Capítulo I: Planteamiento del Problema
Capítulo 2: Marco teórico
3.5. Instrumentos de recolección de datos
Economics frequently draws on the natural sciences to illustrate phenomena, the
most common application of which is the life cycle metaphor, where firm
emergence, growth, decline and exit is perceived as analogous to birth, growth and
death in biological organisms (Child & Kieser, 1981; Penrose, 1952; Whetten, 1987).
The lifecycle paradigm is well established (Greiner, 1972; Levitt, 1965; Utterback &
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1973), products (Abernathy and Utterback, 1978) and technology (Klepper, 1996)
which renders this particular lens apt for consideration by policymakers.
Theorists point to the importance of size, age, technology and legal form in shaping
innovation and growth outcomes for individual firms (Barron, West and Hannan,
1994; Bruderl and Schussler, 1990; 1994; Carroll and Hannan, 1995; Churchill and
Lewis, 1983; Fichman and Levinthal, 1991; Greiner, 1972). Distinguishing between
age and technological progress (production and output technologies) as sources of
growth is challenging. Differentiating between organisational and biological age,
Kimberly (1980) adopts a developmental view suggesting that chronology is just
one of many dimensions to consider in assessing firm maturity or propensity to
grow:
Chronological age may have very little to do with where an organisation is going or where it has been. Calendar time and organisational time are not necessarily identical. Organisations often have rhythms and cycles that are quite independent of their chronological age (p.6).
Employing similar logic, industrial organisation economists characterise age as
young or mature, suggesting that various business and technology iterations occur
naturally as industries grow older (Churchill and Lewis, 1983; Greiner, 1972; Nelson,
1995). Other authors use similar nomenclature at the firm level: early-stage new
ventures 0–4 years old (Low & MacMillan, 1988); entry, post entry or intermediate
and advanced age (Huergo & Jaumandreu, 2004a); infants (0±2 years), adolescents
(3±4 years), middle-aged (5±24 years), or old (25 years or more) as a rough
approximation for seed, start-up, and later stages (Berger & Udell, 1988). Each of
these is suggestive of differential behaviour or economic interaction which requires
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The growth literature commonly applies developmental and stage models (e.g.
Churchill and Lewis, 1983; Greiner, 1972; Kazanjian and Drazin 1989). The
characteristics and challenges of growth stages appear to constitute a more useful
mechanism for calibrating the entrepreneurial process than specific timeframes
(Hite & Hesterly, 2001b). Promoting a more subtle approach, Phelps, Adams, and
Bessant (2007) conclude that lifecycle models are ‘linear, unidirectional, sequenced
and deterministic’ (p.17), calling into question their applicability to the analysis of
firm growth over time. In an extensive analysis of growth models, Levie and
Lichtenstein (2009) found no consensus on basic constructs and no empirical
evidence supporting stages theory. As outlined below, this discord has been
addressed by a number of authors, extending the menu of evolutionary diagnostics
which might be applied to assess system fit within chosen sectors.
Start-ups and Lifecycles
Liabilities of newness and smallness combined appear to dilute the usefulness of
the lifecycle description of organisational change in that most organisations don’t
grow and, high mortality rates among start-ups mean that most new firms face
short term dissolution (Aldrich and Auster, 1986). The US Small Business
Administration as cited in Berger and Udell (1998, p.627) estimates that about
23.7% of small businesses disappear within 2 years and 52.7% disappear within 4
years due to failure, bankruptcy, owner retirement, ill health, or a desire to embark
on a more profitable endeavour. Given the instability of the start-up population,
entrepreneurship policy, rather than the system of innovation, may offer a better
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Firm Age and Growth
Gibrat’s Law predicts discontinuous growth patterns driven by independent random
variables (Hamilton, 2011) however Parker et al. (2010) contend that this is
incompatible with evidence that consistently explains firm growth. Employing
growth as a proxy for innovation, the literature presents supporting and conflicting
evidence of the age-growth relationship. Citing high-technology start-ups as an
exception to generally accepted business growth stages, Churchill and Lewis (1983)
suggest that the entrepreneurs and investors who start them, do so with the
intention of growing them quite rapidly, often with a view to a successful exit (e.g.
IPO or trade sale). While there is some evidence that rapidly growing enterprises
are more concentrated in technologically sophisticated sectors, empirical data
increasingly points to a lack of concentration in the high tech sector (Mason &
Brown, 2013; Parker et al., 2010). Exit patterns and growth potential in high tech
sectors should be a key consideration in SSI design; both from the perspective of
qualifying expectations for job-related growth and the consequences of firm
exit/acquisition for a small open economy.
Once established, organisations benefit from patterns of relationships that
ultimately coalesce into a social structure that enhances their survival prospects.
Beyond the minimum efficient level, however, this study focuses on firm age in
combination with the distinction between technologies and industry lifecycles.
Several aspects of the SI are thought to be prominent in influencing the ultimate
success of innovation as businesses mature from start-up through intermediate and
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that small businesses experience common challenges at similar stages of
development - so while each firm is unique, they all face similar problems and are
subject to the vagaries of changing conditions - and that categorising problems and
growth patterns could help entrepreneurs to navigate them. They highlight pivotal
components as:
Financial resources, including cash and borrowing power; Personnel resources, depth and quality of people at management and staff levels; Systems resources, in terms of the degree of sophistication of information and planning and control systems; Business resources, including customer relations, market share, supplier relations, manufacturing and distribution processes, technology and reputation, all of which give the company a position in its industry and its market (P.40).
By way of illustration, emergent markets are challenging for many young firms as
the timing of market adoption is difficult to predict, and difficulties are often
compounded by straitened capital and human resources, so that the firm is too
drained to bridge the chasm (Moore, 1991). Clearly, an active presence in growing
markets where customer needs and awareness are established can offer young
firms significant advantage (Eisenhardt & Schoonhoven, 1990).
Lifecycles and system contingency
Adopting a growth perspective, Adizes (1979) portrays lifecycles themselves as
contingency models, suggesting that they provide frameworks for prescribing the
actions and decisions likely to be most effective at particular organisational stages.
Employing stages theory, it appears reasonable to assume that development takes
place in identifiable stages during the life course of most firms (Foss, 1997). The
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Models further suggest the potential for firms to foresee problems associated with
growth over time, and to gain insights for effective action (Adizes, 1979).
Articulating parallel factors that condition sector-specific patterns, Klepper (1997)
describes how firms could exploit regularities in evolution, including insights
offered by industry lifecycles. This is supported by La Rocca, La Rocca, and Cariola
(2011) who propose a financial lifecycle model that is homogenous for different
industries and consistent over time.
Child and Kieser (1981) are critical of efforts to support managerial decision-making
by attempting to typify development paths. They suggest that lifecycle models offer
only limited help to those seeking to navigate development, compounded by the
absence of information about potential time lags. Route 128’s minicomputer firms
and Silicon Valley’s semiconductor firms, followed lifecycle organisational and
location logics closely during the 1980s, but competition based on continuous - and
especially radical - innovations undermined the industrial maturity logic implicit in
those models, creating shortened lifecycles. However, Silicon Valley with its
capacity for experimentation, learning and pursuit of multiple technology
trajectories (Saxenian, 1994) privileged the companies involved in that system.
Life course
Child and Kieser (1981) outline the challenge of drawing a sharp distinction,
conceptually or empirically, between development brought about by strategic
choice and that caused by unplanned forces. They suggest that ‘the distinction
between development as a function of strategy and development as a function of
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managers and researchers risks producing spurious analyses of organisational
development. This notwithstanding, Aldrich and Auster (1986) suggest that
research on strategy and context would benefit from investigations which
simultaneously consider both levels of analysis and how they are connected.
Aldrich (1999) suggests substituting the life cycle concept with ‘life course’ (p.196)
as a means of avoiding implied determinism. Borrowing from population
demography, Aldrich and Ruef (2006) employ history rather than time as the
central attribute of firm evolution, suggesting a framework (Table 2-1)
incorporating age, period and cohort effects.
Age effect
Changes produced by processes inherently associated with duration of existence – e.g., decay of a founder’s initial enthusiasm
Period effect
Changes produced by historical events and forces that have similar effects on all organisations, regardless of age (e.g., deregulation of financial markets).
Organisations founded in the same year make up a group that moves together through time, experiencing historical periods and events while they are all the same age.
Cohort effect
Changes produced by historical events and forces that have different effects on organisations of different ages - for example, shortages of essential resources may weaken younger organisations but have little effect on older ones.
Table 2-1: Attributes of firm evolution (Aldrich and Ruef, 2006, p.164).
Many young organisations display drive, flexibility and dynamism derived largely
from the characteristics of their founders and the relative newness of their ideas,
assets and markets, while other young firms may make investments in people,
technology, and assets that they are unable to change subsequently because they
are blinkered or resource-poor (Eisenhardt & Schoonhoven, 1996). In a meta-
analysis of literature on SME age and growth in the US and the UK, Storey (1994)
finds that younger firms grow more rapidly than their more mature counterparts,
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SMEs find that as firms age, they are less likely to drive growth in employment and
turnover. In accord with Storey’s findings on the negative age-employment
relationship, they contend that this is due to owner-managers having achieved the
objectives they formulated at founding, and also that older firms are more likely to
have surpassed the minimum efficient scale of production giving them reasonably
secure position in their markets. One aspect of the aging process is goal-change,
including adjustment to meet broader and/or more achievable targets, and
maintaining the organisation once established (Child and Kieser, 1981). Growth has
been found to decrease with age when firm size is held constant (Evans, 1987). This
suggests that improved understanding of firm dynamics is critical to SSI governance.
Although a large body of literature charts the evolution of market structures,
industries and firms, including theories attempting to predict patterns of growth
over time (Evans, 1987; Klepper, 1997; Phelps et al., 2007), the implications of age,
stage, life course and lifecycle for SSI fit have received limited attention. Sorenson
and Stuart (2000) contend that there have been very few systematic studies of the
relationship between organisational age and firms’ propensity to innovate.
Similarly, Metcalfe (1997) observes wide differences in firms’ abilities to sense
relevant innovation opportunities and to manage technology creation processes.
Echoing that, Phelps et al. (2007) propose a typology of maturity stages of
absorptive capacity, assessing organisations’ abilities to engage with and use new
knowledge. The methodology employed in the current research offers an
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