Strategic comparisons of very large firms to smaller firms in a financial service industry

Strategic comparisons of very large firms to smaller firms in a financial service industry

Larry Pleshko


The paper presents an empirical investigation comparing strategic profiles of very large enterprises (VLEs) to small and medium sized firms (SMEs) in the financial services industry, collecting data from a sample of executives at credit unions. In particular, the authors find VLEs to be larger, more structurally integrated, centralized and complex, more market oriented, and to be more aggressive firms in general when compared to SMEs. The VLEs also show higher levels of perceived relative profitability and relative adaptability than SMEs. However, no differences are found between VLEs and SMEs regarding profitability when considering accounting returns. It appears that size is a critical factor to be considered in the financial services industry.


The purpose of this paper is to examine empirically the relationship of organizational size to the strategic profiles of firms in the financial services industry. In particular, very large firms (VLEs) are compared to small and medium size firms (SMEs) across a range of performance, structural, and strategy constructs. The size of an organization often is viewed as a surrogate for the many detailed dimensions of an organization’s structure and related decision making patterns (e.g., Dalton et al. 1980, Pugh et al. 1968). Therefore, we may expect important strategic profile differences between VLE and SME firms, which in turn may provide guidance to managers and other interested parties for future strategy decisions.

It is important to note that a precise determination of a strategic profile in any firm is not easily accomplished. Inconsistencies in the findings from empirical studies point to a conclusion that an acceptable strategy will depend on the situation (Provan 1989). But, the question of how the size of firms relate to strategy has seen renewed interest in the past decade, as the globalization patterns of large corporations impacts the survival of smaller firms in various countries around the world (Hutchinson et al 2005, Pan and Li 2000). This is especially relevant since the trend in a variety of industries is to fewer but larger firms (Daniels et al 1988). Additionally, many recent works have shown that small firms exhibit differences from larger firms on a variety of factors, which may or may not influence performance (c.f. Bhaskaran 2006, Taymaz 2005, Pompe and Bilderbeek 2005, Acar et al 2005, Biesenbroeck 2005, Smith et al 1986).

Thus, the current study attempts to determine whether company size differences across a profile of strategic constructs is evident in firms in the financial services sector. The majority of studies related to size and strategic decisions appear to investigate manufacturing firms (Van Biesenbroek 2005, Bommer and Jalajas 2002, Schuh and Triest 2000). Thus, this study adds to those in the minority which address non-manufacturing sectors of business (Wernz 2002, Hutchinson et al 2005, Daniels et al 1988). This paper begins with a review of the relevant literature, which is followed by a description of the sample, the measures, the analysis and results, and concludes with a discussion of the findings from the research.


Organizational size is a characteristic of the firm representing how large or small a firm might be. It is measured in a variety of ways depending on the industry under study, including the total sales, number of employees, or asset-holdings of firms (Calof 1993, Dalton et al. 1980, Joaquin and Khanna 2001). Size is an important research variable as it often exhibits an association with the major characteristic descriptors of decision making outcomes: organizational structure, strategy, and performance. In particular, it is widely accepted nowadays that small and large firms differ in many ways, not limited to the availability of funds for activities and management styles and objectives (Beaver 2003). These differences may result in divergent paths to success or failure in many industries.

The literature points to firm size as a determinant of company strategy as indicated by distinctive group membership, but with no clear conclusions evident as to which is better. Size appears to have some influence on export-related activity and strategy, but there is question as to whether it is more advantageous to be of large or small size (Bilkey 1987, Birch 1988, Calof 1993, Edmund and Sarkis 1986, Ekanem 2000, Joaquin and Khanna 2001, Moini 1995, Wolff and Pett 2000). The findings from other studies suggest no relationship between size and strategy (Francis and Colleen 2000, Leonidou and Katsikeas 1996). This may be due to the presence or absence of other important variables, such as export experience or foreign market knowledge, for example (Edmund and Sarkis 1986).

The size of firms is also shown to play an important role in organizational design. Larger firms are found to integrate operations over bigger areas than smaller firms: regional or global even (Dunning 1992). Also, larger firms show a more structured organization in general, being more centralized and using more non-personal forms of control over decision making (Ronen and Shenker 1985).

Other studies have shown an important role for firm size as a moderator variable in relation to various performance indicators. For instance, Pelham (2000) found a significant impact of size on the outcomes of market orientation. Ali and Swiercz (1991) claim that mid-sized firms offer the greatest potential response for increasing export performance. Smith et al (1986) have found that size plays a role in the success or failure of strategies. More specifically, they find that defenders outperform analyzers and prospectors as small firms, while prospectors perform better than defenders and analyzers as medium to large size firms, and analyzers perform better as very large firms. Even productivity seems to be influenced by firm size, in most cases with small firms outgrowing large firms (Sleuwaegen and Goedhuys 2002). However, Biesenbroeck (2005) shows that this is not always the case; in some areas, large firms grow faster than small firms in some African industries (Biesenbroeck 2005).


In the financial services industry, credit union executives are the target of the survey. Data for the study are gathered from a statewide survey in Florida of all the credit unions belonging to the Florida Credit Union League (FCUL). Membership in the FCUL represents nearly 90% of all Florida credit unions and includes 325 firms. A single mailing was directed to the president of each credit union, all of whom were asked by mail in advance to participate. A four-page questionnaire and a cover letter, using a summary report as inducement, were included in each mailing. Of those responding, 92% were presidents and 8% were marketing directors. This approach yielded one hundred and twenty-five useable surveys, a 38.5% response rate. A Chi-squared test of the respondents versus the sampling frame indicates that the responding credit unions are significantly different from the membership firms based on asset size (Chi-sq=20.73, df=7, p<.01). Further analysis of the sample indicates that the smaller asset groups are under-represented.


Firm Size is included as the primary focus of the investigation. Size is oftentimes viewed as a proxy for many other organizational characteristics and has an integral impact on firms’ activities (c.f. Hall et al 1967). In particular, asset size (ASIZE) is the indicator used to represent size of credit unions. Firms are self-classified by marking the box next to the appropriate asset size category and then an approximate ratio-level indicator, ASIZE, is derived from the categories. The largest size category, $50M or above, represents a minimum size for the largest firms. Therefore, the actual size of the largest firms may be much higher. The size of each credit union is assigned to be the midpoint of the self-selected category. This procedure provides an acceptable size estimate when accumulated over an entire sample. ASIZE, therefore, has a possible range from $250,000 to $50,000,000, a mean of $18,000,000, and a standard deviation of $17,121,020. Firms with asset size holdings of $50M or more (the category with largest assets) are considered to be very large firms (VLEs), while all other firms are considered to be small and medium sized enterprises (SMEs). VLEs represent 13.7% (17/124) of the firms, while SMEs total 86.3% (107/124) of the credit unions. The average asset holdings for VLEs are at least $50M, while the holdings of the SMEs are $13.2M. The reader is referred to Table 1, later in the text, for this and other statistics. Note that the VLE credit unions are approximately four times as large as the SME firms.

Organizational structure is measured using relevant company dimensions from the literature: formalization, integration, centralization, and complexity. These four structural characteristics are measured for each firm using a twelve-item instrument ranging from [1] true to [5] not true. Respondents are asked to circle the number which best describes their firm in regards questions such as: “decision making is highly controlled”. The twelve structure variables are subjected to a factor analysis using principal factors followed by a varimax rotation. One of the twelve items was eliminated due to inconsistent loading, leaving eleven items. This procedure results in three dimensions explaining 60% of the original variance: (1) formalization (FORM)–four items, (2) integration (INTE) -three items, and (3) centralization and complexity combined (CNCM)–four items. Summated scales are used for each of the three components to derive overall indicators of the structural dimensions themselves. Reliability, as measured by coefficient alpha is as follows: .791 for formalization, .696 for centrality/complexity, and .642 for integration. FORM ranges from four to twenty, with a mean of 13.1 and a standard deviation of 3.2. INTE is adjusted have a range from four to twenty also, and has a mean of 11.6 with a standard deviation of 3.6. CNCM ranges from four to twenty, with a mean of 9.0 and a standard deviation of 3.1.

Marketing initiative, or aggressiveness, is conceptualized as inclusive of six relevant areas related to marketing strategy: products, advertising campaigns or other promotions, pricing changes, distribution ideas, technology, and markets (Heiens et al 2004, Pleshko et al 2002). Respondents are asked to evaluate on a scale from [1] not true to [5] true whether their firm is ‘always the first’ to take action regarding the six items. A principle axis factor analysis indicates the six items load highly on a single factor explaining approximately 67.9% of the original variance in the items. An overall indicator of strategic marketing initiative (SMI) is constructed by summing the six items. A reliability estimate is found to be .902 using coefficient alpha. SMI has a range from six to thirty, a mean of 13.7 and a standard deviation of 5.7. On the average, credit unions do not exhibit large amounts of marketing initiative.

Market-orientation is defined as a firm’s perspective towards its market environment and, in particular, towards its customers and competitors. The instrument items are adapted from previous research (Pleshko and Heiens 2000, Narver and Slater 1990). Respondents are asked to evaluate their firm’s efforts in the marketplace on a scale form [1] not true to [5] true. The seven items are subjected to a factor analysis using principal axis factoring followed by a varimax rotation. The analysis resulted in two components, three for competitor orientation and four for customer orientation, explaining 69.7% of the original variance. Summated scales were used to represent each of the two components: customer-focus (CUSTO) and competitor-focus (COMPO). CUSTO and COMPO have a possible range from four to twenty-eight. The reliability of the scales, as measured by coefficient alpha was: customer-focus–.834 and competitor-focus–.789. An overall indicator of market orientation (MARKO) is also created by adding the two components, as in previous empirical efforts (Narver and Slater 1990). CUSTO has a mean of 7.8 and a standard deviation of 2.1. COMPO has a mean of 13.5 and a standard deviation of 3.6. Finally, MARKO, the sum of the two dimensions, has a mean of 31.3 and a standard deviation of 4.5.

Regarding firm performance; market share, profitability, and adaptability indicators are included in the study. In addition, both perceptual and accounting variables are included as well, which should alleviate some of the problems associated with each type of measure (Venkatraman and Ramanujam 1986, Rueckert et al 1985, Keats and Hitt 1988, Frazier and Howell 1983). It is also possible that objective measures may lead to different results than perceptual measures (Kirca et al 2005). Also, market share and profits are two distinct goals, each with their own demands on the firm. The inclusion of both marketing goals in the study should greatly add to the findings, especially since different strategies may affect share but not profits, or vice versa (Kirca et al 2005).

The accounting indicators of performance, ROI and ROA are taken from government-mandated accounting reports. The ROA indicator has a range from 0% to 5%, a mean of 2.20%, and a standard deviation of 0.98. The ROI indicator has a range from 1% to 17%, a mean of 7.77%, and a standard deviation of 2.26.

For the perceptual performance indicators of market share and profit, ten items are included on the instrument as described below. Note that these ten items represent relative perceptions of a firm’s performance. The ten items are subjected to a principle axis factor analysis, followed by a varimax rotation. This procedure results in two distinct dimensions explaining 66.4% of the original variance in the ten items. The items load as expected with one dimension representing perceived relative profits and the other representing perceived relative market share. Relative market share (PSHR) is a perceptual indicator measured using a five-item scale, ranging from [1] poor to [5] excellent, as regards five baselines of market share: (1) vs. competitors, (2) vs. goals/expectations, (3) vs. previous years, (4) vs. firm potential, and (5) growth. The overall indicator of relative market share performance, PSHR, is constructed by summing the five. A reliability of .872 is found using coefficient alpha. PSHR ranges from five to twenty-five with a mean of 14.6 and a standard deviation of 3.5. The perceptual indicator of relative profits (PPRR) is derived from five questions also. In particular, respondents are asked about their profit performance on a scale from [1] poor to [5] excellent, relative to five profitability baselines: [1] vs. competitors, [2] vs. goals/expectations, [3] vs. previous years, [4] vs. firm potential, and [5] growth. An overall indicator of PPRR is constructed by summing the five items. A reliability of .870 is found using coefficient alpha. PPRR ranges from five to twenty-five with a mean of 16.0 and a standard deviation of 4.3.

Additionally, a single-item indicator of perceived adaptability (PADP) is included. It is measured using a scale ranging from [1] poor to [5] excellent, as regards a single item: adaptations made to the changing environment. PADP has a possible range from one to five, a mean of 3.3, and a standard deviation of 0.9.

One indicator related to the firm’s perceptions of the external environment is included: Environmental Dynamism (DYNA). The environmental construct is described as the amount of change occurring in an industry environment (Miller 1988, Achrol et al 1983). The respondents are asked to evaluate their perceptions of the environment on a bipolar scale from [1] to [7] across three items representing dynamism: stable/unstable, variable/not variable, and volatile/not volatile. A factor analysis using principal axis factoring followed by a varimax rotation is performed. The three items load on one dimension explaining 58.7% of the original variance. A summated scale is constructed for DYNA with a reliability of .639 using coefficient alpha. DYNA has a possible range from three to fifteen, a mean of 7.3, and a standard deviation of 2.4.


Averages are calculated on all the strategic indicators for the two size groups: VLEs and SMEs. This is followed by an analysis of variance to determine significant differences. Table 1 exhibits the means, test statistics, and summarizes the findings for the two groups and each variable. Regarding the structural indicators, two of three structural dimensions show significantly different levels between VLEs and SMEs. Formalization (FORM, p=.108) shows no differences between large and small firms, while both integration (INTE, p=.007) and centralization/complexity (CNCM, p=.000) exhibit significant differences between the size groups. In both cases the VLEs have higher levels of the structural dimension than the smaller SMEs.

Pertaining to the marketing strategy indicators, two of four constructs show significantly different levels across the groups. For marketing initiative or aggressiveness (SMI, p=.001), VLEs display significantly more initiative than the SMEs. Also, for overall market orientation (MARKO, p=.047), VLEs display significantly higher levels than the SMEs. But this MARKO difference is not based on either of the two dimensions independently, as neither CUSTO (p=.263) or COMPO (p=.074) exhibit differences between VLEs and SMEs.

The table also reveals that size groups differ on performance, with two of five indicators exhibiting significant differences. For the perceptual indicators, relative share (PSHR, p=.089) is not statistically different, although it appears to approach the level of importance. However, both relative profits (PPRR, p=.031) and adaptability (PADP, p=.008) both show statistical differences. VLEs exhibit more adaptability and higher relative profits than SME credit unions. But, the objective accounting performance indicators (ROI, p=.094; ROA, p=.606) show no differences between very large and other firms.

Finally, no differences are shown for the perceptions of the external environment (DYNA, p=.587). Next, a discussion of these findings is presented.


The primary purpose of the paper is to present a strategic profile of both very large firms (VLEs) and the smaller to medium size firms (SMEs) in the financial services industry. Specifically, the author investigates differences between VLEs and SMEs on a variety of structural, strategy, and performance indicators. It is shown that VLE and SME firms exhibit a lot of similarity, with no significant differences on many of the variables: formalization, customer focus, competitor focus, relative market share, ROI, POA, and environmental perceptions. However, there are many important differences between VLEs and SMEs. For every indicator showing significant differences, the VLE firms exhibit higher levels on that variable compared to the SME firms. VLEs are much larger (obviously), have more integrated decision making, a higher level of centralization and complexity in the decision structure, more focus on their markets, more aggressiveness in marketing activities, and higher levels of adaptability and relative profitability.

These findings suggest that very large financial services firms (VLEs) have performance advantages compared to smaller firms, having larger total returns due to their size. But these larger returns are not translated into higher returns on investment or assets, both percentage indicators. It may be that VLEs spend much more money than SMEs adapting to the environment and implementing new marketing programs in hopes of taking advantage of extant opportunities. These additional expenditures may not always lead to enough marginal performance to improve ROI and ROA. Therefore, successful firms that want to make more money may simply try to grow larger, given the average returns in this sector of the industry.

In fact, this striving to become larger–given constant percentage returns–may be what was behind the wave of consolidations in the credit union sector, and financial services in general, during the past twenty years (c.f. Wilson and William 2000). In both Britain and the United States, asset holdings rose dramatically throughout the 1990s (Kaushik and Lopez 1996, Jefferson and Spencer 1998, Wilson and William 2000). In the United States during this time, an industry consolidation led to larger institutions, resulting in stronger competitors within the industry (Kaushik and Lopez 1996, Pleshko and Cronin 1997). Additionally, an easing of restrictions led to cross-industry battles with other types of financial institutions, such as traditional banks and savings banks (Allred and Addams 2000). Thus, it appears that managers all over the industry, pressured to achieve higher overall returns, turned to acquisitions, new markets, or increased penetration to grow assets and become larger firms.

Since most firms in this study show relatively small levels of initiative, but above average emphasis on customers (see Table 1 and refer to text for range of indicators), it may have been a relatively easy step to invest a bit more money and time into more aggressive marketing activities. After all, the entire industry, as would be expected in this type of high-involvement service environment, is heavily focused on serving and keeping customers, as they are the mainstay or the business.

Additionally, as the small firms became medium sized and others became VLEs, organizational designs were altered to provide more adaptable, market-oriented firms, with organizational structures to support this striving for bigness. In doing so, large financial services firms, as noted in this study, needed to be more structurally integrated to handle decision making across a larger company. These larger firms also had to develop systems to allow more centralized control over decision making from a more complex organization, employing skilled managers with a variety of goals.


The paper studies financial services firms to determine if the strategic profiles differ based on the size of the firm. In particular, the authors investigate how very large firms (VLEs) differ from other firms (SMEs) in a sample of executives at credit unions in the USA. The findings suggest that VLEs and SMEs have many similarities in areas such as performance returns, formalization levels, and their focus on customers and competitors. However, there are many other strategic areas where VLEs and SMEs show striking differences. The very large firms, when compared to the SME firms, are more adaptable, more aggressive with marketing activities, have a more integrated and centralized (not to mention complex) organizational design, all leading to higher levels of relative profitability. Growth in an industry such as this one in financial services, where similar percentage returns may be viable for all possible sizes of firms, may be most easily achieved by simply getting bigger.

Caution should be used when generalizing this study to other firms, whether in products or services industries. There several limitations to the conclusions based on the methodology of the study. First, one-shot studies during a single time period are often myopic when investigating strategies. Hatten et al (2004) find that the effects of strategies evolve over time and that it is the implementation of the strategy which is truly important, rather than the classification of the strategic type. Thus, the distinctions derived from the strategic variables might be different if measured at (a) an earlier or later time in the same manner or (b) continuously over time. Also, a more objective indicator of market share, rather than the perceived relative indicator of this study, may lead to other conclusions. In addition, the study should only be cautiously generalized to other firms in the financial services industry outside of credit unions. Credit unions exist in an environment that is more protected than other financial institutions, such as banks, and therefore any generalizations might be suspect. It is suggested that future studies investigate this relationship in banks, savings & loans, and other financial services industries. Future studies might also apply this framework to products industries in both the business-to-business and consumer products area to further test the findings.


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Larry Pleshko, Kuwait University

Inge Nickerson, Barry University

Table 1: Size Group Profiles

Variable/Group VLEs SMEs T ‘p’ Finding

Number 17 117

ASIZE (avg $M) $50M+ $13.2M 30.6 .000 VLE>SME


FORM (avg) 14.2 12.9 1.62 .108

CNCM (avg) 10.9 8.7 3.89 .000 VLE>SME

INTE (avg) 13.8 11.2 2.73 .007 VLE>SME


SMI (avg) 18.0 13.0 3.51 .001 VLE>SME

CUSTO (avg) 18.4 17.7 1.12 .263

COMPO (avg) 14.9 13.2 1.80 .074

MARKO (avg) 33.3 31.0 2.00 .047 VLE>SME


ROI (%) 7.2 7.8 1.70 .094

ROA (%) 2.3 2.1 0.51 .606

PPRR (avg) 18.1 15.7 2.18 .031 VLE>SME

PSHR (avg) 16.0 14.4 1.71 .089

PADP (avg) 3.8 3.2 2.67 .008 VLE>SME


DYNA (avg) 7.0 7.4 0.54 .587

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