Ivan F Rodriguez Business Optimizer Model - The Power of Simplicity – A Correlational Study

Running head: BUSINESS OPTIMIZER MODEL: THE POWER OF SIMPLICITY 1

Business Optimizer Model: The Power of Simplicity – A Correlational Study

Concept Paper

School of Advanced Studies, University of Phoenix

Ivan F Rodriguez

Frank Bearden

September 15, 2013

BUSINESS OPTIMIZER MODEL: THE POWER OF SIMPLICITY 2

Business Optimizer Model: The Power of Simplicity

Introduction

The world is facing several challenges, uncontrolled gains on carbon dioxide (CO2)

emissions, high food prices, water scarcity, aging populations, poor education, gender issues, and

unprecedented unemployment rates, affecting both developed and developing countries (OECD,

2012). Resolving these challenges depends crucially on the pace of innovation in new

technologies, for example in the areas of renewable energy, carbon capture and storage, lower

emissions, bioremediation, smart grids, synthetic biology, bio-informatics, and personalized

medicine (Wynarczyk, 2013). Innovation is increasingly perceived as essential for tackling such

challenges (West, 2002). People are at the heart of the innovation process. “Education systems

play a key role in the development of a highly qualified and flexible labor force” (Rassenfosse &

Potterie, 2009, p. 37). Small and medium-sized enterprises (SMEs) are the blood of modern and

open economies (Rahman, 2012). Their flat structure, their flexible culture, and their low levels

of bureaucracy enable the precious opportunity to move faster than large enterprises and

capitalize new concepts and technologies.

Background of the Problem

In September 2013, the Small Business and Entrepreneurship Council (SBE), a 501 c4

nonprofit, nonpartisan advocacy and research organization, posed revealing data about SMEs’

role in the United States economy. At the end of 2012, SMEs accounted 99.7% of employer

firms in the United States (Sherman, 2013), offering 64% of new private sector jobs and 49.2%

of private-sector employment, representing 42.9% of private-sector payroll and 46% of private-

sector output, offering 43% of high-tech employment, accounting 97.8% of the United States

exporters companies (33.7% of the value of exports) and 97.2% of the importers (23.2% of the


value of importations) (Sherman, 2013). SMEs produced 46% of the private nonfarm GDP in

2008 –most recent year for which the source data are available (Kobe, 2012), and registered 16.5

times more patents per employee than firms with more than 500 employees (Breitzman & Hicks,

2008).

Despite the difficulty in developing one common definition of SMEs (definitions

between countries and even between stakeholders in the same country, vary on the number of

employees and capital structure), SMEs’ statistics are comparable in both developed and

underdeveloped countries (Ayyagari, Beck, & Demirgüç-Kunt, 2003). Ayyagari, Beck, and

Demirgüç-Kunt created a cross-country database that allows making consistent and comparable

comparisons between SMEs in different countries. Using this theoretical framework, one can

observe the high similarity of SMEs’ contribution to countries’ employment and wealth creation

(see Appendix A). Advancing one step farther, the database reveals positive generalizable

correlations (e.g., between gross domestic product (GDP) per capita and SMEs employment

contribution, the higher GDP is the higher SMEs contribution; as income increases, the share of

the informal sector decreases and the SMEs increases) (Ayyagari et al., 2003). SMEs

significance of nations’ wealth creation is undeniable, and so is the importance of understanding

the variables supporting their failure or success.

Statement of the Problem

The general problem is the low survival rate (SR) of SMEs in today’s economy.

Approximately 50% of SMEs survive after five years, and only 30% after 10 (Sherman, 2013).

SRs seem to have a positive correlation with the sector in which SMEs operate. Using SBI’

specialized statistics, one observes SMEs in the finance, insurance, and real estate sectors

registered higher resistance, averaging 58% survival rate after four years of operation.


Conversely, SMEs in the construction sector averaged 47%, and those in the information

technology sector averaged only 37% (SBI, 2013)). The survival of SMEs is critical. Beyond the

statistics SMEs are a reflection of entrepreneurial spirit of a nation, its ability to innovate, and its

potential economic growth. Further support from this claim comes from Meehan and Muir who

demonstrated SMEs’ SR improvement correlates positively to large firms’ profitability. “SMEs

enhance large companies’ competiveness by strengthening industrial linkages, testing concepts,

and ideas before they are made in a larger scale” (Meehan & Muir, 2008, p. 228).

According to Dun and Bradstreet (2004), 88.7% of SMEs’ failures are management

mistakes. They summarized the causes of failure in 12 categories, (1) lack of industry

experience, (2) inadequate financing, (3) lack of adequate cash flow, (4) poor business planning,

(5) management incompetence, (6) ignoring the competition, (7) unrealistic goals, (8) thinning

customer base, (9) inorganic growth, (10) inappropriate location, (11) poor system of control,

and (12) lack of entrepreneurial skills (surprising results because 50.8% of SMEs owners have a

college degree according to the data published by the U.S. Census Bureau (2011)). Similarly,

SBI offers comparable causes for the high SMEs’ failure rate. They assert incompetence (e.g.,

family pressure on time and funds, pride, emotional pricing, living too high for the business, no

knowledge of market dynamics, poor planning, low financial expertise) accounts for 46% of

SMEs’ failure (failure understood as bankruptcy), unbalanced experience (e.g., expanding too

rapidly, inadequate borrowing practices, poor credit granting practices, lack of emotional

intelligence) accounting for 30% of the failure, lack of knowledge (e.g., weak long-term vision

and commitment, wrong supply chain management practices) accounting for 11%, and neglect,

fraud and disaster, accounting for 1% (SBI, 2013).


Despite the vast quantitative and qualitative data explaining the reasons SMEs’ SR is

low, more precise correlations are urgently required to maximize SMEs’ SR. A case in point,

correlation between SMEs’ operating system (SOS) and their business model (BM) to project

SMEs SR is missed (or at best is insufficient) in the literature. Most of the scholar research is

done in the context of two complementary focus streams, (1) SMEs relevancy to nations’

economy development (e.g., Sherman, 2013; Rahman, 2012; Kobe, 2012; Meehan & Muir,

2008; Ayyagari et al., 2003), and (2) SMEs correspondence with specific subjects including

marketing, innovation, strategy, finance, customer satisfaction, globalization, and business

intelligence (e.g., Wynarczyk, 2013; Lee & Shin, 2012; OECD, 2010, 2012; Vancheswaran &

Gautam, 2011; Rassenfosse & Potterie, 2009; Breitzman & Hicks, 2008; Chon, 2007).

Purpose of the Study

The purpose of this quantitative correlational study is to understand if there is a

correlation between SMEs’ operating system (SOS), SMEs’ business model (BM), and SMEs’

survival rate (SR). Specifically, the study will define a model that helps SMEs understanding its

value proposition, differentiate it, and modify timely their leadership, supply chain methods, and

reengineering capacity (the three core elements of the SOS) to maximize their SR.

Significance of the Problem

As globalization increases, the pressure to the SMEs does too (OECD, 2010).

Globalization’ critical success factors (e.g., lower costs, product differentiation, and adaptability

speed) appear to be contrary to the dominant SMEs’ business practices, which are characterized

by high operative and financial costs, long lead times in purchased items, limited networking,

and weak contractual frame (Kobe, 2012). Scale economies and research and development

(R&D) are large firms most used strategic instruments to achieve their success factors (Rahman,


2012). SMEs seem to be at a disadvantage for both these instruments. Today’s economy is

global. It shifted from a local scope to an international one with minimum barriers, exacerbating

the need SMEs have to be aware of market needs, trends, and preferences. Technology

advancements (e.g., nano-microprocessors, integrated chips-on-boards (COB), flexible printed

circuit boards, ball grid arrays, etc.) will continue enhancing the speed and penetration of

globalization, reducing the cost of goods sold (COGS) making SMEs’ viability more uncertain

and ambiguous (Wynarczyk, 2013).

The shift in the economy is changing the paradigms of comparative advantages too.

What few years ago were the principles of competition (e.g., mass production-based industries

with specialized large and rigid manufacturing equipments), changed to information-based

companies, focused on controlling and enhancing information generation, transmission, and use.

More recently a change to knowledge-based industries, with virtual teams spread across the

globe, sharing concepts, and designing products and services for customers who do not exist yet

(Sherman, 2013). This structural transformation requires SOS to adapt efficiently.

Research Questions

This quantitative correlational study will examine 16 BM archetypes (Weill et al., 2004)

and correlate them with the SMEs’ SOS and SR. The following research questions will guide the

study:

1. What is the relationship between SOS and BM?

2. What is the relationship between BM and SR?

3. What is the relationship between SOS and SR?

Hypotheses

Three null hypotheses will be tested:


1. H10: There is no relationship between SOS and BM.

2. H1A: There is a relationship between SOS and BM.

3. H20: There is no relationship between BM and SR.

4. H2A: There is a relationship between BM and SR.

5. H30: There is no relationship between SOS and SR.

6. H3A: There is a relationship between SOS and SR.

Theoretical Framework

The conceptual framework guiding this study relies in two theoretical references. (1) Free

market economy paradigms that embrace Adam Smith’s theory of economy (e.g., self-interested

and competition can lead to economic prosperity, market forces ensure the effectiveness of the

supply-demand process). (2) Conjectural model created by Weill, Malone, D’Urso, Herman, and

Woerner in 2004. These authors proposed 16 BM archetypes. They demonstrated some business

performed better than others and demonstrated business models correlate positively with at least

two broad measures of financial performance, profit and market value. Weill et al. (2004) probed

this predictors are more precise than COMPUSTAT’s traditional classifications. The study relies

intensively on the SMEs’ cross-country database created by Ayyagari et al. in 2003. This

database is unique in that it depicts comparable and consistent information about SMEs

contribution to total employment and GDP across different countries. The database improved

existing publicly available datasets. It extended the coverage of the countries, standardized the

definition of SMEs, and included the informal sector, making the database more relevant to

assess SMEs globally. The selected theory offers rationales that identify factors that support the

correlation between SOS, BM, and SR.


Research Methodology

Considering there is one objective reality (SMEs’ low SR) that is not dependent on

human interpretation, and the post-positivism paradigms underlying this study, a quantitative

research methodology is chosen to satisfy effectively the purpose of the study (maximize SEMs’

SR). This methodology facilitates the process of gathering data through custom made surveys

(see research design) and applying quality assurance metrics (e.g., internal and external

reliability, construct and context validity, and random and deliberate sampling) in the study to

ensure it draws of meaningful results. More important, this research methodology endorses the

use of the deductive process to test the pre-specified concepts, constructs, and hypotheses

described in the problem statement section. Last, this methodology provides observed effects,

number-based, and consequently more generalizable, which is a critical success factor of the

study, allow SMEs in any country in the world improve their SR based on the correlation

findings.

Research Design

A correlational study is selected as the research design of this investigation. Correlational

studies examine variables in their natural environment and minimize researchers’ inference

(Simon, 2006). Additionally, correlational studies depict the relationships between defined

variables applying statistical techniques and parametric analyzes (e.g., cross-tabulation, t-test,

ANOVA, ANCOVA) (Thompson, Diamond, Mc William, & Snyder, 2005). The treatment

group is formed by the SMEs of two BRIICS countries, Brazil and China. SMEs on these

countries are the population of the study. The control group is formed by the SMEs of the United

States, experimental results are compared against this reference.


The study seeks to identify complex relationships between three variables. SOS

(independent variable), SR (dependent variable), and BM (controlled variable). It uses a

customized survey as its research method with simple random sampling in which a random

number table is used to define the number of surveys to send. The survey includes 25 questions

that are answered by SMEs’ owners. The questions cover the three core areas of the SOS (i.e.,

leadership effectiveness (L), supply chain effectiveness (S), and reengineering capacity

effectiveness (R)). Each of them has a subset of factors that facilitate data gathering about SMEs

operating system. For example, under the leadership effectiveness area, five factors are included,

(1) strategy definition, (2) strategy deployment, (3) roles and responsibilities, (4) incentive and

rewards program, (5) balanced scorecard. These factors are measured using the Liker-type scale.

SMEs’ owners rate their company with “1” if they strongly agree with the assertion that referred

factor is defined and implemented effectively in their company, “2” when they agree, “3” when

neither, “4” when disagree, and “5” when they strongly disagree.

Through the use of this research methodology relevant and reliable correlation evidence

is expected. These findings are used to generalize causal inferences between SOS and BM, and

between SOS and SR. Consequently this quantitative correlational study creates evidence-based

projections of SMEs’ SR that help leaders make timely decisions to maximize their SR. To

ensure the quality of the research and given its inherent challenges (e.g., data consistency

between selected countries, broadness, speed of change of defined core SOS’ factors), three

quality indicators are used, (a) measurement (e.g., score reliability and validity), (b) avoidance of

common macro-analytic errors (e.g., failure to interpret structure coefficients, converting

intervallic scaled variables to nominal scale, inappropriate single-dependent methods, test


statistical assumptions), and (c) confidence intervals (e.g., score coefficients reliability, statistics,

and effect sizes) (Thompson et al., 2005).

Definition of Terms

Most of the scholar research on BM is done in the context of e-Business (e.g., business

enhanced by information technology (IT)) with focus in two complementary streams, taxonomies

of business models, and definitions of components of business models (Hedman & Kalling,

2001). For the purpose of this study, BM is defined as (a) what the SMEs do, and (b) how SMEs

make money in so doing. SOS is defined as the SMEs’ operating system, i.e., the combination of

leadership structure and practices (L), supply chain method and practices (S), and reengineering

capacity (R). SR is defined as the SEMs’ survival rate, i.e., the coefficient between the number of

SMEs in operation at a given time x and the number of SMEs that started at a given time y.

Assumptions

Chong’s literature review identified five inhibiting factors for both, large organizations

and SMEs, lack of clarity on a strategic level, lack of IT expertise, absence of a cross-functional

mindset among senior executives, lack of senior executives/leaders, and poor knowledge of

process-oriented approaches (Chong, 2007). These inhibiting factors underlie the SMEs’ SOS

assumption (i.e., SMEs’ effectiveness can be described as a function of leadership effectiveness

(L), supply chain effectiveness (S), and reengineering capacity effectiveness (R)). On the hand,

the 16 Weill et al.(2004) BM archetypes are assumed as descriptive of the entire options public

or private lucrative enterprises have to define what they do and how they do it. Last, the

correlations supported on the cross-country database (Ayyagari et al., 2003) are assumed

accurate.


Limitations and Delimitations

This study will be limited to a random representative sample of SMEs in Brazil and

China. Similarly, the study will be confined to custom made surveys to be distributed as the

primary instrument for gathering data within the given population. Using Cooper’s (1988)

Taxonomy of Literature Reviews, the study is focused on SMEs practices, its goal is integrative

(e.g., SMEs’ low SR problem resolution), its perspective is espousal of position (numerically

supported and generalizable), its coverage is representative (random sampling on selected

populations), and its audience is the public.

Summary

SMEs are the blood of modern and open economies (Rahman, 2012). As globalization

increases, the pressure to the SMEs does too (OECD, 2010). The shift in the economy (local to

global) has changed the paradigms of comparative advantages. The current paradigm (i.e.,

knowledge-based companies) opens an unprecedented opportunity for SMEs to increase their

survival ratio (SR). The novelty of this quantitative correlational study is the introduction of a

comprehensive concept, SMEs’ Operating System (SOS), defined as the combination of SMEs’

leadership effectiveness, supply chain effectiveness, and reengineering capacity effectiveness.

Understanding the correlations between SOS and BM, and those between SOS and SR provide

SMEs with reliable predictors to maximize their SR.


References

Ayyagari, M., Beck, T., & Demirgüç-Kunt, A. (2003). Small and medium enterprises across the

globe: a new database. Worldbank.org. Retrieved from

http://elibrary.worldbank.org/docserver/download/3127.pdf?

expires=1379177250&id=id&accname=guest&checksum=1775B5FBFEEC7626D49907

C9781B3110

Breitzman, A., & Hicks, D. (2008). An analysis of small business patents by industry and firm

size. Small Business Research Summary, 335, 1-60.

Chong, S. (2007). Business process management for SMEs: an exploratory study of

implementation factors for the Australian wine industry. Information Systems and Small

Business, 1, 41-58.

Cooper, H. M. (1988). Organizing knowledge synthesis: A taxonomy of literature reviews.

Knowledge in Society, 1, 104-126.

Hedman, J., & Kalling, T. (2001). The business model: a means to understand the business

context of information and communication technology. Institute of Economic Research

Working Paper Series, 1(4), 42-56.

Kobe, K. (2012, January). Small business GDP: update 2002-2010. SBA.gov. Retrieved from

http://www.sba.gov/advocacy/7540/42371

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model globalization. Technological Forecasting and Social Change, 79(5), 832-842.

Meehan, J., & Muir, L. (2008). SCM in Merseyside SMEs: benefits and barriers. The TQM

Journal, 20(3), 223-232.


OECD (2010). Enhancing the competitiveness of SMEs through innovation. Paper presented at

the Conference for Ministers responsible for SMEs and Industry, Bologna, Italy.

OECD (2012), OECD Science, Technology and Industry Scoreboard. Paris: OECD Publications.

Rahman, N. M. (2012). The effective implementation of global supply chain management in

small to medium-sized companies in Malaysia: an empirical study. International Journal

of Management, 29(3), 274-287.

Rassenfosse, G., & Potterie, B.P. (2009). A policy insight into the R&D–patent relationship. Res

Policy, 38, 779–792.

Statistic Brain Institute SBI (2013, July, 27). Startup business failure rate by industry.

Statisticbrain.com. Retrieved from http://www.statisticbrain.com/startup-failure-by-

industry/

Sherman, A. J. (2013, September 13). Small business facts and data. Sbecouncil.org. Retrieved

from http://www.sbecouncil.org/about-us/facts-and-data/

Simon, M. K. (2006). Dissertation & scholarly research: a practical guide to start & complete

your dissertation, thesis, or formal research project. Dubuque, IA: Kendall Hunt.

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evidence from correlational research for evidence-based practice. Exceptional Children,

71(2), 181-194.

U.S. Census Bureau. (2011). Half of U.S. respondent businesses were home-based, majority self-

financed. Census Bureau Reports. Retrieved from

http://www.census.gov/newsroom/releases/archives/business_ownership/cb11-110.html

Vancheswaran, A., & Gautam, V. (2011). CSR in SMEs: exploring a marketing correlation in

Indian SMEs. Journal of Small Business and Entrepreneurship, 24(1), 85-98.


Weill, P., Malone, T. W., D’Urso, V. T., Herman, G., & Woerner, S. (2004). Do some business

models perform better than others? A study of the 1000 largest U.S. firms. MIT Sloan

School of Management Working Paper No. 226.

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Development, 20(2), 258-278.


Appendix A SMEs Correlations (adapted from

GDP/CAP SME250 SMEOFF INFORMAL INFORMAL_GDP

SME250 0.43a

SMEOFF 0.44a 0.98a

INFORMAL -0.72a -0.35c

INFORMAL_GDP -0.65a -0.32b -0.31b -0.51c

SME_GDP 0.51a 0.68a 0.70a -0.32 -0.17

Notes: (1) The variables are defined as follows: GDP/CAP is the real GDP per capita in US$. SME250 is the SME sector’s share of formal employment when 250 employees is used as the cut-off for the definition of SME. SMEOFF is the SME sector’s share of formal employment when the official country definition of SME is used. INFORMAL is the share of the shadow economy participants as a percentage of the formal sector labor force. INFO_GDP is the share of the shadow economy participants as a percentage of GDP. SME_GDP is the SME sector’s contribution to GDP. (2) The official country definition of SME is used. (3) Values are 1990-99 averages for all the variables. (4) a , b and c stand for significance levels at 1, 5 and 10 percent, respectively.


Appendix B Business Model Archetypes (adapted from Weill et al., 2004)

Business Model Asset Involved

Financial Physical Intangible Human

Creator (ownership of asset with significant transformation)

1Entrepreneur

2Manufacturer

3Inventor

4Human Creatora

Distributor (ownership of asset with limited transformation)

5Financial Trader

6Manufacturer

7Intellectual Property Trader

8Human Distributora

Landlord(use of asset)

9Financial Landlord

10Wholesaler / Retailer

11Intellectual Landlord

12Contractor

Broker(matching of buyer and seller)

13Financial Broker

14Physical Landlord

15Intellectual Property Trader

16Human Resource Broker

Notes: a Models illegal in most countries as they involve selling people (included for logical comprehensiveness).

Ivan F Rodriguez Business Optimizer Model - The Power of Simplicity – A Correlational Study

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