Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Optimizing the Firm’s Strategic Decisions by Using the
OSPP Computer Matrix Program Empirical Evidence
Dan Kipley, Alfred Lewis and Orlando Griego
Over the last thirty years researchers have developed analytical tools that
systematically attempt to establish a firm’s future competitive position and future
potential of the industry in which it competes. Many of these analysis tools
utilized positioning matrices to depict the firm’s relative position based on two
variables such as market share or growth rate. As researchers began to
understand the importance of internal and external factors, the analysis tools
were enhanced to enable management’s deeper interpretation of the firm’s
position. H. Igor Ansoff further contributed to academia and industry with his
Strategic Success Paradigm and subsequently his Ansplan-A computer program.
All of these assessments are invaluable strategic tools for firms. However, it
could be argued that these positional analyses fall short as each only analyze the
firm from a singular perspective and none of the matrices considers the
interactions of the other.
This research paper is an extension of an earlier conceptual paper published in
2012 and provides empirical evidence of a computer program that combines the
previous disjointed matrices and provides management with several critical views
of the firm’s strategic position such as its future competitive position, the
industry’s future prospects, and its strategic posture.
Extending the conceptual application to empirical research was requisite to
determine the efficacy of the computer program. Using SPSS analytics, our
study analyzed 43 publicly traded firms in various industries including retail,
technology, manufacturing and the service sector to determine if a relationship
exists between the firm’s center of gravity (COG) matrix position as indicated by
the computer program and key performance determinants as indicated by the
following financial ratios; current ratio, GPM, NPM, ROE, ROA, ROCI, EBITA,
revenue growth, P/E, EPS, and D2E.
Research findings indicate that there is a strong relationship between the firm’s
COG position and multiple key performance indicators thus providing support for
the efficacy of this computer model as a reliable strategic planning tool for
management in providing critical high-level data for optimal strategic formulation
and implementation.
Keyword: Ansoff, ANSPLAN-A, optimal strategic performance matrix, strategic
diagnosis, strategic management, firm performance, OSPP.
Introduction
The work of Mintzberg and Lampel (1999) was an important source of influence
in the study of strategic planning. They explored the critical characteristic of a
“formal” strategic planning process and the requirements that the process, “not
be just cerebral but formal, decomposable into distinct steps, delineated by
checklists, and supported by techniques”. Hence, the process of strategic
planning consists of adaptable set of concepts, procedures, tools, and practices
that are intended to assist organizations in determining where they are, what they
____________________________________________________
Dr. Dan Kipley, Azusa Pacific University
Dr. Alfred Lewis, University of Liverpool
Dr. Orlando Griego, Azusa Pacific University
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
should be doing, how to do it, and why (Bryson, 2004). Traditionally, the historic
paradigmic approach used by management was to follow a three-step method;
1). Setting goals and objectives 2). Determining alternative solutions and
strategic formulation 3). Implementing the feasible alternative (Ackerman, 1970;
Allison, 1970; Bower, 1970; Carter, 1971; Cyert & March, 1963; Mintzberg,
Raisinghani, & Theoret, 1976; Witte, 1972). Several normative approaches to the
design of strategic planning systems have been offered by Ackoff (1970); Grant
and King (1982); Andrews (1971); Ansoff (1971, 1984); Porter (1980,1985);
Bryson (2004); Cohen, Eimicke, and Heikkila (2008); Mulgan (2009); and Niven
(2008); however, these models have had limited applicability and utility in the
decision-making process when involving multiple critical variations in decision
situations. Nonetheless, the three-step method in the strategic decision-making
process is fundamental in determining the future course and optimal strategy of a
firm as well as having an impact on the functions of the firm, its direction,
administration, and structure. In spite of this, the three-step method falls short
when examining the interrelationship between all of the strategic decision-making
variables, specifically, the firm’s strategic posture and strategic budget, with the
future industry prospects and the firm’s future competitive position.
Extant research conducted by Bourgeois, 1984; Eisenhardt & Zbaracki, 1992;
Glaister, 2008; Hrebiniak, Joyce, & Snow, 1988 support the necessity of
integrating the elements of normative strategic planning with an analysis of the
external environment and the strategic choice perspectives in models of the
strategic decision process.
The purpose of this article is, thus, to contribute to the understanding of strategic
positioning and performance in organizations. More specifically, it addresses two
issues: Is there a relationship to be found among the firm’s financial ratios in the
context of high performance and the positional display on the (Optimal Strategic
Performance Positioning) OSPP matrix? Is there continuity in the performanceposition relationship between certain classes of businesses?
In this study, strategic analyses were conducted on manufacturing, service,
merchandise, and trade. Comparisons were made with respect to certain
attributes that are specified in the first section of this paper. The second section
consists of methodology discussions and choices, while the third section
presents the relatedness patterns and performance effects. The findings and
limitations are finally discussed and this leads to conclusions, implications, and
recommendations for further studies.
History
The OSPP computer model was developed as an extension of Dr. H. I. Ansoff‘s seminal
work on a robust, interactive, strategic management computer program designed to
integrate the analytical power of the computer with the experiential heuristics of senior
management. Ansoff‘s ANSPLAN-A was based on the empirically proven Strategic
Success Paradigm (Ansoff et al., 1993), which was confirmed reliable as a Tier 3,
strategic analysis system. At introduction, Ansoff‘s ANSPLAN-A was at the forefront of
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
the industry in strategic analytics. The program was explicitly designed to be robust as
well as intricately detailed in data assessment yet simple to use requiring that managers
only need to respond to a detailed list of possibilities for strategy and capabilities as well
as providing an estimate of the future competitive importance and probability of the
respective potentials.
The data collection for the ANSPLAN-A was achieved using four modules; Module 1
focused on the firm‘s current practice and the prospects available in the strategic business
area (SBA). During this analysis, the historic and present performance as well as its
products and strategies are held in abeyance with the overarching focus on the
identification of those threats, opportunities, growth, and profitability prospects that are
available to the SBA.
Module 2 estimates how well the SBA will perform if it follows its current strategy using
a range of uncertainties from pessimistic to optimistic.
Module 3 combines the previous modules and arrives at a decision point between an
immediate commitment to a future strategy or a gradual commitment based on keeping
options open for as long as possible. At this point, the software will guide the manager
through a series of consequences for each choice. If immediate commitment is chosen,
current strategies, capabilities to support the strategy, and the requisite strategic
investment are calculated to support the choice.
Module 4 is the final step in which the program identifies those programs and subprojects
that must be launched to ensure implementation of the project.
Although Ansoff‘s intent was to design a management tool with ease of use, it was all but
that as navigation was difficult, data screens were unintuitive, and the program was
plagued with constant ‗freezing‘ during data input and computations.
Current Industry Analysis Tools
The SWOT analysis is a simple structured framework that is used to evaluate the firm‘s
strengths, weaknesses, opportunities as well as the threats. Although it is important to
have an awareness of these four factors, the SWOT alone does not provide management
with the firm‘s future competitive position, the industry prospects, nor its strategic
posture. An extension of the data derived from the SWOT, the Internal Factors
Evaluation/External Factors Evaluation (IFE/EFE) are simplistic strategic tools used to
evaluate the firm‘s internal strengths and weaknesses (IFE) and the firm‘s external
available opportunities and threats (EFE).
The Competitive Profile Matrix (CPM) is a valuable strategic management tool that
compares one firm to other firms (competitors) in the industry. The CPM uses critical
success factors and assigns various weights relative to the importance in the firm‘s
industry. The CPM is very valuable as it shows the relationship of one firm to its
competitors, however, that is all the information the tool provides. The BCG GrowthShare Matrix utilizes a 2x2 grid to facilitate management in determining which areas of
the firm (products or business units) deserve more resources and investment based on the
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
relative market share and market growth rate. The GE-McKinsey Matrix is similar to the
BCG Matrix in that it positions business units on a 9-cell grid of the industry based on the
business unit strength and industry attractiveness. Although both matrices show a
position of current performance for the firm it remains a singular view of the firm
strategic position. The Strategic Position and Action Evaluation (SPACE) Matrix
provides the company with a recommended type of strategy to pursue based on analyzing
the internal strategic dimensions (financial, competitive) and the external dimensions
(industry strength, environmental stability) although this matrix is more advanced than
the previous matrices, it still falls short of a total analysis of the firm‘s future competitive
position and future industry prospects.
It is thus evident that the current strategic analysis tools, although important and useful,
fall short of providing management with a complete positional/performance analysis of
the firm.
The OSPP Model
The Optimal Strategic Performance Positioning (OSPP) model is based on the principles
of Ansoff‘s ANSPLAN-A model which has its foundation embedded in the Strategic
Success Paradigm, specifically; industry environmental turbulence level (ETL)
assessment, firm‘s strategic aggressiveness (SA), and general management capability
responsiveness (CR) must be in alignment in order for the firm to achieve optimal
strategic potential. Des Thwaites and Keith Glaister (1993) state, ―to succeed in an
industry, an organization must select a mode of strategic behavior which matches the
levels of environmental turbulence, and develop a resource capability which
complements the chosen mode‖.
The OSPP adds robustness to the analysis by integrating industry data on two future
variables:
1. Future competitive position and
2. Future industry prospects
Comprised of 11 data collection screens, a data summary output screen, and an
assessment screen. The OSPP matrix is based on four measured variables.
1.
2.
3.
4.
Strategic Posture
Strategic Investment
Future Competitive Position and
Future Industry Prospects
The results of the four variables are plotted on the matrix illustrating the firms‘ position
relative to the ―optimal strategic position.‖
Strategic Posture
Strategic Posture is defined as the combination of the Environmental Turbulence Level,
Strategic Aggressiveness, and general management Capability Responsiveness. In this
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
first phase, managers perform a detailed analysis of the industry‘s future ETL from a list
of 36 turbulence-level ―‗descriptors‖ encompassing both Industry Assessment (Figure 1)
Figure 1. ETL Assessment – Industry
and Macro Environment Assessment (Figure 2) and classify their input on a range from 1
(placid and stable) to 5 (surpriseful and discontinuous). The ETL results are then
calculated and transferred to the summary output screen.
Figure 2. ETL Assessment – Macro Environment Assessment
Analyst will thereafter assess the firms‘ ‗SA‘ level by measuring both their Innovation
Aggressiveness and Marketing Aggressiveness. Managers complete a series of 14
innovation-aggressiveness ―descriptors‖ (Figure 3) and 11 marketing-aggressiveness
―descriptor‖ (Figure 4) questions and the results are calculated and transferred to the
summary output screen.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Figure 3. Present Strategic Aggressiveness – Innovation
Figure 4. Strategic Aggressiveness - Marketing
At this point, the program has calculated the first ―gap‖ analysis between the ETL and
SA. The Strategic Aggressiveness Gap is entered into the summary output screen.
The final component used to determine the firm‘s Strategic Posture variable is the
assessment of Firm‘s Capability Responsiveness (CR). Capability Responsiveness
assesses the firm‘s propensity and its ability to engage in strategic behavior that will
optimize attainment of the firm‘s near and long-term objectives in two complementary
ways: (a) by observing the characteristics of the firm‘s responsiveness behavior—for
example, whether the firm anticipates or reacts to discontinuities in the environment and
(b) by observing the capability profiles of the firm that produce different types of
responsiveness.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
The data gathered to assess CR is generated by completing five survey screens
measuring;
1). General Managers Capabilities (Figure 5), the function responsible for the overall
performance of the firm.
Figure 5. Capabilities Responsiveness – General Managers
2). Firm Culture (Figure 6), whether hostile, passive, or predisposed to change. The
propensity toward risk—whether as a group, management avoids, tolerates, or seeks risk
(familiar or novel). The time perspective – past, present, or future based. The action
perspective – internal or externally focused. The goals behavior – seeking stability,
growth, or innovation. The change trigger – crisis, unsatisfactory performance, or
continual change.
Figure 6. Capabilities Responsiveness - Culture
Proceedings of 7th Annual American Business Research Conference
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3). Firm Structure (Figure 7) - whether formalized, centralized, flexible, and management
focus.
Figure 7. Capabilities Responsiveness – Structure
4). Firm Systems (Figure 8) – The organization system for decision-making strategy
ranges from extrapolation to deductive analysis to impact analysis/stochastic. Each
represents a family of systems within which, each type of the specific systems share a
distinctive perception of the future environment of the firm. The figure shows that
although different in intent, the systems are built of building blocks that perform identical
functions in each system. The management system, even if responsive to the firm‘s
needs, will be ineffective if other components of the firm‘s capability do not support it.
Figure 8. Capabilities Responsiveness – Systems
5). Firm Technology (Figure 9)– Assesses the current analytical model being used, the
firm‘s level of technological development, research & development, product and process
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
investments relative to the industry leader as well as the type of surveillance system
currently in use. Figure 9 is used to determine the technological aggressiveness of the
firm‘s strategy and identification of the gaps. Each factor lists several descriptors that
together determine what is called the Present Systems Responsiveness Level. The
importance of the respective factors to the firm‘s future business strategy can be assessed
as follows:
 Determine the gaps between the future environment and the firm‘s historical
strategic position.
 Provide an estimate of the firm‘s future technological competitive position if the
firm continues using its historical strategy.
 Identify the changes in the technological strategy factors that should be made.
 If the technology assessment shows that the firm‘s technologies are turbulent and
that they play an important role in the future success of the firm and that R&D
investment will be significant, it becomes desirable to synthesize the strategic
variables into a statement inclusive of the firm‘s technology strategy.
Figure 9. Capabilities Responsiveness – Management Technology
6). Capacity of Management (Figure 10) simply assesses whether the firm has a sufficient
number of general managers and support staff to create and sustain strategic and profit
making activities.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Figure 10. Capabilities Responsiveness – Management Capacity
The results of each of the seven surveys are calculated and entered into the summary
output (Figure 13); in addition, the ―component gaps‖ for each assessment are entered
into the summary page. At this point, a second Capabilities Responsiveness Gap is
determined and entered into the summary output.
Strategic Budget (ISTRATEGIC)
The Strategic Budget is defined as the investment committed by management to the
strategic development of the firm. Economic theory and practice both suggest that the
firm‘s profitability will be proportional to the size of its investment. The Strategic Budget
is assessed using two screens measuring the firm‘s total commitment of resources for
facilities and equipment (operations), developing the firm‘s products and processes
(R&D), market position (marketing) as well as the supporting capabilities in
management, production, sales, (operations) relative to the market leader.
Figures 11 & 12 illustrate the surveys used for both the Capacity Investment (CI; 9
descriptors) and Strategic Investment (SI;12 descriptors) of the firm relative to the market
leader, which is the ISTRATEGIC Opt. The Strategic Investment Ratio is determined and
expressed in the formula; ISTRATEGIC Opt = 5 (coef CI) (coef SI), in which the firm‘s
competitive position will be in proportion to the ratio of the firm‘s investment into an
SBA to the level of investment that will produce optimal profitability.
The Strategic Budget completes the second variable required to determine the firm‘s
matrix position.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Figure 11. Strategic Budget – Capacity Investment
Figure 12. Strategic Budget – Strategic Investment
Future Competitive Position
The firm‘s future competitive position is assessed relative to the industry by using both
hard data (obtained from measurements and statistical data such as financials, market
share, ROI, ROA, ROE, etc.) and soft data (views and opinions from qualified
individuals, such as industry analysts or experts in the industry who contribute their
expertise, judgments, and predictions about the inputs, estimation process, and
outcomes). Managers complete a 26-point survey (Figure 13) of ―competitive
descriptors‖ to determine the firm‘s future competitive position in its industry. For each
attribute managers identify and enter the characteristic that best describes the future
conditions of the industry using a numerical scale and assign numbers to each element.
Proceedings of 7th Annual American Business Research Conference
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Figure 13. Future Competitive Position
Future Prospects of the Industry
The final variable required to complete the OSPP assesses the future prospects of the
industry (i.e., is the industry still relevant to serve the consumers ―need‖?). The data used
to determine industry future prospects are gathered from industry publications, statistics,
governmental reports, as well as informed subjective estimates from managers,
customers, and industry news.
Managers complete an 18-point survey (Figure 14) analyzing the future prospects of the
industry the computer program allows the user to assign numbers to each factor, averages
the data, and enters the results in the summary page.
Figure 14. Future Prospects of the Industry
Proceedings of 7th Annual American Business Research Conference
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OSPP Company Assessment
The logic of the OSPP is illustrated in Figure 15. As mentioned, the first step of the
analysis was to determine the future ETL of the industry. The initial assessment of the
environment is a critical element for the entire diagnosis as its value specifies the type of
strategic behavior necessary for success. Research conducted by Davis, Morris, and Allen
(1991) and Calantone, Garcia, and Dröge (2003) confirm the proper assessment of the
industry‘s ETL as a foundational cornerstone to formulating a successful strategic plan.
The ETL assessment is illustrated in Figure 15 as 3.21. The SA of the firm in display
reveals two components— Innovation Aggressiveness and Marketing Aggressiveness.
Both are combined and the average SA of the firm is determined. In addition, each
component gap is displayed for manager‘s assessment allowing an increased granularity
for strategic planning. The SA is displayed in Figure 15 as 3.01 with a gap of 0.20.
The assessment of the General Management Capabilities Responsiveness (CR), is
displayed (3.11) as well as the total CR gap 0.10 and each of the general management
component gaps.
The component gaps are also displayed by the application of an enunciator panel of
green, yellow, and red lights. Green indicates an acceptable gap, yellow is cautionary,
and red indicates a gap >0.50. Manager and analysts can quickly assess the firm‘s overall
alignment via the enunciator panel.
The four matrix variables are now displayed indicating the results of the Strategic
Posture, Strategic Budget, the Firm‘s Future Competitive Position, and the Future
Industry Prospects. Figure 15 illustrates the results as 4.05, 3.27, 3.16, and 3.14.
Figure 15. Company Assessment
A rigorous investigation on both the classification mechanism and key performance
indicators (KPI‘s) was conducted on 43 organizations to determine the relationship
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
strength between the center-of-gravity (COG) position as illustrated on the OSPP
computer model and financial KPI‘s.
The OSPP Matrix Screen
The results from the matrix variables are now plotted on the display matrix as illustrated
in Figure 15. The nexus of the vertical and horizontal variables are displayed indicating
the firm‘s ―Center of Gravity‖ (COG). The COG is the firm‘s performance position
relative to optimal performance positioning.
Optimal performance positioning on the matrix is achieved by positioning the firm in the
highest proximity to the top right corner of the matrix. (Note: Managers must consider
that industries and industry conditions vary, and as such, what may be the optimal
position for one industry, may be different for another industry; hence a lower position on
Interpreting the Matrix
As can be seen from Figure 15, the Firm‘s Strategic Posture, Strategic Investment, Firm‘s
Future Competitive Position, and Future Industry Prospects are positioned as a result of
the OSPP Summary Page, with the firm‘s COG indicating a relatively strong position on
the matrix in zone 1. The horizontal and vertical lines intersect in one of the sixteen cells
in the OSPP matrix. The firm‘s performance position is then indicated by the intersection
of the x-axis and the y-axis (COG) along with a recommended strategy. The four zones
are illustrated in Figure 16.
Zone 1 indicates the firm performance position is Optimal. The Strategic Posture and
Strategic Budget are both very high and are in alignment. As well, the Firms Future
Competitive Position and Future Industry Prospects are very high. Strategic
recommendations are for aggressive, intensive, grow and build strategies. Recommended
strategies should focus on market penetration, market development, and product
development. From the operational perspective, backward integration, forward
integration, and horizontal integration should also be considered.
Zone 2 indicates that the firms Strategic Posture ranges from the Moderate to Optimal
range. As well, the Firms Future Competitive Position range is from Poor to Moderate.
What is critical in this region is the position of the Strategic Budget and Future Industry
Prospects. If the firm has a low Strategic Budget (<3.0) and high Future Industry
Prospects (3>) management has a misalignment with its Strategic Posture and Strategic
Budget and recommendations are to increase the Strategic Budget. Such increases will
improve the Firms Future Competitive Position and its Optimal Performance. If however,
the Strategic Budget is high (>3) and the Future Industry Prospects are low (<3) the firm
should consider aggressive cost management strategies and/or divestment or harvesting
strategies.
Zone 3 indicates that the firm is in a Moderate to Poor position. Although the Future
Industry Prospects range from Moderate to High (>3), the Strategic Budget is Low (<3).
What is of critical importance in this region is the relationship of the Firms Strategic
Posture and the Firms Future Competitive Position. If the Firms Strategic Posture is low
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
(<3), the firm is gravely misaligned with its Strategic Aggressiveness and its Capabilities
to the prevailing level of Environmental Turbulence, this misalignment coupled with a
low Strategic Budget indicate a Poor performance position. Major strategic posture
corrections are required as well as a complete assessment and improvement of the firms
supporting capabilities. If, however, the Firms Strategic Posture is high (>3) the
recommendation is to increase the Strategic Budget to support the Strategic Posture.
Such increases will both improve the Strategic Budget Position and improve the Firms
Future Competitive Position and its Optimal Performance.
Zone 4 indicates that the firm is in a Poor performance position. Most typically the firms
Strategic Posture is poor (<3) and the Strategic Budget is poor (<3) as well as the Future
Industry Prospects (<3). Strategic recommendations include following an exit end-game
strategy of harvesting or divestment.
Figure 16. OSPP Assessment
The OSPP is both descriptive and prescriptive as managers can now easily assess which
area of the firm in which to concentrate its resources to improve its performance position.
In this case, the matrix reveals that management can increase its Firm Strategic Posture
and Firm‘s Future Competitive Position.
Research Findings
Corporate Performance has commonly been measured in relatedness studies by
accounting or market-based objective measures. (Farjoun, 1998; St. John & Harrison,
1999). As optimal firm performance is the context of this study and is also a corporate
construct, the measures chosen for this study was the firm‘s financial ratios and matrix
position. Market-based objectives as well as other similar external measures are
generally more distant from the firm‘s internal activities, such as strategic aggressiveness
and capabilities responsiveness, than are financial-based measures.
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Results
Data were based on 61 organizations assigned to a zone position based on the OSPP Zone
or on company category. Zone 1 companies accounted for 57.4 percent, Zone 2 for 6.6%,
Zone 3 for 16.4 percent, and Zone 4 for 19.7 percent. In addition, there were 18.0
percent manufacturing companies, 45.9 percent merchandising or trade companies, and
36.1 percent service companies (see Tables 1 and 2).
Table 1. Frequency and Percent of Zone Position
Zone Position
Frequency
Percent Cumulative Percent
1.0
35
57.4
57.4
2.0
4
6.6
63.9
3.0
10
16.4
80.3
4.0
12
19.7
100.0
Total
41
100.0
Table 2. Frequency and Business Category
Business Category
Frequency Percent
Manufacturing
11
18.0
Merchandising or Trade
28
45.9
Service
22
36.1
Total
41
100.0
Cumulative Percent
18.0
63.9
100.0
An Analysis of Variance was done to determine differences between the 4 zone positions
and the 15 key indicators. To assess a violation of variance a test of homogeneity of
variance was accomplished. Strategic Posture, Current Ratio, NPM, EBITDA, ROE,
ROCI, and D2E violated assumptions (see Table 3). When variances are very unequal,
and/or the group sample sizes are small and unequal, as was the case in this study, a
Welch F test was used as an alternative to avoid Type I errors. Post hoc tests that met the
assumptions were analyzed using a Tukey HSD while those that failed to meet
assumptions used Games-Howell which takes into account unequal variance. The
ANOVA or Welch F Test determined there were eight significant findings. They are as
follows:



There was a significant difference in Zone position in Strategic Posture using the
Welch F Test [F (3, 16.52) = 5.52, p = .008]. Further post hoc analysis using
Games-Howell determined Zone 1 (M = 3.47, SD = 0.63) had significantly higher
Strategic Posture than Zone 3 (M = 2.60, SD = 0.82). Additionally, Zone 2 (M =
3.64, SD = 0.23) was significantly higher than Zone 3 (M = 2.60, SD = 0.82).
There was a significant difference between Zones in Strategic Budget [F (3, 57) =
31.39, p < .001]. The Tukey HSD post hoc analysis determined Zone 1 (M = 3.34,
SD = 0.72) scored significantly higher on Strategic Budget than both Zone 3 (M =
1.85, SD = 0.74) and Zone 4 (M = 1.51, SD = 0.58). In addition, Zone 2 (M =
3.92, SD = 0.51) scored significantly higher than Zone 3 (M = 1.85, SD = 0.74)
and Zone 4 (M = 1.51, SD = 0.58).
There was a significant difference in Zones in Future Competitive Position [F (3,
57) = 31.39, p <.001]. The post hoc test using Tukey HSD determined Zone 1 (M
Proceedings of 7th Annual American Business Research Conference
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= 3.67, SD = 0.39) had significantly greater Future Competitive Position than
Zone 2 (M = 2.53, SD = 0.38) and Zone 4 (M = 2.50, SD = 0.47). Moreover,
Zone 3 (M = 3.29, SD = 0.46) scored significantly higher than both Zone 2 (M =
2.53, SD = 0.38) and Zone 4 (M = 2.50, SD = 0.47).






The Future Industry Prospects showed significant differences in Zones [F (3, 57)
= 5.46, p = .002]. Further post hoc analysis using Tukey HSD determined Zone 1
(M = 3.67, SD = 0.39) had significantly greater scores than Zone 2 (M = 2.75, SD
= 0.12).
There was a significant difference in the four Zones related to NPM using the
Welch F Test [F (3, 11.71) = 3.55, p = .049]. A Games-Howell post hoc analysis
determined Zone 1 (M = 14.93, SD = 24.37) scored significantly higher than Zone
4 (M = -2.85, SD = 15.13).
There was a significant difference in the Zone quadrants in ROE using the Welch
F Test [F (3, 18.10) = 17.12, p < .001]. A post hoc analysis using Games-Howell
determined Zone 1 (M = 23.34, SD = 13.02) showed significantly higher ROE
than Zone 2 (M = 2.16, SD = 3.96).
There was a significant difference in Zones within ROA [F (3, 57) = 9.10, p <
.001]. A post hoc analysis using Tukey HSD determined Zone 1 (M = 11.17, SD
= 7.81) showed significantly higher ROA than Zone 2 (M = -9.03, SD = 18.43)
and Zone 4 (M = -5.82, SD = 18.67).
There was a significant difference in Zones in ROCI [F (3, 10.02) = 7.91, p =
.005]. Using a Games-Howell post hoc test showed Zone 1 (M = -17.54, SD =
11.17) was significantly greater than Zone 3 (M = -0.54, SD = 16.30) and Zone 4
(M = -16.76, SD = 31.02).
Both GPM and EBITDA showed significant results. However, robust tests of
equality of means cannot be performed because at least one group had zero
variance.
Table 3. Test of Homogeneity of Variances Using Zone Position
Key Indicator
Strategic Posture
Strategic Budget
Future Competitive Position
Future Industry Prospects
Current Ratio
GPM
NPM
EBITDA
Revenue Growth
ROE
ROA
ROCI
D2E
PE
EPS
Levene Statistic
.019*
.734
.938
.210
.019*
.096
.004*
.028*
.718
.000*
.123
.010*
.000*
.378
.279
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
*p<.01, Indicates violation of assumptions and Welch F Test used
Table 4. ANOVA for Zone Position on Key Indicators
Mean
(SD)
Zone 1
3.47
(0.63)
Mean
(SD)
Zone 2
3.64
(0.23)
Mean
(SD)
Zone 3
2.60
(0.82)
Mean
(SD)
Zone 4
2.79
(1.09)
Strategic Budget
3.35
(0.72)
3.92
(0.51)
1.85
(0.74)
1.51
(0.58)
34.71*
Future Competitive
Position
3.67
(0.39)
2.53
(0.38)
3.29
(0.46)
2.50
(0.47)
28.64 *
Future Industry Prospects
3.67
(0.39)
2.53
(0.38)
3.20
(0.17)
3.11
(0.35)
5.46*
Current Ratio
2.06
(1.38)
0.00
(0.00)
2.46
(3.00)
1.43
(0.40)
0.85
GPM
43.50
(18.80)
0.00
(0.00)
27.36
(22.34)
38.68
(15.50)
†
NPM1
14.93
(24.37)
-38.00
(53.63)
3.40
(9.84)
-2.85
(15.13)
3.55*
EBITDA
17.22
(12.50)
0.00
(0.00)
5.62
(12.64)
-2.34
(18.17)
†
Revenue Growth
11.72
(13.42)
8.78
(10.54)
5.05
(6.99)
2.32
(8.20)
2.39
ROE1
23.35
(13.02)
2.18
(3.96)
3.97
(20.90)
-34.11
(101.21)
17.12*
ROA
11.17
(7.81)
-9.03
(18.43)
0.66
(9.55)
-5.82
(18.67)
9.10*
ROCI1
17.54
(11.34)
-21.50
(36.72)
-0.54
(16.30)
-16.76
(31.03)
7.91*
D2E
6.74
(23.06)
1.28
(1.72)
0.88
(0.55)
45.62
(87.74)
1.66
PE
127.04
(534.97)
-52.90
(141.15)
8.90
(21.07)
2.99
(8.03)
0.52
EPS
11.04
(36.10)
-0.56
(0.83)
0.83
(3.12)
-1.33
(2.76)
0.84
Key Indicator
Strategic Posture1
F or Welches F Test
5.52*
*p < .01, 1 = Welch F Test analysis, † = Cannot determine significance since one group
had zero variance
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Another Analysis of Variance was done to determine differences between the 3 business
categories and the 15 key indicators. Once again a test of homogeneity of variance was
accomplished. There were no violation of assumptions (see Table 4). As before, post
hoc tests that met the assumptions were analyzed using a Tukey HSD.
There was only one significant finding. There was a significant difference in Business
Category in GPM [F (2, 57) = 3.22, p = .047]. The further post hoc analysis using Tukey
HSD determined Service (M = 44.91, SD = 20.38) scored significantly higher than
Merchandising or Trade (M = 33.21, SD = 20.05). There were no other significant
findings. See Table 5.
Table 4. Test of Homogeneity of Variances Using Company Type
Key Indicator
Levene Statistic
Strategic Posture
.199
Strategic Budget
.617
Future Competitive Position
1.000
Future Industry Prospects
.913
Current Ratio
.179
GPM
.920
NPM
.666
EBITDA
.069
Revenue Growth
.650
ROE
.405
ROA
.192
ROCI
.415
D2E
.096
PE
.065
EPS
.328
*p<.01, Indicates violation of assumptions and Welch F Test used
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Table 5. ANOVA Company Type on Key Indicators
Mean
(SD)
Manufacturing
2.95
(0.75)
Mean
(SD)
Merch - Trade
3.45
(.70)
Mean
(SD)
Service
3.03
(.97)
Strategic Budget
2.79
(0.87)
2.99
(1.08)
2.50
(1.19)
1.21
Future Comp Position
3.41
(0.75)
3.30
(0.61)
3.25
(0.65)
0.22
Future Industry Prospects
3.19
(0.29)
3.19
(0.33)
3.27
(0.34)
0.40
Current Ratio
1.88
(0.97)
1.94
(1.19)
2.20
(2.30)
0.20
GPM
38.75
(21.87)
30.21
(20.05)
44.92
(20.38)
3.22*
NPM
11.35
(11.41)
4.44
(37.28)
5.50
(14.69)
0.26
EBITDA
17.32
(17.59)
10.75
(10.80)
6.34
(18.80)
1.89
Revenue Growth
6.37
(13.59)
9.32
(8.53)
8.75
(14.95)
0.24
ROE
16.86
(26.09)
9.70
(34.44)
-0.04
(72.72)
0.45
ROA
7.62
(9.89)
4.78
(10.98)
3.36
(18.38)
0.34
ROCI
11.58
(17.35)
5.07
(20.00)
2.37
(31.82)
0.52
D2E
0.60
(0.60)
18.58
(57.13)
12.29
(36.67)
0.64
PE
14.48
(20.59)
138.97
(602.98)
14.07
(13.28)
0.70
EPS
6.05
(12.79)
9.47
(39.74)
2.04
(8.45)
0.43
Key Indicator
Strategic Posture
*p < .01
F
2.34
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Discussion
A summation of the findings brings to light key differences. Within the business
category, the one significant finding was Service scoring significantly higher than
Merchandising / Trade in GPM. Most of the significant differences were related to
the OSPP Zone quadrants. In summation, the following were significant and
noteworthy:









With respect to Strategic Posture, both Zones 1 and 2 showed significantly
higher scores than Zone 3.
Comparing Strategic Budget, both Zone 1 was significantly greater than
Zone 3 and 4. Additionally, Zone 3 was significantly higher than Zone 4.
There was a significant difference in OSPP Zone positions in Future
Competitive Position. Zone 1 indicated higher Future Competitive Position
than Matrix 2 and Matrix 4. Moreover, Zone 3 was significantly higher
than Zone 2 and Zone 4.
Future Industry Prospects showed significance with Zone 1 scoring higher
than Zone 2.
There was a significant difference in Zones related to NPM. Zone 1 recorded
significantly higher than Zone 4.
There was a significant difference in Zone position in ROE. A post hoc analysis
using Games-Howell determined Zone 1 showed higher ROE than Zone 2.
There was a significant difference in Zones with respect to ROA. Zone 1 was
significantly higher than both Zone 2 and Zone 4.
There was a significant difference in Zones in ROCI. Zone 1 was significantly
greater than both Zone 3 and Zone 4.
Both GPM and EBITDA showed significant results, but there were violations in
group variance. No other significant results were found outside of these.
Contributions of this study
The purpose of this study was to provide empirical validation of the OSPP computer
application as an effective tools for determining a firm‘s strategic position and to validate
its ability. The OSPP computer application provides positional plotting versus the firm‘s
financial performance plus determines if a relationship of position/performance did
indeed exist. The results of the study indicate that the overall approach and
methodological framework was proven valid as it clearly shows a significant difference
in OSPP Zone position and financial performance.
As a result, it can be stated that the OSPP computer program does provide managers and
analysts with an effective analytical tool in which to identify the firm‘s strategic position,
thus enabling management with valuable information to plan proactive measures to best
guide the firm. Moreover, the study shows that the OSPP is more effective at
determining strategic position than industry grouping alone (e.g., manufacturing,
merchandising/trade, or service).
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Conclusion
The paper presents result based on extending a previous study which examined the
contribution by Ansoff (1986). The extension was to strengthen the instrument utilized to
determine a firm‘s strategic position. The Optimal Strategic Performance Position
Matrix© (OSPP, 2012) extended Ansoff‘s ANSPLAN-A model by adding and
integrating two critical variables; future competitive position and future industry
prospects to the existing four variables measuring; environmental turbulence, strategic
aggressiveness, capabilities responsiveness and strategic budget.
SPSS 22.0 software was utilized to analyze 61 publicly traded firms in a variety of
industries such as manufacturing, merchandising/trade, or service in order to ascertain if
there is a relationship between a firm‘s center of gravity (COG) matrix position as
indicated by the OSPP and key performance determinants as indicated by the following
financial ratios; current ratio, GPM, NPM, ROE, ROA, ROCI, EBITA, revenue growth,
P/E, EPS, and D2E.
The results indicate that a strong relationship exists between a firm‘s COG position and
multiple key performance indicators. As such, the OSPP matrix computer model is a
reliable strategic planning tool for management in providing high-level data which can be
utilized in the strategic planning and management process to achieve optimal strategic
formulation and implementation.
Recommendations for Business Managers and Analysts
The previously mentioned conclusions illuminate the effectiveness of the OSPP program
and as such provides managers with an analysis tool that is relatively easy to use, yet
affords those decision makers with a comprehensive assessment tool for identifying the
firm‘s strategic position.
Unlike traditional analysis tools, the OSPP computer application is a dynamic analysis
program that can benefit management by identifying and analyzing the multiple critical
variables for organizational success and their interrelationship with each other to
determine an all-inclusive evaluation of the firm.
Therefore it is recommended that both managers and analysts include in their strategic
decision making process a tool that can provide a holistic view of the firm‘s strategic
position.
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