Multiple Organizational Strategies: Performance Analysis Using PLS

Path Modeling

Alexandru Stancu 1 2

, Bernard Morard 1 and Christophe Jeannette

1

1 University of Geneva, HEC Executive, Switzerland

2 IATA (International Air Transport Association), Geneva, Switzerland

Abstract. This paper compares multiple organizational strategies that have been categorized in two main

groups: well performing vs. non-performing companies. The organizational performance is generated using

the same key performance indicators collected from both types of organizations. The paper proposes a

process of constructing an optimal organizational performance model based on the Partial Least Square (PLS)

Path Modeling. The two optimal organizational performance models, one for the well performing companies

and another one for the poor performing companies are then analyzed and compared. It will be noted that the

each group of companies follow a certain and common strategy that can be used as a pattern to predict the

future changes. Furthermore, a new company can be categorized against the good vs. poor performing pattern

and the information can be used as a baseline for the company’s forecast.

Keywords: Balanced Scorecard (BSC), Key Performance Indicators, Performance Measurement, Partial

Least Squares (PLS), Principal Component Analysis (PCA), Bootstrap.

1. Introduction

In recent years, several methods to analyze a company’s performance and its associated strategy have

been developed. One of the current most appreciated performance and management tool was advanced in the

early 1990s by Robert Kaplan and David Norton called the Balanced Scorecard (BSC). Identifying several

limitations of previous management tools, the BSC offers a comprehensive direction as to what companies

should focus in order to “balance” the financial perspective with other critical areas. One of the main

advantages and characteristics of the BSC is that the model has the ability to articulate the company’s

strategy with a complex cause-and-effect chain. Following this statement, it can be assumed that (hypotheses

1) different well performing companies should have similar BSC, while the same approach should be

considered for the bad performing companies (hypotheses 2). However, what is more important to

demonstrate is that the strategy of good performing companies is different than the bad performing

companies (hypotheses 3).

The main purpose of our study is to demonstrate the three above mentioned hypotheses using a rational

approach based on PLS Path Modeling. The paper is structured as follows. In the next section, we will

present the high-level concepts of the BSC. We will stress the “idealistic” process of 4-axes construction

followed by a more rational framework allowing for the identification of the number of strategic perspectives

as well as the performance indicators connected to each perspective. We will set forth a tentative modeling

of Optimal Performance Framework that can used to benchmark any specific company. This framework is

validated with key performance indicators collected from a small sample of companies using a modified

version of the bootstrap technique to propose an alternative quality criterion that seeks and selects the most

reliable cause-and-effect sequence among all possible combinations. Finally, we will develop a critical part

Corresponding author. Tel.: +41 78 853 0845; fax: +41 22 770 2612

E-mail address: stancua@iata.org

2014 3rd International Conference on Business, Management and Governance

IPEDR vol.82 (2014) © (2014) IACSIT Press, Singapore

DOI: 10.7763/IPEDR.2014.V82.3

14

about the BSC concept from Kaplan and Norton’s perspective, and present the limits of our own study and

the possibilities that it further offers.

2. Literature Review

2.1. Balanced Scorecard

As noted by Fielden [4], companies worldwide use the ability of BSC for translating the vision and the

strategy into a set of measurable objectives. As showed in Figure 1, with only four perspectives (Learning &

Growth, Internal Processes, Customer, Financial), the BSC cut down information surplus by restricting the

number of performance indicators used and forces the organization to focus on a handful of vital measures.

Current studies estimate that over 60 percent of Fortune 1000 companies have worked with the BSC [5].

Adopters include top companies such as KPMG, Peat Marwick, Allstate Insurance, and AT&T, amongst

others. Even in the medical sector recent study covering the years 1991 to 2011 concludes that over 6,300

documents (conference, articles) containing the words such as "balanced scorecard" were mentioned the

same period [6].

Fig. 1: BSC cause-of-effect link between the 4 strategic perspectives

One of the main features of the BSC is that it groups the performance indicators into strategic

perspectives and it identifies a sort of cause-and-effect relations between various components of an

organization [7]. This postulates the essence of the model, which is allowing measures in financial and non-

financial areas to be used to predict future organizational performance [8].

However, the BSC also presents several weaknesses with some of its main assumptions and relations

highlighted by numerous authors from the specialized literature. Nørreklit [9] states that there is not a causal

but rather a logical relationship between the strategic perspectives analyzed. Moreover, the BSC is not a

representative strategic management tool because it does not consider any connection between the

organization and its competition. Consequently, a discrepancy should be admitted between the company’s

strategy emerging from its actions and the assumed strategy [9].

Kanji also summarizes several BSC limitations emphasizing that the model is overly abstract and not

user-friendly as a measurement model [10]. Furthermore, he notes that the relations between strategic

perspectives are not clearly explained and the causal relationships are problematic (more like

interdependence, rather than correlations).

Lastly, Malina & Selto [11] stress that the BSC is very difficult to put into practice, furthermore finding

controversy between the organization and its stakeholders. They also determined that the performance

indicators selected in the model are biased or inaccurate, the communication about the BSC within a

company is strictly top-down and the comparisons between companies using the BSC are inappropriate due

to lack of standardization, even though these benchmarks are widely used for assessments.

2.2. Partial Least Squares (PLS)

Financial

Internal

Processes

Learning

& Growth

Customer

Vision and

Strategy

15

Within this intensive environment of BSC uncertainty and criticism, some authors have invited

management accounting researchers to make better use of Structural Equation Modeling (SEM) [3], [12],

[13], [14]. SEM is a statistical method comprising Path Modeling, Partial Least Squares (PLS) and latent

variable SEM, which allows the simultaneous analysis of multiple structural equations. These methods are

specifically useful when a dependent variable in one equation becomes an independent variable in another

equation [15].

The use of PLS, despite its intrinsic constrains (being a limited-information technique, aimed to

maximize prediction, rather than fit), proves to be a way in which statistical modeling in management

accounting can progress without the need of large samples, something which management accounting

researchers have found challenging. Requiring less challenging assumptions, another advantage of PLS is its

ability to accommodate non-normal data [16].

The “PLS approach” concept is fairly large and combines PLS for path models and PLS regressions.

Implementing Martens recommendation [17], this paper uses the term PLS for Structural Equation Modeling

to describe the use of “PLS Path Modeling” as illustrated in Figure 2.

Fig. 2: Illustration of PLS Path Modeling example

The internal model (also called or structural model) describes the causality between the latent variables. A

latent variable is called exogenous if it is only affecting other variables (basically any variable from which

arrows will only emanate). Any variable that regresses on another variable is defined to be an endogenous

variable (any variable receiving an arrow). Each endogenous latent variable in a structural model is linked to

other latent variables using the following multiple regression equation:

∑

where (path coefficient or correlation index) expresses the impact of exogenous latent variables on

endogenous latent variable . The only assumption of this model is that residual vector has a mean of

zero and is uncorrelated with the predictors [18]. The main constraint of the structural model is not to have a

loop in the model, which is the major characteristic in the so-called recursive models.

The external model (also called or measurement model) connects the manifest variables to their own

latent variable. The latent variable is the common cause shared by all the manifest variables that can be

formalized by a simple regression. In other words, the latent variable exists only theoretically but cannot be

observed. It influences the indicators, explaining their inter-correlations. There is therefore a first set of

equations linking the manifest variables of the latent endogenous variables (x) to their associated

measurement errors (ε) and to the exogenous latent variables:

ξ1

ξ2

ξ3

λ11

λ12 λ31

λ32

Internal (structural) model

External (measurement) model

x11

x12

x21

x22

x23

ε12

ε11

ε13

ε21

ε22

β1

3

β2

3

ζ3

ε31

ε32 x32

x31

λ21

λ22

λ23

16

where represents the manifest variable of the block , the loading associated with the manifest

variable of the block , the number of endogenous latent variables, the number of manifest variables of

the block and, finally, the error value of the variable from the block. The assumption behind this

model is that the residues have a zero mean and is uncorrelated with the latent variable of the same block

[19].

Even if newer and more complex PLS programs are available today (e.g. PLS-Graph or SmartPLS), a

better analysis and continuous understanding of the PLS Path Modeling allowed us to develop our own

software from scratch (PLS Assistant) for our research purposes. The objective was to combine all statistical

methods we are using in one single and reliable tool: compute the principal component analysis (PCA),

estimate the path weighting scheme and, finally, generate bootstrap validation procedure and assess the best

model from all possible graphs [8].

3. Data and methodology

In order to assess our hypothesis and establish a general strategic pattern for the over-performing versus

underperforming companies, we decided to choose American companies traded at the New York Stock

Exchange (NYSE). The Standard & Poor’s (S&P) 100 Index is a subset of the bigger S&P 500 and measures

the performance of large cap companies in the United States across multiple industry groups [26].

Considering the financial theory in an efficient market, the share price should reflect the entire

information available and therefore the financial state of a firm. The share price of any quoted company is

calculated on the same basis so we based our selection on the notion of the share price. For each of the 100

companies, we collected the monthly traded closing price between the 31 December 2009 and the 31

December 2012. As the data concerning ABBV (AbbVie Inc.) was not available at the time, we based our

analysis on the remaining 99 companies. Once the data has been gathered, we computed a yearly average

price for each company. Finally, we calculated the mean of all the companies’ averages in order to have an

S&P index reference: $USD 52.4 (2009), $USD 52.5 (2010), $USD 62.2 (2011) and $USD 71.15 (2012).

The second step was to compute the % variation between 2009-2012 years and found an average of 35.8%

for the S&P index reference. We then ordered the 99 companies in two groups: companies with a variation

below 35.8% on one hand (52 companies) and companies with a variation above 35.8% on the other (47

companies). We repeated this step for the variations 2009-2010 (S&P index reference: 0.2%), 2010-2011

(S&P index reference: 18.5%) and 2011-2012 (S&P index reference: 14.3%). Finally, we choose 10

companies that had the higher (respectively the lowest) 2009-2012 variations, and we verified that those

companies had a variation above (respectively below) the S&P index reference for each year.

Having the final list of these companies, we tried to find the most relevant performance indicators about

each of them from 2009 to 2012. We have used both the Thomson Reuters database and the firm’s annual

reports to collect financial facts from Balance Sheet and Cash-flow statement. After having collected all the

data, we ran two distinct PLS path model, one for each category of performance. The results obtained

completely matched with our expectations. We managed to obtain two different significant models: one for

the over-performing companies and one for the underperforming ones. Even though the first results were

pretty reassuring, we wanted to reinforce our results with a bigger sample size.

In order to be coherent with our previous selection method, we replicated the same procedure with 2

major differences: first, we based our choice on the much larger S&P 500 index1 being widely regarded as

the best single gauge of large cap U.S. equities and, second, we choose 30 companies of each category of

performance instead of 10 in the first assessment.

The results for the S&P index reference (the mean of the means) were: $USD 42.5 (2009), $USD 46.1

(2010), $USD 53.7 (2011) and $USD 56.7 (2012). Afterwards, we computed the variations for each year

between 2009-2012 for all the firms and for the S&P 500 obtaining 33.3%. Furthermore, we were able to

separate our sample in two groups of companies: the first one with a 2009-2012 variation below 33.3% (199

1 There is over $USD 5.l4 trillion benchmarked to the S&P 500 index, with assets comprising approximately $USD 1.6 trillion of this

total. The index includes 500 leading companies and captures approximately 80% coverage of available market capitalization. 17

companies), and above 33.3% for the second category (304 companies). Finally, we repeated this step for the

variations 2009-2010 (S&P index reference: 8.4%), 2010-2011 (S&P index reference: 16.5%) and 2011-

2012 (S&P index reference: 5.6%). As illustrated in Figure 3, we were then able to choose 30 companies that

had the highest (respectively the lowest) 2009-2012 variations, and we verified that those companies had a

variation above (respectively below) the S&P reference for each year.

Fig. 3: Distribution of selected companies throughout different industry sectors

We can observe from Figure 3 that the two repartitions are quite different and some sectors seem to have

a higher success rate than others during 2009-2012 period. We can notice that Retail, IT and Financial

sectors represented an important part of the over-performing companies. Adversely, Financial, Technology

and Energy sectors have been negatively impacted during the same period.

The key performance indicators from all companies have been grouped in 3 main strategic perspectives

as per the following: Value, Profitability and Risk. The Value perspective corresponds to the market value

of the firm and has a total of 3 metrics: the Beta of the Firm, the Reinvestment rate and the Total Shareholder

Equity. The Profitability perspective contains a total of 8 indicators: the Account Receivable turnover, the

Asset turnover, the Fixed Asset turnover, the Net Margin, the Pretax ROA, the Pretax ROE, the Revenues

and, finally, the Net Income. The final group is labeled the Risk perspective and holds 5 main performance

indicators: the Asset/Equity ratio (also called the Equity multiplier), the Cash & Cash Equivalents, the

Debt/Equity ratio, the Long Term Debt and the Total Liabilities.

4. The two optimal performance model

As noted previously, we generated two individual performance models: one for the over-performing

companies and another one for the poor performing companies.

4.1. Performance model of the successful companies

Figure 4 illustrates the model given the PLS approach on the successful enterprises. This model

describes that successful firms have as their basic objective the value of their company. This is the first

preoccupation of those firms: maximizing their market value that will drive their strategy. The second

objective is the profitability and, finally, the top priority and objective is the risk mitigation.

A possible interpretation could be as follows: the top executives want to first satisfy their shareholders

and the best way is to maximize their wealth by increasing the company’s stocks value. Consequently, this

stock market strategy will have an impact on the profitability of the firm. We can notice the correlation

coefficient from Value perspective to Profitability perspective of 0.868 that reinforce this statement. The

positive coefficient indicates a direct relationship between the two latent variables, an increased value of the

Energy, 3% Industry

, 10%

General Goods,

13%

Financial

Services, 14% Technol

ogy, 10%

IT, 27%

Retail, 23%

Overperforming companies

Education, 8%

Energy, 12%

Industry, 12%

General Goods,

4%

Financial

Services, 24%

Technology, 20%

IT, 8%

Retail, 12%

Underperforming companies

18

firm implies an increased profitability. Finally, the last preoccupation of the company is to manage the risk

and it is negatively correlated with a coefficient of -0.548. This last coefficient indicates an indirect

relationship between the latent variables: an increase of company’s profitability implies a diminution of their

risk. These over-performing companies are able to reduce their debts and liabilities and are able to invest

their profit in new productive assets.

Fig. 4: Performance model of over-performing companies

Our interpretation makes sense and it appears that the Value Based Management goes in the same

direction. As explained by McKinsey, the “senior managers are fully aware that their ultimate financial

objective is maximizing value; that they have clear rules for deciding when other objectives (such as

employment or environmental goals) outweigh this imperative; and that they have a solid analytical

understanding of which performance variables drive the value of the company” [27]. To do so, managers

must understand the whole organization and define value drivers that can have an impact on the company.

McKinsey also explained that there are four processes in order to implement a Value Based Management.

The first one is to develop such a strategy to maximize value at all levels of the firm. The second one is to

define the value drivers and the short and long term performance targets. The third one is to build a

comprehensive action plan and the fourth is to implement performance measurement in order to measure if

the objectives are achieved or not [27].

Considering the outcome of our first model, we concluded that successful firms are using a Value Based

Management strategy. Their ultimate goal is Value maximization and in order to achieve this, value drivers

are defined so that the second axis is the Profitability. Those drivers will allow the shareholders to evaluate

the profitability and the performance of the firms decreasing the overall Risk.

4.2. Performance model of the unsuccessful companies

Defining the term “failure” was a challenging task for many researchers of the 70’s. The most relevant is

given by the McGraw-Hill dictionary of Modern Economic in 1973 as “the cessation of operations by a

business concern because of involvement in court procedures or voluntary actions which result in loss to its

Beta

Reinvestment

rate

Shareholder

equity

.783

.761

-.735

Value

.868

-.548

Profitability

A/R Turnover

Asset Turnover

Fixed Assets

Net Margin

Pretax ROA

Pretax ROE

Revenues

Net Income

.882

-.022

.051

.407

.862

.845

.427

.007

Risk

Asset / Equity

Cash & cash

equivalents

Debt / Equity

Long Term Debt

Total Liabilities

.921

.872

.928

.558

.805

19

creditors” [28]. Considering this definition, it is important to understand what the main cause for the failure

of a certain firm. The performance of a firm is influenced by two major groups of factors. The first group is

represented by outside factors, such as the economic environment, the market outlook, etc. The second group

is characterized by the internal factors, like its resources, its management, etc.

A firm is considered successful if it satisfies three requirements [28]. The first one is that the firm needs

to reach a solid position in the market by increasing its sales and therefore gaining market shares. The second

one, as noted by the agency theory, the shareholders require the firm to produce certain financial

performance results in term of profitability, growth and liquidity. The third and final requirement is that the

interest of the stakeholders such as the employees, the clients and the suppliers should be taken into

consideration and fulfilled. A failure position can be assumed if a firm doesn’t meet one or more of those

requirements.

Figure 5 presented below illustrates the PLS model approach for the unsuccessful companies. It is

composed by the same three strategic perspectives: Profitability, Risk and Value, however the causal links

are completely different.

Fig. 5: Performance model of underperforming companies

Taking a first glimpse to unsuccessful performance model, we can notice that the primary and ultimate

objective is the Value perspective. This could imply that the non-performing firms are abandoning or

neglecting the interests of their shareholders. Following the “failure” definition given above, this would be

enough to define a firm as a non-successful. However, we can detail further the interpretation of this model

and more precisely the correlation coefficients between the latent variables (or the strategic perspectives).

This model illustrates that unsuccessful companies have as their basic objective the Profitability. The first

preoccupation of those firms facing difficulties is to quickly make profit. The second concern of the non-

performing companies is the Risk, having a negative coefficient. As with the previous model, we conclude

that increasing profitability will enable reducing the exposure risk. One interpretation could be that these

companies are using their profit to pay their creditors and decrease their debts volume. However, this

.802

Beta

Reinvestment

rate

Shareholder

equity

.975

-.024

.976

Value

Risk

Asset / Equity

Cash & cash

equivalents

Debt / Equity

Long Term Debt

Total Liabilities

.961

.945

.837

.740

.687

Profitability

A/R Turnover

Asset Turnover

Fixed Assets

Net Margin

Pretax ROA

Pretax ROE

Revenues

Net Income

.647

.420

-.178

.410

.339

.288

-.491

-.222

.868

20

decision of using their short term profits in order to reduce their exposure to debt is not well perceived by the

company’s shareholders and the market participates to a decrease of the company’s value. This can be

noticed by the positive coefficient between Risk and Value perspectives, therefore a direct relationship

between the two. This indicates that when the risk is reduced the value of the company is also diminished.

As the market is founding its opinion on the future expected cash flows, when earnings are used to pay

debt and not to reinvest, the market value will collapse. This strategy can be defined as a method to “stop the

bleeding" in order to avoid bankruptcy as the short-term profit made is used to reimburse creditor debts.

However, as the companies are facing a crisis, directors are constrained to adopt this type of strategy in order

to minimize the damages, but this means that they cannot prepare for the future, as the shareholders would

like them to.

5. Conclusion

Norton and Kaplan defined a rigid model of BSC that every company can use, independently of its type,

and independently of its performance, in order to reflects the strategy. As we have demonstrated in our study,

the over-performing companies seem to have a common strategic pattern (validation of hypotheses 1)

compared to the underperforming companies that also have a common strategic pattern (validation of

hypotheses 2) but different than the former (validation of hypotheses 3). We have also illustrated that, based

on the PLS approach, the firms have different Balanced Scorecard and thus different strategy, depending on

their performance. For Norton and Kaplan the BSC is a unique model, whereas in our model, each group of

company has its own personalized BSC.

Extrapolating the above, we can now think of larger applications of those results and the method used.

Currently, a lot of financial analysts and investors have their sample of companies split into two major

groups depending only on the quantitative measures. Our proposal can be seen as an optimal solution to

predict if a company is going to improve or not its performance in the future.

In our model, those causal links do not follow a certain rule, but depend on the group of companies and

their performance indicators. This implies that our model gives a clearer and straightforward explanation of

the relation between the strategic axes and the performance variables, which allows the executives a better

understanding of the model.

However, even though the findings from our study are promising, we cannot deny several limitations.

Our analysis is based on two samples of 30 quoted firms from the S&P 500 index. All those firms are based

in the United States and therefore our results may not be generalizable to companies from other countries. A

second limitation is the time frame of our study which is based on variations of the ratios of each company

between the years 2009 - 2012. This specific period following the financial crisis has probably impacted the

results of our study. Even though we have tested the stability of our models, we were not able to test their

sustainability and therefore our schema is likely to evolve in time. All these limitations can be diminished by

reconstructing and reapplying our procedure over time using larger samples of companies from other

countries. Only then this method can be reinforced and can be used at a larger and universal scale.

The PLS approach mixed with the concept of Balanced Scorecard enables us to check which factors have

the major impact on the performance of the firm. Furthermore, it allows us to analyze the chain of causality

between those factors that lead to performance. The suggested method can be used as a predictive approach

of performance but could also be used in a corrective way. The executive management will ultimately be

able to adjust their strategic goals in order to match with the over-performing pattern.

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