention raised here is that a differentiation competitive
strategy also affects PMO, on the basis of the notion that
marketing activities are likely to be influenced by strategic
choices ([75] Slater and Olson, 2001). Specifically, a
differentiation strategy affects PMO, because this strategy
drives a firm to develop not only an intimate understanding of
customers (i.e. RMO or unique insights) but also an
understanding of their customers' latent needs (i.e. PMO or
unique foresights). Furthermore, PMO is becoming more
important in the context of firm performance as more and more
firms develop the RMO capability. PMO inherently involves
exploratory learning that includes searching for a broader range
of information and knowledge that allows the firm to move
beyond the limitations of experience and engage in
experimentation ([5] Atuahene-Gima et al. , 2005).
Consequently, firms using PMO engage in scanning the markets
more widely ([18] Day, 1994) and working integrally with lead
customers ([42] Leonard-Barton, 1995), both of which provide
unique foresight into a firm's customers. Thus, we hypothesise
the following:
H3. Differentiation strategy is positively related to proactive
market orientation.
The essence of PMO is that it leads the firm beyond its existing
experience and encourages experimentation, through new
knowledge of markets that may be different from the firm's
existing market ([80] Tsai et al. , 2007). Proactively market-
oriented firms work closely with lead users and engage in
experiments, both of which are linked to innovative

developments ([43] Lilien et al. , 2002). Therefore, a cost-
leadership strategy will lead to higher levels of PMO because
proactively market-oriented firms experiment with new
technological developments that allow for increased internal
efficiencies. Furthermore, focusing on future customer needs
may also alert the firm to new market and technology
developments and increase its abilities to integrate new
developments into lowering costs. In addition, because it is
important that firms adopt both competitive strategies
concurrently, these new technologies enable the firm to
simultaneously engage in such activities as new product
development whilst emphasising cost-leadership. Thus, we
hypothesise the following:
H4. Cost-leadership strategy is positively related to proactive
market orientation.
RMO, PMO and firm performance
The key premise of the RB theory is that competitive advantage
lies in the heterogeneous firm-specific capabilities held by
firms ([52] Montgomery and Wernerfelt, 1988). Moreover, RB
theory contends that capabilities are the most important source
of an organisation's success ([18] Day, 1994). Firm capabilities
have gained widespread attention in recent years because they
are difficult to duplicate ([20] Dierickx and Cool, 1989).
Similarly to [5] Atuahene-Gima et al. (2005), we conceptualise
RMO and PMO as capabilities. Market orientation has been
linked to the organisational response to consumers' needs and
wants (e.g. [69] Ruekert, 1992), and has been argued to be a
source of competitive advantage that influences firm
performance (e.g. [36] Jaworski and Kohli, 1993; [55] Narver
and Slater, 1990). Although there is a debate regarding the
direct effects of market orientation on firm performance (e.g.
[48] Matear et al. , 2002), we adhere to the assertion that there
has been a broad consensus regarding the positive relationship
between market orientation and firm performance (e.g. [38]
Kirca et al. , 2005). For example, a meta-analysis conducted in
23 countries in five continents suggests that the relationship

between market orientation and firm performance is positive
and consistent ([11] Cano et al. , 2004). However, previous
conceptualisations have viewed market orientation primarily as
a responsive capability (RMO), attempting only to understand
customers' expressed needs and satisfying them (e.g. [40] Kohli
and Jaworski, 1990; [55] Narver and Slater, 1990).
Nevertheless, there is strong support for a positive relationship
between RMO and firm performance. Thus, we hypothesise the
following:
H5. Responsive market orientation is positively related to firm
performance.
Market orientation can be a source of competitive advantage
when it is rare in a firm's industry ([6] Barney, 1991). With the
widespread research on RMO and its relationship to firm
performance, firms are increasingly investing in being market-
oriented in the traditional notion of RMO. Consequently, as
RMO becomes common, in the long run, competitors can imitate
it ([56] Narver et al. , 2000). Therefore, [57] Narver et
al. (2004) argue that to develop and maintain a competitive
advantage, firms increasingly must complement RMO with
PMO. Furthermore, to understand and discover latent needs and
to respond with new solutions, proactively market-oriented
firms are more likely to scan the markets more widely than are
firms that focus on RMO ([18] Day, 1994) and work integrally
with lead customers ([42] Leonard-Barton, 1995). Owing to the
more sophisticated market-sensing and linking processes
involved in PMO, as opposed to RMO, we argue that firms
adopting PMO are more likely to understand not only the
expressed needs but the latent needs of the customers, allowing
firms, for example, to uncover new market opportunities and to
undertake market experiments to improve marketing strategies
([5] Atuahene-Gima et al. , 2005), all of which affect customer
value and firm performance. Thus, we hypothesise the
following:
H6. Proactive market orientation is positively related to firm
performance.

Method
Research design
The mail survey was comprised of pre-existing scales found in
the literature. RMO was measured using [19] Deshpandé et al. 's
(1993) measure; PMO by [57] Narver et al. 's (2004) measure
and differentiation and cost-leadership competitive strategies by
[27] Frambach et al. 's (2003) measures. Measures were
anchored with scale poles from 1=strongly disagree to
7=strongly agree. Firm performance was measured with [35]
Hooley et al. 's (2005) measure using sales volume, market
share, overall profit, profit margins and return on investment
compared with competitors. Anchors were 1=much worse than
competitors to 7=much better than competitors. Following [82]
Venkatraman and Ramanujam's (1986) two-step method of
pretesting, the survey was pretested with five marketing
academics and doctoral students in marketing and with 17
senior managers from the sample frame. The sample frame was
obtained from databases of Australian firms that are publicly
available on the internet. These websites provided public access
to the mailing addresses of more than 8,000 firms across
Australia. A systematic sampling process was used because of
its representativeness and ease of implementation. For example,
every fourth firm on the databases was chosen as the sample
unit for the mail survey to be sent.
For the final survey, 1,400 key informants in organisations
across various industries listed on a publicly available website
were mailed the surveys. Of the 1,400 surveys mailed, 189
useable surveys were returned, resulting in a response rate of
13.5 percent. This response rate is within the 10-20 per cent
range for top management survey response rates ([49]
Mennon et al. , 1996). The RB theory does not place any
significant emphasis on firm size because that measure is
primarily focused on resource-based rather than monopoly-
based advantages ([21] Fahy, 2002). Therefore, the population
of interest included all firms (SMEs)[1] , focusing on firms
having fewer than 200 employees. Furthermore, the industries

represented included software, construction, automotive,
engineering and mining. The majority of the firms were in the
services industry (63.6 per cent) and the remainder were in the
manufacturing industry (36.4 per cent). Moreover, 70 per cent
of the firms were in the business-to-business sector, whilst 24
per cent had both businesses and individual customers. The
firm-level variables in this study are strategic (i.e. firm
capabilities, competitive strategies) and as such senior
executives are appropriate informants, because they have the
greatest insight into the firm's strategic practices ([14] Child,
1997). A descriptive analysis suggested that 47 per cent were
managing directors, 24 per cent chief executive officers, 13 per
cent were owners and the remaining 16 per cent of the
respondents included general managers and marketing
managers. To verify the respondents' knowledge of the key
constructs, each survey instrument contained a self-report item
on the informant's knowledge and confidence of the area being
studied ([41] Kumar et al. , 1993). The final sample showed a
high mean score of 5.36 on a scale of 1-7, where 1=not
confident and 7=very confident.
Analysis procedures
The hypotheses were tested using partial least squares (PLS), a
multivariate, variance-based technique used for estimating path
models involving latent constructs indirectly observed by
multiple indicators. Moreover, because PLS focuses on the
explanation of variance using ordinary least squares, this
technique is better suited for investigating relationships in a
predictive rather than confirmatory fashion ([24] Fornell and
Bookstein, 1982). In this study, our primary concern is with
maximizing the prediction of dependent endogenous constructs
using exogenous constructs, including strategy to PMO-RMO
and PMO-RMO to firm performance. Because we used PLS, we
also avoided the necessity of a large sample size, as research
similar to this study has done as well. For example, [63] O'Cass
and Ngo (2007b) used a sample size of 180, which is
comparable to this study (189 respondents). Furthermore, PLS is

not sensitive to the assumptions of normality, thus
circumventing the necessity for the multivariate normal data.
Moreover, the preliminary analysis indicated that some items
had moderate to high levels of skew and kurtosis, indicating
that PLS was suitable procedure ([63] O'Cass and Ngo, 2007b).
Finally, PLS is increasingly being used in marketing literature
(e.g. [39] Klemz et al. , 2006; [62], [63] O'Cass and Ngo,
2007a, b; [77] Slotegraaf and Dickson, 2004)[2] to analyse data
to test theory in marketing strategy.
Also, because PLS is not based on distributional assumptions,
providing definite statistics tests are contrary to the soft
modelling philosophy, the evaluation of the model is as such not
based on any single statistical index, but uses several indices
([22] Falk and Miller, 1992). These indices are used and
assessed on the basis of their ability to explain the data
congruence with hypotheses and their precision. As a result, we
used several indices to assess the hypotheses (see [25] Fornell
and Cha, 1994): r2 , average variance explained (AVE), average
variance accounted (AVA), regression weights and loadings.
Furthermore, we also assessed important indices such as the
variance explained by the paths and critical ratios for the inner
model. Because the hypotheses are one-tailed, to reject a null
hypothesis at the 0.05 level the observed t -value should be
greater than 1.645 and 0.01 at 1.96 ([58] Ngo and O'Cass,
2008). Moreover, two sets of linear relations specify the model:
the outer model relationships between the latent and the
manifest variables and the inner model where the hypothesised
relationships ( H1 -H6) between the latent variables are
specified ([58] Ngo and O'Cass, 2008).
Measurement issues
Convergent and discriminant validity
Given that a single source of information can introduce spurious
relationships among the variables, and as this study collected
data via single source methods (self-report scales), the need to
test for common method variance was evident. This test was
conducted by adopting Harmon's one-factor test ([64] O'Cass

and Pecotich, 2005), in which all items, presumably measuring a
variety of different constructs, were subjected to a single factor
analysis. The results indicated that one factor was not present
(or a common factor underlying the data) and because the
majority of the variance was not accounted for by one general
factor, we determined that a substantial amount of common
method variance was not evident.
Assessing measurement validity is important. [26] Fornell and
Larcker (1981) argue that convergent validity is achieved if the
AVE in items by their respective constructs is greater than the
variance unexplained (i.e. AVE>0.50). Therefore, to assess the
constructs, convergent validity, the squared multiple
correlations from the factor analysis were used to calculate the
average variance explained. All factors had an AVE greater than
or equal to 0.50, therefore meeting the recommended criteria for
convergent validity. Table I [Figure omitted. See Article
Image.] presents the AVEs, composite reliabilities and
component loadings for each construct.
Having computed the composite measures, we next assessed
discriminant validity as recommended by [29] Gaski and Nevin
(1985) and [61] O'Cass (2002). First, the discriminant validity
is exhibited if the square root of the AVE is greater than all
corresponding correlations ([26] Fornell and Larcker, 1981). As
shown in Table II [Figure omitted. See Article Image.], these
values are consistently greater than the off-diagonal
correlations, suggesting discriminant validity. Second, [29]
Gaski and Nevin (1985) and [61] O'Cass (2002) suggest that
satisfactory discriminant validity among constructs is obtained
when the correlation between two constructs is not higher than
their respective reliability estimates. Table II [Figure omitted.
See Article Image.] demonstrates that no individual correlations
(which ranged from 0.72 to 0.18) are higher than their
respective reliabilities (which ranged from 0.94 to 0.85),
indicating satisfactory discriminant validity.
Hypotheses testing
Because the measurement models were satisfactory, we applied

PLS to test the hypothesised relationships depicted in H1 -H6 .
We used the bootstrapping procedure in PLS Graph to test the
significance of the regression coefficients. Table III [Figure
omitted. See Article Image.] presents the path coefficients,
variance due to path (recommended to be greater than
0.015), r2 values (recommended to be greater than 0.10) and
critical ratios (CR) (recommended to be greater than 1.645 for a
one-tailed test). Differentiation strategy positively affects RMO
(path=0.36, CR=3.55) and cost-leadership positively affects
RMO (path=0.22, CR=2.28). Hence, H1 and H2 are supported.
The findings also suggest that differentiation affects PMO
(path=0.41, CR=4.44) and cost-leadership positively affects
PMO (path=0.20, CR=2.77). Hence, H3 and H4 are supported.
Lastly, both RMO (path=0.20, CR=1.86) and PMO (path=0.40,
CR=4.01) positively affect performance[3] .
Hence, H5 and H6 are supported. Furthermore, all the r2 are
greater than 0.10 (RMO=0.22, PMO =25, and
performance=0.33) and the AVA is 0.26. In summary, the
findings suggest that all hypotheses are supported.
Indirect and direct effects
Additional or supplementary findings can be an important
component of research, as seen in [34] Homburg et al. (2004).
In their work, they undertook further analysis to investigate the
indirect effects of differentiation strategy on performance
through market orientation. We build on this philosophy by
exploring the effect of strategy on performance through market
orientation. Therefore, in the same vein as [34] Homburg et
al.(2004), we analysed the indirect effects of the competitive
strategies through RMO and PMO, which highlights the
intervening role of market orientation. This allows us to
understand the role of market orientation in the implementation
of competitive strategies - specifically, whether the indirect
effects of competitive strategies on the performance through
RMO and PMO are important when compared with the direct
effects of strategy on performance.
PLS is a well-established method for examining the direct and

indirect effects of several variables simultaneously. The indirect
effect is determined by understanding the product of a particular
variable on a second variable through its effect on a third
intervening or mediating variable ([1] Alwin and Hauser, 1975).
Furthermore, "the sum of the direct and indirect effect reflects
the total effects of the variable on the endogenous variable"
([60] O'Cass, 2001, p. 56). Table IV [Figure omitted. See
Article Image.] illustrates that the indirect effects are stronger
than the direct affects. The results show that, first, in the
context of RMO as an intervening variable, the effect of
differentiation on performance is 0.04, the indirect effect on
performance is .08 and the total effects are 0.12. Second, in the
context of RMO as an intervening variable, the effect of cost-
leadership on performance is 0.00, the indirect effect on
performance is 0.06 and the total effects are 0.06. In the context
of PMO as an intervening variable, the effect of differentiation
on performance is 0.04, the indirect effect on performance is
0.13 and the total effects are 0.17. Last, in the context of PMO
as an intervening variable, the effect of cost-leadership on
performance is 0.00, the indirect effect on performance is 0.06
and the total effect is 0.06. Collectively, these results highlight
the importance of indirect effects and the intervening role of
RMO and PMO between competitive strategies and
performance.
Furthermore, we applied [8] Baron and Kenny's (1986, p. 1177)
four-step method for identifying mediation to examine the
indirect effects and to corroborate the results of PLS. The
results support those obtained through PLS, showing that when
the independent variable differentiation and the mediator RMO
were regressed on firm performance, differentiation became
non-significant ( p >0.1), whilst RMO positively affected
performance (p<0.001). When the independent variable cost-
leadership and the mediator RMO were regressed on
performance, cost-leadership became non-significant (p >0.5)
whilst RMO positively affected performance (p<0.001). When
the independent variable differentiation and the mediator PMO

were regressed on performance, differentiation became non-
significant (p >0.5), whilst PMO positively affected
performance (p<0.001). Last, when the independent variable
differentiation and the mediator PMO were regressed on firm
performance, differentiation became non-significant (p >0.5)
whilst PMO positively affected performance (p<0.001).
Discussion and implications
This study is couched in the research addressing strategy
implementation, and it adds to this research domain by
conceptualising and testing the antecedents and consequences of
RMO and PMO. Particularly, by integrating the strategy
implementation approach and the RB theory, this study
examined the role of two key marketing capabilities, i.e. RMO
and PMO, in the competitive strategies-firm performance
relationship. Therefore, the current study extends the works of
[27] Frambach et al. (2003) and [34] Homburg et al. (2004), and
contributes to the strategy implementation literature by
examining the effects of competitive strategies on both PMO
and RMO. This model essentially shows:
- a direct link between differentiation (H1 ) and cost-leadership
strategies (H2 ) on RMO;
- a direct link between differentiation (H3 ) and cost-leadership
(H4 ) on PMO; and
- a direct link between RMO (H5 ) and PMO (H6 ) on
performance.
This study applied PLS to analyse complex relationships by
simultaneously examining both the direct and indirect effects.
The findings support the conceptual model. Differentiation and
low-cost strategies drive RMO and PMO, and RMO and PMO in
turn affect firm performance. Furthermore, the findings suggest
that the treatment of market orientation as RMO and PMO is
important, as they can fully capture the benefits of the
competitive strategies and, more importantly, act as critical
mechanisms for transmitting the benefits of competitive
strategies to performance.
The findings contribute in several ways to the market

orientation literature, specifically the emerging literature
related to RMO and PMO (i.e. [5] Atuahene-Gima et al. , 2005;
[16] Coltman et al. , 2008; [57] Narver et al. , 2004; [70] Saini
and Johnson, 2005). The study confirms the argument that RMO
and PMO are statistically related but theoretically distinct
constructs. Furthermore, it provides evidence that RMO and
PMO are capabilities that are important sources of competitive
advantage and uniquely contribute to performance ([5]
Atuahene-Gima et al. , 2005). Therefore, it is possible to pursue
RMO or PMO individually, with some success. Last, it validates
the claim that PMO should have higher impact on performance
than RMO, as [57] Narver et al.(2004) and [16] Coltman et
al. (2008) argue. For example, the relative importance of PMO
is evidenced by the higher coefficient between PMO and
performance (0.40) than the coefficient between RMO and
performance (0.20). This further validates the argument that
industry and customer foresight (PMO) may be the most
important component of market orientation ([16] Coltman et
al. , 2008) with empirical evidence. It is important, however,
that as the findings suggest that these capabilities together fully
intervene between competitive strategies and performance, for
optimal performance outcomes, firms must simultaneously
invest in and nurture both RMO and PMO when implementing
competitive strategies.
Extant literature that has related PMO to performance (i.e. [5]
Atuahene-Gima et al. , 2005; [57] Narver et al. , 2004) has only
related this capability to one specific performance outcome:
new product success. However, this study conceptualises
performance as comprising five items relating to market share
and financial performance and empirically finds a positive
relationship between PMO and performance. Although this
finding has been extensively confirmed in the context of RMO,
this study is amongst the first to empirically illustrate the
essential nature of PMO as it affects various important broader
performance outcomes.
The findings also show that differentiation strategy had a

stronger effect on RMO and PMO (as evidenced by the
coefficient on both RMO, 0.36, and PMO, 0.41), than did cost-
leadership strategy (on RMO, 0.22, and PMO, 0.20). This is
consistent with the argument that market-oriented firms are
inherently externally focused, emphasising understanding
customer needs (both expressed and latent) and attempting to
satisfy them better than their competitors ([55] Narver and
Slater, 1990; [74] Slater and Narver, 1996; [81] Vazquez et al. ,
2001). This does not discount the pursuit of cost-leadership
strategy, but it does show that differentiation is more likely to
be more successfully implemented with RMO and/or PMO.
Marketing scholars have called for adopting the strategy
implementation approach ([10] Bonoma, 1984; [13] Chebat,
1999). In the context of competitive strategies and RMO and
PMO, we contribute to the discussion by showing that the extent
of market orientation in a firm must be congruent with the
competitive strategy pursued (see also [17] Conant et al. , 1990)
to ensure successful implementation. This study contributes to
the strategy implementation literature by finding empirical
support for this approach. In fact, the findings strongly support
the strategy implementation approach, as the competitive
strategies only affect performance through RMO or PMO.
Essentially, the findings suggest that competitive strategies
create and shape RMO and PMO, which then result in increased
performance outcomes. Furthermore, the strategy
implementation literature is increasingly emphasising
organisational capabilities, as opposed to traditional
organisational dimensions such as organisational structure, as
key intervening dimensions between strategy and performance.
This study conceptualises RMO and PMO as capabilities, and
because the findings illustrate the fully intervening nature of
these capabilities, they give credence to the RB theory's claim
that capabilities are critical for strategy implementation.
Practical implications
There are several implications for practice. First, the empirical
findings present managers with an insight into the role of firm

capabilities in the competitive strategies-performance
relationships. It provides a viable path for building competitive
advantage. For example, little research exists on the
interrelations among competitive strategies, capabilities
(specifically RMO and PMO) and performance. The findings
suggest that these interrelationships collectively serve to
provide performance outcomes and therefore are important to
understand.
Second, strategy implementation is a valid route to
organisational performance. Specifically, the development of
RMO and PMO is essential for the effectiveness of competitive
strategies, suggesting that in their quest for competitive
advantage, managers must not only develop competitive
strategies but simultaneously develop capabilities that act as
key mediators. Therefore, an important message from the
evidence to managers is that having competitive strategies in
the absence of RMO and PMO is likely to be substantially less
effective in facilitating the firm's achievement of relevant
performance outcomes. Third, the findings suggest that
managers should emphasise strategy implementation over
strategy formulation; strategy implementation is more likely to
be effective because it is more operational than the intellectual
process underlying the strategy formulation approach ([34]
Homburg et al. , 2004).
Last, the manner in which market orientation is conceptualised
has implications for practice. Whereas market orientation has
traditionally been viewed as being responsive, PMO highlights
the importance of understanding latent needs. This approach
encourages organisations to adopt a holistic view of market
orientation that includes proactively understanding customers'
latent needs in examining the influence of competitive
strategies on performance.
Limitations and future research directions
Applying the strategy implementation approach, this study
examines two important capabilities as intervening variables.
However, other capabilities may play an important intervening

role. A firm's ability to create, disseminate and utilise
knowledge, or organisational learning , has been argued to be
important. For example, [71] Sinkula et al. (1997, p. 316)
suggest that "cultivating a learning culture may indeed become
one of the primary means to attain and maintain a competitive
advantage". Furthermore, because the business environment is
characterised by technological uncertainty, competitive
advantage depends on the firm's capability to adopt new
technologies in a strategic manner (e.g. [67] Porter, 2001).
Therefore, a firm's ability to understand and respond to new
technologies, or technological opportunism, is critical ([78]
Srinivasan et al. , 2002). Lastly, leadership is important in
implementing strategy. Future research could examine the role
of strategic leadership capability ([84] Voola et al. , 2004) in
the competitive strategies-performance relationships.
This study emphasised two competitive strategies:
differentiation and cost-leadership. There is scope for future
research to include focused competitive strategy and
simultaneously examine the concurrent effects of all three
competitive strategies on firm capabilities and performance.
Furthermore, another dominant conceptualisation of competitive
strategies is that of [50] Miles and Snow (1978) typology,
which has been extensively applied in marketing (e.g. [54]
Morgan et al. , 2003). Therefore, understanding the
relationships among [50] Miles and Snow's (1978) typology,
RMO, PMO and performance would provide an alternative
perspective and would be a fruitful extension to the strategy
implementation approach.
Conclusion
We argue that businesses strategies influence two key marketing
capabilities: RMO and PMO, which in turn influence
performance. The findings support this contention. These
arguments and findings contribute to the strategy
implementation and the RB theories, especially the market
orientation literature, by empirically showing that RMO and
PMO fully capture the benefits of the competitive strategies and

act as fundamental mechanisms for transmitting the benefits of
competitive strategies to performance. Therefore, this research
contributes to the identified need for work in the area by [85]
Vorhies and Morgan (2003) who contend that organising
marketing activities in ways that successfully enable business
strategy implementation is a difficult challenge facing
managers, and yet researchers know little about how marketing
activities should be organised to enable business strategy
implementation.
Footnote
1. Our focus on SMEs and the industries represented is
consistent with a number of studies found within the literature
that reported comparable sample size, and/or firm size focus
(SMEs), and/or industries represented within samples. Such
studies, like this study, did not specifically test for differences
across firm size or industry (see, for example, [63] O'Cass and
Ngo, 2007b; [27] Frambach et al. , 2003).
2. We followed similar procedures outlined in the literature by a
number of scholars who also studied various aspects of either
strategy or capability and performance issues, with similar
sampling procedures and sample sizes and all adopted PLS for
data analysis (see, for example, [63] O'Cass and Ngo, 2007b;
[77] Slotegraaf and Dickson, 2004; [88] White et al. , 2003).
3. Moreover, [33] He and Wong (2004) highlight the issue of
ambidexterity in the context of fit as matching and fit as
moderating. Utilising their procedures, our regression results
show that there is evidence for fit as moderating, but no
evidence for fit as matching. As such, there is evidence of
ambidexterity in firms pursing both RMO and PMO. We thank
one of the reviewers for highlighting these procedures.
Ting, C. (2010). Corporate competitive strategies in a

transitional manufacturing industry: An empirical
study.Management Decision, 48(6), 976–995. doi:
10.1108/00251741011053497 (ProQuest Document ID:
578010952)
Introduction
With the liberalization of international trade and financial
markets, an increasingly interconnected global economy has
been emerging ([19] Dicken, 2007). Nowadays, companies are
facing more radical changes than ever before to which they must
adapt to survive and prosper ([32] Gereffi, 2001). These
changes have been widely felt across many sectors of industry
and commerce, including the US textile industry (e.g. [4]
Anson et al., 2003; [42] Kilduff, 2005).
In the past two decades, the US textile industry has been
experiencing a fundamental transition similar to those unfolding
in many other US manufacturing sectors ([14] Chi, 2009). As
pillar of industrialization, it has been at the forefront of
globalization in terms of confronting international competition,
and has seen the emergence of large retail groups exercising
control over the product agenda while seeking out lowest cost
sources of supply ([33] Gereffi and Memedovic, 2003).
Competitive pressures have steadily escalated as a result of
continued international trade liberalization, including the phase-
out of textile and apparel quotas under the World Trade
Organization (WTO), the creation of the North American Free
Trade Area (NAFTA), and the growing number of US bilateral
preferential/free trade agreements (P/FTAs) ([2] Amponsah and
Boadu, 2002; [59] Taplin, 2003). Against this backdrop, the
industry as a whole has experienced a sharp downturn since
1997 ([42] Kilduff, 2005).
Table I [Figure omitted. See Article Image.] exhibits the US
mill fiber consumption by end-use destination in 1992, 1997,
2002, and 2007 respectively. The drastic contraction in apparel
and home textile productions since 1997 and the downward
trend in carpet production in recent years are evident while

fiber consumption in technical type products has remained much
more resilient. This situation has been further reinforced by a
wave of technological innovation over the last few years that
has advanced process and product technologies, and diversified
the numbers and applications of technical textile products ([12]
Chang and Kilduff, 2002). [15] Chi et al. (2005) estimated that
the value of technical textile shipments in the USA was around
$20 billion in 2002, accounting for some 33 percent of total
value of shipments by the US textile industry. The total
workforce in this sector of the industry increased slightly
between 1997 and 2007, reaching some 230 thousand in the
latter year ([64] United States Department of Labor, 2009). This
contrasts sharply with the apparent decline of overall textile and
apparel employment over the same time period. [58] Smith
(2001) indicated that the US technical textile sector has
established strong position in the domestic market and the rapid
growth of international markets creates even broader
opportunities for this sector. The definition and scope of
technical textiles are provided in the Appendix. As competition
continues to escalate across traditional textile manufacturing
sectors, many US apparel-related and household end-use yarn
and fabric manufacturers are seeking to switch over to technical
products to survive and grow ([12] Chang and Kilduff, 2002;
[14] Chi, 2009).
Given the bright future and growing importance of technical
textile sector within the US textile economy, there has been
very little empirically based research devoted to understanding
this critical sector. This is in part because much of the literature
focuses on aggregate trends in textiles and apparel (e.g., [38]
Hunter et al. , 2002; [42] Kilduff, 2005; [54] Rees and
Hathcote, 2004). It is also because technical textile sector was a
relatively small fraction of industry activity in the past and this
has perhaps led to an unconscious neglect ([14] Chi, 2009).
In an effort to fill this gap in the literature, as an exploratory
study, this research took a strategic approach to analyze how the
US technical textile manufacturing companies managed their

business operations and to determine whether there are
differences on competitive priorities between high performing
companies and low performing companies. By identifying the
differences, the high performers will be able to maintain and
further improve their competitiveness while the low performers
will be able to find the problems and adjust or redesign their
strategies. Competitive priority model consisting of four
constructs low cost, quality, delivery performance, and
flexibility, one of the most widely accepted operations strategy
frameworks, was utilized to construct the analysis. Primary data
was collected through a survey of senior executives in the US
technical textile companies. Using 202 eligible survey returns,
exploratory factor analysis (EFA) and confirmatory factor
analysis (CFA) within structural equation modeling (SEM) were
carried out to assess the model-to-data fit, unidimentionality,
reliability, and validity of the model.
The remainder of this article is organized as follows. The next
section reviews the relevant literature. Competitive priority
model is then introduced with the corresponding measures and
scales for each construct in the model. In the methodology
section, the survey subjects, data sets, and statistical methods
are described respectively. The results and discussion follow
thereafter. Next, the conclusions are drawn based on the
findings and the implications for both academic researchers and
industrial practitioners are presented. Finally, some limitations
of this study are addressed and some directions for future
research are offered.
Literature review
Over the last four decades, the acceptance and use of strategic
approaches to manage manufacturing organizations have
experienced a continued growth. Since [56] Skinner's (1969)
early work in the field, a common thread in operations strategy
research has been the need of companies for choosing among
and achieving one or multiple key capabilities ([67] Ward and
Duray, 2000). Consistent with the mainstream of literature, the
term competitive priorities has been broadly used to describe

companies' choice of these competitive capabilities (e.g., [16]
Chopra and Meindl, 2009; [37] Hayes and Wheelwright, 1984;
[68] Ward et al. , 1995; [67] Ward and Duray, 2000). There are
some other terms or classifications also proposed and/or used to
describe and explore these concepts. For instance,
manufacturing tasks was used by [56] Skinner (1969), [55]
Richardson et al. (1985), and [5] Berry et al. (1991). developed
a typology from a strategic perspective to categorize companies
into one of the four groups namely prospector, analyzer,
defender, and reactor.[46] Miles and Snow (1978) [1] Adam and
Swamidass (1989) proposed to use content and content
variables. [23] Ferdows and De Meyer (1990) labeled as
organizational priorities and generic capabilities. [29]
Fitzsimmons et al. (1991) named dimensions of competition. In
spite of the differences in terminology, there is a general
agreement in the literature that competitive priorities can be
expressed in terms of low cost, quality, delivery performance
(speed and reliability), and flexibility (e.g., [5] Berry et al. ,
1991; [13] Chen and Paulraj, 2004; [37] Hayes and
Wheelwright, 1984; [56] Skinner, 1969, [57] 1985; [67] Ward
and Duray, 2000). These four constructs collectively measure
the content of a company's competitive strategies ([68] Ward et
al. , 1995).
Although all manufacturers are concerned to some degree with
cost, most do not compete solely or even primarily on low cost .
Companies that emphasize cost as a competitive priority usually
focus on lowering production costs, improving productivity,
maximizing capacity utilization, and reducing inventories ([37]
Hayes and Wheelwright, 1984; [68] Ward et al. , 1995).
Engineering, marketing, manufacturing, and service functions
have often been described as possessing different definitions of
quality ([68] Ward et al. , 1995). Manufacturing's traditional
observance of quality control reflects a focus on the
conformance dimension of quality such as providing high
performance design, offer consistent and reliable quality, and
conformance to product design specification ([30] Flynn et al. ,

1990; [67] Ward and Duray, 2000).
Delivery performance comprises reliability and speed. Delivery
reliability is the ability to deliver according to a promised
schedule. Here the business unit may not have the least costly
nor the highest quality product but is able to compete on the
basis of reliably delivering products as promised ([30] Flynn et
al. , 1990). For some customers, only delivery reliability is not
good enough, delivery speed is also necessary to win the order.
Although the two dimensions are separable, long run success
requires that promises of speedy delivery be kept with a high
degree of reliability ([7] Boyer and Pagell, 2000; [30] Flynn et
al. , 1990; [67] Ward and Duray, 2000).
Flexibility in manufacturing companies has traditionally been
achieved at a high cost by using generic purpose machinery
instead of more efficient special purpose-built machinery and
by deploying more highly skilled workers than would otherwise
be needed ([68] Ward et al. , 1995; [69] Ward et al. , 1996).
Advanced manufacturing technologies, when properly
implemented, have reduced the cost of achieving flexibility ([7]
Boyer and Pagell, 2000).
[57] Skinner (1985) stressed that each of these four competitive
priorities must be given a weight by the company that reflects
the degree of emphasis required to achieve the overall goals at a
corporate level. The weights associated with each priority
provide a broad measure of what a manufacturer deems
important at a particular time.
The links between company competitive priorities and its
business performance were affirmed by [65] Vickery et
al. (1993). They found there is covariance relationship between
competitive priorities and production competence with business
performance. In an empirical study of Singaporean
manufacturing companies, [68] Ward et al. (1995) found that a
quality, delivery performance, and/or flexibility emphasis aimed
at building capabilities for product or service differentiation
while a cost emphasis is not. This is consistent with the
viewpoint of [53] Porter (1980). proposed that a company can

achieve profitability over its competitors in two fundamentally
different approaches to strategy[53] Porter (1980) -
differentiation or cost leadership. He views differentiation and
cost leadership as mutually exclusive strategies. Differentiation
strategy offers customers unique products or services that are
differentiated in such a way that customers are willing to pay a
price premium that exceeds the additional cost of the
differentiation. In contrast, cost leadership strategy aims to
provide an identical product or service at a lower cost. indicated
that a company pursuing both strategies simultaneously is stuck
in the middle, which almost guarantees low profitability.[53]
Porter (1980) [68] Ward et al. (1995) stressed there is no one
particular strategy that is applicable to all types of
circumstances. [70] Wardet al. (1998) further developed a more
comprehensive instrument for measuring competitive priorities.
[70] Ward et al. (1998) addressed several issues related to the
adequacy of measurement based on the data collected from 114
manufacturing plants in the USA They concluded that
competitive priorities have long served as a foundation for
strategy research, and that the choice of competitive priorities
impacts company business performance.
Complete and accurate measurement of a company's business
performance is still viewed as one of the challenges in
operations management research ([44] Lancioni et al. , 2000).
Typically, business performance is measured using financial
metrics. [39] Jahera and Lloyd (1992) proposed that return on
investment (ROI) is a valid performance measure for midsize
firms. [68] Ward et al. (1995) used self-reported changes in
profit before tax to measure firms' performance. [50] Morash et
al. (1996) measured firm performance relative to competitors
using return on asset (ROA), ROI, return on sales (ROS), ROI
growth, ROS growth, and sales growth. [21] Duray et al. (2000)
measured firm performance using the respondent's perception of
performance in relation to competitors. The measures used were
ROI, ROS, market share, growth in ROI, growth in ROS, growth
in market share, and growth in sales. [60] Stock et al. (2000)

indicated that financial perspective measures such as market
share, ROI, and sales growth is more likely to reflect the
performance assessment of a company.
Conceptual model and survey instrument development
Competitive priority model provides the theoretical foundation
for this study, as shown in Figure 1 [Figure omitted. See Article
Image.]. The model consists of four latent constructs - low cost,
quality, delivery performance, and flexibility. Each of these
four latent constructs is captured by multiple measures in a
survey instrument. The use of such multi-item constructs
increases the ability to draw finer distinctions among
respondents over the use of single item ([30] Flynn et al. ,
1990). The five-point Likert scales employed in this study
provide a relative assessment on a continuum and are commonly
used for collecting primary data for empirical research in
operations management, and more generally in management
research ([70] Ward et al. , 1998). Respondents answered all
questions with respect to a particular product line in their
companies. The product line contributed the most sales value in
dollar terms for their companies. The measures for each latent
construct in the survey instrument are also illustrated in Figure
1 [Figure omitted. See Article Image.].
The measures for four latent constructs were developed based
on previous empirical literature ([6] Boyer, 1998; [47] Miller
and Vollmann, 1984; [67] Ward and Duray, 2000). The five-
point Likert scales for each measure are 1=No emphasis,
2=Little emphasis, 3=Moderate emphasis, 4=Strong emphasis,
and 5=Extreme emphasis.
In addition, in order to reveal the differences in competitive
priority between high performing companies and low
performing companies, based on prior research, in this study,
business performance is measured using the respondent's
perception of performance in relation to competitors. The
measures are comprised of market share, sales growth, profit
margin, ROI, and ROA. The five-point Likert scales for each
measure are 1=Significantly lower, 2=Lower, 3=Approximately

equal, 4=Higher, and 5=Significantly higher. The developed
survey instrument was first examined by academic and
industrial experts. These provide the proof of content validity of
the measures ([62] Swink et al. , 2005).
Methodology
Subjects
The US technical textile manufacturing companies were the
research subjects. Although the US technical textile sector has
proven less vulnerable than the apparel-related textile sector to
global competition, it nevertheless has been confronting
growing pressure from competitors in both developing and
industrialized countries. In this sense, the sector is an epitome
of the entire US manufacturing industry.
A sample of subjects was taken using the mailing list provided
by the Industrial Fabrics Association International (IFAI). IFAI
is a US based nonprofit trade association whose more than
2,000 members represent the majority of US technical textile
companies. The Industrial Fabrics Foundation (IFF), a
charitable organization associated with the IFAI, provided
financial support and survey cooperation. The subjects targeted
all occupied high-ranking management positions with an
overview of the company's business operations to ensure they
had knowledge of the issues the survey addressed.
Data collection
The developed survey instrument was pre-tested through five
on-site interviews with senior executives of technical textile
companies. The instrument was thus refined with regard to
content, arrangement, wording accuracy, and relevance. This
procedure helped make the final survey instrument more valid
and clearer. A postal mail survey was selected as the principal
method of data collection. The survey package was sent to a
sample of 995 US technical textile companies. To improve the
response rate, the targeted respondents each received a follow-
up email written by the IFAI president four weeks after the
initial postal mailing. This email was constructed to solicit
those people who did not respond to the postal mail survey and

invited them to return the questionnaire or, if they preferred, to
complete the survey online. The web-based questionnaire was
identical to the postal mail instrument.
Among the 995 mailed surveys, six were returned owing to
incorrect contact information. The adjusted survey sample size
was therefore 989. After eight weeks, 207 responses were
received, of which 95 were from the postal mail survey and 112
were from the follow-up web survey. Some 202 out of 207
returns were eligible and complete responses. The adjusted
response rate was 20.4 percent (202/989), which was very
satisfactory compared to the response rates in previous
empirical studies (e.g., [63] Tracey and Tan, 2001, 9 percent;
[66] Vonderembse and Tracey, 1999, 13.4 percent), particularly
in light of the difficult conditions prevailing in the US textile
industry. For an industry survey, [20] Dillman (2000) indicated
that there is no generally accepted minimum response rate and it
really depends on the survey topics and industries chosen.
Table II [Figure omitted. See Article Image.] shows the profile
of survey respondents. It covers a broad diversity of businesses
in technical textile sector. Among the respondents, 52 percent
were owner/president/CEO, 15.5 percent were vice presidents,
and the remainders were general managers or other positions.
This indicates that most respondents were high-ranking
executives and had the knowledge to provide relatively accurate
answers to the survey questions.
Statistical methods
Non-response bias testing
Non-response bias was evaluated using the t -test on
demographic variables. As a convention, the responses of early
and late groups of returned surveys were compared to provide
support of non-response bias ([43] Lambert and Harrington,
1990).
Factor analysis
[22] Fabrigar et al. (1999) recommended using exploratory
factor analysis (EFA) to identify measurement models and
confirmatory factor analysis (CFA) to test the full model. In

this study, the four measurement models are the latent
constructs of low cost, quality, delivery performance, and
flexibility. The full model is a second-order CFA model for
competitive priority. The four first-order constructs are
collectively represented by a second-order construct.
EFA with varimax rotation method was utilized to reduce
attribute space from a larger number of measures to a smaller
number of factors. SPSS software was employed in the EFA
analysis. The extraction criterion was set as eigenvalue above
one. The measures with low factor loadings (<0.50), high cross-
loadings (>0.40), and item-to-total correlations (<0.30) ([17]
Comrey, 1973; [40] Janda et al. , 2002) were excluded from the
factor matrices. The deduction of certain measures required the
recomputation of factor loadings, coefficient alpha, and item-to-
total correlations and a reexamination of factor structure using
the reduced number of measures. This iterative procedure was
repeated until all requirements were met.
CFA represents a special case of structural equation modeling
(SEM) ([10] Byrne, 2005). The primary goal of testing CFA
models is to determine the goodness of fit between the proposed
model and the sample data. The full model was tested by CFA
using LISREL involving three levels: measures, first-order
latent constructs (low cost, quality, delivery performance, and
flexibility), and a second-order latent construct (competitive
priority).
Assessment criteria
Model-to-data fit
Goodness-of-fit indices are used to assess the model-to-data fit,
which is the extent to which the data matches the proposed
model. There are many goodness-of-fit indices and no single
test best describes the model-to-data fit. In this study, the
indices adopted for the model-to-data fit assessment included
Normed Chi-square (χ2 ), the root mean squared error
approximation (RMSEA), goodness-of-fit index (GFI), the
Normed Fit Index (NFI), the Non-Normed Fit Index (NNFI), and
the comparative Fit Index (CFI).

A Normed Chi-square (χ2 ) less than 2 indicates no significant
difference between the observed and estimated covariance
matrices. The RMSEA measures the discrepancy between the
observed and estimated covariance matrices per degree of
freedom. ([49] Maruyama, 1998) The lower the RMSEA value,
the better the fit between the model (predicted data) and the
actual data. Values less than 0.08 are deemed acceptable. The
value of GFI should be larger than 0.9. ([9] Byrne, 1998) The
NFI compares the fit between the proposed model and nested
baseline or null model. An index score of 0.90 or higher are
acceptable threshold for the NFI. The NNFI also compares the
fit between the proposed model and the null model. It also
measures parsimony by evaluating the degree of freedom from
the proposed model to the degree of freedom of the null model
([48] Marsh et al. , 1988). The NNFI is highly recommended
because of its resilience against variations in sample size. An
index score of 0.90 or higher is acceptable for the NNFI. The
CFI measures how the proposed model compares with other
possible models with the same data ([49] Maruyama, 1998). An
index score of 0.90 or higher is acceptable for the CFI ([34]
Hair et al. , 1995).
Unidimensionality, reliability, and construct validity
The measurement properties of the constructs in the model were
assessed by the following criteria: unidimensionality,
reliability, and construct validity. These criteria have been
widely utilized by previous empirical studies (e.g., [13] Chen
and Paulraj, 2004; [68] Ward et al. , 1995). [13] Chen and
Paulraj (2004) noted that these represent a three-stage
continuous improvement cycle lying at the heart of the
instrumentation.
Unidimensionality has been described succinctly by [36] Hattie
(1985) as a set of variables forming a latent construct that all
measure just one thing in common. This is a most critical and
basic assumption for measurement theory. [45] Levine (2005)
further indicated that unidimensionality is a prerequisite to
meaningfully interpret the reliability of a measurement. In order

to prove unidimensionality, [68] Ward et al.(1995) suggested
that the [11] Carmines and Zeller (1979) criteria should be met:
the first indicator should explain a large proportion of the
variance in the constructs (i.e. > 40 percent);
subsequent indicators should explain fairly equal proportions of
the remaining variance, except for a gradual decrease;
all or most of the constructs should have sizeable loadings on
the first indicator (i.e. > 0.3); and
all or most of the constructs should have higher loadings on the
first indicator than on the subsequent indicators.
Also, the achievement of the model-to-data fit demonstrates
sufficient internal consistency.
After all measures show unidimensionality, their reliability is
then tested. Reliability is the consistency of a set of
measurement variables in a latent construct. Cronbach's
coefficient alpha and the construct reliability for each latent
construct are calculated respectively to compare to criterion
value. A Cronbach's coefficient alpha of 0.70 and above
suggests adequate reliability ([51] Nunnally, 1978) while
construct reliability values of greater than 0.50 indicate
adequate reliability ([31] Fornell and Larcker, 1981).
Construct validity consists of convergent validity and
discriminant validity. All of the measurement loadings are
significantly high and all of the goodness of fit indices met
recommended values to suggest convergent validity. An
additional indication of convergent validity was the average
variance extracted (AVE), which is the percentage of the total
variance of a measure represented or extracted by the variance
due to the construct, as opposed to being due to error ([31]
Fornell and Larcker, 1981). The desired threshold AVE score is
above 0.5. Discriminant validity is shown by the confidence
interval of 2 standard errors around the correlation between
each respective pair of constructs in the model. If the
confidence interval does not include 1.0, discriminant validity
is then demonstrated ([3] Anderson and Gerbing, 1988).
Results

As the measures for business performance showed
unidimensionality, a single set of composite scores of these
measures were used to represent the construct ([68] Ward et al. ,
1995). The 202 responses were sorted in descending order in
terms of their mean scores from the five business performance
measures. The first half of the responses were designated as
relatively high performers and the second half were designated
as relatively low performers. [35] Hambrick (1984) indicated
that dividing the sample into separate high and low performance
sub-samples in this manner is a practical analytical technique
for strategy research. This method has been successfully applied
in various prior studies (e.g. [35] Hambrick, 1984; [68] Ward et
al. , 1995; [67] Ward and Duray, 2000).
The iterative procedure of data analysis was repeated for both
the relatively high performers sub-sample and the relatively low
performers sub-sample and resulted in 14 final measures in both
sub-samples. Achieve/maintain lowest inventory in low cost
construct and make rapid design changes in flexibility construct
were dropped due to low factor loading.
Non-response bias testing results
The non-response bias testing shows there are no significant
differences between early and late groups of returned surveys.
Results of model-to-data fit, unidimensionality, reliability, and
construct validity
Table III [Figure omitted. See Article Image.] summarizes the
final results from factor analysis. The results suggest that all
four measurement constructs for both high performers sub-
sample and low performers sub-sample met the
unidimensionality criterion of [11] Carmines and Zeller (1979).
The measures capture four distinct dimensions and the
individual measures contribute to the expected construct. The
eigenvalues for each factor are relatively large, from 1.453 to
4.327 for high performers sub-sample and from 1.573 to 3.293
for low performers sub-sample. The four constructs
cumulatively account for 69.8 percent of the variance in
competitive priority for high performers sub-sample and 67.4

percent for low performers sub-sample. They are very
satisfactory. Cronbach's coefficient alphas and construct
reliability scores all are above 0.70 for both sub-samples, the
evidence of reliability is then established for both sub-samples.
Table IV [Figure omitted. See Article Image.] exhibits the AVE
scores of all four constructs for both sub-samples. All of the
AVE scores are above the desired threshold of 0.5 ([3]
Anderson and Gerbing, 1988), which indicates the criterion of
convergent validity is met.
Table V [Figure omitted. See Article Image.] shows none of the
confidence intervals (of 2 standard errors around the correlation
between each respective pair of factors in the model) capture
1.0. Therefore, the criteria of discriminant validity are met for
both sub-samples.
Table VI [Figure omitted. See Article Image.] summarizes the
goodness of fit indices of all four constructs for both sub-
samples. The results show all constructs meet the model-to-data
fit requirements.
Results of the second-order CFA model
Figure 2 [Figure omitted. See Article Image.] illustrates the
second-order CFA models for high performers and low
performers respectively, including the standardized factor
loadings and corresponding t -values. The final CFA model
showed an excellent fit to the collected data. The four
constructs designed to measure competitive priority, low cost,
quality, deliver performance, and flexibility, all exhibited high
and significant factor loadings.
Discussion
Competitive priority model was rigorously tested using
collected survey data from the US technical textile industry.
The self-perception answers from senior executives were relied
on in this study. The competitive priorities embraced by senior
executives are crucial and affect many other decision-making
processes such as supply chain arrangement ([52] Pagell and
Krause, 2004). For example, if manager perceives the company
mainly competes on low cost, its supply chain arrangement

might be lean oriented rather than be agile focused in order to
maximize profit through minimizing cost in each operations
stage ([28] Fisher, 1997). Thus, many researchers have argued
that the use of perceptual measures of competitive priority
permits a stronger test of the relationships between strategy
orientation and other key corporate decisions ([68] Ward et al. ,
1995). The establishment of unidimensionality, reliability, and
validity of constructs and the model-to-data fit make perceptual
measures viable and dependable in large-sample empirical
studies. It is consistent with the prior research (e.g., [13] Chen
and Paulraj, 2004; [41] Ketokivi and Schroeder, 2004)
The results of factor analysis show that there are distinctions on
emphasis of competitive capabilities between high performers
and low performers. For higher performers, quality contributes
the most in the variance of competitive priority at 24.5 percent,
followed by delivery performance at 17.1 percent, low cost at
14.8 percent, and flexibility at 13.4 percent. This indicates that
higher performers consider quality and delivery performance as
the most important competitive capabilities although low cost
and flexibility are also given certain emphasis. According to
[68] Ward et al. (1995), such strategic approach aims at
building capabilities for product or service differentiation. In
contrast, for low performers, low cost contributes the most in
the variance of competitive priority at 19.2 percent, followed by
quality at 18.3 percent, delivery performance at 15.5 percent,
and flexibility at 14.4 percent. This reveals that low performers
grant very close weights to all four types of competitive
capabilities although the emphasis on low cost and quality is a
little greater than delivery performance and flexibility.
According to [57] Skinner (1985), each of these four
competitive priorities must be given different weights by the
company in order to achieve the overall corporate goals. Equal
emphasis means no emphasis.
Nowadays, dynamism is the most prominent environmental
characteristic facing the US technical textile sector ([14] Chi,
2009). The companies are confronting increasing uncertainty in

domestic and international markets. There are rapid and
discontinuous changes in supply, demand, competitors,
technology, and regulations/rules ([12] Chang and Kilduff,
2002). In this environment, the large scale, mass production
model that brought the industry great prosperity in the past has
been no longer ensured future competitiveness ([8] Bruce et al. ,
2004). Market needs have become more changeable and
fragmented. These explain why differentiation strategies,
including quality and delivery service were emphasized more by
the high performing companies in the US technical textile sector
over low cost strategy. In contrast, the lack of clear emphasis on
strategies could be one of the reasons resulting in a relatively
low business performance.
Conclusions and implications
The US textile industry is undergoing a radical transition from
traditional labor-intensive sectors such as apparel-related
textiles and home textiles to more technology- and capital-
intensive sectors such as technical textiles. This study
represents the first empirical investigation into corporate
strategy issues in the US technical textile sector. The adequacy
of the measurements and validity of the model are rigorously
addressed. The confirmation process followed the typical
standards of measure and scale development in management
research ([9] Byrne, 1998; [13] Chen and Paulraj, 2004). The
results of this study are offered as an effort in a process of
continued advancement in the understanding of corporate
competitive strategies.
Overall, this study contributes to the literature in four ways.
First, based on previous theoretical and empirical research, it
develops a survey instrument for effectively measuring
corporate competitive strategies in four distinct constructs - low
cost, quality, delivery performance, and flexibility. Second,
using the primary data from an industry survey, it statistically
assesses the unidimensionality, reliability, validity, and model-
to-data fit of competitive priority model and proves the model is
valid and the survey instrument can generate reliable data.

Moreover, these four constructs can capture most of the
variance in competitive priority. The influences of each
construct on the variance of competitive priority are also
determined. Third, the differences on emphasis of competitive
capabilities between high performing companies and low
performing companies are quantitatively identified. Finally, the
statistical analysis reveals the possible cause in terms of
strategic approach for low performing companies. As previous
studies indicated (e.g. [53] Porter, 1980; [61] Swamidass and
Newell, 1987; [68] Ward et al. , 1995), differentiation that is
embraced by high performing companies is an appropriate
strategy in an increasingly complex, dynamic, and hostile
environment.
This study also imparts several implications. For academia, this
study provides a springboard for future studies of corporate
competitive strategies and its relationships with other key
decisions (such as supply chain arrangement) and outcomes
(such as business performance). Although the measures and
scales were tested in the US technical textile sector, the
methodology may, therefore, be transferred to other industries
and to other market sectors. In addition, this study substantiates
that an effective survey strategy can lead to higher response
rates. First of all, cooperation with the industry trade
association, IFAI was vital in providing privileged access to
member companies through access to the association's database
and, more importantly, a personal communication from the
association president. A second factor was perhaps that senior
executives perceived the content of the study as an important
issue. A third factor was perhaps the use of a mixed-mode
survey method, which included a postal mailing with a follow-
up email that provided an online version of the questionnaire.
As [20] Dillman (2000) indicated that a mixed-mode survey
may be the only alternative for immediately gaining access to
all members in the survey sample.
For industrial practitioners, as they continue to experience
intensifying international competition, shifting market needs,

and constant technological innovations, the business
environment is likely to become even more dynamic, complex,
diverse and hostile. Under such turbulent conditions, the
configuration and deployment of effective strategies and other
organizational arrangements is imperative to achieve superior
business performance, and perhaps, even just to survive. To be
effective, it is essential for senior executives to understand the
characteristics of their environment so they can choose
appropriate competitive capabilities accordingly. Companies
also need to constantly monitor their environment for shifts so
they can make timely adjustment.
Limitations and future studies
This study overcame some limitations of previous research by
using a well-developed survey instrument, an effective
industrial survey strategy, and the rigorous application of EFA
and CFA techniques for data analysis. However, there are still
several limitations that need to be addressed and also can be
considered as possible directions for future studies.
First of all, one of most obvious limitations is about time
constraint. This research provides a measure of what a
manufacturer deems important at a particular time. With the
changes of business environmental characteristics, it is worth
conducting follow-up studies in the future. Second, as an
exploratory study, this research is dedicated to understanding
strategic emphasis in a transitional industrial sector. In future
studies, the relationships between corporate competitive
strategies and other key arrangements such as supply chain
management can be examined. A decision making model can be
developed accordingly. Finally, although four constructs - low
cost, quality, delivery performance and flexibility can capture
most of the variance in corporate competitive strategies, some
underlying factors that contribute to the unexplained variance in
the model can be identified in the future.

Lahovnik, M. (2011). Corporate strategies in the post-transition
economy: The case of Slovenian companies.Journal of Applied
Business Research, 27(1), 61–68. (ProQuest Document ID:
849563355)
This paper argues that unrelated diversification strategies
outperform related diversification strategies. The author
identifies three phases of the internationalisation process. More
detailed analyses of the internationalisation process shows that
companies are trying to develop more complex forms of
international business activities. The author also identifies four
groups of competencies that are the cornerstones of corporate
strategies. This study reveals that 40.6% of companies
diversified through external means, 36.2% diversified through
internal means, while 23.2% diversified through both internal
and external methods. There appears to be no statistically
significant performance differences among companies regarding
external and internal growth strategies. Internal growth and
joint ventures are the most important forms of diversification.
These companies also tend to develop various forms of long-
term strategic cooperation. This process can be crucial for
developing competitive advantages. By comparing the
performance of companies regarding ownership structure, the
author found that companies with international ownership
structure performed better. In other words, foreign ownership
had a positive influence on company performance.
Keywords: Slovenia; corporate strategy; diversification strategy
INTRODUCTION
This PaPer deals with some factors determining performance of
corpegies in the PostTransition economy in Slovenia. The
article proceeds as follows. The next section deals shortly with
some important theoretical issues. The third section presents the
characteristics of the Slovenia's business environment. The
empirical results of this study are presented in the fourth
section. In conclusion the author discusses the results and some

implications for managers. Slovenia is a small open economy. It
has become a member of the European Union in 2004 and a
member of OECD in 2010. Among those transition economies
that entered the new larger Europe, the Slovenian economy is
the most developed, with a GDP of approx. 90% of average
GDP in the European Union and therefore bigger than in some
older EU member states like Portugal. Slovenia has become a
'benchmark' for other post-transitional economies in the region
due to its successful transition process. Not only it is a member
of EU and OECD on one hand but, as a former Yugoslav
republic, it offers a starting point for strategic investors in the
region of South-eastern Europe on the other.
The principal concern of corporate strategy is to identify die
business areas in which a company should participate in order
to maximise its long-run profitability (Hill, Jones, 1998). To
create value, a corporate strategy should enable a company or
its business units to perform one or more of the value creation
functions at a lower cost or perform one or more of the value
creation functions in a way that allows differentiation. Thus, a
company's corporate strategy should help in the process of
establishing a distinctive competency and competitive
advantage at the business level. It is a link that many companies
appear to have lost of sight of.
The empirical research was based on a fully-structured
interview that was prepared with pre-coded responses. A firm
had to have specific characteristics to fall within the research
sample:
* it should have had at least 250 employees;
* it should have had at least USD 5 million in annual income;
and
* it should have been a j oint-stock company.
The author mailed the questionnaire to 185 companies in
Slovenia. Sixty-nine companies responded which gives us 37.3
percent respond rate. The responses of the top managers were
recorded on a standardised Likert scale.
The author compared the performance of the various corporate

strategies by using four different criteria: ROA (return on
assets), ROE (return on equity), ROS (return on sales) and value
added per employee and formulated three basic research
hypotheses:
* H^sub 0^: There are no performance differences between
specific types of corporate strategies.
external and internal growth strategies.
companies regarding the ownership structure.
The author defined three basic criteria to determine whether two
businesses are related or not. In order for one business to be
related to another and to consider diversification as related, at
least two of the following three criteria had to be fulfiUed: (1)
similar type of markets served, (2) similar type of products sold
and, (3) similar technology used in production.
THEORETICAL BACKGROUND
A fundamental part of any firm's corporate strategy is its choice
of what portfoho of businesses it is to compete in. There are two
main types of diversification: related diversification and
unrelated diversification. Related diversification is
diversification in a new business activity that is linked to a
company's existing business activity. In most cases, these
linkages are based on manufacturing, marketing or
technological synergies. The diversified company can create
value in three main ways. First, by acquiring and restructuring
poorly run enterprises. Second, by transferring competencies
among businesses. Third, by realising economies of scope Table
1 lists the sources of value and costs for each strategy.
Scholars have analysed the performance of related vs. unrelated
diversification strategies. The empirical evidence on this issue
is however mixed. The author can identify at least three
different groups of authors with contradicting results.
According to the lion's share of the academic literature the
diversification strategy should reflect the superiority of related
diversification over unrelated diversification (Singh,

Montgomery, 1987; Rumelt, 1974). This first group of scholars
found that well-managed organisations had used a »sticking to
the knitting« strategy (Collis, Montgomery, 1998). Another
group of scholars argues that performance differences depend
on the characteristics of the markets in which firms operate
rather than on the strategic relationship between existing and
new businesses (Lecraw, 1984; Bettis, Hall, 1982). However,
the third group of scholars found that unrelated diversification
performs better than related version (Chatterjee, 1986; Little,
1984).
On the other hand, some scholars suggest that the traditional
ways of measuring relatedness between two businesses is
incomplete because it ignores the strategic importance and
similarity of the underlying assets residing in these businesses
(Markides, Williamson, 1994). Researchers have traditionally
regarded relatedness as being limited primarily because it has
tended to equate the benefits of relatedness with the static
exploitation of economies of scope, thus ignoring the main
contribution of related diversification to long-run competitive
advantage. This is the potential of a firm to expand its stock of
strategic assets and create new ones more rapidly at a lower cost
than its rivals which are not diversified across related
businesses.
According to the Porter study of 33 prestigious US companies,
each company entered an average of 80 new industries and 27
new fields. Just over 70% of the new entries were acquisitions,
22% were start-ups, and only 8% were joint ventures (Porter,
1987). Entry into new product-markets, which represents
diversification for the existing firm, may provide an important
source of future growth and profitability. In his study Porter
identified four concepts of corporate strategy that have been put
into practice: portfolio management, restracturing, transferring
skills and sharing activities. The concepts are not always
mutually exclusive, but each rests on a different mechanism by
which the corporation creates shareholder value and each
requires the diversified company to manage and organise itself

in a different way. The first two require no connections among
business units, the second two depend on them.
Research of 358 executives over a 45-year period revealed
growth to be the most frequently used corporate strategy (Hill,
Jones, 1998). This strategy has been used six times more often
than stability and seven times more often than retrenchment.
Growth strategies are extremely popular because most
executives tend to equate growth with success (Wheelen,
Hunger, 1999). Corporations in the dynamic environment must
grow in order to survive. Growth is a very seductive strategy for
at least three key reasons:
* To exploit economies of scale, as well as the effect of the
experience curve.
* A growing firm can cover up mistakes and inefficiencies more
easily than a stable one. Larger firms also have more clout and
are more likely to receive support in the case of impending
bankruptcy.
* Growth, per se, is exciting and ego-enhancing for CEOs. A
growing corporation tends to be seen as a winner.
The factors that determine why and how one business
outperforms another have been the subject of considerable
research. In general, the debate has centred on competitive
positioning, resource- or competence-based theory and
knowledge-based approaches. The first of these approaches, the
subject of Porter's work, concentrates on developing a strategic
framework by viewing a firm in the context of its environment
(Porter, 1985). The second sees superior performance as a
consequence of the special resources of an individual
organisation (Grant, 1991). This approach is called the
resource-based theory. The third approach is based on core
competencies that can be defined as a combination of resources
and capabilities that are unique to a specific organisation and
which are responsible for generating its competitive advantage
(Prahalad, Hamel, 1990). The knowledge-based theory is the
fourth approach focused on the importance of knowledge
management and organisational learning in building and

mamtaining a competitive edge (Whitehill, 1997). Although
each of these approaches provides a method by which superior
performance can be investigated, it is the knowledge-based
approach that in more recent times offers the best perspective
from which the determinants of company's competitive
advantage can be analysed. Successful corporate strategies are
based on certain competitive advantages of companies that can
be explained by these theories.
Some management studies have suggested that managers make
different decisions when owners are actively involved in the
firm (owner-controlled) versus the situation where paid
managers are relatively free to set the firm's strategy (Tosi,
Katz, Gomez, 1997; McEachern, 1975). Managers and internal
owners have managed to forge specific coalitions in many
companies that are controlled by insiders. These companies are
in fact controlled by managers and they behave differently to
companies controlled by strategic outside investors. An
insiderdominated firm may generate neither the resources
needed for restructuring activities, such as investment, nor have
the incentive to sell the firm to outsiders who have those
resources (Blanchard, Anghion, 1995). Prasnikar and Svejnar
(1998, page 19) found some strong arguments in their research
to support this thesis regarding the role of insiders in Slovenian
companies. Therefore, the author decided to compare the
performance of companies regarding the ownership structure
also.
Rumelt argued (1984) that a firm's competitive position is
defined by a bundle of unique resources and relationships with
competitive advantage arising from the sources of potential
rents ranging from changes in technology and consumer tastes
to innovation and legislation. The ability of a firm to develop
and sustain a competitive advantage from these sources depends
on its ability to develop isolating mechanisms. These can take
the form of specialised assets and resources, especially those
that provide specialised information, enhance brand name,
image and reputation, and restrict entry. It is evident that core

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