The Relationship between Project-Portfolio Success and multi-dimensional Business Success

Table of Contents

Abstract

Abstract (German version)

Table of Contents

List of abbreviations

List of figures

List of tables

List of formulas

Explanation of terms used

1 Introduction
1.1 Study subject and objectives
1.2 Structure

2 Theoretical Basis
2.1 Definition of examined companies
2.2 Multi-project Management
2.3 Success and Performance
2.3.1 Success measurement
2.3.1.1 Subjective
2.3.1.2 Objective
2.3.1.3 Summary
2.3.2 Project-portfolio success
2.3.3 Business success
2.3.3.1 Definition
2.3.3.2 Complexity
2.3.3.3 Perspectives
2.3.3.4 Measurement

3 Study design
3.1 Method
3.2 Identification of essential key figures

4 Data Analysis
4.1 Results
4.2 Discussion
4.2.1 Limitations of this thesis
4.2.2 Further studies of the examined subject
4.2.3 Recommendations for further research

5 Conclusion

Appendix
A) Notes on research methodology
B) Further regression models and results

Bibliography

Online sources

Abstract

This thesis investigates the relationship between subjectively measured project-portfolio success and objectively measured business success. In doing so the investigational background is initially explained, namely the determination of examined companies and the general construct of multi-project management. In approaching the issue the two kinds of success measurement utilized (subjective and objective) are discussed. Furthermore, the two main fields within this thesis are described:first, key success factors for project-portfolio success andsecond, the complex construct of business success. Due to the many facets of business success, this thesis also follows up with the different perspectives on business success and its measurement dimensions. In doing so, particular key figures will be discussed in more detail. While examining the issue, subjectively measured data that represent key figures for portfolio success were collected within the 5thbenchmarking study at the Technical University of Berlin. Objective business success data were extracted from the companies’ annual reports between 2008 and 2010.

Although the multiple regression analysis delivers several statistically significant results, these were not sufficient to lead to a unambiguous conclusion concerning the examined relationship. Consequently, there must be additional factorsthat influence overall business success and the goodness offit of the regression modelsused. However, following further studies and the statistically significant findings within this thesis, there is a trend which confirms the hypothesized relationship. Possible implications for companies complete this thesis.

Abstract (German version)

Ziel dieser Arbeit ist die Untersuchung des Zusammenhangs zwischen subjektiv gemessenem Projektportfolio Erfolg und objektiv gemessenem Geschäftserfolg. Dazu wird zunächst der Untersuchungshintergrund näher beschrieben. Dies ist einerseits die Beschreibung und Einordnung der untersuchten Unternehmen und andererseits das allgemeine Modell des Multiprojektmanagements. Um sich der Kernfrage weiter zu nähern, werden die beiden Messmethoden (subjektive und objektive) genauer diskutiert. Weiterführend werden die beiden Hauptthemen der Arbeit genauer vorgestellt. Dabei wird als erstes der Projektportfolio Erfolg genauer beschrieben und seine Erfolgsfaktoren, die auch in dieser Arbeit verwendet werden, definiert. Als zweites widmet sich diese Arbeit dem Geschäftserfolg. Dazu werden seine Komplexität, die verschiedenen Perspektiven des Erfolgs sowie Dimensionen der Messung vorgestellt. Die Beschreibung und Diskussion ausgewählter Kennzahlen vervollständigen dieses untersuchte Thema. Um den gesuchten Zusammenhang zu überprüfen, werden subjektive Daten, die den Projektportfolio Erfolg darstellen, mit Hilfe der 5. Benchmarking Studie der Technischen Universität Berlin erhoben. Die objektiven Kennzahlen werden aus den Unternehmensgeschäftsberichten von 2008 bis 2010 entnommen.

Obwohl die multiple Regressionsanalyse ein paar statistisch signifikante Ergebnisse liefert, bleibt eine eindeutige Schlussfolgerung schwierig. Es wird vielmehr deutlich, dass noch weitere Faktoren den Geschäftserfolg beeinflussen sowie die Güte der verwendeten Regressionsmodelle. Nach einer Diskussion mit weiteren Studien und die Verwendung der signifikanten Ergebnisse dieser Arbeit lässt sich jedoch eine Tendenz erkennen, die den untersuchten Zusammenhang bestätigt. Mögliche Implikationen für Unternehmen vervollständigen diese Arbeit abschließend.

List of abbreviations

illustration not visible in this excerpt

List of figures

Figure 1: Structure of this thesis

Figure 2: Conceptual model of project-portfolio success dimensions

Figure 3: Conceptual model of testing business success key figures

Figure 4: Established relationships

Figure 5: Appendix - Frequency of examined industries

Figure 6: Appendix - Histogram about sales growth

Figure 7: Appendix - Normal P-P Plot about sales growth

Figure 8: Appendix - Histogram about ROA

Figure 9: Appendix - Normal P-P Plot about ROA

Figure 10: Appendix - Histogram about ROE

Figure 11: Appendix - Normal P-P Plot about ROE

Figure 12: Appendix - Histogram about TSR

Figure 13: Appendix - Normal P-P Plot about TSR

Figure 14: Appendix - Histogram about Altman's Z

Figure 15: Appendix - Normal P-P Plot about Altman's Z

List of tables

Table 1: Correlations of independent variable with Pearson

Table 2: Regression results for sales growth

Table 3: Regression results for sales growth (ANOVA)

Table 4: Regression coefficients for sales growth

Table 5: Regression results for ROA

Table 6: Regression results for ROA (ANOVA)

Table 7: Regression coefficients for ROA

Table 8: Regression results for ROE

Table 9: Regression results for ROE (ANOVA)

Table 10: Regression coefficients for ROE

Table 11: Regression results for TSR

Table 12: Regression results for TSR (ANOVA)

Table 13: Regression coefficients for TSR

Table 14: Regression results for Altman's Z

Table 15: Regression results for Altman's Z (ANOVA)

Table 16: Regression coefficients for Altman's Z

Table 17: Appendix - Frequency analysis of used measures in literature and practice

Table 18: Appendix - Rolls of asked managers

Table 19: Appendix - Questions to determine project-portfolio success

Table 20: Appendix - Scatter diagrams about examined relationships

Table 21: Appendix - Regression results for sales growth without outliers

Table 22: Appendix - Regression results for sales growth without outliers (ANOVA)

Table 23: Appendix - Regression coefficients for sales growth without outliers

Table 24: Appendix - Regression results for ROA without outliers

Table 25: Appendix - Regression results for ROA without outliers (ANOVA)

Table 26: Appendix - Regression coefficients for ROA without outliers

Table 27: Appendix - Regression results for ROE without outliers

Table 28: Appendix - Regression results for ROE without outliers (ANOVA)

Table 29: Appendix - Regression coefficients for ROE without outliers

Table 30: Appendix - Regression results for Altman’s Z without outliers

Table 31: Appendix - Regression results for Altman's Z without outliers (ANOVA)

Table 32: Appendix - Regression coefficients for Altman's Z without outliers

Table 33: Appendix - Regression results for sales growth (only listed corporations)

Table 34: Appendix - Regression results for sales growth (only listed corporations) (ANOVA)

Table 35: Appendix - Regression coefficients for sales growth (only listed corporations)

Table 36: Appendix - Regression results for ROA (only listed corporations)

Table 37: Appendix - Regression results for ROA (only listed corporations) (ANOVA)

Table 38: Appendix - Regression coefficients for ROA (only listed corporations)

Table 39: Appendix - Regression results for ROE (only listed corporations)

Table 40: Appendix - Regression results for ROE (only listed corporations) (ANOVA)

Table 41: Appendix - Regression coefficients for ROE (only listed corporations)

Table 42: Appendix - Regression results for Altman's Z (only listed corporations)

Table 43: Appendix - Regression results for Altman's Z (only listed corporations) (ANOVA)

Table 44: Appendix - Regression coefficients for Altman's Z (only listed corporations)

List of formulas

Formula 1: Sales growth in %

Formula 2: Return on sales in %

Formula 3: Operating return on sales in %

Formula 4: ROI in %

Formula 5: Return on assets in %

Formula 6: Return on equity in %

Formula 7: Total Shareholder Return in %

Formula 8: Tobin's Q

Formula 9: Approximate Q

Formula 10: Market value added

Formula 11: Economic value added (capital charge)

Formula 12: Economic value added (value spread)

Formula 13: Altman's Z-score (general form)

Formula 14: Altman's Z-score (adjusted form)

Explanation of termsused

„Between-method“ investigation

Investigation of both subjectively and objectively collected data

„Within-method“ investigation

Investigation of only subjectively collected data

1 Introduction

Globalization increases the dynamics of the environments in which companies must operate. Due to highly efficient logistics solutions,enterprises construct their value creation chains all over the world. Besides this, the market becomes a world-wide one and firms are easily able to satisfy more and more consumer needs. This results in dynamic organizational structures and processes, because every company must satisfy the demand of their customers more specificallyto secure their market shareor even to gain access to the respective market. Hence, firms are more and morebecoming project-oriented organizations.^[1]

1.1 Study subjectand objectives

Within firms which are characterized bya project-oriented organization, typically dozens, even hundreds of projects are going on simultaneously.^[2] On the background of such multi-project settings, the traditional single project goals within the triangle of time, budget and performance^[3] are necessary conditions, but no longer sufficient ones for a company to be successful.^[4] In the current situation with dynamic markets, a highfixed cost environment, and multi-product firms^[5] an effective multi-project management may contribute high benefits to corporate performance.^[6]

However, what is business success and how is it measurable? A purely financial approach is not adequate to this constellation. There are numerous groups of stakeholdersinterested in the firm. Each of these groups has their own idea about organizational performance. Thus, a multi-dimensional approach for the measurement of success is necessary.^[7] Many authors recommend a measurement in different categories.^[8] This idea, however, leads to the problem of the availability of requested information. There is a legal obligation ofdisclosure for so called joint-stock companies (section 325 of the German Commercial Code). But small and medium-sized enterprisesare under no such obligation, making it significantly more difficult to acquire information concerning their success or lack thereof.

One possibility of solving this problem is subjective data collection with so called key informants.^[9] These could be managers of a particular firm for example. However, subjectively generated data is easily influenced by failures or inaccuracy.^[10] Hence, the validity of results and the interchangeability to objectively generated information is doubtful.

In this context empirical data were collected, analyzed and evaluated by means of the 5thbenchmarking study about multi-project management (MPM) at the Technical University of Berlin. This study is carried out regularly since 2003 and it examines critical success factors of management within project environments of large and medium-sized enterprises. So far, more than 1.000 members in over 400 firms took part in this study, so it is a well known and internationally recognized instrument for comparing enterprises based on “best practices” in multi-project management.^[11]

The situation summarizedabove results in the following tasks:A convincing definition of organizational performance needs to be formulated, taking into account the different stakeholders and the dynamic development of the economic environment. Furthermore, it is essential to developmethods for validating subjectively collected information on project-portfolio success. After these two fundamental steps have been taken,the relationship between the subjective evaluation criteria and objective key indicators of organizational performance must be statisticallyanalyzed.

Thus, there are three core issues, which are to be answered in this thesis:

1. Which indicators adequately depictproject-portfolio success?
2. How to measure business success taking into account all achievements of a company?
3. Is there arelationship between subjectively measured project-portfolio success and objectively measured multi-dimensional business success?

The answer to these questions and the case of a positive relationship, implying that efficient project-portfolio management delivers additional benefits to business success will clarify why certain companies perform better than others. Considering this possible result, firms may also develop a higher awareness regarding project management and in doing so enable themselves to improve their project management abilities and create more value. Consequently, this thesis will also provide some evidence regarding high investments in project management. This makes the subject of this thesis very important for research and business practice.

Although there are many studies on business success and also on project-portfolio success, there are only a few studies that examine the relationship between these success dimensions. These studies examine the correlations between several single success factors of project management activities and their impact on organizational benefits.^[12] Meskendahl (2010) examines the relationship of project-portfolio success and business success as a whole in a subjective measure “within-method” investigation.^[13] However, there is no study for the examination of the relationship between project-portfolio success and multi-dimensional business success by using a “between-method” investigation, namely subjectively measured portfolio success and objectively measured business success.

The next sub-chapter will give an overview of the argumentationused, leading to a clear answer of the questions mentioned above.

1.2 Structure

Chapter two elaboratesthe theoretical basis to ensure a fundamentalunderstandingofthe examined cases. First off, the firmswhich were examined in this thesis willbe described. After that, multi-project management (MPM) is described regarding its definition and its significance for companies. MPM is a generic term for all topics of project management and it will be used as the framework for this thesis.

The mainsection of the theory chapter will deal with success and performance. Due to the use of both subjective and objective measurement within this thesis the chapter will critically discuss these performance measurement methods and summarize the current state of research. Based on this, project-portfolio success and its indicators willfurther be explained as part of the subjective data collection, followed by a discussion of business success as part of the objective data collection. The latter will initially be specifiedby adefinition of success in the context of complexity and different perspectives on performance. After that, key figures as essential elements for objective performance measurement will be shown and grouped into particular categories.Key figures are the most important form of data compression,^[14] thus they are very important for this investigation.

The third chapter represents the bridge between theory and practice. For that,the most important key figuresfor the investigation are identified in the context of MPM and the examined companies. Furthermore, the study design as well as the method of data collection andthe structure of data analysis as well as the assumptions are described.

In chapter four the formulated relationship is examined with a regression analysis. In doing so, the results arepresented and evaluated. After that the results arediscussed critically and further potential for researchis shown. The last chapter concludeswith a summary of the findings of the thesis.

Figure 1: Structure of this thesis

illustration not visible in this excerpt

2 Theoretical Basis

For a basic understanding of the examinedcases,initially it’s essential to describe and explain the subject areas touched upon. The approach of this chapter isto begin with the definition of different companies with their categories and difficulties regarding information collection. An explanation of multi-project management follows this. The main theoretical section deals with success. For this a comparison between subjective and objective measurement is made followed by a definition of project-portfolio management and its success factors. A section about business successconcludes this chapter. The last section will represent the major part due to the complex constellation of business success and its multidimensional character.

2.1 Definition of examined companies

To develop an idea ofthe companies thatare relevant for the investigatedtopic and which also took part of the evaluated benchmarking-study, a division into two major groups is useful – small and medium-sized enterprises being one and large enterprises being the other.

Small and medium-sized Enterprises (SME)

Containing more than 99% of all companies in Germany, this category is by far the larger one.^[15] Hence, this group of enterprises is an important factor for employment and a main pillar for the German economy.^[16] However, neither a legal nor an agreed upon definition for political and economic scope exists.^[17]

There are three recognized approaches. The first one is based on a quantitative perspective, which defines a SME by financial turnover and number of employees. This approach is a recommendation of the Commission of the European Communities from May 6th,2003 and it is important for the legal support of the respective enterprises. The recommendation of the IfM-Bonn, which is important for science and practice, differs regarding the number of employees. Due to the fact that independent departments of a company often formally are independent SMEs too, this approach has its particular limits.^[18]

The second approach uses a qualitative perspective. This means that the ownership or the conduct of afirm is more important for defining a company. Indeed, the differences between the two approachesare measureable, but they are not large.^[19]

The third approach takes into account the connections to other companies.^[20] This idea considers firms as large companiesif the number of their employees and financial turnover exceed the defined limits due to their relation to other companies with the right of supervision. This also leads to a different share of all companies being defined as SME.However, the difference is also slight.^[21]

For measurements, comparisons or evaluations of SMEs, it is essential to define quantitative facts, because qualitative facts are limited regarding their statistical evaluation.Thus, quantitative criteria were developed from a supporting to a sufficient level.^[22] Today, science and practice use the following maximum criteria: less than 500 employees and less than 50 millioneuroturnover p.a. Regarding legal support the maximum limits are: less than 250 employees and less than 50 millioneuro turnover p.a. tooor less than 43 millioneuro balance-sheet total.^[23] The second definition has a special “OR”-rule. That means a company also belongs to the group of SME, if it does not exceed either the employee limit or the turnover limit.^[24]

In reality it is difficult to find a common definition, because there are so many different companies from a little tradesman to big businesses,^[25] which belong either to the qualitatively determined group or to the quantitative group. For example a firm leadby the owner can also employ more than 500 employees or generate more than 50mil. euro turnover p.a. Maybe this could be a reason why there is still no agreed upon and legal definition on small and medium-sized enterprises.

Large Enterprises

On the other end of the scale are big businesses. That means more than 250 or 500 employees and more than 50 millioneuro turnover p.a. (as well as more than 43 million euro balance-sheet total). Nevertheless this definition is not generally agreed upon. Most authors use the recommendation of the Commission of European Communities with a strong interpretation of amount of employees and turnover p.a. for statistical investigations. However, Kless and Valdhues (2008) use more than 249 employees or more than 50million. euro turnover p.a. to describe large enterprises, whereas May-Strobl et al. (2010) only use the turnover limit in case of no availability of employee data. Hence, it is very important to find out the limits used for defining SME within a study in order to ensure comparable data.

Most large enterprises are joint-stock companies so the publication of their corporate data is mandatory. Nevertheless, this group only has a share of 0.4% of all enterprises.^[26] Divided by legal forms there are, however, 16% joint-stock companies in Germany.^[27] These will be the fundamental basis for this examination.

2.2 Multi-project Management

For a better understanding of project-portfolio success and its significance for organizational performance it is useful to take a look at multi-project management. During the literature research, the fact that there is no common understanding about multi-project management regarding both the term and the content stood out.^[28] Some authors use different terms like program management, project-portfolio management or multi-project management synonymously.^[29] However, for a clear argumentation within this thesis, the different terms will be determined as followed:

Single-project management is the lowest level of project management and deals with all activities for the best possible preparation, planning and execution of projects^[30] within the triple bottom line of time, budget and performance. Today, however, client’s satisfaction and welfare as well as alignment with corporate strategy should also be considered animportant indicator of success.^[31] The main characterization is the allocation of limited resources to achieve the determined objectives and in doing so the effective and efficient control of a single project.^[32]

Program Management represents the next level regarding project management. Artto et al. (2009) point out that there is no common definition for this level in literature. Recent research shows that the definition depends on the different contexts like number of projects or size and resource type. An appropriate definition was made by the Project Management Institute (2006).They argue thatProgram Management is

“[…] the centralized coordinated management of a program – a group of related projects managed in a coordinated way to obtain benefits and control not available from managing them individually – to achieve the program’s strategic objectives and benefits.”^[33]

Here, a program management is responsible for theallocation of resources, the coordination of activities and the combination of projectsto reduce complexity and realize synergies. These combined projects altogether belong to a large project. Thus, the program is limited regarding the time of the main project.^[34]

Portfolio Management (or project-portfolio management) is one level above program management and based on a portfolio. A portfolio is “a collection of projects or programs and other work that are grouped together to facilitate effective management of that work to meet strategic business objectives.”^[35] Project-portfolio management is characterized by a strategic decision process, which consists of identification, allocation, categorization and prioritization of projects as well as of a strategic allocation of resources to different programs and projects. These tasks also aim to achieve specific strategic business objectives.^[36] Compared to program management, project-portfolio-management is closely related to strategic decisions and should implement strategic objectives into project management alignment. Furthermore, it is not limited regarding the time of any project.

Multi-project Management is a generic term for all subjects relating to project management and the terms mentioned above. Hence, program management, project-portfolio management and single-project management are respectively subsets of multi-project management.^[37] Furthermore, taking into account the includedproject management dimensions, it is also a set of organizational and methodical activities as well as processes and a system of incentives that contribute to the management of a multi-project environment.^[38] That means this level of project management is responsible for organizational structures to promote project-oriented control, e.g. a central coordination by a project management office (PMO).^[39] The qualification of employees and the development of a project management culture are also tasksof multi-project management. Consequently, the multi-project level comprises three dimensions of project management with additional activities and tools to ensure a functioning and efficient project environment.

Why do we need multi-project management? Executives increasingly realize that projects are the basis for the future profitability of a firm, leading to their growinginterest in how projects are selected and managed.^[40] Also many new trendsnecessitate multi-project management, for example more project-oriented organizations as mentioned in the introduction, increased complexity due to shorter project duration or a higher diversity.^[41] A large number of projects share and compete for the same limited resources^[42] and interact with each other,leading to strong interdependencies such as double and redundant work as well as synergies.^[43] Also, the dynamic enterprise environment results in many changes within the project environment. Often a lack of information about the ongoing and planned projects is a consequence.^[44] Therefore enterprisesrequire goodmanagementof all their projects in the context of their strategic objectives to avoid such problems.

Dietrich and Lehtonen (2005) suggest that many of the presented models and approaches in literature are merely theoretical constructions about solving managerial problems.^[45] However, there are also some empirical studies, which suggest a positive influence of multi-project management on organizational performance.^[46] Thus, multi-project managementdeliversadditional benefits for the performance of an organization.

The presented argumentation emphasizes project-portfolio management and its successas a field in its own right, taken in the context of project management, or rather multi-project management. It contributes to achieving organizational objectives and especially the field of project-portfolio management and its success is only one indicator for these objectives and consequently business success. The formulated definitions make a clear dissociation regarding the terms used and improve the understanding of the examined topic in the context of multi-project management.

2.3 Success and Performance

In order tounderstand what success really is, especially for a project-portfolio and for a business, as well as how to measure it, this chapter describes and defines the required context. First, an overview is given about measuring success and why this is important for the results of this examination. Second, project-portfolio and its success indicators are defined as part of subjective measurement followed by, third, business success as objective measurement. The definition of business success will take the major part due to its complexity and different dimensions involved therein.

2.3.1 Success measurement

Within this thesis project-portfolio success and business success are examined and measured. There are multiple ways to partition performance measurement, but the most popular is between subjective and objective measures.^[47] Each of them has its advantages and disadvantages. Here, both types of measurment are used, thus an explanation is useful for understanding the empirical part of this thesis.

2.3.1.1 Subjective

According to Muckler and Seven (1992) “definitions of objectivity and subjectivity […] usually hinge on the degree to which human experience, judgment, and feeling (subjective factors) are involved in the derivation of reality.”^[48] Thus, measurement is subjective when one or more persons evaluate a particular question or subject. These persons, also called key informants, are supposedly knowledgeable about the issues being researched. They are also able and willing to communicate about the requested issues.^[49]

Investigations with key informants often use five- or seven-pointLikert-scales due to their high significance within economical and social science.^[50] Theseordinal – or by using symmetric formulations and visualization intervals – scales are mostly used in questionnaires.They cover a strong negative to a strong positive attitude like “strongly disagree” to “strongly agree”. These facts emphasize the subjective character of data collection by key informants. The person gives his own opinion regarding a requested item.

Consequently, there are both advantages and disadvantages associated with the use of subjective measurement. First, researchers are able to define and tailorthe questions directly to fit the investigated subjects.^[51] In doing so they are able to define individual or aggregate items to cover different dimensions of performance. This fact allowsthe gathering ofinformation that would have not been possible to collect with standardized objective data. In practice some studies used this advantage. Narver and Slater (1990) sent questionnaires to different business units in one corporation and asked their managers about the market orientation of the firm. All the business units had their own strategy and the managers were responsible for sales and profit,^[52] which is necessary for identifying a business and also for identifying well-informed persons, who are suitable for a research study.^[53] Within the scope of market orientation the managers were asked about customer orientation, competitor orientation and interfunctional coordination. Other examplesare the usage of key performance indicators (KPI) in project-oriented management (George 1998) and the manager’s satisfaction about provided information of KPIs (Weber and Sandt 2001). These items point out that subjective measurement by using questionnaires allows for the gathering of specific and personal information whereas objective would not be able to do so.

Another advantage is the flexible character of subjective data collection with questionnaires due to the lack of fixed reference points.^[54] Hence, measures are not anchored to any definite objects, they can also be relative. In a study of 68 US firms across industries Pelham and Wilson (1996) examined the subjective evaluation of business position relative to expectations.^[55] Slater and Narver (1996) investigatedthe subjective evaluation of return on assets (ROA) and sales growth relative to competitorsin 228 manufacturing firms across industries.^[56]

A third advantage is the measurement of organizational performance with consideration of the strategic background and industry characteristics. Lenz (1981) emphasizesthe absolute performance of a business entity depends on more than one indicator. There are diverse dimensions that similarlyaffect organizational performance.^[57] For instance, market share and distribution is much more important for a company with a cost-leader strategy than forfirms with a differentiation or focus strategy. Managers know the firm’s strategy and evaluate questioned items in context of it.

The possibility to ask about future trends, developments or company alignment is a further advantage. Objective information with past-oriented data is limited regarding its significance for future developments.^[58] Managers certainly are well-informed regarding the enterprise’s future alignment and its meaning for the performance of the respective organization. This fact also leads to more appropriate and reliable data by using subjective measurement.

Despite all these advantages there are also a few disadvantages to subjective information gathering. Muckler and Seven (1992) point out in accordance to Rosenthal (1976) that the interaction of experimenter and respondent is a major source of data distortion.^[59] This, however, is not applicable when using questionnaires, which managers complete without an observer or experimenter. Another problem is cognitive distortion.^[60] Taylor and Brown (1988) say Managers tend to unrealistically positive views of themselves and their actions,^[61] whereas Mezias and Starbuck (2003) identify an error scope of less than -75% to more than +200% for 59% of all respondents.^[62] This could be attributed to memory failure or an inability to accurately recall past events as well as memory distortion as Kumar et al (1993) collected from different authors.^[63] A third disadvantage is the “Halo Effect”. It is characterized by a prejudice which leads to errors in evaluating particular subjects.^[64] Nisbett and Wilson (1977) confirm this effect within questionnaires in a study with 118 students.^[65] In doing so they establishan influence of questions to each other, that causes an evaluation distortion. Wall et al. (2004) also constitute another two errors on subjective measurement. The first one, a random error, appears if the respondent does not have all information necessary for a valid answer. In that case the respondent guesses, which results in a wrong or inaccurate information. The second one is a systematic error. According to Wall et al. (2004) this is more serious due to a negative influence of all data collected from one person.^[66] It appears if the respondent is requested to evaluate his own performance. Thus, there is a general tendency to a positive or negative evaluation.

Consequently, subjective measurement is a useful method for informationgathering. Especially the collection of data that is not easily available, the flexible character and the evaluation with consideration of particular backgrounds make this type of measurement very valuable. On the other hand the influence through individual perceptions can be enormous and leads to the question of the validity of the collected data.

2.3.1.2 Objective

In contrast to the measurement above there is also an objective type ofmeasurement. Many authors argue this is an ideal of science and a sufficient criterion for the quality of a study.^[67] Indeed, the central characteristic of objectivity is defined as direct measures of countablebehaviors or outcomes, the eliminating of subjectivity thus leaving no systematic bias or random errors and the possibility of verifiability.^[68] Hence, objective information should ideally be statistical measures which are based on facts and recognized standards of data collection, availability, definition, comprehensiveness etc. Objectivity is also characterized by direct units of measurement, which are either absolute or relative to another particular measure.^[69] The access to objective data can be provided through accounting or management information systems, for instance. By contrast, external sources for those data can be commercial databases that provide these data for research.

The access and availability of data regarding business success point out the main advantage of objective measures. Due to the standardization and the often automatic and computer-aided collection of information, objective data is quickly, easily and reasonably accessible. The standard conventions, especially by using a data base, ensure comparability. In addition, objective measurement scales can also adopt a ratio characterization depending on the examined indicator. Thus, measures and indicators can be used in various further functions like thecreation of new indicators. This leads to a more specific usage and enables a more detailed analysis.^[70]

Nevertheless, there are also some disadvantages and limitations to objectivity. The main advantage – the standard definitions and thus a good comparability – is also its major disadvantage. The lack of flexibility does not allow the examination of particular issues.Consequently, gathering useful and sufficient data is more difficult compared to subjective measurement.^[71] Many authors confirm this problem in different studies.^[72]

Wall et al. (2004) also argue that objective measurement is not able to focus on overall performance in the waysubjective measures are.^[73] This is also supported by Benito and Benito (2004). They say only the subjective approach facilitates the measurement of complex dimensions of performance,^[74] which is necessary for covering the entire performance and thus for a valid evaluation of a firm.

In addition, the objective approach cannot develop a reliable measurement of cross-sectional analysis through sectors and markets or qualitative characteristics.^[75] However, differences in performance can appear through internal and external factors like company size or industry affiliation.^[76] In fact, this is a direct contrast to subjective measures, which are able to quantify performance in the context of particular strategies or in comparison to objectives and competitors. Moreover, there is no agreed upon set of measures or indicators for performance. Instead, there are dozens of possible measures, which the researcher must select for his objectives.^[77]

Bommer et al. (1995) also point out there is no objectivity without subjectivity:

“… no measure could “objectively” measure all relevant performance aspects. Some key measures are necessarily subjective.”

An appropriate example for this argument is the balancing of accounts.^[78] In some issues it is the accountant’s or manager’s choice whether the company creates reserve funds or moves important purchases into other periods.These optional inclusions as assets lead to information distortion. Hence, also objective data can easily be contaminated by subjective influences.

2.3.1.3 Summary

There are several studies, which examine the relationship of subjective and objective performance. The major part (50%) of studies analyzed by Benito and Benito (2004) reported a stronger relationship for subjective, the minor part (20%) for objective.^[79] Furthermore Kolloge (2009) and Richard et al. (2009) could not find a clear definition regarding subjective or objective performance measurement. Both approaches, however, are prone to errors thus a conclusion about which is the most appropriate measurement is not possible.^[80] This thesis also uses both subjective data collection with questionnaires and objective measures in annual reports for instance. Therefore a few closing remarks regarding a comparison of both measurements are inserted here and some improvements for reliable measuresare given.

First, by gathering subjective information researchersneed to beware the single source bias, which leads to distortion due to prejudice – especially when managers must evaluate their own performance. Another manager within the same company or business unit with similar information could be able to answer the same questionnaire. A combination of both first and second respondent makes the measures more reliable.^[81] Furthermore all respondents should work in the same fields, because managers of different sectors do not have the same knowledge regarding a particular subject for which reason the measures can vary significantly.^[82]

On the objective side it is important to create a valid causal inference with measures of success. For more reliable measures use of both success data taken from different periods to eliminate extreme influences in certain periods and multiple types of performance in measuring business success is recommended.^[83] Hult et al. (2008) also emphasize a comparability of measures and thus the need to avoid different accounting standards for international enterprises and its accounting optionsregarding particular evaluations of a firm’s assets.^[84]

Currently there is no clear argumentation in research for an advantageousness of one of these measurement types. Instead, an ideal approach for measurement of firm performance is the consideration of both subjective and objective data sources whenever possible.^[85] Hence, a comparison of subjective measures with objective measures secures reliable information. Moreover, by using both subjective and objective data the advantages of the former approach can compensate the disadvantages of the latter one and vice versa.If this is not possible, researchers must take into account disadvantages of each approach and try to avoid them to make measures more valid.

2.3.2 Project-portfolio success

As distinguished above between subjective and objective performance measurement, the project-portfolio success, which represents the subjective measureswithin this thesis, will be further describednow. The theoretical analysis will lead to a definition, which also follows the criteria within the used empirical benchmarking study. However, this is only another contribution to a common assessment of project or project-portfolio performance respectively, because there is no agreement on standards, or even an operative framework for measuring project success.^[86]

Definition

First, there are different ideas regarding a definition of project-portfolio success in literature. Martinsuo and Lehtonen (2007) argue that aligning projects to portfolio objectives influence the project-portfolio efficiency in a positive manner, whereas Meskendahl (2010) directly uses the term project-portfolio success regarding activities that deliver benefits to a project-portfolio.^[87] Assuming that efficiency leads to success, this thesis determinesthe term project-portfolio success as basis for a clear definition.

Furthermore, there are two perspectives on successful portfolio management. The strategic view evaluates success of projects and portfolios through their contribution to an organization’s strategy.^[88] Consequently, the research question becomeswhether a contribution to the organization’s strategy also contributes to the firm’s business success. To answer that question is the aim of this thesis. However, for an examination of this question the second perspective is necessary – the multi-project context. This more detailed view allows an examination of success factors “that lead directly or indirectly to thesuccess of the project or business.”^[89] Hence, the determination of convincing factors defines project-portfolio success.

Various studies and approaches emphasize the influence of single-project performance on project-portfolio success.^[90] Hence, this thesis determines the average single-project success as first important factor for project-portfolio success. Nevertheless, several studies criticize thatprojects are often evaluatedindependently and analyzed isolatedly. In today’s dynamic markets and companies with multi-project environments this view is not enough.^[91] Blomquist and Müller (2006b)acknowledge in accordance with Turner and Speisera simultaneous consideration of project, program and portfolio performance.^[92] Müller et al. (2008) support this argument with a worldwide study on middle manager’s practices, roles and responsibilities in program and portfolio management. They establish that portfolio performance is multidimensional and includes the project, portfolio, and organizational level.^[93] This leads to another well established approach in literature. The realization of synergies, rather the coordinated management of all projects within a portfolio delivers higher benefitsin opposition to managing them individually.^[94] Thus, realizing synergies is the second main success factor for project-portfolio management used within this thesis. Some authors also emphasize the linkage of project alignment to an organization’s strategy.^[95] Empirical studies show that companies with a project-portfolio alignment to strategic objectives perform better.^[96] This linkage as a project-portfolio objective is also determined in the portfolio management definition mentioned above. Consequently, linking projects to an organization’s strategy is the third important factor for project-portfolio success. Moreover, Martinsuo and Lehtonen (2007), Cooper and Edgett (2003) and Cooper et al. (2002) constitute the balance across projects as a main success factor. A balanced portfolio is able to exploit advantages and reduce the effects of disadvantages simultaneously for each project characteristic. For instance a mix of short and long term projects with higher and lower risks or across various markets, technologies etc. ensures the company’s performance more appropriately than “putting all one's eggs in one basket.”^[97] Although a few authors also considerclear goals, resource allocation, prioritization or information quality as success factors in a multi-project environment^[98], this thesis strongly remains on the definitions for project-portfolio and multi-project management above. Regarding these definitions, factors such as clear goals, information quality etc. areconsidered to be more of a set of tools, which belong to multi-project management and are therefore not taken into consideration in the context of project-portfolio success.

Based on the discussion above this thesis proposes project-portfolio success is defined by (1) the average single-project success, (2) the realization of synergies between projects, (3) the portfolio fit to the organization’s strategy, and (4) the portfolio balance. This definition is also supported by Cooper et al. (2002), Dammer (2008) and Meskendahl (2010).^[99]

illustration not visible in this excerpt

Figure 2: Conceptual model of project-portfolio success dimensions

Averagesingle-project success

Martinsuo and Lehtonen (2007) argue that single-project success is not a sufficient, but minimally a necessary condition for successful portfolio management.^[100] Hence, this dimension is the first main factor, which indicates project-portfolio success. Recent studies emphasize that managing projects only to fulfill time, budget and specification goals is too limited.^[101] For instance, the Sydney opera house is Australia’s most famous landmark and every tourist wants to see it, but the construction needed much more time and money than calculated. However, a consideration of these indicators is still important, but a wider scopeis more appropriate for a successful project management. Some authors support the satisfaction of consumer needs as a critical success factor for project management thus a project should also deliver a benefit to the customer.^[102] Continuing the example, the Sydney opera house would not be a successful project by using the criteria of the “iron triangle”. In consideration of consumer satisfaction, however, it is successful. Furthermore Shenhar et al. (2001) realize that “project management is a multi-faced, multi-dimensional concept” and they identify different dimensions and timeframes to assess a project’s success.^[103]

In reflection of the discussion above and limitations of the study design used, this thesis measures the average single-project success of all projects within the portfolio by evaluating time, budget and specification goals. These criteria are extended by the customer satisfaction. Customers are determined through (internal or external) recipients of deliverables.

Use of synergies

Within a company, which manages many projects simultaneously, there are also some dependencies across the projects. These dependencies could be based on strategy,technology, resources or budget.^[104] The management of a complex multi-project environment with numerous interdependencies is very difficult, because managers have to know the single-project milestones, resources and technologies and in addition the impact on other projects through changing one of these factors.^[105] Nevertheless, managing interdependencies delivers additional benefits. Buuren et al. (2010) examined tensions and synergies between spatial development projects. They find that the management of dependencies within a portfolio realizes synergies and “can result in cost and efficiencygains, increase the possibility of creating package-dealsand opportunities for sharing and reducing risks.”^[106] This fact is well established in literature and proven by several studies.^[107] Kaplan and Norton (2006) also emphasize the importance of synergies in a strategic perspective, thus, this factor is equally applicable to the portfolio level.

Therefore, this thesis defines as second success factor for project-portfolio management the use of synergies between projects regarding technologies and markets.

Strategic Fit

According to the statement that firms are becoming more project-oriented organizations made in the introduction, projects develop into an important tool for directing companies. Hence, “project managers must become the new strategic leaders, [because] projects in the future will become engines that drive strategy in new directions.”^[108] Krüger (2008) points out that project planning, rather project-portfolio management must relate to corporate strategy for an appropriate reaction to external developments.^[109] Dietrich and Lehtonen (2005) have a more detailed view of strategicfit. They measure the fit in three dimensions. First, the project objectives are aligned with corporate strategy. Second, the resource allocation to projects is aligned to corporate strategy and third, the current project-portfolio reflects the corporate strategy.^[110] This approach explains not only the importance of strategic project alignment, but in particular the significance of resource allocation within a portfolio.

As defined above, strategic resource allocation as well as the other tasks of portfolio management in alignment with the strategy of the organization is an important success factor for project-portfolio management. Therefore, this thesis also follows the measurement concept of Dietrich and Lehtonen (2005) and assesses the alignment with corporate strategy regarding project objectives, resource allocation and strategy reflection.

Portfolio-balance

The theory of balanced portfolios has its origin in the stock exchange. Markowitz (1959) argues a good portfolio delivers security and opportunities simultaneously:

“A good portfolio is more than a long list of good stocks and bonds. It is a balanced whole, providing the investor with protection and opportunities with respect to a wide range of contingencies.”^[111]

Since the 1970s this approach was adapted by several management consultancies for organizational concerns. Applied to project management, a successful combination of projects within a portfolio delivers several benefits for both the company and the customer, e.g. minimizing risks, gaining of competitive advantages or a better satisfaction of consumer needs through abetterexploitation of the organization’s strengths.^[112] However, whereas the literature on portfolio management is in agreement that a successfully balanced portfolio should cover a range of dimensions,there is no common convention on these particular dimensions.^[113] In practice a worldwide survey of the largest private and public companies (229 responses from 196 companies in the total survey sample) point out that managers mostly take account into the balance of cash generation vs. consumption (79%), growth vs. profitability (78%), risk vs. return (71%), short-term vs. long-term value creation (68%) and exploiting existing vs. exploring new capabilities (41%).^[114]

With respect to theory and practice, this study covers a wide range of dimensions that are as heterogeneous as possible to each other. Consequently, the fourth success factor for portfolio management is determined by a balance regarding the constant generation of cash flow as well as the constant utilization of resources along the project execution, the risk level and existing technologies and their respective areas of application.

2.3.3 Business success

The business success covers the objective dimension of data perception within this thesis. Although it is frequently examined and analyzed in various studies there is no common agreement on a standard definition of business success in literature. Therefore, the following part will develop a better understanding of business success, which is used within this thesis. In doing so, first a common term will be determined. Second, the complexity of business success and its different perspectives will be described and third, the different dimensions of measurement for business success will be introduced.

2.3.3.1 Definition

Research on business success comprises numerous theoretical and empirical examinations. Nevertheless, authors use different terms in their studies, namely organizational performance, organizational effectiveness and business success. Venkatraman and Ramanujam (1986) determine a difference between performance and effectiveness. They argue that business performance is a subset of the overall concept of organizational effectiveness.^[115] Richard et al. (2009) follow this concept and define organizational effectiveness as a broader set of measures which captures organizational performance (financial and product-market performance as well as shareholder return) plus internal performance outcomes.^[116] Devinney et al. (2010) consider performance to be about evaluating units or departments whereas effectiveness concerns overall company performance.^[117] However, other authors use the term organizational performance for a company’s overall performance.^[118] The use of the term business success regarding its measurement is very rare in literature and was only used by Meskendahl (2010).

However, this thesis concentrates on the results of manager’s performance with their project-portfolio, thus it is less important whether internal processes are efficient or effective. Moreover, organizational performance is more about financial or operational achievements, but provides no final information regarding the evaluation of these achievements. Therefore, this thesis defines business success as the final outcome which comprises all more or less effective operations within the company and the firm’s performance. Under the assumptionsthat companies are mainly project oriented and a successful project-portfolio management as part of a successful multi-project management contributes benefits to the company, overall business success is the predominant result of successful manager’s performance.

2.3.3.2 Complexity

The measurement of overall business success is very difficult due to the diverse perspectives, categories of measures as well as the complexity of business success itself. Hence, this part will introduce the dimensions that make business success complex. As Carton and Hofer (2006) emphasize, business success depends on stakeholders who have different expectations about success, the time periods in which success should be measured, and indirectly the company’s strategy.^[119] Richard et al. (2009) as well as Devinney et al. (2010) define these dependencies in three dimensions, namely stakeholder, company and environmental characteristics as well as timeframe.^[120]

Stakeholder
Business success may mean different things to different stakeholders,^[121] thus there are many different versions about what represents a successful company. These versions arise through the different views and people’s expectations of people who are associated with the organization. Freeman (2010) clearly defines:

“A stakeholder in an organization is (by definition) any group or individual who can affect or is affected by the achievement of the organization’s objectives.”^[122]

In accordance with Neely and Adams (2003) key stakeholders are a combination of “investors, principally shareholders but including other capital providers; customers and intermediaries; employees and labor unions; suppliers and alliance partners; regulators, pressure groups, and communities [and] their relative importance will vary from organization to organization.”^[123] This points out that there is a large scope of ideas regarding business success that leads to increased complexity. For example, employees or people who are living in the city of a company’s plant are more interested in working conditions and social responsibility whereas financial investors (also called shareholders) mainly consider profits. Even these shareholders are divided into long- and short-term oriented investors. Therefore, for companies it is important to select a perspective of success, which conforms mostly to the key stakeholder’s interest,^[124] because identifying and evaluating organizational relationships with its main stakeholders improves business success.^[125]

The question remains which interest groups should be considered for increasing business success? A debate about shareholder (valued-based view) vs. stakeholder approach tries to clarify this situation. Whereas one group of authors prefers the stakeholder approach, because shareholders only represent one of the dimensionsinfluencing the company, another group arguesthat a consideration of shareholders also leads to satisfaction of other stakeholders needs. In accordance with Zimmermann (1998) both approaches are valid simultaneously and influence each other. On the one hand, in saturated markets the company must satisfy consumer needs with high quality products while meeting social and environmental responsibilities to realize profits and in doing so satisfy shareholder needs. On the other hand, in open capital markets shareholders can easily switch their investment to other companies. In that case the company is prone to fail and therefore it obviously cannot satisfy the other stakeholder’s needs.^[126] This emphasizes the complexity for researchers and companies that must consider shareholders, but also the other stakeholders to realize business success. The measurement should also consider these different views or at least the relevant perspectives for researchers to provide valid data.^[127]

Companies’ and environmental characteristics

The characteristicsof a company andits environment represent the second main complexity dimension regarding business success.Followed by the market-based view (MBV), which describes the external view of a company, competitive advantage of a firm is attributable to the market structure with characteristics such as industry growth or competition within the market.^[128] Consequently, business success depends on these different characteristics and is difficult to evaluate across industries. Due to a lack of empirical evidence for the validity of the MBV, especially regarding success differences of companies within an industry, the internal view of companies was developed. This approach, the resource-based view (RBV), describes that competitive advantages of a firm are provided by heterogenic, valuable firm-level resources that competitors are unable to reproduce.^[129] Such a resource is anything “which could be thought of asa strength or weakness of a given firm.”^[130] Hence, in RBV business success depends on characteristics within the firm that are different from other firms. This diversity of influencing factors regarding MBV and RBV leads to different evaluations of business success.

Another influence on business success and its measures is the firm’s strategic choice.^[131] For example, organizations in earlier stages of development or firms with a cost leader strategy as described by Porter (1998) concentrate on sales as the main business success indicator and may neglect profitability. Enterprises with a focus or differentiation strategy following Porter (1998) usually realize a higher profit margin.^[132] The question is whether companies with higher profitability or with higher sales are more successful?Here, there is no general answer, but it also clarifies the issue at hand. Different characteristics of companies and their environment drive the complexity of business success and its measurement. Furthermore, when evaluating a firm’s success, its strategy and objectives must be considered.^[133]

Timeframe
The third complexity dimension for business success is the considered timeframe. It is divided into three fundamental ideas. First, measures of business success depend on the chosen start and end points.^[134] Especially sensitive measures such as stock prices are easily influenced by random movements in the market that may be completely unrelated to any specific action within a company. Second, some success measures are themselves time dependent^[135] and may also load on short-term success evaluation whereas other measures load on the long-term. Thus, each group of measures has its limitations and may distort the evaluation of business success, if inappropriately applied. Third, different time periods also deliver different results for business success. McGahan and Porter (1999) show that incremental industry effects persist longer than incremental business-segment and corporate parent effects.^[136] Thus, an effect on business success is only noticeable in particular time periods. Tsai et al. (1991) empirically support this fact. They show that strategic initiatives initially reduce return on investment, but lead to increased return on investment after four years.^[137] Hence, researchers and companies must also consider the timeframe for receiving valid data regarding business success.

The constellations mentioned above underline the complex nature of business success and its measurement respectively. In addition, this part also points out the difficulty of a valid success evaluation and a comparison among enterprises. Thus, the choice of appropriate measures must align exactly with the researcher’s phenomenon of interest.^[138]

2.3.3.3 Perspectives

The previous section pointed out the fact that the complexity of business successmakes an appropriate measurement thereof which covers the most important factors difficult. This part, moreover, describes the measurement of business success under different perspectives.

Accounting
The accounting perspective represents a post hoc measurement and is able to determine the effectiveness of managerial decisions or general of value creation in the past. In doing so the utilization of key performance indicators (KPI) is well established in literature and practice. These KPIs or key figures compress complex structures and processes into a quantifiable and comparable form.^[139] Hence, an appropriate combination of KPIs may reliably represent whether a business is successful or not. Moreover, accounting standards like GAAP (Generally Accepted Accounting Principles), IFRS (International Financial Reporting Standards) or DRS (Deutsche Rechnungslegungsstandards – German accounting standards) produce financial reports that are materially accurate and comparable across organizations in similar industries. Furthermore, the accounting measures provide information for all users of financial statements, thus they take a multi-constituency and multi-dimensional view of success.^[140] However, this approach does not consider information about the future. It only represents quantitative and ignores qualitative values and even within the standards there are accounting options for different valuations of the respective items on the balance sheet.^[141] These and further disadvantages of the accounting perspective limit the meaningfulness regarding overall business success.

Intellectual Capital

The intellectual capital perspective could be seen as the opposite of the accounting perspective. If a firm mainly creates value and is successful due to its intangible assets, financial measures are inadequate and insufficient.^[142] Furthermore, an empirical longitudinal study among different companies found a gap between market value to book value, which maybe could be explained through intangible assets or intellectual capital.^[143] In literature these intangible assets are distinguished in human capital, structural capital, and customer or relational capital.^[144] Consequently, intellectual capital covers an internal and external view of the firm with several subsets that also leads to a multi-constituency and multi-dimensional perspective of success. Although, for example, Villalonga (2004) proves that the availability of intangible assetsare positively related to the persistence offirm-specific profits or losses,^[145] this view alone is not appropriate for representing the overall business success due to the lack of financial measures.

Balanced Scorecard

The Balanced Scorecard combines financial tangible measures with intangible measures of intellectual capital. Furthermore, the scorecard, which was developed by Kaplan and Norton (1992), also covers operational measures that consider investments in future opportunities.^[146] In detail, these measures are divided out in four main perspectives, namely financial, internal business, customer as well as innovation and learning. Each of them contains a particular combination of measureable KPIs that are chosen in the context of cause-effect relationships and in alignment with the firm’s objectives and strategy.^[147] Hence, the balanced scorecard is not only an approach for measuring overall business success, but also a management system to achieve the firm’s objectives by using a multi-constituency and multi-dimensional view. In contrast, there are also some disadvantages. One of the scorecard’s strengths is simultaneously also its weakness. The flexibility of the concept to adapt to a firm’s particular objectives with unique measures results in less reliable comparisons across companies based on balanced scorecards.^[148]

Strategic Management

This perspective represents business success from a strategic point of view. The task of strategic management is the realization of the firm’s long-term success. In detail, strategic management formulates a strategy and translates it into business success by utilization of structures and systems.^[149] Thus, this approach rather considers the entire company as one unit in the context of its success.In strategic management literature there are many conceptualizations of organizational performance with two main critical aspects. First, the constituencies for whom the organization performs, and second the dimensions which are to be measured.^[150] For example, Freeman (1984) arguesthat the consideration auf stakeholders is necessary for effective management.^[151] Frost (2000) also follows this approach.

“Our business is to create value for our stakeholders; our first job is to know who those stakeholders are and what they value in our performance.”^[152]

The aspect regarding measured dimensionsin strategic management is examined by numerous authors with different opinions. Whereas Drucker (1954) argues that the ultimate measure for organizational performance is survival, Ansoff (1965) prefers return on investment and Venkatraman and Ramanujam (1986) determine financial and operational performance as well as the influence of stakeholders as important strategic dimensions. The list could be endlessly continued. However, although there is no agreement on how overall business success should be measured and for whom organizations perform, all views of individual authors are both multi-constituency and multi-dimensional perspectives about strategic management.^[153]

Entrepreneurship

The entrepreneurship perspective is similar to strategic management under the assumption that the goals of the founding entrepreneurs are the goals of the organization, thus it is a one-dimensional view of business success.^[154] Nevertheless the entrepreneurship view also considers the perspective of different stakeholders. For example a growth driven organizational view versus a security driven view of family business owners. Some other trade-offs regarding goals of venture capitalists, angel investors or simply fans of the company could also appear.Hence, the entrepreneurship perspective is also a multi-constituency and multi-dimensional perspective of business success. In addition, entrepreneurship research considers interdependencies of stakeholder’s objectives and their trade-offs. In accordance with Murphy et al. (1996) the improvement of performance on one dimension can impairperformance on another dimension.^[155]

Microeconomic

Based on the shareholder value perspective, business success within this perspective is characterized through value maximization for asset owners and investors.^[156] The theoretical concept behind this approach is defined by achieving the shareholders expectations. So long asthe created value or the achieved business success deliversa return that is satisfactory relative to the risk the shareholders take, they will further contribute their assets to the organization. If the value created is lowerthan expected, the shareholders will change their investment to another exposure where they can achieve the required return.^[157] Consequently, business success is defined as the economic rent for the shareholder. Although this perspective only considers one group of stakeholders a more diverse view of goals could be seen in detail. For example the shareholder’s interest could be attributed to security, prestige or personal relationship regarding the company.^[158] However, due to the mainly financial objectives, the microeconomic perspective stands in contrast to the perspectives discussed abovefor a one-dimensional view of business success.

[...]

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^[1] Cf. Dammer et al. (2005), p. 16;Hirzel et al. (2009), p. 15; Holtschke et al. (2009), pp. 62f; Kalkowsk and Otfried (2002), p. 122.

^[2] Cf. Martinsuo and Lehtonen (2007), p. 56.

^[3] Cf. Shenhar et al. (2001), p. 702.

^[4] Cf. Shenhar et al. (2001). p. 705ff.

^[5] Cf. Lemak et al. (1996), p. 4.

^[6] Cf. Meskendahl et al. (2011), p. 20.

^[7] Cf. Richard et al. (2009), p. 721ff.

^[8] Cf. Carton and Hofer (2006); Devinney et al. (2009); Venkatramen and Ramanujam (1987).

^[9] Cf. Richard et al. (2009), p. 734.

^[10] Cf. Richard et al. (2009), p. 736.

^[11] Cf. http://www.multiprojectmanagement.org; access 05.04.2011 - 17:30.

^[12] Cf. Cooper et al. (2004a, b); Dietrich and Lehtonen (2005); Killen et al. (2008); Shenhar et al. (2001).

^[13] See „explanation of used terms“ above for further information.

^[14] Cf. Hahn and Laßmann (1993), p. 259; Reichmann (2006), p. 19.

^[15] Cf. http://www.ifm-bonn.org; access 22.03.2011 – 14:30.

^[16] Cf. Kay and Richter (2010), p. 12; Kless (2008) p. 1.

^[17] Cf. Kless (2008), p. 1.

^[18] Cf. Amtsblatt der Europäischen Union (2003); Günterberg and Wolter (2003);Koch und Kössler (2008);Linnemann (2007); Wolter and Hauser (2001).

^[19] Cf. Günterberg and Wolter (2003); Haunschild and Wolter (2010);Linnemann (2007); Wolter and Hauser (2001).

^[20] Cf. Koch and Kössler (2008)

^[21] Cf. Sturm et al. (2009).

^[22] Cf. Wolter and Hauser (2001), p. 30.

^[23] Cf. Amtsblatt der Europäischen Union (2003); Kay and Richter (2010); Kless (2008); Koch and Kössler (2008); Linnemann (2007); Wallau et al. (2007).

^[24] Cf. KochaundKössler (2008), p. 29.

^[25] Cf. Kay and Richter (2010), p. 12.

^[26] Cf. Günterberg (2011), p. 1.

^[27] Cf. Nahm and Philipp (2005), p. 943.

^[28] Cf. Hiller (2002), p. 22; Lomnitz (2008), p.22.

^[29] Cf. Lycett et al. (2004) ;Schawel and Billing (2009).

^[30] Cf. DIN 69901-1 (2009), p. 5; Schawel and Billing (2009), p. 158.

^[31] Cf. Shenhar et al. (2001), p. 702.

^[32] Cf. Holtschke et al. (2009), p. 61.

^[33] PMI (2008), p. 139.

^[34] Cf. Lomnitz (2008), p. 28.

^[35] Cf. PMI (2008), p. 4.

^[36] Cf. Holtschke et al. (2009), p. 61; PMI (2008), p. 6.

^[37] Cf. Dammer et al. (2005), p. 17.

^[38] Cf. Dammer (2008), p.16; Dammer et al. (2005), p. 17; Holtschke et al. (2009), p. 60.

^[39] Cf. Bernstein (2000), p. 4; Kerzner (2003), p. 13; Valle et al. (2008), p. 3.

^[40] Cf. Levine (2005), p. 16.

^[41] Cf. Hirzel et al. (2009), p. 20f., Holtschke et al. (2009), pp. 63f.

^[42] Cf. Archer and Ghasemzadeh (1999), p. 208.

^[43] Cf. Hirzel et al. (2009), p. 18.

^[44] Cf. Dammer et al. (2005), p. 17; Holtschke et al. (2009), p. 63.

^[45] Cf. Archer and Ghasemzadeh (1999); Dietrich and Lehton (2005), p. 386; Englund and Graham (1999);Spradlin and Kutolowski (1999); Wheelwright (1992).

^[46] Cf. Cooper et al.(2000, 2004a, 2004b); Dammer (2008); Killen et al. (2008);Light et al. (2005); McDonough (2003).

^[47] Cf. Bommer et al. (1995), p. 588.

^[48] Muckler and Seven (1992), p. 441.

^[49] Cf. Campbell (1955), p. 339; Richard (2009), p. 734.

^[50] Cf. Greving (2009), p. 73.

^[51] Cf. Richard (2009), p. 736.

^[52] Cf. Narver and Slater (1990), pp. 22f.

^[53] Cf. Aaker and McLoughlin (2010), p. 3.

^[54] Cf. Richard (2009), p. 736.

^[55] Cf. Pelham and Wilson (1996), pp. 27, 33.

^[56] Cf. Slater and Narver (1996), pp. 164ff.

^[57] Cf. Lenz (1981), p. 131f., p. 141.

^[58] Cf. Aichele (1997), p. 88f, p. 91; Küting and Weber (2009), p. 75; Leffson (1984), p. 65.

^[59] Cf. Muckler and Seven (1992), p. 443.

^[60] Cf. Richard (2009), p. 736.

^[61] Cf. Taylor and Brown (1988), p. 195.

^[62] Cf. Mezias and Starbuck (2003), p. 9.

^[63] Cf. Kumar et al. (1993), p. 5.

^[64] Cf. Rosenzweig (2009), pp. 72-89.

^[65] Cf. Nisbett and Wilson (1977), pp. 252ff.

^[66] Cf. Wall et al. (2004), pp. 98f.

^[67] Cf. Himme (2009), p. 485; Muckler and Seven (1992), p. 441.

^[68] Cf. Bommer et al. (1995), p. 588, p. 599; Himme (2009), p. 485; Muckler and Seven (1992), pp. 441f., 445.

^[69] Cf. Gräfer (2008), p. 18; Preißler (2008), p.12ff.; Weber and Sandt (2001), p. 10.

^[70] Cf. Wall et al. (2004), p. 97.

^[71] Cf. Benito (2004), p. 802.

^[72] Cf. Bommer et al. (1995), p. 599; Caruana et al (1998), p. 59; Pitt et al. (1996), p. 9.

^[73] Cf. Wall et al. (2004), p. 97.

^[74] Cf. Benito and Benito (2004), p. 802.

^[75] Cf. Gräfer (2010), p. 9; Hooler et al. (1999a), p. 278; Hooley et al (1999b), p. 268.

^[76] Cf. Dess and Robinson (1984), p. 266; Harris (2001), p. 29.

^[77] Cf. Muckler and Seven (1992), p. 443.

^[78] Cf. German Commercial Code §§ 248, 250, 274.

^[79] Cf. Benito and Benito (2004), p. 802.

^[80] Cf. Bachmann (2007), p. 99.

^[81] Cf. Bagozzi et al. (1991), pp. 424f.; Gerhart et al. (2000), p. 807.

^[82] Cf. Glick et al. (1990), p. 309.

^[83] Cf. Hult et al. (2008), p. 1070.

^[84] Cf. Hult et al. (2008), pp. 1071f.

^[85] Cf. Bachmann (2009), p. 99; Hult et al. (2008), p. 1072.

^[86] Cf. Shenhar et al. (2001), p. 701.

^[87] Cf. Martinsuo and Lehtonen (2007), p. 57; Meskendahl (2010), p. 808.

^[88] Cf. Dietrich and Lehtonen (2005), p. 387; PMI (2008), p. 6.

^[89] Cooke-Davis (2002), p. 185.

^[90] Cf. Fricke and Shenhar (2000), p. 266; Martinsuo and Lehtonen (2007), p. 62, Meskendahl (2010), p. 808.

^[91] Cf. Shenhar et al. (2001), p. 700.

^[92] Cf. Blomquist and Müller (2006b), p. 54.

^[93] Cf. Müller et al. (2008), p. 34.

^[94] Cf. Cooper and Edgett (2003), p. 55; Henard and Szymanski (2001), p. 369

^[95] Cf. Cooper et al. (2002), p. 5; Cooper and Edgett (2003), p. 55; Elonen and Artto (2003), p. 395; Killen et al. (2008), p. 32; Martinsuo and Lehtonen (2007), p. 56.

^[96] Cf. Dietrich and Lehtonen (2005), p. 389.

^[97] Cf. Cooper et al. (2002), p. 5; Cooper and Edgett (2003), p. 55;Martinsuo and Lehtonen (2007), p. 57.

^[98] Cf. Fricke and Shenhar (2000), p. 266; Martinsuo and Lehtonen (2007), pp. 57f.

^[99] Cf. Cooper et al. (2002), p. 5; Dammer (2008), pp. 47f.; Meskendahl (2010), p. 808.

^[100] Cf. Martinsuo and Lehtonen (2007), p. 61.

^[101] Cf. Shenhar et al. (2001), p. 702.

^[102] Cf. Atkinson (1999), pp. 340f.;Dvir et al. (1998), pp. 917f.; Shenhar et al. (2001), pp. 702f.

^[103] Cf. Shenhar et al. (2001), pp. 713ff.

^[104] Cf. Gardiner (2005), p. 11.

^[105] Cf. Patanakul and Milosovic (2009), p. 225.

^[106] Cf. Buuren et al. (2010), p. 675.

^[107] Cf. Cooper and Edgett (2003), pp. 55f.;Henard and Szymanski (2001), p. 369; Verma and Sinha (2002), p. 457.

^[108] Shenhar et al. (2001), p. 703.

^[109] Cf. Krüger (2008), pp. 270ff.

^[110] Cf. Dietrich and Lehtonen (2005), p. 388.

^[111] Markowitz (1959), p. 3.

^[112] Cf. Mikkola (2001), p. 427.

^[113] Cf. Archer and Ghasemzadeh (1999), p. 211; Cooper et al. (2002), p. 5; Killen et al. (2008), p. 25.

^[114] Cf. Rubner and Nippa (2010), pp. 4f., 10.

^[115] Cf. Venkatraman and Ramanujam (1986), p. 803.

^[116] Cf. Richard et al. (2009), p. 722.

^[117] Cf. Devinney et al. (2010), p. 922.

^[118] Cf. Carton and Hofer (2006);Frederico and Cavenaghi (2009); Murphy et al. (1996).

^[119] Cf. Carton and Hofer (2006), pp. 5, 7, 35.

^[120] Cf. Devinney et al. (2010), p. 924; Richard et al. (2009), pp. 723ff.

^[121] Cf. Shenhar et al. (2001), p. 702.

^[122] Freeman (2010), p. 46.

^[123] Neely and Adams (2003), p. 2.

^[124] Cf. Adams and Neely (2000), p. 1; Frost (2000), p. 31.

^[125] Cf. Laan et al. (2008), p. 307.

^[126] Cf. Zimmermann (1998), pp. 5f.

^[127] Cf. Carton and Hofer (2006), p. 5.

^[128] Cf. Porter (1979), pp. 218f.

^[129] Cf. Grant (1991), pp. 117f.

^[130] Wernerfelt (1984), p. 172.

^[131] Cf. Devinner et al. (2010), p. 924.

^[132] Cf. Porter (1998), pp. 35ff.

^[133] Cf. Steers (1975), p. 554.

^[134] Cf. Devinney et al. (2010), p. 924.

^[135] Cf. Richard et al. (2009), p. 726.

^[136] Cf. McGahan and Porter (1999), p. 152.

^[137] Cf. Tsai et al. (1991), p. 23.

^[138] Cf. Carton and Hofer (2006), p. 5.

^[139] Cf. Reichman (2006), p. 19.

^[140] Cf. Carton and Hofer (2006), pp. 41f.

^[141] Cf. Aichele (1997), pp. 88ff.

^[142] Cf. Moon and Kim (2006), p. 253.

^[143] Cf. Martin-de-Castro (2011), pp. 649f.

^[144] Cf. Bontis (1996); Edvinsson and Malone (1997); Hsu and Fang (2009); Martinez-Torres (2006).

^[145] Cf. Villalonga (2004), p. 226.

^[146] Cf. Carton and Hofer (2006), p. 42; Kaplan and Norton (1992), p. 71.

^[147] Cf. Kaplan and Norton (1996), pp. 64f.

^[148] Cf. Carton and Hofer (2006), p. 42; Chavan (2009), p. 398.

^[149] Cf. Hungenberg (2004), pp. 4f, 24.

^[150] Cf. Carton and Hofer (2006), p. 43.

^[151] Cf. Freeman (1984), p. 48.

^[152] Frost (2000), p. 31.

^[153] Cf. Carton and Hofer (2006), pp. 38, 45.

^[154] Cf. Carton and Hofer (2006), p. 45.

^[155] Cf. Murphy et al. (1996), p. 21.

^[156] Cf. Zimmermann (1998), p. 2.

^[157] Cf. Carton and Hofer (2006), p. 46.

^[158] Cf. Zimmermann (1998), p. 3.