Structural configurations in a financial service industry.

Pleshko, Larry P. ; Mohammad, Ali Husain

ABSTRACT

The paper presents an investigation of the strategic profiles of three structural configurations in a sample of executives at financial services firms. In particular, the authors investigate how the firm's in each structural configuration differ in regards strategy, performance, environmental perceptions, and their structural dimensions. Findings indicate that, for nearly every strategic variable, the order from high to low is generally high-structure firms, followed by mixed-structure firms, and then low-structure firms. High structure firms are characterized as larger firms which are aggressive marketers, market-oriented focusing on competitors, showing high levels of perceived performance and adaptability, as well as lower yet acceptable ROI. By definition, the high-structure firms exhibit high levels across all the structural dimensions. On the other hand, low-structure firms are shown to be smaller firms which are passive marketers with less focus on competitors, showing lower levels of perceived performance and adaptability, but higher levels of ROI. Again, by definition, low-structure firms exhibit low levels on all the structural dimensions. The profile of mixed-structure firms are in-between both categories.

INTRODUCTION

For many decades, researchers have studied the various constructs which comprise the strategic nature of organizations and how these constructs interrelate (Chandler 1962, Rumelt 1974, Hall & Saias 1980, Mintzberg 1979). However, an area that has been neglected within this body of research is the examination of the specific structural configurations implemented by firms, as well as how these structural configurations are related to strategy, performance, and environmental factors pertaining to the firm. Most research related to organizational structure focuses on the design, or dimensions, of the company (c.f. Miller 1988). But, rarely does a study investigate the overall structural design, which may be even more relevant than the specifics of the dimensionality (Mahajan & Vakharia 1990, Porter 1980).

The problem with focusing on the dimensions of an organization rather than on the overall structural design is that researchers are required to formulate a myriad of oftentimes contradictory multivariate hypotheses (Shani 1994). Any coherent theme(s) may be obscured by the simple task of interpreting numerous oftentimes different findings related to the dimensions of structure. An even more serious shortcoming of this approach is that reality is oftentimes not expressed in terms of linear relationships.

Additionally, contingency theory suggests that the levels of structural dimensions, as well as there relationships with other strategic firm characteristics, will depend on the context in which they occur (c.f. Ruekert et al 1985). For example, the centralization of power may correlate positively with innovation in small organizations pursuing a stable task, while the same relationship may be reversed in high-technology firms, where experts are often given broad freedoms to accomplish their goals (Miller 1983). Therefore, structural relationships cannot be divorced from their context. So the 'few variables at a time' alternative of relating strategy to structure may be cumbersome, as well as conceptually inaccurate at times.

It is therefore likely that the dimensions of organizational structure cohere within common configurations, as do those of strategy (c.f. Miles & Snow 1985). If true, then the task of relating the characteristics of the firm to the details of structure will be much simpler. In particular, commonalities across the structural dimensions may accurately reflect the idea of broad structural configurations. The purpose of this paper follows from these ideas and is twofold: (a) to determine if a coherent broad structural configuration is evident and (b) to examine empirically the strategic differences among the structural configurations in areas such as performance or strategy. A sample of executives at financial services firms is selected as the target to study. Firms are grouped into three structural configurations based on the specific structural dimensions: (i) low-structural form, (ii) mixed-structural form, and (iii) high-structural form. Then the profiles of these configurations are examined.

STRUCTURAL FORM

Numerous structural characteristics are common in the literature. However, four major structural dimensions are prevalent: (1) formalization, (2) integration, (3) centralization and (4) complexity (c.f. Child 1974, Ford 1977, Fry 1982, Dalton et al 1980, Hall 1977, Van de Ven 1976, Fredrickson 1986, Miller & Droge 1986, Miller 1987, Miller 1988). Centralization refers to the degree to which the right to make decisions and control activities is concentrated (Fredrickson 1986). In other words, a high degree of centralization within an organization means that the critical decisions are made at the top management level. Formalization can be defined as the extent to which an organization uses rules and procedures to prescribe behavior such as the details on how, where, and by whom tasks are to be performed (Fredrickson 1986). Formalization restricts the activities of employees to those proscribed in advance. Complexity describes the many, usually interrelated, parts of an organization (Fredrickson 1986). This includes the number of hierarchical levels, the span of control, or the geographical dispersion of operating sites, among others. Structural integration refers to the coordination of activities among the different specializations within the firm (Miller 1987). Highly integrated firms allow contacts between the experts within each department and also with the top level decision-makers.

Porter (1980) claims that organizations require a high degree across all of the structural dimensions in order to implement generic strategies. Thus, the use of consistent structural configurations may lead to better performance. Mahajan and Vakharia (1990) support this empirically in a dynamic environment, where higher performing firms are found to have constant or similar levels across all or most of the structural characteristics. Thus, it may be that the structural configuration plays a significant role in an organization's performance. In other words, perhaps the driver of performance is not the structural dimensions (formalization, integration, etc.) independently, but rather the combination of structural dimensions--referred to as "structural configuration". For this study, three broad structural configurations are possible from which firms might choose: (1) an organic firm: a low-structure, (2) a mechanistic firm: a high-structure, or (3) a hybrid firm: a mixed-structure configuration. Firms implementing a low-structure internal environment will exhibit lower levels across all the structural dimensions than other firms. Firms implementing a high-structure internal environment will exhibit higher levels across all the structural dimensions than other firms. Finally, a mixed-structure firm will show a variety of levels of structural dimensions versus other firms.

OUTCOMES OF STRUCTURAL FORM

Firms with a high-structural configuration (the more mechanistic firms), are said to be more controlling of decision making than other firms, especially the low-structure firms (the more organic firms). Those firms exhibiting a mixed-structure (the hybrids), are most likely the most adaptive firms as they design the structural configuration to match the demands of the market environment. The strategic differences among these configurations have not been extensively delineated and they will now be addressed regarding the general relationships to other characteristics of the firm.

The relationship between organizational structure and performance can best be summarized as inconsistent, since the relationships between key structural dimensions and performance is not strongly supported (Dalton et al 1980). The findings on the associations of integration, centralization, complexity, and formalization to performance do not offer a consistent pattern, oftentimes being positive and other times being negative (c.f. Miller 1988, Dalton et al. 1980, Nwachukwu & Tsalikis 1990). Argument for the positive relationship between high levels of structure and performance stems from the fact that high degrees of formalization contribute to the reduction of role ambiguity and role conflict. Reducing these work stress factors results in increases in related employees attitudes (job satisfaction, organizational commitment, turnover, absenteeism, etc.), and eventually firm performance. Recent studies reveal that corporate boards believe centralized strategic guidance by skilled top management teams produces higher firm performance than widely dispersed management teams (Phan 2000). However, firms with more complex structures are generally found to be better performers, thus, suggesting that maybe not always will the more mechanistic firms be better performers.

Although the inconsistency of the impact of structural dimensions on performance is accepted, it is widely known that specific structural characteristics do indeed influence performance in some way (Miller 1988). In particular, it may be that the 'fit' between organizational structure and organizational strategy is the key criteria in a given situation (c.f. Venkatraman 1989, Miller 1986). Miller (1988) finds that integration and formalization are relevant for performance for specific strategic types. Therefore, it may be that certain structural dimensions must be present with given strategies in order for the firm to achieve high performance levels (Zeffane 1989). This would suggest that a 'fit' or, alternatively, an interaction between strategy and structure is relevant to performance. Regardless of the explanation, in many cases the benefits of highly structured firms and programs (i.e. TQM, ISO 9000) are apparent for management continuity, quality, and innovation, eventually resulting in improved performance (Shah 2000, Aiken & Hage 1971).

The relationship between structural configuration and environmental perceptions seem to be dependent on environmental uncertainty. The literature on structure and environment indicate that organizations are most effective when their overall design characteristics match or fit with the environment and the levels of uncertainty (Doty et al 1993, Miller 1986, Ruekert et al 1985). Specifically, the more scarce, dynamic, and complex is the environment, then the more organic (low structure) the recommended firm structure. Similarly, the more abundant, stable, and simple is the environment, then the more mechanistic (high structure) should be the structural configuration.

The relationship of structural configuration to marketing factors is not well defined in the literature (c.f. Ruekert et al 1985). Research to date suggests that formalization and centralization, as well as departmentalization, are inversely related to information utilization, which is important for marketing programs (Deshpande & Zaltman 1982, Hage & Aiken 1970). Therefore, it appears that formalization and centralization are inversely related to an organization's responsiveness: more formalized and centralized firms are slower to react to market changes or opportunities. We might expect the more mechanistic firms to be less market-oriented. However, there is reason to believe that the more mechanistic organizational structures may actually result in increased market-orientations. Firms which are more highly structured may more often have the required resources (and personnel) to implement the systems necessary to be truly market-oriented with long-term focus on customers and competitors and attention to the dynamics of the market environment. This may be important because a critical factor pertinent to a market-orientation is the integration of marketing thought throughout the firm--something requiring systems and policies to accomplish (Narver & Slater 1990).

The relationship of structural configuration to innovation, and marketing initiative or leadership, is better understood. Zaltman et al. (1973) draw on numerous studies to argue that organizational dimensions such as formalization, centralization, and departmentalization may have undesirable effects on the planning stages of innovative behavior: more highly structured firms are less innovative. However, the innovative activities may be more successful once implemented under a more mechanistic structure. Therefore, we might expect higher levels of marketing initiative or leadership to be associated with any of the three structural configurations, depending on the innovative activity under investigation. But again, due to the requirements that marketing systems are in place in order for marketing activities to be successful, it may be that more highly structured firms are more supportive of these types of activities.

INDUSTRY/SAMPLE DESCRIPTION

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.

MEASURES

Structural configuration (STRUCFN) is derived from the relevant dimensions of organizational structure, including formalization, integration, centralization, and complexity. Then, the configuration indictor used in the analysis is derived from these four dimensional measures. The firms' structural characteristics are measured 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". From these twelve items the three possible structural forms of firms is derived, as follows: (i) High-Structure, (ii) Low-Structure, and (iii) Mixed-Structure. 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 analysis resulted 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.

In order to assign firms to the three structural groups (Low, Mixed, High), first a median split is used to divide each of the individual structural dimensions into high and low categories. Thus, each of the one-hundred nineteen useable respondent firms is now classified as having either high or low levels of formalization, high or low levels of integration, and high or low levels of centralization/complexity. The structural configuration indicator (STRUCFN) is then derived in the following manner. Firms which exhibit high levels across all of the structural dimensions are categorized as High-Structure (26%, n=31/119). Firms which exhibit low levels across all of the structural dimensions are categorized as Low-Structure (15%, n=18/119). Firms which exhibit inconsistent levels across the structural dimensions are categorized as Mixed-Structure (59%, n=70/119).

Firm Size is included as another relevant structural characteristic, as it may oftentimes be viewed as a proxy for many other organizational characteristics (c.f. Hall et al 1967). In particular, asset size (ASIZE) is the indicator used to represent size of the credit unions. Firms are self-classified by marking the box next to the appropriate asset size category and then ASIZE is estimated from the categories. The size of each credit union is estimated to be the midpoint of the category. This should arrive at an acceptable estimate when accumulated over the 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.

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, with a possible range from six to thirty. A reliability estimate is found to be .902 using coefficient alpha. SMI has a mean of 13.72 and a standard deviation of 5.72.

Market-orientation is defined as a firm's perspective towards its market environment and, in particular, towards its customers and competitors and the variable items are adapted from previous research (Pleshko & Heiens 2000, Narver & 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 & Slater 1990). CUSTO has a mean of 7.87 and a standard deviation of 2.13. COMPO has a mean of 13.52 and a standard deviation of 3.61. Finally, MARKO, the sum of the two dimensions, has a mean of 31.38 and a standard deviation of 4.51.

Regarding firm performance, both market share and profitability 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 & Ramanujam 1986, Rueckert et al 1985, Keats & Hitt 1988, Frazier & Howell 1983). It is also possible that objective measures may lead to different results than perceptual measures (c.f. 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 objectives in the study should greatly add to the findings, especially since different strategies may affect share but not profits, or vice versa (c.f. Kirca et al 2005).

The accounting indicators of performance, ROI and ROA are derived from government-mandated accounting reports. The ROA variable has a range from 0% to 5%, a mean of 2.20%, and a standard deviation of 0.98. The ROI variable 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. 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 profits and the other representing perceived market share. Perceptual market share (PSHARE) 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 market share performance, PSHARE, is constructed by summing the five. A reliability of .872 is found using coefficient alpha. PHARE ranges from five to twenty-five with a mean of 14.64 and a standard deviation of 3.56. The perceptual indicator of profits (PPROF) is derived from five questions. 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 PPROF is constructed by summing the five items. A reliability of .870 is found using coefficient alpha. PPROF ranges from five to twenty-five with a mean of 16.06 and a standard deviation of 4.35.

Additionally, a single-item indicator of perceived adaptability (PADAPT) is included. Adaptability is said to have an impact on other aspects of firm performance (c.f. McKee et al 1989). Adaptability is measured using a single-item scale ranging from [1] poor to [5] excellent, as regards a firms adaptations made to the changing environment over the past year. PADAPT has a possible range from one to five, a mean of 3.29, and a standard deviation of 0.91.

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 7.35, and a standard deviation of 2.43.

ANALYSIS/RESULTS

Averages are calculated on all the strategic indicators, for each of the three structural groups. This is followed by an analysis of variance to determine significant differences. If differences are evident, as noted with the 'p'-values, then a post-hoc test using least-squared differences is performed. These post-hoc contrasts are noted to be significant only if they exhibit a 'p'-value of .05 or less. Table 1 exhibits the means, test statistics, and summarizes the findings for each group and each variable.

As noted in the table regarding structural indicators, all three of the structure dimensions show significantly different levels across the groups. However, since the structural configuration was based on the four dimensions, it is expected that the three groups will exhibit significant differences--and it is so. All three, formalization (FORM, p=.000), centralization and complexity (CNCM, P=.000), and integration (INTE, p=.000), show similar significant differences in structural dimensions with high-structure firms exhibiting larger levels than mixed-structure firms, which have higher levels than low-structure firms. Note also structurally that high-structure firms are significantly larger than mixed-structure firms, which are again larger than the low-structure firms. Large firms have an average asset size of $31.9M, while the small firms have an average size of only $6.8M: a very significant difference. Even the mixed-form firms are barely half the size of the high-structure firms.

As noted in the table regarding strategy indicators, three of the four constructs show significantly different levels across the groups. For marketing initiative or aggressiveness (SMI, p=.000), high-structure firms display significantly more initiative than either mixed-structure of low-structure firms, while mixed-structure firms have a higher level of initiative than the low-structure firms. For overall market orientation (MARKO, p=.012), the low-structure firms are significantly less market oriented than the other firms. This market orientation difference is based on the level of competitor-focus (COMPO, p=.000) implemented by the firms, with the low-structure firms displaying significantly lower levels of competitor-focus than do the other firms.

The table also reveals that the groups differ on perceived performance as well, with four of the five indicators exhibiting significant differences. For the perceptual indicators, the results are the same: high-structure firms are the better performers in regards profits (PPROF, p=.010), share (PSHARE, p=.000), and adaptability (PADAPT, p=.000). However, the accounting indicators of percentage returns reveal a different story. No differences are found for ROA (p=.217). But, the low-structure firms show better returns on investment (ROI, p=.009) than other firms and the high-structure firms the lowest ROI, an interesting finding.

Finally, no differences are shown for the perceptions of the external environment (DYNA, p=.191). All three structural configurations seem to have similar evaluations of the environment in which they operate.

DISCUSSION/IMPLICATIONS

The primary purpose of this research is to investigate whether broad structural configurations are evident in the financial services industry and to determine if important strategic differences exist in regards other relevant characteristics of the firm. The statistics reveal that most firms implement a hybrid configuration (a mixed structural form), with the mechanistic (high structural form) and the organic (low structural form) configurations evident in much smaller proportions. The results show that these configurations are significantly different across all four of the main structural dimensions, as expected by definition. The results also show that these broad structural configurations are highly related to other characteristics of the firm.

Most characteristics of the firm in this study show important differences across the configurations. Where differences exist, except for ROI, the high-structure firms show higher levels on the strategic constructs than the lesser-structured firms. Also, in most cases the mixed-structure firms exhibit higher levels on the strategic constructs than low-structure firms, except for ROI and perceptual profits. Note no differences are found between the groups regarding environmental perceptions, ROA, and customer orientation.

These findings suggest that firms implementing a mechanistic or highly-structural form, characterized by high levels across all the structural dimensions, are better performers in services industries than other firms, unless the main goal is percentage returns. If returns are the primary focus, then the lesser-structured, more organic form might be the better choice. A hybrid or mixed-structural form might also be acceptable in either case, but with lesser perceived and/or accounting performance. But, regarding performance levels, the low-structure form seems to definitely be the lesser performers, except for ROI, at least on the constructs of this investigation.

In regards marketing efforts, the low-structured firms are definitely less market-oriented and aggressive. However, this may actually contribute to the better percentage returns when compared to other firms. Since low-structure firms do not invest large sums of money and effort into activities related to market leadership, it may be possible that this lower investment level leads to better performance. Possibly, being a follower firm in the financial services industry is an acceptable form of competitive strategy.

CONCLUSIONS/LIMITATIONS

The paper studies executives from financial services firms to determine if the strategic profiles differ based on the structural configuration of the firm. In particular, the authors investigate how the firm's in each structural configuration differ in regards strategy, performance, environmental perceptions, and their structural dimensions. Three possible configurations are identified from four dimensions of structure: (i) low-structural form, (ii) mixed-structural form, and (iii) high-structural form. Findings indicate that, for nearly every strategic variable, the order from high to low is generally high-structure firms, followed by mixed-structure firms, and then low-structure firms. The one major exception is that low-structure firms show better ROI than other firms. High structure firms are generally characterized as being aggressive marketers that are market-oriented focusing on competitors, and showing high levels of perceived performance and adaptability. By definition, the high-structure firms exhibit high levels across all the structural dimensions. On the other hand, low-structure firms are passive marketers with less focus on competitors, while showing lower levels of perceived performance and adaptability, but higher returns. Again, by definition, low-structure firms exhibit low levels on all the structural dimensions. The profile of mixed-structure firms are in-between both categories. It appears that firms wishing to achieve better returns might opt for the low-structure/follower strategy, while other firms wishing for higher market shares or perceived profitability or adaptability opt for a high-structural form. A mixed-structural form is always an option for a middle, safe place in the market.

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, as mentioned previously, conclusions might not be as applicable to smaller firms as to medium and large sized firms. Second, 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 differences among the structural configurations may be different if measured at (a) an earlier or later time in the same manner or (b) continuously over time. Also, more objective indicators of market share 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 P. Pleshko, Kuwait University

Ali Husain Mohammad, Kuwait University

Table 1: Structural Group Profiles

Variable / Group Low Mixed High F/[X.sup.2] 'p' Finding

Number 20 72 32
 Structure
FORM (avg) 9.6 12.9 15.6 32.9 .000 H>M>L
CNCM (avg) 5.7 8.5 12.2 54.2 .000 H>M>L
INTE (avg) 7.4 11.6 14.1 31.6 .000 H>M>L
ASIZE (avg $M) 6.8 15.4 31.9 20.6 .000 H>M>L
 Strategy
SMI (avg) 7.7 13.5 17.8 27.5 .000 H>M>L
CUSTO (avg) 18.0 17.8 17.7 0.06 .938
COMPO (avg) 10.7 13.8 14.5 8.54 .000 H,M>L
MARKO (avg) 28.7 31.6 32.3 4.63 .012 H,M>L
 Performance
ROI (%) 9.3 7.5 7.3 4.98 .009 L>M,H
ROA (%) 2.4 2.2 1.9 1.55 .217
PPROF (avg) 14.0 15.8 17.7 4.79 .010 H>M,L
PSHARE (avg) 11.6 14.6 16.2 11.0 .000 H>M>L
PADAPT (avg) 2.4 3.3 3.6 16.0 .000 H>M>L
 Environment
DYNA (avg) 7.5 7.6 6.6 1.67 .191