Powerful competitors and rapid technological change have made the
quest for competitive advantage more difficult and its accomplishment
less sustainable (D'Aveni, 1994). Corporate entrepreneurship in
general and innovtion in particular are frequently regarded as important
means of achieving superior performance in such competitive
environments. Corporate entrepreneurship has been variously
conceptualized as the strategic renewal of established corporations, and
innovtion and venturing within established corporation (Guth and
Ginsberg, 1990), and innovation within existing businesses (Sandberg,
1992). Lumpkin and Dess (1996) argue that innovation is a key element of
a firm's entrepreneurial orientation, and Covin and Slevin (1991)
note that innovation is an important dimension of a firm's
repertoire of entrepreneurial behaviors. In fact, innovation is so
important to corporate entrepreneurship that it may be considered the
essence of such activity (Covin and Miles, 1999). Hence, the management
of innovation ha s become a subject of significant research interest
(e.g., Hitt et al., 1999).
The research question examined in this article asks that impact top
management them (TMT) demography has on the effectiveness of firms'
product-market innovations. As Lumpkin and Dess (1996) note, top
management team characteristics are a key contingency factor influencing
the relationship between firm-level innovation and firm performance.
Contingency models advance our understanding of organizational phenomena
because they move beyond bivariate relationships and explicitly
recognize the need for increased model specification (Rosenberg, 1968).
Hence, to enhance our understanding of how innovation may contribute to
performance outcomes, we examine the impact of management team
characteristics upon that relationship.
However, TMT demography is generally modeled as an independent or
dependent construct rather than in a contingency model (Finkelstein and
Hambrick, 1996). Briefly, upper-echelons research frequently posits that
the decisions of top management are primary drivers of firm performance,
and those decisions are influenced by the demographic makeup of the top
management team. The upper-echelons literature has met with equivocal
results (c.f., Finkelstein and Hambrick, 1996), particularly when
attempting to link TMT demography directly to firm performance (e.g.,
Murray, 1989; West and Schwenk, 1996). We believe that a perspective
that recognizes an interaction effect between strategy and the top
management team may more accurately reflect the strategy formulation and
implementation process. We discuss and test that perspective in this
study.
The article is organized as follows. In the next section we provide
the theoretical background and development for two hypotheses regarding
1) the direct effect of innovation on firm performance and 2) an
interaction effect between innovation and top-management team
characteristics on firm performance. Next, we discuss the sample, data
and statistical procedures. The article concludes with a discussion of
the results of our hypotheses testing, implications of this study for
practitioners and scholars, limitations of the study, and avenues for
future research.
CONCEPTUAL BACKGROUND AND HYPOTHESIS
Miller and Friesen (1978) cite product-market innovation, that is,
innovation comprised of product design, market research, and other
marketing-related activities, as an important element of a successful
innovation strategy. Other authors (e.g., Maidique and Patch, 1982)
discuss technological innovation--an emphasis on research and
development, and technical expertise related to new or improved products
and processes--as the driver of a successful innovation strategy.
Lumpkin and Dess argue that while the distinction between product-market
innovation and technological innovation may provide a useful means to
conceptualize innovation, in practice the distinction between the two is
frequently blurred, "... as in the case of technologically
sophisticated new products designed to meet specific market demand"
(1996: 143). Furthermore, making such a distinction unnecessarily
fragments the classification of innovation (Van de Ven, 1986). Other
authors have developed definitions that comprise both elements of
innovat ion. For instance, Morris and Sexton's definition of
innovativeness as "... the seeking of creative, unusual, or novel
solutions to problems or needs" seems to encompass both
technological and product-market innovation (1996: 6).
Nohria and Gulati (1996) note that prior research has not yet
developed a definitive measure of innovation. Accordingly, these authors
adopted a very broad definition of innovation that includes "any
policy, structure, method or process, product or market opportunity ...
perceived to be new" (Nohria and Gulati, 1996:1251, emphasis
added). The Austrian perspective also emphasizes new actions carried out
by firms in an effort to disrupt the competitive status quo, causing
disequilibrium (status quo and equilibrium are defined here as ordinary
competitive behavior). By contrast, Nelson and Winter argued that
"non-new" or commonplace actions are "... regular and
predictable business behavior plausibly subsumed under the heading
'routine,' especially if we understand that term to include
the relatively constant disposition and strategic heuristics that shape
the approach of the firm" (1982: 15). Our definition of
product-market innovation is consistent with these definitions: the
firm's realized product-market act ions that go beyond the status
quo of the market process and are perceived to be new.
Innovation has been associated with improved firm performance in
both theoretical and empirical research. For instance, Nelson and Winter
(1982) posit that firms need not engage only in radical innovation but
may also undertake many incremental innovative activities as a means to
success. Caves and Ghemawat (1992) found a positive linkage between new
products and new processes, and firm performance. Rapid and frequent new
product introduction can significantly enhance organizational
performance by facilitating the acquisiton of market share, providing
pricing power, and permitting the company to establish industry
standards (Zahra and Covin, 1993). Ravenscraft and Scherer (1982) and
Smith et al. (1992) found that R&D efforts were more strongly
associated with firm performance than were marketing efforts. Finally,
Banbury and Mitchell (1995) linked the introduction of product
innovations to market share acquisition.
In sum, despite some conflicting evidence (Nelson and Winter,
1982), theory and empirical research suggest a positive relationship
between innovative activity and firm performance. Accordingly, we
propose the following hypothesis:
H1: Product-market innovation will be positively associated with
firm performance.
Top Management Teams and Decision Making
While we expect that innovation will be directly associated with
firm performance, we also expect that the nature of the management team
will influence the innovation-performance relationship. Contingency
modeling such as that performed here allows a "... more precise and
specific understanding" (Rosenberg, 1968: 100) of the relationship
between product-market innovation and performance by increasing model
specification. The mechanisms by which TMT demographic heterogeneity
enhances the impact of product-market innovation on performance are
discussed in the following paragraphs.
The nature of the top management team is central to the type and
quality of firms' strategic choices (Andrews, 1971; Hambrick,
1989), including decisions regarding entrepreneurial posture
(Khandwalla, 1987). Upperechelons theory posits that managers make
strategic choices based upon their values, cognitions, and perspectives,
and that organizational activities or outcomes reflect the collective
cognitive biases and abilities of the TMT (Hambrick and Mason, 1984).
A significant body of research concerning innovation and
organizational leadership has examined the link between the management
team and innovative or creative behavior on the part of the management
or the firm. For example, Bantel and Jackson (1989) found that
demographically diverse management teams were associated with higher
levels of creativity and innovation. Similarly, Wiersema and Bantel
(1992) linked top-management team heterogeneity and propensity to engage
in strategic change. Other researchers, such as Murray (1989), have
attempted to directly link TMT characteristics to firm performance.
This study takes a different approach to the question of how
leaders matter to firm innovation by incorporating firm performance into
a model of TMT characteristics and firm-level innovation. Other authors
have recently examined the link between TMT heterogeneity and
performance (e.g., Hambrick et al., 1996). These authors examined the
direct effects of TMT heterogeneity on various characteristics of firm
competitive actions and rivals' responses, and the direct effect of
TMT heterogeneity on firm performance. They sum up the relevance of the
top management team to competitive activity by noting that ". ..
undertaking competitive actions is foremost a function of being able to
create, or generate, those actions" and call for research on the
antecedents of competitive behavior ". . . to include the
characteristics of the decision makers, in particular, the
company's top management team" (1996: 665). Our study explores
how the interaction between innovation and TMT heterogeneity influences
the relationship betw een innovation and performance.
High-quality decisions spring from both the collective cognitive
capability of the team and the decision-making process used by the team
(Amason, 1996). The collective mental capability of a demographically
heterogeneous top management team provides the "requisite
variety" (Ashby, 1956) necessary for the team to cope with complex,
ambiguous, and multifaceted decisions such as those associated with
developing strategy (Mintzberg et al., 1976). The alternatives
considered by a demographically heterogeneous top management team are
likely to be characterized by "diversity, novelty, and
comprehensiveness" (Wiersema and Bantel, 1992: 96). "In other
words, cognitive diversity is a valuable resource. The presence of
people with differing points of view ensures consideration of a larger
set of problems and a larger set of alternative potential
solutions" (Bantel and Jackson, 1989). For instance, Bantel and
Jackson (1989: 109) found that top management teams that were
heterogeneous in terms of educational background ma de more innovative
decisions than less diverse teams. Hence, demographic heterogeneity
among the members of the top management team is an indicator of
creativity in decision making.
Demographic diversity also impacts decision-making processes
(Jackson et al, 1995). As Simons et al. note, demographic ". . .
diversity represents a potential for more thoughtful decision
making" (1999: 664). There is substantial research that suggests
that decision-making processes that synthesize the diverse knowledge
bases, values, and perspectives of demographically dissimilar team
members enhance decision quality. The members of a demographically and
cognitively diverse group are likely to view strategic decisions
differently from one another (Mitroff, 1982), engendering debate
regarding the most appropriate alternative. Schweiger et al. note that
such decision-making processes have the twin benefits of preventing
". . . the uncritical acceptance of the seemingly obvious and
(tapping) the knowledge and perspectives of group members" (1989:
747). In an empirical study, they found that dialectical inquiry (DI)
and devil's advocacy (DA) techniques produced closer evaluation of
competing assumption bases, an d faster, higher quality decisions. The
benefits of conflict over consensus in strategic decision making are
shown in Schweiger and Sandberg (1989). Those authors found that DI/DA
techniques applied to strategic decision making were significantly more
effective than consensus-seeking techniques in exploiting the different
capabilities of team members. Thus, conflict among team members
regarding the most appropriate course of action can enhance decision
making by unearthing the assumptions underlying each potential course of
action and causing management to critically evaluate the merits of each
alternative. Indeed, such conflict is vital to the development of
high-quality decisions (Amason, 1996).
Overall, we expect the conflict, debate, decision
comprehensiveness, etc. engendered by TMT heterogeneity to improve
environmental scanning and decision-making quality relating to
product-market innovations. This, in turn, should lead to enhanced firm
performance because improved decision making should enhance the efficacy
of realized firm actions. Demographic diversity may be particularly
important for decisions regarding innovative products or processes, as
these are outside of the organization's standard operating
procedures, and thus by definition, necessitate alternative perspectives
(Nelson and Winter, 1982). Thus, the following hypothesis is proposed:
H2: Top management team (TMT) demographic heterogeneity will
interact with product-market innovation. To the extent that firms engage
in product-market innovation, firms with heterogeneous TMTs will exhibit
higher performance than firm with homogenous TMTs.
METHOD
Sample
Consistent with the behavioral model of corporate entrepreneurship
(Covin and Slevin, 1991; Zahra, 1993), an effective way to develop large
sample, multivariate research designs is through content analysis of
published histories about firms (Ginsberg, 1988). Because the strategies
of the largest, market-leading firms are likely to be the most
observable (Fombrun and Shanley, 1990), we first drew a sample of market
leading firms that were members of the Fortune 500 and were number one
or number two in their industry in terms of U.S. market share. We
cross-validated these market share rankings with the industry rankings
list of Ward's Business Directory. Second, to ensure that news
accounts of firm strategies and product-innovation actions pertain to
the line of business on which these firm are most highly dependent
(Chen, 1996), only those firms having Rumelt's (1974)
specialization ratios greater than 0.70 (dominant or single business
firms) were selected. Finally, firms were eliminated from the sample if
the y did not have top management team datalisted consistently in Dun
& Bradstreet Reference Book of Corporate Managements during
1987-1993. Thus, the sample includes the largest, relatively
non-diversified U.S. firms so as to be certain that their competitive
actions are carried out to improve their respective competitive
positions in their primary industries.
This sampling process is consistent with that used in prior
research (e.g., Ferrier et al., 1999) and yielded a final research
sample consisting of a pooled, seven-year cross sectional database for
the two largest single business firms across 33 industries with the
firm-year being the unit of analysis.
Measures
Product-market Innovation. We first adopted a general definition of
product-market actions consistent with research in competitive dynamics:
externally directed, specific, and observable competitive moves
initiated by a firm to enhance its relative competitive position
(Ferrier et al, 1999; Young et al, 1996). This definition is also
consistent with corporate entrepreneurship research, which views
competitive action as behavior that is overt, demonstrable, and
aggressive towards competitors and is carried out to improve competitive
position and to outperform competitors in the marketplace (Covin and
Slevin, 1991; Lumpkin and Dess, 1996).
Using structured content analysis (Jauch et al., 1980), we
categorized the competitive actions of each firm into six specific
action categories (i.e., pricing actions, marketing actions, new product
actions, capacity-related actions, service actions, and overt signaling
actions) based on the appearance of one of the keywords listed in Table
1 in the headlines and abstracts of news reports found in the U.S.
series of F&S Predicasts. This procedure and resultant action
categories are consistent with that used in previous competitive
dynamics research (Chen et al., 1992; Ferrier et al., 1999; Young et
al., 1996) and are consistent with the view within corporate
entrepreneurship that business strategy involves a firm's
collection of competitive tactics that includes, among other things, new
products, service, warrantees, advertising, price policy, etc. (Covin
and Slevin, 1991). Internal actions such as layoffs, restructurings,
etc. are not considered "competitive actions" by competitive
dynamics researchers and w ere not included in our sample. The final
data set contains a total of 4,617 product-market actions. Table i
contains a list of these keywords and several sample news headlines
across the six action type categories.
We tested the reliability of our coding process using Perreault and
Leigh's (1989) index of reliability. Using the key words listed in
Table 1, two academic experts separately recoded a representative sample
(N = 300) of actions into each of the six categories listed above. This
approach yielded an index value of 0.91, which indicates a high degree
of reliability in categorizing these actions (Rust and Coil, 1994).
To distinguish innovative product-market actions among all ordinary
product-market actions across five of the six action type categories
(all but "product actions"), we further coded actions as
innovative if the news headline or abstract also contained a keyword
describing the overall level of innovativeness surrounding the action
(e.g., new, innovative, unique, first-ever, etc.). Consequently, we
created a tally of innovative product-market actions each firm carried
out over each year of the time panel (1987-93). This tally represents
the sum of the number of product actions which are, by their very
nature, innovative, and the number of innovative product-market actions
among the other five action type categories. The tally represents our
measure of product-market innovation, whereby higher values represent
higher levels of product-market innovation.
Top Management Team Heterogeneity. We adopted Wiersema and
Bantel's (1992) approach to measure educational background
heterogeneity, functional background heterogeneity, and industry tenure
heterogeneity. To calculate TMT educational heterogeneity, we used
Blau's (1977) index of heterogeneity and included each TMT
member's highest degree received across six different degree
categories: business, science, liberal arts, engineering, law, and
other. We also used Blau's index to calculate functional background
heterogeneity, whereby functional experience was categorized as
engineering/R&D, finance/accounting, legal, human resources
management, manufacturing, logistics, purchasing, public relations, and
general management. Since industry tenure is a continuous variable
measured in years, we calculated industry tenure heterogeneity using the
coefficient of variation, defined as the standard deviation divided by
the mean for the number of years each of the TMT members was active in
the focal industry.
Because TMT heterogeneity may be considered as a meta-construct
that is manifested along a number of different, yet correlated
dimensions, we employed a parsimonious composite measure of TMT
heterogeneity calculated as the sum of the three standardized individual
TMT heterogeneity measures noted above (see Ferrier, in press; Ferrier,
2000). Also, to avoid placing disproportional weight on time/experience,
we included only one time-related TMT dimension (i.e., industry tenure
heterogeneity) in our composite measure. Consistent with the individual
TMT measures, high scores for our composite TMT measure indicate that
the TMT possesses, overall, a diverse set of experiences, cognitive
perspectives, and backgrounds.
Performance. Firm performance is a multi-dimensional construct.
Since corporate entrepreneurship may influence various dimensions of
firm performance differently (for instance, the expenditure of resources
necessary to grow revenues or market share may adversely impact
short-term profits), multiple measures of performance are preferable to
single measures of performance (Chakravarthy, 1986; Lumpkin and Dess,
1996; Zahra and Covin, 1995). Accordingly, we tested our hypothesis
using two different performance measures: Altman's Z-score and
market share gain.
Altman's Z-score is a weighted composite of profitability,
efficiency, slack, and stock market performance factors, calculated as:
Z = 0.012 [X.sub.1] + 0.014 [X.sub.2] + 0.033 [X.sub.3] + 0.006
[X.sub.4] + 0.999 [X.sub.5],
where [X.sub.1] Working Capital / Total Assets, [X.sub.2] =
Retained Earnings / Total Assets, [X.sub.3] = Earnings Before Interest
and Taxes / Total Assets, [X.sub.4] = Market Value of Equity / Book
Value of Liabilities, and [X.sub.5] = Sales / Total Assets (see Altman,
1968; Chakravarthy, 1986). Chakravarthy argued that although the Z-score
was "essentially constructed to predict bankruptcy, it can also be
a valuable index of the company's overall well-being. By measuring
distance from bankruptcy, Z-score could be a surrogate index of
strategic performance" (1986: 446). The use of a composite
performance measure that captures multiple dimensions of firm well-being
is consistent with prior research in corporate entrepreneurship (e.g.,
Covin and Slevin, 1986; Zahra and Covin, 1995). Z-scores greater than
3.0 indicate a condition of strong performance, whereas Z-scores lower
than 1.8 indicate poor performance.
Consistent with several other studies exploring the effect of
competitive strategy on market share, we calculated market share gain as
the positive year-to-year change in percent of firm sales to total
industry sales in the focal firm's primary industry (e.g., Ferrier
et al., 1999). This measure also accounts for market share erosion,
measured as the negative annual change in market share. Data for both
performance measures were collected from Compustat and Ward's
Business Directory.
Control Variables. Previous research suggests that several
industry-and firm-specific variables influence firm performance. For the
sake of parsimony, we calculated a composite measure for barriers to
entry, represented by the sum of the year-by-year pooled industry means
for investments in R&D, selling activities, and total assets,
respectively (see Ferrier et al., 1999; Young et al., 1996). We measured
industry concentration using the Hirschman-Herfindahl Index, one of the
most widely used measures of this construct (Scherer and Ross, 1990). We
controlled for the effect of industry growth on firm performance by
including the year-to-year percentage change in gross industry sales.
Finally, because TMT size may affect cognitive heterogeneity, social
integration, and consensus in the decision-making process (see
Finkelstein and Hambrick (1996), we also included TMT size as a control,
measured as the number of managermembers that comprise the TMT. Table 2
reports the means, standard deviations, and correlations among all
variables in our analyses.
ANALYSIS AND RESULTS
To control for potential bias due to serial correlation and
industry-specific factors, we used the PROC MIXED regression technique
found in SAS, which allowed us to model the linear regression error term
into separate components: a) the first-order autoregressive function
(AR1), b) random industry-level factors, and c) and random error
(Wolfinger et al., 1991). We report the covariance parameter estimates
for both industry random error and serial correlation in Table 3.
Table 3 reports the results of the moderated hierarchical mixed
regression analyses. In stage i of each model, we entered 1-year lagged
product-market innovation, TMT heterogeneity, and TMT size, as well as
the current-year industry controls. We lagged the models by one year to
allow time for the product-market innovations to impact performance. We
limited the lag time to one year because research using datasets of this
type (e.g., action counts) shows that once an innovative marketing
campaign, unique new product, and so on hits the market, rivals tend to
initiate a countervailing response within, on average, 18 months (Smith
et al, 1992). With regard to the direct effects for the variables of
interest, we found that product-market innovation was positively and
significantly related to market share gain (b = .002, p < .01).
Hence, hypothesis one (H1) was partially supported. Top management team
heterogeneity was not significantly related to either performance
variable.
The inclusion of the product-market innovation X TMT heterogeneity
interaction term significantly improved the predictive efficiency of
both the Z-score and market share gain models (i.e., significant change
in -2 log likelihood, see note 'e' in Table 3). Consistent
with hypothesis two (H2), which predicted that product-market innovation
would have a more strongly positive influence on firm performance for
firms with heterogeneous TMTs than firms with homogeneous TMTs, the
interaction of product-market innovation with TMT heterogeneity in stage
2 was significant and positively related to Z-score (b = .169, p <
.05). The interaction term was also significant in the market share gain
model (b = .015; p < .01). Therefore, hypothesis 2 is fully
supported.
Post hoc Analyses. To check the validity of our results, we also
ran separate models using four other common measures of firm performance
as dependent variables. Other aspects of the model remained the same as
in the original analysis. These models were also lagged by one year.
Hypothesis one (H1) was not supported in models predicting return on
sales, return on equity, or standardized net income before taxes. A
model predicting return on assets was marginally significant (b = .095,
p < .10). We found results consistent with hypothesis two (H2) for
three out of four of these models. In particular, the product-market
innovation TMT X heterogeneity interaction term was significant for
models predicting return on sales (b .007, p < .05), return on assets
(b = .667, p < .05), and standardized net income before taxes (b =
.049, p < .10). A model using return on equity was not significant.
DISCUSSION AND CONCLUSIONS
The results of our hypothesis testing, as well as the results of
the post-hoc analysis, appear to affirm the basic premise of the study:
a demographically heterogeneous team makes better decisions regarding
innovative strategies and tactics. That is, more heterogeneous
management teams appear to achieve better results with innovation
strategies than less-heterogeneous teams. The results for the market
share gain model suggest that innovation has a positive relationship
with market share gain, but innovation pursued by a heterogeneous top
management team has a stronger positive relationship. The results for
the Z-score models suggest that while innovation may lead to gains in
market share, it is also expensive and therefore has a negative impact
on financial performance. However, when innovation is undertaken by a
heterogeneous top management team, there is a positive impact on
financial performance, though the relationship is not as strong as that
of the market share gain model. These findings suggest that, i ndeed,
the influence of innovation on Firm performance can be contextual and,
in this case, contingent upon the nature of the top management team.
While economies of scale, market power, and reputational advantages
stemming from high market share have been associated with higher profits
(Porter, 1980), executives may pursue market share based on a competitor
orientation," or an emotional commitment to beating competitors
(Armstrong and Collopy, 1996). However, as this and other research
suggests, an orientation that emphasizes beating competitors interms of
market share is not always associated with higher profits (Armstrong and
Collopy, 1996). Our findings suggest that heterogeneous teams are better
able to achieve both market share and profitability than are more
homogeneous TMTs. This may occur because heterogeneous TMTs are less
subject to groupthink (Janis, 1972) and therefore are better able to
balance the desire to beat competitors against the need for
profitability, or because they just make better decisions, as argued
above.
Future studies should expand upon these findings by examining the
competitive environments where the influence of demographic
heterogeneity on the innovation-performance relationship may be the most
important and, conversely, those industries where heterogeneity does not
provide benefits or even has adverse effects. For instance, strategic
change has been shown to disrupt firm routines and decrease firm
performance (Amburgey et at, 1993). Likewise, innovation can disrupt
organizational routines as organizations struggle to "unlearn"
old ways of doing things and focus on implementing new processes or
products (Nystrom and Starbuck, 1984). This effect may be particularly
pronounced for firms in placid industries since those firms are likely
to have well-established routines and be unaccustomed to change.
Further, firms in placid industries may benefit more from TMT cohesion
and its implied consequences of increased communication (Zenger and
Lawrence, 1989), consensus (Dess, 1987), and decision-making speed (Eis
enhardt and Schoonhoven, 1990). Hence, such firms are particularly
likely to suffer the disruptive effects of innovation and change and,
consequently, their performance may deteriorate during such periods
(Amburgey et at., 1993). Our research suggests that heterogeneous teams
may be better able to cope with the disruptive consequences of
innovation and change. Thus, there may be complex interrelationships
encompassing environmental as well as organizational characteristics
such as the nature of the top management team, or the firm's
innovation implementation climate (Klein and Sorra, 1996) that more
fully delineate the relationship between innovation and firm
performance.
These results have important implications for management practice.
As Hitt et al. (1999) note, top management teams bear final
responsibility for the selection and implementation of firm actions in a
manner that generates wealth. Thus, to ensure that a firm's pursuit
of product-market innovation results in profitable market share
gains--avoiding the overzealous pursuit of innovation or market share
for its own sake--managers should actively incorporate open debate using
more complex and diverse points of view in the strategic decision-making
process. Our results suggest that management teams characterized by
members with wide diversity in demographic attributes may be successful
on projects that intuitively benefit from marked dispersion of
attitudes, interests, and perspectives, such as those requiring the
evaluation of innovative or creative ideas. Some authors have argued
that high-performing management teams benefit more from the potential
conflict induced by dissimilarity than from consensus. In fact, un
necessarily striving for consensus may be a waste of scarce executive
time. As suggested by Katzenbach, "real teams do not avoid
conflict--they thrive on it" (1997: 85).
The results of this study have important consequences for academic
research as well. They suggest that TMT demography may be useful as a
moderator construct. The research implications of this reach beyond the
upper-echelons literature to encompass research on executive hiring,
selection, and development, as well as the strategy-making process and
implementation, and other areas. The results of our study suggest that
the relationship between heterogeneity and performance may be more
complex than at first thought and suggests a possible explanation for
the equivocal results that have plagued the upper echelons research
(c.f., Finkelstein and Hambrick, 1996). For instance, while there has
been some difficulty in linking demography directly to firm performance
(Finkelstein & Hambrick, 1996; West and Schwenk, 1996), dispersion
of demographic characteristics among the members of a TMT may enhance
firm performance when tasks are undertaken that require creativity or
novel thinking, such as the pursuit of an innovati on strategy.
There are a number of limitations to this research. First, the
inner workings of the top management team are a "black box"
(Lawrence, 1997) in demography-based research. The theoretical
perspective taken here is that demographic heterogeneity leads to
conflict among TMT members and such conflict has a salutary effect on
decision making. The demography-based perspective is well grounded in
theory and has a substantial and diverse supporting literature (e.g.,
Hambrick and Mason, 1984; Eisenhardt et al., 1997). However, there is an
important alternative perspective. Specifically, that perspective
assesses conflict directly, distinguishing between affective, or
dysfunctional emotional conflict, and cognitive, or beneficial
task-related, conflict (Jehn, 1995; Amason, 1996). If we had been able
to measure and distinguish between affective and task-related conflict
within the teams in our sample, we might have found a weaker, or even
negative, effect on the relationship between product-market innovation
and performa nce for heterogeneous teams whose interactions were
characterized by affective conflict, and a stronger effect on
performance for teams characterized by task-related conflict.
Second, and on a related note, future research should also employ
more substantive measures of TMT heterogeneity such as executive power,
psychographics and judgment (Priem et al., 1999; Miller et al., 1998) in
order to generate more "fine-grained" (Harrigan, 1983)
insights into the relationships described above. This will likely
necessitate a sample of smaller firms where executives are relatively
more accessible so that data can be collected (Hambrick and Mason,
1984).
This research was conducted to explore the relationship between
innovation and firm performance in the context of the nature of the top
management team. As such, it addresses an important contingency in the
relationship between corporate entrepreneurship and firm performance,
and takes a step towards answering the question of "how corporate
entrepreneurship creates competitive advantage" (Covin and Miles,
1999: 48). Further, this research addresses an issue seldom examined in
upper-echelons research--the contingency effect of top management team
heterogeneity on the relationship between firm behavior and performance
(Finkelstein and Hambrick, 1996).
Table 1
Coding Keywords and News Examples for Individual Action Types
Variables Content Analysis Coding Scheme
Pricing Actions Key words: price, rate, discount,
fares, etc.
Marketing Actions Key words: ads, spot, promote,
distribute, campaign, markets,
pushes, sales force, pitches,
distribute, package, bundle
Product Actions Key words: introduce, launch,
unveil, roll out, offer, line,
version, etc. (with some concrete
product or service)
Capacity Actions Key words: raises, boosts,
increases with capacity- or output-
related keywords like plant,
capacity, output, production level,
line, etc.
Service Actions Key words: service, warrantee,
guarantee, financing, after-sale,
customer training, help line,
tech-help, customer service, etc.
Signaling Actions Key words: vows, promises, says,
seeks, aims, declares, to focus on,
targets, etc. (with some
strategically salient statement,
not just a promise of better
returns, etc.)
Product-Market Innovation Actions a) Product actions
or
(see Covin & Slevin, 1991; Lumpkin b) Pricing, marketing, or service
& Dess, 1996; Austrian literature actions qualified by presence of
in corporate entrepreneurship, key words: new, unique,
etc.) experimental, test, innovative,
creative, first-ever, radical,
change, pioneer, next-generation,
etc.
Variables Examples of Headlines
Pricing Actions "FedEx offers rate discounts on
day short haul service."
Marketing Actions "United launched ads to counter
American's campaign."
Product Actions "Merck introduces Mevacor, to
reduce serum cholesterol."
Capacity Actions "Mobil raises lube stock capacity
10% via recent improvements."
Service Actions "Sears offers Kid Vantage frequent
buyer warrantee program."
Signaling Actions "Reebok's Fireman vows to retake
lead in athletic shoe market by
end of 1995."
Product-Market Innovation Actions "Nike's creative air shoe ads to
illustrate gravity."
(see Covin & Slevin, 1991; Lumpkin "Boeing launches first-ever global
& Dess, 1996; Austrian literature ad campaign to encourage business
in corporate entrepreneurship, air travel."
etc.) "American Airlines radically alters
pricing structure."
"Fed Ex introduces new software and
hardware package to help clients
track shipments."
"Wal-Mart experiments with
innovative environmentally friendly
supercenter in Nebraska."
"Alcoa unveils next-generation
alloys for aero-space industry."
"IBM tries unique incentive program
to boost high-end PS/2 sales."
TABLE 2
Descriptive Statistics and Correlations (N = 462)
Mean Standard 1 2 3 4
Variables Deviation
1. Z-Score 4.16 3.28
2. Market share gain .02 1.84 .16
3. Product-market innovation .14 .28 .25 .09
4. TMT heterogeneity .57 1.77 -.01 .06 -.07
5. TMT size 5.44 2.11 -.07 -.09 .36 .19
6. Industry concentration .22 .15 -.02 -.10 -.01 -.05
7. Industry growth .18 .18 .14 -.03 .02 -.01
8. Barriers to entry 2,598 3,561 -.17 -.06 .28 -.12
5 6 7
Variables
1. Z-Score
2. Market share gain
3. Product-market innovation
4. TMT heterogeneity
5. TMT size
6. Industry concentration -.04
7. Industry growth .04 -.07
8. Barriers to entry .19 .08 -.15
NOTE: All underlined correlations are significant at p < .05 level or
better.
TABLE 3
Hierarchical Regression Results of Firm Performance on Product-Market
Innovation and Top Management Team Heterogeneity (N = 462)
Model 1:
Z-Score
Non-Std. Std.
Coefficients Error
Stage 1: Main Effects (a)
Product-Market Innovation (b) .001 .002
TMT heterogeneity (b) .018 .053
TMT size (b) .116 .046 **
Industry concentration -.028 1.585
Industry growth .134 .281
Barriers to entry (c) -.499 .326 +
Intercept 3.558 .592 ***
-2 Log Likelihood (d) = 1238.400 *
Est. of Industry Random Error = 12.785 ***
AR(1) = .655 ***
Stage 2: Interaction Term (a)
Product-market innovation x .169 .090 *
TMT heterogeneity (b)
-2 Log Likelihood (e) = 1225.100 *
Est. of Industry Random Error = 13.084 ***
AR(1) = .652 ***
Model 2:
Market Share
Gain
Non-Std. Std.
Coefficients Error
Stage 1: Main Effects (*)
Product-Market Innovation (b) .002 .001 **
TMT heterogeneity (b) -.240 .207
TMT size (b) -.068 .061
Industry concentration -1.256 .867 +
Industry growth -1.252 .681 *
Barriers to entry (c) -.199 .114 +
Intercept 1.290 .607 *
-2 Log Likelihood (d) = 1250.700 **
Est. of Industry Random Error = .047 *
AR(1) = .196
Stage 2: Interaction Term (a)
Product-market innovation x .015 .006 **
TMT heterogeneity (b)
-2 Log Likelihood (e) = 1263.500 *
Est. of Industry Random Error = .003 *
AR(1) = .239 *
Values reported are non-standardized coefficients accompanied by
standard errors. One-tailed tests were used, which were directionally
predicted in the hypothesis:
+ p <.1;
* p < .05;
** p < .01;
*** p < .001.
NOTES
(a) Coefficients are reported "at stage" because the t-vaules for the
direct effects that comprise the interaction terms are influenced by
linear transformations of those variables (Cohen, 1978). Therefore,
stage 2 direct effects coefficients are not reported in order to
discourage unjustified interpretation of those variables.
(b) Variables were lagged by one year.
(c) Standardized measure of variable was used in analyses.
(d) Significance for -2 log likelihood obtained by comparing values to
those obtained from a nested model containing only a constant.
(e) Significant for -2 log likelihood represents significant improvement
of fit over Stage 1 models.
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