managing strategy risks through balanced scorecard (bsc)
TRANSCRIPT
Managing Strategy Risks through
Balanced Scorecard (BSC)
A Survey Study in the Iranian Petroleum
Equipment Industry
Author: Azizi Shalbaf, Elnaz; Mian, Nabira
Ashfaq; Sohaib, Muhammad Numair
Tutor: Kirsi-Mari Kallio
Examiner: Helena Forslund
Term: VT21
Subject: Business Process Control & Supply
Chain Management
Level: Master’s Level
Course code: 5FE04E
Master’s Degree Project
II
Abstract
Purpose-This thesis aims to identify the role of Balanced Scorecard (BSC) for
managing strategy risks as well as the types of strategy risks that can be managed using
four perspectives of the BSC in the Iranian Petroleum Industry Equipment
Manufacturers (IPIEM).
Design/ approach/ methodology- In this thesis cross-sectional design and the
deduction approach are used. For collecting data for quantitative analysis, a
questionnaire was conducted by the research team. Then the data collected from
respondents was then analyzed through running simple linear regression analysis in the
SPSS software.
Findings- The first research question (RQ) is about BSC’s roles in managing strategy
risks in IPIEM. These roles are risk assessment, risk controlling and collecting data for
decision making of strategy risks. It was proved by the research team that BSC can play
a role of assessment of strategy risks in IPIEM. This means by using BSC as an RM
tool in IPIEM, companies can assess strategy risks through identifying, analysing and
evaluating strategy risks. However, the results indicate risk controlling and collecting
data for decision making cannot be managed by using BSC.
The second Research question is about the types of strategy risks that four perspectives
of BSC can manage. The results shows that from the 8 strategy risks chosen for this
thesis, 6 of them which are “liquidity risk” from the financial perspective; “risk of
clients’ opposition to pilot testing of the product” from the customer perspective; “risk
of improper design of product at development stages”, and “risk of improper selection
of international partners” from the internal perspective; “risk of incorrect evaluation &
selection of technology options” and “the risk of not enough operational experience in
similar previous projects” from the learning and growth perspective can be managed
through using BSC as a RM tool in IPIEM. Based on the conclusion of RQ1, the effect
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can now be adjusted into RQ2 findings. This study concludes that IPIEM can use BSC
for risk assessment of the above-mentioned six different strategy risks. It can also be
concluded that the BSC cannot be a full RM tool for managing strategy risks in the
companies, since it only can apply for one of the three processes of RM; risk
assessment.
Key Words
Risk management, Balanced Scorecard, Strategy risks, Petroleum Industry, Petroleum
equipment Industry, Linear regression analysis, Quantitative method
IV
Acknowledgements
The research team would like to thank each personnel that became a part of the
completion of this thesis directly or indirectly. A special thanks to associate Professor
Kirsi-Mari Kallio for helping us develop our research topic and continuous support and
feedback throughout the entire research work and providing us with guidance, and
correcting us where required. We would also especially like to thank our examiner,
Professor Helena Forslund for providing us with valuable and important feedback in
each seminar.
Furthermore, we are very grateful to Mr. Sadat Rasoul (CEO), Mr. Songhori (Deputy
CEO) and Mr. Raisi (Marketing Manager) at Sharif Fund to help us in distributing our
survey questionnaire to the organizations in IPIEM. It would not have been possible
without their help in getting in contact with these companies, whose response was to
play an essential part in this research work. All the managers and employees who took
part in the questionnaire are also thanked for their participation. Also, a special thanks
to all our fellow students for providing us with their constructive criticism and
feedback, which helped us improve our paper throughout the whole work in process.
Finally, special gratitude to our families for their love, support and encouragement
during the entire study in Sweden.
Monday, 31 May 2021
Azizi Shalbaf, Elnaz; Mian, Nabira Ashfaq; Sohaib, Muhammad Numair
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Table of Contents
1 Introduction ............................................................................................................. 1
1.1 Background ........................................................................................................ 1
1.2 Problem Discussion ........................................................................................... 5
1.2.1 RQ1- Role of BSC in Managing Strategy Risks......................................... 5
1.2.2 RQ2- Managing Strategy Risks through Four Perspectives of BSC .......... 7
1.3 Purpose .............................................................................................................. 9
1.4 Disposition ......................................................................................................... 9
2 Methodology ........................................................................................................... 10
2.1 Research Philosophy ........................................................................................ 10
2.1.1 Positivism .................................................................................................. 11
2.1.2 Realism ..................................................................................................... 11
2.1.3 Interpretivism ............................................................................................ 12
2.1.4 Postmodernism .......................................................................................... 12
2.1.5 Pragmatism ............................................................................................... 12
2.1.6 Research Philosophy of this Thesis .......................................................... 12
2.2 Research Strategies .......................................................................................... 13
2.2.1 Research Strategy for this Thesis .............................................................. 13
2.3 Research Approach .......................................................................................... 14
2.3.1 Research Approach Used in this Thesis ................................................... 14
2.4 Research Design ............................................................................................... 15
2.4.1 Research Design for this Thesis ................................................................ 16
2.5 Data Collection Method ................................................................................... 17
2.5.1 Primary and secondary data ...................................................................... 17
2.5.2 Surveys ...................................................................................................... 17
2.5.3 Questionnaire and Questionnaire Design ................................................. 18
2.5.4 Population and Sample ............................................................................. 20
2.6 Data Analysis methods ..................................................................................... 22
2.6.1 Data Analysis Method for this Thesis ....................................................... 23
VI
2.7 Research Quality .............................................................................................. 24
2.8 Ethical Considerations ..................................................................................... 26
2.8.1 General Data Protection Regulation ......................................................... 26
2.9 Individual Contribution .................................................................................... 26
3 Literature review ................................................................................................... 27
3.1 Balanced Scorecard (BSC) .............................................................................. 28
3.2 RQ1- Role of BSC in Managing Strategy Risks ............................................... 30
3.2.1 Risk Assessment ....................................................................................... 31
3.2.1.1 Risk Identification ....................................................................................................... 32
3.2.1.2 Risk Analysis .............................................................................................................. 32
3.2.1.3 Risk Evaluation ........................................................................................................... 32
3.2.2 Risk Control .............................................................................................. 33
3.2.3 Risk Data collection for Decision making ................................................ 37
3.3 Model and Hypotheses related to RQ1 ............................................................ 39
3.4 RQ2- Managing Strategy Risks through Four Perspectives of BSC ................ 39
3.4.1 Risks Categories ....................................................................................... 39
3.4.2 Balanced Scorecard-Risk Management (BSC-RM) ................................. 41
3.4.2.1 Financial Risk Perspective .......................................................................................... 42
3.4.2.2 Customer Risk Perspective .......................................................................................... 42
3.4.2.3 Internal Risk Perspective ............................................................................................. 42
3.4.2.4 Learning and growth Risk Perspective ........................................................................ 43
3.4.3 Types of potential strategy risks in IPIEM ............................................... 46
3.4.4 Conceptualization of strategy risks selected ............................................. 49
3.4.4.1 Liquidity risk ............................................................................................................... 49
3.4.4.2 Financing risk .............................................................................................................. 49
3.4.4.3 Rejection of the product after its release to the market ............................................... 49
3.4.4.4 Clients' opposition to pilot testing of the product ........................................................ 50
VII
3.4.4.5 Improper design of product at development stages ..................................................... 50
3.4.4.6 Improper selection of international partners ................................................................ 50
3.4.4.7 Not enough operational experience in similar projects ............................................... 50
3.4.4.8 Incorrect evaluation and selection of possible technology options ............................. 51
3.5 Model and Hypotheses related to RQ2 ............................................................ 51
4 Empirical Study ..................................................................................................... 53
4.1 Pretest of Questionnaire .................................................................................. 53
4.2 Data Collection ................................................................................................ 54
4.2.1 Non-response ............................................................................................ 55
4.3 Reliability Test ................................................................................................. 56
4.4 Descriptive Analysis ......................................................................................... 57
4.5 Testing Assumptions ......................................................................................... 59
4.5.1 Normality Tests ......................................................................................... 59
4.5.1.1 Histogram .................................................................................................................... 60
4.5.1.2 Normal P-P Plot .......................................................................................................... 60
4.5.2 Homoscedasticity Test .............................................................................. 61
4.5.3 Linearity Test ............................................................................................ 61
4.6 Linear Regression Analysis .............................................................................. 62
4.6.1 Testing hypotheses related to RQ1 ........................................................... 62
4.6.1.1 Testing Hypothesis A1 ................................................................................................ 62
4.6.1.2 Testing Hypothesis B1 ................................................................................................ 63
4.6.1.3 Testing Hypothesis C1 ................................................................................................ 63
4.6.2 Testing hypotheses related to RQ2 ........................................................... 64
4.6.2.1 Testing Hypothesis D1 ................................................................................................ 64
4.6.2.2 Testing Hypothesis E1 ................................................................................................ 65
4.6.2.3 Testing Hypothesis F1 ................................................................................................. 65
4.6.2.4 Testing Hypothesis G1 ................................................................................................ 66
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4.6.2.5 Testing Hypothesis H1 ................................................................................................ 66
4.6.2.6 Testing Hypothesis I1.................................................................................................. 67
4.6.2.7 Testing Hypothesis J1 ................................................................................................. 68
4.6.2.8 Testing Hypothesis K1 ................................................................................................ 68
4.6.3 Summary of Hypotheses Test Results ...................................................... 69
5 Conclusion .............................................................................................................. 70
5.1 Discussion ........................................................................................................ 70
5.2 Conclusion........................................................................................................ 74
5.3 Limitations........................................................................................................ 76
5.4 Suggestions for Further Study .......................................................................... 76
6 List of References .................................................................................................. 78
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List of Figures
Figure 1: Disposition for this study ................................................................................ 10
Figure 2: Model and hypotheses related to RQ1 ............................................................ 39
Figure 3: Illustration of Kaplan’ (2009) three levels risks .............................................. 41
Figure 4: An Example of RM-BSC model presented by Calandro and Lane (2006) ..... 44
Figure 5: BSC-enterprise logistics risks presented by Yongsheng and Li (2010) .......... 45
Figure 6: Model and hypotheses related to RQ2 ............................................................ 52
X
List of Tables
Table 1: Own Illustration of identified strategy risks (level 2) in PEI ........................... 46
Table 2: Own Illustration of categorization of selected strategy risks in four perspectives
of BSC ............................................................................................................................. 48
Table 3: Result of data collection ................................................................................... 54
Table 4: Result of Reliability test ................................................................................... 56
Table 5: Companies' experience in PEI (year)................................................................ 58
Table 6: Subsidiary of another foreign company ........................................................... 58
Table 7: Job title of respondents .................................................................................... 58
Table 8: Companies using BSC for managing strategy risks ......................................... 59
Table 9: Linear regression output for HA1 ..................................................................... 62
Table 10: Linear regression output for HB1 ................................................................... 63
Table 11: Linear regression output for HC1 ................................................................... 64
Table 12: Linear regression output for HD1 ................................................................... 64
Table 13: Linear regression output for HE1 ................................................................... 65
Table 14: Linear regression output for HF1 ................................................................... 65
Table 15: Linear regression output for HG1 ................................................................... 66
Table 16: Linear regression output for HH1 ................................................................... 67
Table 17: Linear regression output for HI1 .................................................................... 67
Table 18: Linear regression output for HJ1 .................................................................... 68
Table 19: Linear regression output for HK1 ................................................................... 69
Table 20: Summary of hypotheses test result ................................................................. 69
Table 21: RM-BSC model for this study inspired by Calandro and Lane’s (2006) RM-
BSC model ...................................................................................................................... 73
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Appendices
Appendix 1: Questionnaire Guide .................................................................................. 87
Appendix 2: Normality test for HA1 .............................................................................. 93
Appendix 3: Homoscedasticity test for HA1 .................................................................. 93
Appendix 4: Linearity test for HA1 ................................................................................ 93
Appendix 5: Normality test for HB1 .............................................................................. 94
Appendix 6: Homoscedasticity test for HB1 .................................................................. 94
Appendix 7: Linearity test for HB1 ................................................................................ 94
Appendix 8: Normality test for HC1 .............................................................................. 95
Appendix 9: Homoscedasticity test for HC1 .................................................................. 95
Appendix 10: Linearity test for HC1 .............................................................................. 95
Appendix 11: Normality test for HD1 ............................................................................ 96
Appendix 12: Homoscedasticity test for HD1 ................................................................ 96
Appendix 13: Linearity test for HD1 .............................................................................. 96
Appendix 14: Normality test for HE1 ............................................................................. 97
Appendix 15: Homoscedasticity test for HE1 ................................................................ 97
Appendix 16: Linearity test for HE1 .............................................................................. 97
Appendix 17: Normality test for HF1 ............................................................................. 98
Appendix 18: Homoscedasticity test for HF1 ................................................................. 98
Appendix 19: Linearity test for HF1............................................................................... 98
Appendix 20: Normality test for HG1 ............................................................................ 99
Appendix 21: Homoscedasticity test for HG1 ................................................................ 99
Appendix 22: Linearity test for HG1 .............................................................................. 99
Appendix 23: Normality test for HH1 .......................................................................... 100
Appendix 24: Homoscedasticity test for HH1 .............................................................. 100
Appendix 25: Linearity test for HH1 ............................................................................ 100
Appendix 26: Normality test for HI1 ............................................................................ 101
Appendix 27: Homoscedasticity test for HI1 ............................................................... 101
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Appendix 28: Linearity test for HI1 ............................................................................. 101
Appendix 29: Normality test for HJ1 ........................................................................... 102
Appendix 30: Homoscedasticity test for HJ1 ............................................................... 102
Appendix 31: Linearity test for HJ1 ............................................................................. 102
Appendix 32: Normality test for HK1 .......................................................................... 103
Appendix 33: Homoscedasticity test for HK1 .............................................................. 103
Appendix 34: Linearity test for HK1 ............................................................................ 103
Appendix 35: Regression analysis outputs related to HA1 .......................................... 104
Appendix 36: Regression analysis outputs related to HB1........................................... 105
Appendix 37: Regression analysis outputs related to HC1........................................... 106
Appendix 38: Regression analysis outputs related to HD1 .......................................... 107
Appendix 39: Regression analysis outputs related to HE1 ........................................... 108
Appendix 40: Regression analysis outputs related to HF1 ........................................... 110
Appendix 41: Regression analysis outputs related to HG1 .......................................... 111
Appendix 42: Regression analysis outputs related to HH1 .......................................... 113
Appendix 43: Regression analysis outputs related to HI1 ............................................ 114
Appendix 44: Regression analysis outputs related to HJ1 ............................................ 115
Appendix 45: Regression analysis outputs related to HK1 .......................................... 117
XIII
List of Abbreviations
ANOVA Analysis of Variance
BSC Balanced Scorecard
ERM Enterprise Risk Management
GDPR General Data Protection Regulation
IFAC International Federation of Accountants
IPEI Iranian Petroleum Equipment Industry
IPIEM Iranian Petroleum Industry Equipment Manufacturers
KPI Key Performance Indicator
MA Management Accounting
MAG Management Accounting Guideline
MANCOVA Multivariate Analysis of Covariance
MANOVA Multivariate Analysis of Variance
MDA Multiple Discriminant Analysis
PEI Petroleum Equipment Industry
PMS Performance Measurement System
RM Risk Management
RMS Risk Management System
RQ Research Question
SEM Structural Equation Modelling
SIPIEM Society of Iranian Petroleum Industry Equipment Manufacturers
1
1 Introduction
The first chapter presents the area of research that highlights the main subjects of this
thesis. It starts with the background, first a brief introduction to the industry of
petroleum equipment in Iran that has been focused for this study. Then it will continue
with presenting the theme of the study consisting of defining related key concepts such
as risks and different levels of risks, Balanced Scorecard (BSC) and Risk management
(RM). This part also shows the importance of the study of BSC and risks. It is then
followed by the problem discussion indicating current research gaps, which is the role
of BSC for managing strategy risk and types of strategy risk that can be managed with
the four perspectives of BSC. Furthermore, research questions are derived with the
acknowledgement of existing problems. After that, the aim of the paper is presented.
The disposition of the thesis is presented at the end of this chapter.
1.1 Background
As the world’s population is rapidly growing and an improvement in global economic
growth, especially in the developing countries, has led to an increased global
consumption of petroleum products during the last five decades (Yazdani and Pirpour,
2020). Due to this the demand for fossil fuels experienced a boom as it reached almost
2.5 times to what it was in 1971 (ibid).
The petroleum industry in Iran is one of the biggest sources for the country’s income as
the economy of Iran is heavily dependent on this single source (Mohamedi, 2010). Iran
was the first Persian Gulf country to find oil in 1908 and has been one of the most
important industries since the 1920s. Even though an attempt by Iran was made to
broaden their economy, the petroleum industry still stood out as a critical growth factor
for the country’s economy. Currently Iran holds the position of the fifth largest crude oil
manufacturer in the world and hence also shows the potential to play a major and vital
part internationally in the petroleum products market (Hosseini and Stefaniec, 2019).
In order for the petroleum industry to grow, a supporting role from another industry is
very crucial. The petroleum equipment industry plays a major and vital part in the
growth of the petroleum industry. This study will focus on the Iranian Petroleum
Equipment Industry (IPEI).
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Iranian Petroleum Industry Equipment Manufacturers (IPIEM) are spread widely: over
1400 types of equipment from upstream to downstream in different groups. These
equipment are classified in ten categories: Fixed equipment; Rotating Equipment;
Control systems, automation and instrumentation; Drilling equipment (sea and land);
electrical equipment; Pipes and fittings; Chemicals and catalysts; Industrial valves and
borehole equipment; safety and firefighting equipment; and Public goods and services
(SIPIEM, 2020).
One of the complexities is the development of various technologies related to this
industry. Most of the shortcomings of the oil-rich countries of the Middle East and Iran
are from the levels of research and development to the construction of equipment, and
the main condition to reach these levels is to acquire remarkable technological
capabilities (Naghizadeh et al., 2017).
In the past two decades, implementation of petroleum projects required extensive
technical and human assistance from developed countries. Therefore, localization and
access to new knowledge and technologies (equipment) has always been one of the
petroleum industry’s main concerns (Naghizadeh et al., 2017). After struggling for a
long period to localize new knowledge and technologies, the share of Iranian
manufactures and suppliers in supplying the petroleum products and equipment has now
reached 85% locally (Mehr News, 2020).
Despite localizing the knowledge and technologies in the Iranian petroleum industry,
technology development projects require significant capital investment which contain
many potential risks. Also, technology development in this industry is not favourable
compared to other countries and hence prone to more risks (Naghizadeh et al., 2017).
Hence, looking at the size and importance of the petroleum equipment manufacturers
especially in Iran gives the research team a reason to choose this industry.
Looking at the complex and dynamic environment that the companies are operating in
today, it is not wrong to say that every company has to go through a phase of
uncertainty at some stage. These uncertainties can come at any time and for different
reasons. These uncertainties may arise due to the use of different resources, complexity
in business processes and demand and supply (Monica and Pangeran, 2020). These
uncertainties are termed as risks in the business field (Rasid et al., 2017). These risks
3
can cause different problems for the organizations. A few examples of risks are late
deliveries and financial losses (Monica and Pangeran, 2020). Companies’ survival is
also affected by the uncertainties in supply chains, therefore, the research for finding the
reasons and reducing the effects of these events is increasing day by day. Todays’ fast
production makes supply chains to face unpredictable circumstances at any stage
because the new manufacturing systems only concern efficiency and then restrict
adaptability to new situations (Baryannis et al., 2019).
Not all the companies can avoid risks but can try to mitigate them by managing
efficiently. These risks if not taken care of can have a considerable impact on the
company’s overall performance (Monica and Pangeran, 2020).
How to manage risks is what any organization should focus upon but in order to do that
risks first need to be identified (Hamdi et al., 2018). Earlier definition of risk had been
derived from study of Enterprise risk management (ERM) that states any disruptions in
the results or interference in the achievement of designed goals with unexpected
happenings is called as risk (Baryannis et al., 2019). Kaplan (2009) classified risks into
three levels based on their degree of predictability, controllability, administration, and
the importance of their outcomes to the enterprise. Level three is the lowest category,
including routine operational and compliance risks. These risks stand from mistakes in
procedure, systematized, and foreseeable processes that expose the company to a
significant loss.
Level two is strategy risks which are non-avoidable. These are defined as the risks that
relate to a company’s strategy goals, can be measured and controlled. For instance,
accepting the risk of failure when increasing credit to customers; or investing in
constructing a completely new product line or starting a new geographic market can be
riskier. Potential strategy risks include “financial risk; customer, brand, and reputation
risk; supply chain risk; innovation risk; environmental risk; human resources risk; and
information technology risk” (Kaplan, 2009, p.3). Level two risk management directs
the “known unknowns.” (Kaplan, 2009, p.5). Level one is global enterprise risks. Many
companies’ failures are caused by level one risk; the “unknown unknowns” (p.5) which
an unforeseeable and unheard incident creates existential risk. Such events are known as
“black swan”(p.5) events. “natural acts (earthquakes, storms, tsunamis), global
economic phenomena (dramatic changes in energy prices, currency exchange rates,
4
interest rates, economic growth rates, or regulation), or competitors’ action” are the
examples of level one risks (ibid, p.5). According to Kaplan (2009), if companies
cannot measure what they do, they cannot manage it. Hence, it was decided to study
strategy risk -level two- as they are quantifiable and measurable.
RM is an essential element of every firm. It can be defined “as a firm’s processes to
cope with risks in order to minimize the volatility of returns and to ensure survival”
(Rehman & Anwar, 2019, p.208). A common RM framework consists of four steps
identifying risks, measuring, mitigating and monitoring and reporting risks (ibid).
Companies’ survival is more promised when they are using RM from the beginning. It
starts with determining the environment of an organization. It also includes risk analysis
and choosing the right method to mitigate risk. In the last step, the outcomes of the risk
management system (RMS) are monitored for feedback and for better RM techniques in
future (Lavruk et al., 2018). RM helps organizations to assess and recognize their
threats; in this way firms can be prepared to confront those future uncertainties.
Companies can also eliminate unexpected costs related to risks and improve their
operations if they already know the future happenings. They can be able to resolve the
future problems and make the most suitable decisions. Also RM informs firms new
methods of doing business and supports in establishing new business lines (Kusserow,
2020).
In order to manage risk, there are tools that fulfill this purpose, BSC is one of them. Its
adaptability and internal control makes BSC a tool that helps management to stay
aware, capable and ready to accept any uncertainty (Oliveira, 2014). Also, RM follows
a few steps such as looking for and identifying risks, what measurement technique to be
used and how to assess if there is any relationship between the risks, identifying a way
to control the risks and to come up with strategies to limit them and a system where
these risks are continuously being assessed and evaluated. All these steps are also easily
covered by BSC and hence can be used (Oliveira, 2014).
BSC has four perspectives: financial, customer, internal, and learning and growth.
Different studies indicated the use of BSC to control risks. For instance, Massingham et
al. (2019) have included risk controls in the learning and growth perspective of BSC.
Another study conducted by Oliveira (2014), indicated that BSC also has RM
dimension in the internal control perspective. According to her, BSC helps to build
5
effective internal controls and to monitor operations by following specific guidelines
and standards. It is also considered to support planning and checking of performance
measures that are linked with risk management in many firms. Cheng et al. (2018) have
not been limited to only one perspective and considered risk controls in all the four
perspectives of BSC. According to Ratri and Pangeran (2020), the four BSC
perspectives implement a complete view of strategic planning and a comprehensive
view of the potential risks and RM.
1.2 Problem Discussion
1.2.1 RQ1- Role of BSC in Managing Strategy Risks
Most petroleum business experts believe that petroleum projects are risky (Askary et al.,
2016). The broadness, complexity and variety of projects in the petroleum industry have
doubled the importance of managing these projects. In the implementation of large
projects, especially in oil and gas, risks are one of their inherent and natural features,
and identifying and evaluating these risks will help project managers for better planning
(Gharib and Ghodsypour, 2017).
Rasid et al., (2017) shows the use of a BSC to manage some types of risks that are
involved with strategies, market, finance, accounting and business operations. Several
researchers studied the integrated use of RM with BSC in order to explore theoretically
how risk can be managed (Rasid et al., 2017; Wu & Hua, 2018).
Papalexandris et al., (2005) presented roles related to RM containing two levels; risk
assessment and risk control. They believe that BSC is useful for assessing risks, which
starts with identifying potential risks and uncertainties, then examining and prioritizing
them, and finally planning for contingency and mitigation measures. Scholey (2006),
believes that BSC can integrate with RM to control risks. To reach this, he suggested
four steps. The first is providing a list of all risks the firm faces through brainstorming.
Then it needs to make risk assessment charts based on each category of risks. In the
third step, the company prepares the risk report card, and finally enters the results
achieved into the BSC to assess the exact overall performance. According to Scholey
(2006), BSC if used efficiently by any organization can lead to many potential benefits
6
in terms of mitigating the effects of risk which is a factor of utmost importance in
achieving organizational goals.
Oliveira (2014) has stated BSC scope will assess and control potential risks. According
to her, the framework of BSC supports a variety of risks. It enables the company to
identify its significant perspectives and define areas where relevant risks can be
considered. If these perspectives cover the company's whole activities from customer to
suppliers and industrial environment, the main business risks can be looked for through
the BSC framework (Oliveira, 2014). The BSC scope will identify and control potential
risks. This helps the company define more reliable strategies and allocate resources
based on priorities.
Cheng et al. (2018) also deemed the BSC as a strategic performance management
system which is specifically formed to aid managers and provide them with information
enabling them to monitor and evaluate the business strategies. Therefore, according to
them, BSC is considered as a process for carrying information required to make
managerial judgments based on strategy risks.
Different authors have studied the integration of BSC and RM to find out what exactly
are the roles of BSC in managing risks and how BSC can be integrated with RM.
BSC’s roles can be risk assessment (Papalexandris et al. 2005; Calandro and Lane,
2006; Oliveira, 2014), risk control (Papalexandris et al. 2005; Scholey, 2006; Calandro
and Lane, 2006; Oliveira, 2014), and a process that provides managers with the
information they need to make decisions based on strategy risks (Cheng et al., 2018).
Most of the studies have been conducted theoretically (e.g. Papalexandris et al. 2005;
Beasley et al. 2006; Scholey 2006; Oliveira 2014; Rasid et al., 2017; Wu & Hua, 2018;
Cheng et al., 2018) have not been done empirically. There are also some case studies
(e.g. Pangeran, 2020; Ratri and Pangeran, 2020; Safitri and Pangeran, 2020) that studied
the integration of BSC and RM. According to Bell et al. (2019) case studies generate an
in-depth inspection of a case or cases, which create the foundation for theoretical
analysis. Therefore, the research team intends to generalize these case studies’ findings
in the IPIEM by applying survey and quantitative methods to realize what exactly is the
role of BSC in managing strategy risks for different companies within this industry.
7
Based on the study’s aim, the following research question (RQ) is formulated by the
research team:
RQ1: What roles does BSC play in managing strategy risks in the IPIEM?
1.2.2 RQ2- Managing Strategy Risks through Four Perspectives of BSC
There have not been many studies on assessing the risks of technology development and
localization projects, especially in the petroleum equipment industry (Naghizadeh et al.
(2017). Most of the research has focused on aspects of product development.
Naghizadeh et al. (2017) have identified four potential risk areas for product
development: technology risk (product design and platform development,
manufacturing technology, and intellectual property), market risk (consumer, public,
commercialization, and potential competitors' actions), operational risk (Internal, project
team, partnership with external suppliers and procurement), and financial risk
(commercialization). In another study by Wu and Wu (2014), the most common risks in
technological innovation and product development were stated as follows:
Technological risk (accelerated planning, conflicting specifications, unrealistic design,
ineffective project leaders, lack of communication and coordination between developers
and the technology life cycle), market risk (Change of suppliers, availability of
alternative products and shortage of complementary goods), financial risk (limited
financing for product development and problems with new customers), cooperation risk
(fraud, distortion of information and allocation of resources for oneself) and
institutional risk /regulatory (industrial policies, poor protection of intellectual property
rights).
How to manage risks is what an organization should focus on (Hamdi et al., 2018). In
order to manage risk, there are tools that fulfill this purpose. BSC is one of the tools
helping any organizations in achieving that objective where it has been successfully
aligned with RM in large corporations such as in Mobil and Chrysler (Olson, 2015).
BSC considers both long term and short term strategic goals (Kaplan, 2009), and works
as a strategy management system as well (Wang et al., 2010).
8
An analysis of BSC opens doors to various strategic perspectives. The main principle is
to determine four vast areas; financial, internal business process, customer and learning
and growth that are strategically important and identify solid measures that can help
managers in determining how the company performs in different areas. This not only
allows to view things from a different point of view but also helps to analyse different
risks (Olson, 2015). Calandro & Lane (2006) mentioned two categories of risks which
are market risks and non-market risks related to firms. The firm risks are further divided
into three levels: global enterprise risks, strategy risks and routine operational and
compliance risks by Kaplan (2009). He stated that level two or strategy risks are
controllable and can be identified with BSC. Strategy risks create hindrance in
achieving strategic goals. These risks can be internal or external too. But the role of
external forces is less in these risks (Safitri & Pangeran, 2020).
According to Kaplan (2009), the BSC and strategy map include the company's all
strategic goals and their relationships with each other. The learning and growth
perspective comprises goals linked to people and technology; the internal process
perspective includes goals for managing operations, clients, innovation, and
environmental, administrative, and social processes; the customer perspective consists
of goals associated to customer value proposition and customer outcomes; and the
financial perspective entails the goals related to income, cost, price, and margin.
Therefore, the strategy map presents a natural framework for recognizing, decreasing,
and regularly managing the strategy risks with firm’s strategic goals in an integrated and
inclusive way (Ibid). Some firms have practically integrated RM with the BSC
(Calandro & Lane, 2006; Wang et al., 2010).
RM processes can differ related to each specific type of risk (Kaplan, 2009). Several
types of risks fit into different perspectives of BSC to manage the risks (Wu & Hua,
2018). Some authors integrated risks and their measures with different perspectives of
BSC. For instance, Calandro & Lane, (2006) stated that the measures of operating risk,
technological and environmental risks can be incorporated into two perspectives of BSC
internal or customer perspectives (Calandro & Lane, 2006). Grembergen & Haes,
(2005) also stated that IT risks with other IT operations and processes can be managed
with the support of a framework that is based on the perspectives of BSC. Besides,
Nugroho and Pangeran (2021) studied the integration of BSC and RM and tried to
9
identify types of risks. They evaluated risks by using the ISO 31000 RM framework and
four perspectives of BSC. They could identify several types of risks such as “financial
risk, operational risk, technology risk, business ethics risk, health and safety risk,
economic risk, legal risk, political risk, market risk, and project risk” with one case
study (Nugroho and Pangeran, 2021, P.23).
The research team realized a research gap as a lack of empirical study to indicate the
types of risks that were managed by BSC perspectives especially in IPIEM. There have
been studies where this implementation was used as case studies (e.g. Iwata, 2018;
Nugroho and Pangeran, 2021). The research team intends to generalize case studies’
findings as well as contributing more by applying a different research method which is
survey and quantitative methods in the IPIEM. The aim of this study is to realize what
types of strategy risks can be managed with the four perspectives of BSC by studying
IPIEM. Based on the study’s aim the following RQ is formulated by the research team:
RQ 2: What types of strategy risks in the IPIEM can be managed with each of the four
perspectives of the BSC?
1.3 Purpose
The research team aims to find out what roles BSC plays in managing strategy risks in
the IPIEM. Further it will find out which strategy risks in the IPIEM can be managed
with each of the four perspectives of the BSC. Strategy risks will be categorized into
BSC four perspectives according to its relevance. This study will specifically focus on
the BSC as a tool to manage strategy risks and then the emphasis will be on studying
management of different types of strategy risks through four perspectives of BSC.
1.4 Disposition
The research is divided into five chapters which indicate a step by step approach for
presenting the study. After the introduction, the second chapter will present the research
methodology used for this study. The methodology includes the research strategies,
design, data collection and analysis method, population, sample, research quality,
ethical considerations, and personal contribution. The third chapter will be a literature
10
review that focuses on previous studies conducted by different researchers. At the end
of this chapter hypotheses will be made from the literature review that will be tested
later. Empirical data will be collected in chapter four. This will be followed by the
analysis of the data collected where hypotheses will be tested. Finally the last chapter
will discuss the findings, then present conclusion, limitations and suggestions for further
study.
Figure 1: Disposition for this study
2 Methodology
The second chapter explains the research methods of the business management field and
then highlights the particular choices regarding the specific method for this thesis.
These specific methods support the research and make the direction of the study clear
by providing deeper knowledge about the different ways for conducting a research. For
gathering or processing data, most suitable options are decided upon after the
understanding of research methods.
2.1 Research Philosophy
To carry out research in a unique manner and with more effectiveness, research
philosophy is important to understand. It is all based on assumptions related to reality
and also provides new meaning to a research topic by depending on the researchers’
mind. These philosophical assumptions make the shaping of research results by
collecting and analyzing data. (Saunders et al., 2019) New knowledge is generated
through these research philosophical beliefs in a certain field. (Bell et al., 2019;
Saunders et al., 2019). These philosophical assumptions are of three kinds: Ontological,
epistemological and axiological (Bell et al., 2019; Saunders et al., 2019). Ontological
11
assumption is concerned with the phenomena that what we study exists objectively “or
whether they are ‘made real’ by the activities of humans and the meanings which
observers attach to them” (Bell et al., 2019, p. 26). Epistemological assumption follows
ontology but also works with “a particular understanding of what reality is” (Ibid, p.29).
Finally, axiological assumption deals with “extent and ways your own values influence
your research process” (Saunders et al., 2019, p. 130). Better results about reality are
made if these all assumptions are compatible with chosen research methods in a study
(Saunders et al., 2019).
Research philosophy helps in conducting research throughout the process. The
assumptions also ensure the right selection of research strategy, methods and data
analysis techniques. Research philosophy creates a consistency with more clarity within
a study (Saunders et al., 2019). According to Saunders et al., (2019), there are five
major research philosophies in the business field that are named as positivism, empirical
realism, interpretivism, postmodernism and pragmatism.
2.1.1 Positivism
Positivism examines the reality with the use of a natural approach to social sciences and
yields general concepts in the theory (Saunders et al., 2019). It uses theories to create
results and work as a deductive method. Positivism is a method of epistemology that is
based on realistic knowledge (Bell et al., 2019). Knowledge (Hypothesis) is generated
by studying and observing realities that is used for further examination of social
phenomenon (Saunders et al., 2019).
2.1.2 Realism
Realism or critical realism pretends to be an actual picture of happenings whilst the
reality is different from what we understand. It tends to explain the reality of social
events. With a different image through human senses, we see a thing differently in
contrast to a real structure. The reality is altered by previous knowledge or our
experiences (Saunders et al., 2019).
12
2.1.3 Interpretivism
Interpretivism says that humans and other objects are totally different from each other
and it concerns the study of those objects or the phenomenon that people develop. As
realism this philosophy also considers, things are distinct due to the different knowledge
and backgrounds of people and all they need to understand thoroughly due to multiple
influencers for a single thing that changes its meaning. The world around humans is
studied deeply from the point of view of every different human. Organizations are taken
into account from the perspective of every individual and happen to be complex
(Saunders et al., 2019).
2.1.4 Postmodernism
It focuses on roles and procedures rather than objects. Instead of language, its roles are
examined deeply in this philosophical method. Moreover, it rejects the views that are on
the surface and commonly known by a wide range of people but enforces to see other
submissive aspects too. Hidden realities are emphasized to bring the actual truth which
is behind the view. Data creates the objects that are in front of our eyes (reality visible
to us). Hence, the data is also important in this other than the end results (Saunders et
al., 2019).
2.1.5 Pragmatism
Pragmatism is different from the other four philosophies due to its focus on practice
rather than theoretical work. If we go practical, this method used to be more useful. It
believes in actions that strive to find solutions to some real problems (Saunders et al.,
2019).
2.1.6 Research Philosophy of this Thesis
This research will develop hypotheses after reviewing literature that further will be
tested. According to Bell et al. (2019) & Saunders et al. (2019), hypothesis testing is
done in positivism. It confirms the reliability of knowledge (Bell et al., 2019).
13
The RQs will be answered by testing hypotheses, so this research adopts positivism
philosophy as focused on variables and hypotheses testing. Moreover, positivism is
suitable when quantitative methods of research are used (Bell et al., 2019).
2.2 Research Strategies
Research strategy is a road map to achieve research purposes. Strategy is selected by
considering research philosophy and chosen data collection and analysis methods. The
consistency of methods and their effects on the research project are important to
consider. The research strategies can be qualitative, quantitative or both combined that
are further divided into many types (for qualitative). Quantitative research is the method
of research in which numbers are involved. Basically it produces results in numbers first
that are further analyzed to present the findings or answers to problems. Qualitative
research observes the behaviors and uses or produces non-numeric figures such as
words or statements (Saunders et al., 2019). Quantitative method is used to measure the
relationship between two or more variables or phenomena (Bell et al., 2019).
Sometimes research demands both ways according to the nature of the project or the
purpose. The topic requires researchers to explore the theory/statistics and also analyze
data through multiple ways. Hence, both the qualitative and quantitative methods are in
use in business for a single research project. The combination of both these methods
make it possible to get larger perspectives on research to generate better results
(Saunders et al., 2019).
2.2.1 Research Strategy for this Thesis
This thesis will be conducted by using quantitative research methods as data will be
gathered through surveys in IPIEM in the form of close-ended questionnaires which
will be analysed by statistical methods. Both data collection and analysis will use
quantitative methods. Some of the main features of quantitative methods that come step
by step will also be followed in this project and are as follows: First, developing theory
which will lead to generating hypotheses that will be tested later. The second step using
a specific research design that will go with the quantitative strategy is the survey
method for this thesis. Operationalization is the third step where the measures of the
main concepts will be determined. Research site is selected in the fourth step which is
14
the respondents from IPIEM in this case. The tool to be used for research is
administered and data is collected after the planning. Questionnaire guide will be made
to ensure the reliability of research and further self-made questionnaires will collect the
data. That data will be processed and analyzed by using quantitative techniques to draw
the results for reaching some conclusions of the thesis (Bell et al., 2019).
2.3 Research Approach
Research design also has one more very important aspect that is research approach.
Research approach is the way of development of theory which is decided by the
research itself according to the subject of the research (Saunders et al., 2019).
Bell et al. (2019) and Saunders et al. (2019) defined two research approaches to theory;
deductive and inductive. Then a third approach named abductive was also added in
research that is now commonly used by business researchers (Saunders et al., 2019).
In a deductive approach, research starts with some theories that lead the conclusions. In
this resultant statements are made through the previous knowledge. These statements or
theories are tested to form new information. Deductive reasoning is based on theories
while inductive reasoning forms through empirical data. Also in an inductive approach
results are not confirmed. It generates theory by using existing information. The last
approach abductive reasoning starts from a conclusion which generates logics that
further lead to new conclusions (Ibid).
In contrast to deduction and induction; from theory to empirical data or empirical to
theory, abductive reasoning works in a different way. It uses data to get new theories
and then again data is collected for testing those theories. Abduction is the combination
of both deductive and inductive theories as it starts from empirical data like inductive
reasoning and leads to generating data or empirical data like deduction (Saunders et al.,
2019).
2.3.1 Research Approach Used in this Thesis
The deduction will be used for this thesis. As this thesis involves hypotheses and the
approach goes from to theoretical findings on the basis of collected data. This study will
test the theory to generate new theoretical findings with the help of empirical
15
data/surveys. Data collection is completed in one go by using this approach. Therefore,
it is convenient to use deduction in this project. Moreover, deduction uses quantitative
research method while induction focuses on qualitative study. As this research will be
using a quantitative research method; deduction is the right approach to be used
(Saunders et al., 2019).
2.4 Research Design
Research design is a structure to collect and analyze data for the research purpose. (Bell
et al., 2019; Saunders et al., 2019) A research design shapes the whole research project
(Bell et al., 2019).
It leads the research for data collection, analysis and provides guidance for the research
method. Research design has been divided into five categories primarily: Experimental,
case study, cross-sectional, longitudinal and comparative design. (Bell et al., 2019)
Cross sectional design has many cases for comparison and focuses on variation. Data is
collected at the same time for all the cases. Variation is important in cross sectional
design that can be gained through quantifiable data. Quantitative method has a standard
approach (Bell et al., 2019) so it relates to cross sectional design by establishing
variation. Cross sectional design contains questionnaire or structured interview method
(Bell et al., 2019).
Longitudinal research design is used to make changes in management studies. This
study is based on different times for the different events for the same study that is
involved in cross sectional. But in longitudinal, the difference of events times can alter
the results (Bell et al., 2019).
Experiment compares two variables and sees the effect of one thing on another variable.
It is mostly used for research in science subjects where nature is involved. It involves
hypothesis testing and not answering open research questions. Hypothesis are the
statements of some phenomenon that can be proved false in future.
The fourth design is a case study method. Case study looks into a particular subject by
using a real case. That case can be an individual or an organization. It refers to study
within a specific location where the research is focused to solve the problem related to
16
the particular organizations involved as a case. This design is mostly used in qualitative
research (Bell et al., 2019). Case study is deep exploration and involves all dynamics of
a phenomenon so it is considered more valid than experiments. Case study can include a
single case or multiple cases (Saunders et al., 2019). Comparative design has more than
one case that are compared with each other. It can be in both qualitative or quantitative
research (Bell et al., 2019).
There are different strategies for a particular research design. Saunders et al. (2019)
mentioned these research strategies which are named as experiment, case study, survey,
archival research, documents, ethnography, inquiry, grounded theory and others. Some
of these are explained further in detail (Ibid). Surveys, observations and experiments are
for quantitative research while qualitative research uses grounded theory, ethnography,
qualitative interview or focus group (Bell et al., 2019). Saunders et al. (2019) points out
that archives and documents and case study strategies are used in both qualitative and
quantitative research.
Surveys are widely used in business research. These are involved in collecting data
from many participants and then it is analyzed on the basis of their answers. The third
method Archival and document research is associated with data gathering as secondary
data by online sources in the form of documents. It is an easy accessible way to find
data for research analysis. Data about different institutions can be gathered by using the
Internet and this information can be in the form of text, numbers, audio, visual or others.
Archival or documentary research can be used for qualitative, quantitative or both ways.
Also the data collection method can differ from the data analyzing method. For
example, documents are in quantitative expression but they can be used for qualitative
analysis (Saunders et al., 2019).
2.4.1 Research Design for this Thesis
In this research all the variables will be on the same questionnaire. So a respondent will
answer them at once in a single specific time. According to Bell et al. (2019), it is the
cross sectional design that this thesis will follow. In addition to that, this thesis will use
survey strategy with the aim to fit it with the research design. Surveys are referred to the
cross sectional design. This design helps to keep the meaning of the traditional survey
17
while realizing the relevance of the cross-sectional research design where it does not
only collect data via questionnaires (ibid).
2.5 Data Collection Method
Data collection is an essential part of any research. Some data collection methods have a
structured approach. In a structured approach, researchers set the comprehensive
outlines of what they want to realize and design the research. Questionnaires and
structured interviews are examples of structured methods. Researchers make questions
that will provide data to be gathered to answer particular research questions in the
former. In the latter, researchers apply the kind of interview in survey studies which
involves questions created for specifically the same goal (Bell et al., 2019). Many data
collection methods, such as participant observation and semi-structured interviewing,
are less structured than the questionnaire and the structured interview. Since these
methods stress an open-ended form of the research process, there is less limitation on
the topics and matters being investigated. They enable researchers to keep an open mind
about what they need to know; therefore, ideas and assumptions can develop from the
data. Although such research is organised to answer research questions, they have less
explicitly than structured research methods (Bell et al., 2019). In this thesis the
structured method which is the questionnaire within the surveys will be utilized.
2.5.1 Primary and secondary data
Bell et al. (2019. p.12) stated that "Primary data analysis means that the researcher who
collected the data conducts the analysis." However, in secondary data analysis,
researchers utilize data that have not been involved in collecting them. The secondary
analysis can be used for the analysis of both quantitative and qualitative data (Bell et al.,
2019). In this thesis, the primary data will be collected. Since the research team will use
a questionnaire, primary data will be collected.
2.5.2 Surveys
Surveys are mainly used with the deductive research approach. What, Who, How much,
how many and where; these types of research questions are answered by using surveys.
18
It mostly uses a questionnaire method that is an easy and cost effective approach.
Questionnaires also help to get many responses more conveniently and rapidly. Surveys
are conducted to collect answers from people reflecting their attitude and behaviors on a
certain matter. The results from surveys are derived easily and they are simple in terms
of understanding the people's responses. Survey answers are further analysed with
statistical techniques as they are used in quantitative methodology. Survey strategy
creates the justification for the relation between different variables. Structured
interviews and structured observation methods can also be used with surveys. Survey is
the choice for this thesis due to all the above mentioned characteristics; also it is used
with deductive approach and for positivism philosophy (Saunders et al., 2019).
2.5.3 Questionnaire and Questionnaire Design
Saunders et al. (2019. p.503) stated that “we use the questionnaire as a general term to
include all methods of data collection in which each person is asked to respond to the
same set of questions in a predetermined order.” Also, instrument is an alternative term
of questionnaire, which researchers broadly utilize. The questionnaire is one of the most
broadly employed data collection methods within the survey approach. It implements an
effective way of collecting answers from a wide sample before quantitative analysis
since each respondent responds to the same collection of questions. However, it is
crucial to know that producing a proper questionnaire is very difficult because it should
be made to collect the exact data needed to reach the research questions' answer and
meet the research purposes (Saunders et al., 2019).
Questionnaire is not fit for exploratory or other studies that demand large numbers of
open-ended questions. It is suitable for standardized questions that researchers can be
assured that all respondents will comprehend the same way. Therefore, it utilizes
descriptive or explanatory research. Descriptive research includes attitude, opinion, and
organizational practices questionnaires that allow researchers to recognize and describe
variability in diverse phenomena. Explanatory research allows researchers to assess and
describe relationships among variables, especially the cause-effect relationships (ibid).
Since this study will investigate the relationship between variables the explanatory
research will be conducted by the research team.
19
There are different modes of designing the questionnaire which depend on whether it
will be completed by respondents (self completed) or a researcher as well as how it will
be delivered and collected. Self completed modes containing Internet questionnaire
(web questionnaire or mobile questionnaire), SMS questionnaire, Mail questionnaire,
and Delivery and collection questionnaire. Researcher completed modes include
Telephone questionnaire and Face-to-face questionnaire. Several factors influence the
selection of questionnaire mode, such as the respondents' characteristics from whom
researchers wish to collect data; significance of attaining an appropriate person as
respondent; significance of respondents' answers not being affected or falsified; and the
sample size, types and number of questions (ibid).
In this research, a self-completed questionnaire via web questionnaire survey will be
conducted. Most of the self-completion questionnaires are designed within closed
questions. Two types of closed answers are horizontal and vertical (Bell et al., 2019).
Utilizing closed questions enables researchers to pre-code them which is convenient
when it comes to processing data with computer analysis (ibid). Web-based
questionnaires work by asking respondents to visit a website where the questionnaire
can be observed and completed online. According to Bell et al., (2019) web-based
questionnaires provide a much wider diversity of embellishments regarding the
appearance, which is not possible in the email questionnaires. This thesis will be
completed using a questionnaire which will comprise closed questions. It is designed
with a control question which indicates whether the respondents are aware of the RM
process in their company. After that, a few general questions will be created that will
give us some background information about each company. It starts off with a question
which is answered in terms of years and the respondent has five answer options to
choose from. The options are coded as follows; 1-5(1), 6-10(2), 11-15(3), 16-20(4) and
20 and more (5). Some questions are answered using Yes/No options where Yes is
coded as 1 and No is coded as 2 for statistical purposes. One question consists of
different options in terms of the job title of the respondent. It will be answered using
one of the four options and is coded as follows; Risk manager (1), General Manager (2),
Member of board of directors (3) and Other (4). It is then followed by some questions
related to the purpose of the study based on the two RQs for this paper. These questions
are answered using a vertical format seven point likert scale and are coded as follows;
20
Strongly disagree (1), Disagree (2), Somewhat disagree (3), Neutral (4), Somewhat
agree (5), Agree (6) and Strongly agree (7).
The research team will first create a questionnaire in English and then translate it into
Persian for better understanding of the respondents since the population for this study is
in Iran. Once the data is collected it will then be translated back into English for further
analysis in SPSS as the codings will remain the same for both the English and the
Persian version.
2.5.4 Population and Sample
It is hard to study a complete selected population due to the limited access to all the
aspects and less resources. Therefore a small part of the data is studied that is called a
sample of the population. A sample is selected from the relevant population and is taken
as a generalization for all the cases. Instead of studying the whole population, only a
sample is studied that saves time. (Saunders et al., 2019) The target population for this
paper is IPIEM.
Deciding on a suitable sample size is very crucial. According to (Saunders et al., 2019),
if the sample size is large, the error rate in concluding the sample size becomes lower.
Probability sampling comprises the accuracy of the data collected and the resources that
are willing to be invested in terms of time and money for the checking and the analysis
of the data. The decision of sample size within this is controlled by the confidence in the
data, acceptable margin of error, types of analysis and finally the extent of the target
population (Ibid.) Sampling is of two types termed as probability and non-probability.
Probability sample has an equal chance of all cases. When the mathematical description
of a target population is needed, a probability sample is chosen to use. While a non-
probability sample is chosen through judgment and each sample does not have the equal
chance to be selected. Instead most relevant and useful cases are selected (Saunders et
al., 2019). In this thesis all samples have an equal chance of being selected. Hence,
probability sampling will be applied.
Moving on, a high response holds the utmost importance. According to (Saunders et al.,
2019), Having a high response rate removes the uncertainty of the result being biased. A
higher response rate reduces the risk of non-response bias and also tries to make sure
21
that the result is the representation of the whole sample. Having non-responses is very
likely. Non-response result due to the respondents unwilling or unable to respond due to
any reason. In this scenario the result derived has chances of being biased as the non-
respondent will now not be a part of the target population, this is also known as non-
response bias. Also, for each non-response it is necessary to add a new respondent to
fulfill the required sample size, this however is time consuming as well as expensive
(Ibid.). To avoid being in this situation, chances of non-response can be reduced by
paying more attention towards the data collection method used. On the other hand, some
respondents may not meet the research requirements and will hence be ineligible. In this
scenario, ineligible and unreachable respondents will not form part of the sample.
The research team intends to answer the RQs by studying IPIEM. The target population
for this research is all members of the Society of Iranian Petroleum Industry Equipment
Manufacturers (SIPIEM). It was established in 2000 to aim for synergy, pursuing
professional demands and common problems of members, and participation in the
decision-making process in policy-making institutions. So far, over 820 companies have
become members of this society (SIPIEM, 2020). To know the sample size, cochran’s
formula was calculated. When the population is known the below formula is used for
computing the sample size (Cochran, 1977).
Equation 1: Cochran formula
𝑛 = 𝑠𝑎𝑚𝑝𝑙𝑒 𝑠𝑖𝑧𝑒 𝑧 = 1.96 (95% 𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙) 𝑝 = 0.5; proportion q=1-0.5 𝑁 = 820; total population d = acceptable margin of error for mean (0.05)
22
Finding company profiles and people responsible for answering the questionnaire as
well as communicating with them is very difficult and time-consuming. Also, they may
ignore it even if the questionnaire is sent to them because they do not know the
researchers. To address these difficulties, the research team has contacted the Sharif
fund and asked for support in distributing the questionnaire. It is an organization that
works with the SIPIEM and provides financial services such as surety bonds, loans, and
counselling to the members. Since one of the research team members previously had
working experience in the Sharif fund, its executive accepted to corporate with
distribution of the questionnaire. Therefore, the questionnaire will be distributed among
the members of the SIPIEM with the help of the Sharif fund.
2.6 Data Analysis methods
According to (Bell et al., 2019), there are three types of quantitative data analysis.
These are Univariate analysis, Bivariate analysis and Multivariate analysis. Univariate
analysis covers only one variable for analysis, Bivariate analysis is where two variables
are analysed together to identify if there is any relation between the two variables.
Multivariate analysis on the other hand stands for the analysis of three or more
variables.
Several approaches have been identified by (Bell et al., 2019) to conduct the Univariate
analysis. These approaches consist of using the frequency tables, diagrams, measures of
central tendency where the mean, median and mode are identified. Measures of
dispersion is another approach where dispersion is measured by two different
techniques. The first technique to measure dispersion is via range. It is simply the
difference of the maximum and minimum values within a distribution which are
“associated with an interval/ratio variable” (Ibid, p.320). The second technique is the
standard deviation which basically identifies the average amount of variation around the
mean (p.320).
For Bivariate analysis, Contingency tables are considered to be the most flexible out of
all the methods for analysis of relationships as they can be used with any set of
variables, however they are not always considered to be the most suitable in terms of
efficiency for some variables. Pearson’s r is another method in the Bivariate analysis to
23
examine the relation between interval/ratio variables. According to this method, if the
coefficient lies between 0 to 1 or -1 it determines the strength of the relationship with 0
meaning no relationship and 1 or -1 meaning a perfect relation. Coefficient being
negative or positive will indicate the direction of the relationship. Moving on,
Spearman’s rho which is also represented as “ρ” is designed to be used for the pairs of
ordinal variables but can also be used in the case one variable being ordinal and the
other being interval/ratio. The outcome of this is similar to that of Pearson’s r method.
Furthermore, Phi and Cramer’s V are statistics which are very close. Phi is used to
analyze the relation between two dichotomous variables and has an outcome similar to
that of Pearson’s r statistically. However Cramer’s V on the other hand even though
uses the same formula as Pearson’s r but cannot identify the direction of the relation
between two variables but can only indicate their strength, hence only a positive value.
(Bell et al., 2019) also mentions that methods are also commonly “reported along with a
contingency table and a chi-square test” (ibid, p.325). Lastly there is another method
where the mean is compared to eta (η). “Eta is a very flexible method for exploring the
relationship between two variables, because it can be employed when one variable is
nominal and the other interval/ratio. Also, it does not make the assumption that the
relationship between variables is linear” (Ibid, p.325).
Moving forward towards the third method for quantitative analysis, the multivariate
analysis, Hair et al. (2014) mentions some statistical techniques that can be carried out
in this analysis. These are Factor analysis, Simple regression, Multiple regression,
Multiple discriminant analysis (MDA), Logistics regression, Canonical regression,
Multivariate analysis of variance (MANOVA), Multivariate analysis of covariance
(MANCOVA), Cluster analysis and Structural equation modelling (SEM).
2.6.1 Data Analysis Method for this Thesis
For this thesis, Simple Linear regression analysis will be used to analyse data for RQ1
and RQ2 as this method helps in investigating relationships between dependent and
independent variables. Regression analysis holds a three way purpose. The first purpose
aims to form an association among the response variable (y) and regressors x1,x2,....xn.
The second purpose is predicting y on the basis of “set of values of x1, x2, · · · , xn”
(Yan and Su, 2009, p.4). The third purpose is to screen variables x1, x2, …, xn for
24
identification of variables which are higher in importance in comparison to others for
explaining “the response variable y so that the causal relationship can be determined
more efficiently and accurately” (Yan and Su, 2009, p.4). In this thesis the first purpose
of the regression analysis will be considered.
Given the scope of the RQ1 where the research team is going to find the relationship
between BSC and RM in terms of the role of BSC in managing strategy risks, as well as
the scope of RQ2 is find the relationship between each perspective of BSC and a
specific type of strategy risk, Bivariate Linear regression analysis is the best suited
method for the analysis. This relationship can either be expressed in the form of an
equation or a model which connects the dependent variable with the independent
variable (Chatterjee and Hadi, 2012).
2.7 Research Quality
Quality of the research and its results is the main concern in a research design. In
business studies, quality is assessed through two dimensions; reliability and validity
(Bell et al., 2019; Saunders et al., 2019). Reliability is about being consistent and
remaining the same throughout the research and same results after replication (Bell et
al., 2019). Validity demands measuring accurately, measuring right and use of results
rightly (Saunders et al., 2019).
In business research, validity can be internal, external or related to measurement. The
types of validity are named as measurement validity, internal validity, external and
ecological validity. Among all types measurement validity is for quantitative research
also called construct validity. It makes sure that whether a concept measures the exact
phenomenon which it tends to be. Measurement validity is tested in many ways that are
face validity, concurrent validity, predictive validity, convergent and lastly discriminant
validity (Bell et al., 2019).
Face validity means the validity of measures used in the research should indicate and
communicate the real concept of the research question. The measure should be
concerned with the actual concept. Measures are made valid and more definitive by
using experts’ judgement. Concurrent validity is answered by this question: Does the
measure really answer the concept that is to be measured. This can be the problem if the
25
measure is not related or does not have the relation with the actual concept. The
measure not having the relation with the concept will lead to providing false
conclusions. This concurrent validity error can occur due to the different nature of
understanding of people. Predictive validity tests the measures of the future that are
based on the future events. It is done by asking the respondents about what they will be
behaving in future in relation to a certain phenomena. In Convergent validity, the
validity of a measure is checked by using some different method, for example
observation is done instead of using actual measure. Discriminant validity ensures that
the measure of one concept should be different from the measure of another similar
concept. One concept measure should not overlap with the other measure even if the
concepts are homogenous (Bell et al., 2019).
Reliability can be internal or external. Some errors and bias should be eliminated in
order to get a reliable research project with reliable findings. These threats are further
discussed as Participant error: This involves some factors that affect the performance of
the participant. It can be neglected by preparing and deciding everything with the
participants in advance. Participant bias: It is about wrong input by the participants. The
reasons can be general; any mistake on the behalf of the researcher or a wrong answer
by intention. This bias can be lessened by considering environmental conditions.
Researcher error: researcher understanding is altered with some inappropriate reasons.
Guide is prepared to eliminate researcher error. Researcher bias: It is explained as bias
on the side of researchers for the answers of participants (Saunders et al., 2019). As a
researcher presence while collecting data can influence participants’ answers in any
way. In case of online (web-based or email) questionnaires, the bias is minimum due to
non-interaction of researchers with the participants in the process of answering the
survey (Bell et al., 2019). Transparency of research work is important for judgements
by others in order to avoid these errors. In this way, research will be reliable (Saunders
et al., 2019). Researchers' error is minimized in this thesis as it will use questionnaires
that are well thought and pre planned. Researcher bias will be minimum as this research
will be done by using online questionnaires hence, the research team will not be present
when respondents are answering the questionnaire to influence the selection of their
answers. Further, the questionnaire quality will be confirmed by doing Cronbachs’
Alpha test to measure reliability and pre-test for the validity. However, the validity of
26
this thesis is ensured by using the judgement of the examiner and tutor. The reviews of
these professionals supported the research quality throughout the process of writing the
thesis.
2.8 Ethical Considerations
Ethics are important throughout the study whether in data gathering or analysis.
Decisions should be made while considering basic ethics of the research (Saunders et
al., 2019; Bell et al., 2019). The research project has been consulted with a tutor and
supervisor throughout the study to ensure the ethics based decisions (Bell et al., 2019).
Data management, copyright and privacy are some ethics for research. Data collection
from the internet may be used for the other purpose than the original use (Bell et al.,
2019). How much information and for what purpose it can be used. These are important
aspects to consider. This also includes the permission and anonymity of the participants
(Bell et al., 2019; Saunders et al., 2019). Access issues are also essential to put in
research projects where on the internet everything is available (Saunders et al., 2019)
but there is a need to take permission (Bell et al., 2019). Preparation in advance, time
management and research structures help to maintain ethics (Saunders et al., 2019).
2.8.1 General Data Protection Regulation
According to the General Data Protection Regulation (GDPR) personal data collected
from sources must be handled and taken care of with extreme care. The information that
belongs to a person comes in this law. According to this regulation data must only be
collected if it is necessary and should be avoided in situations not required.
Furthermore, according to this regulation entities or people whose personal data is going
to be collected or used must be informed and should not be kept unnecessarily. The
nature of this thesis does not necessitate the use of personal data but if necessary will be
dealt with according to the data protection ordinance (Linnaeus University, 2021).
2.9 Individual Contribution
This topic was decided and selected after careful and detailed discussion between the
members of the research team by taking into consideration the knowledge and interest
27
of all the members. Since the nature of the study was related to the IPIEM, it required to
study some Persian articles for more in depth knowledge from which relevant
information was then discussed and translated. English questionnaire which was
prepared by the entire research team was then translated into Persian for better
understanding of the respondents. The responses received were again translated into
english for further analysis. This translation process was conducted by one of the team
members who bore Iranian ethnicity. Equal contribution has been put in by all the
members of the team throughout all the chapters of the research paper. Frequent
meetings were conducted throughout the work in progress for discussion about the
writings and to provide each other with updates about information collected for
maintaining a strong grip of all the members on the research. These meetings also bore
the purpose of giving improvement suggestions to each other as well. The entire
research team attended all the mandatory seminars as well as the tutoring sessions with
the supervisor and aimed for a healthy and active participation. Overall, the group
collaboration worked positively and the communication was good throughout the thesis
as it supported the completion of this study.
3 Literature review
This chapter presents all the relevant literature on RQ1 and RQ2. First general insights
on the BSC have been given for the introduction of the main tool that is the focus of this
study. BSC different generations over the years are also elaborated. Then literature on
the BSC-RM is reviewed which presents a new approach of the BSC as a different tool.
BSC has been described to use in various roles of the RM process. RM has been
described in different categories and subcategories. Studies on each particular category
in the RM process together with BSC effects have been considered in this chapter. In
the last, hypotheses for RQ1 are made by using literature review.
Literature related to RQ2 starts from the explanation of three different categories of
risk. Then theory on the risks into four perspectives of BSC is given. Further on strategy
risks of IPIEM are presented and then among them some strategy risks are chosen for
this thesis. The focused strategy risks are categorized into four perspectives based on
their characteristics. Finally, hypotheses for RQ2 are made by using literature chapters.
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3.1 Balanced Scorecard (BSC)
After Johnson and Kaplan (1987) new claims that Management accounting (MA) had
lost its relevance since focusing more on financial measures; it created implications for
MA (Bhimani, 2006). As a result researchers created new MA models and systems that
are based on strategies and markets. BSC is one of them that has a different approach
from traditional systems for controlling and measuring operations (Nilsson, Olve &
Parment, 2011).
BSC is a Performance measurement tool (Perkins et al., 2014) that was developed from
extensive research on performance measurement in large firms in the US (Kaplan &
Norton, 1992, 1993, 1996).
Performance measurement System (PMS) is a tool consisting of measures to make the
efficient and effective allocation of resources to create value and be more competitive to
other firms. Traditional PMS only had financial measures that do not measure intangible
assets. These assets can only be evaluated with non-financial measures. BSC is a
modern PMS tool that considers both financial and non-financial measures and
indicators. BSC perspectives are divided into two different types of measures that are
called lagging and leading indicators. Financial perspective of performance is a lagging
indicator that is led by other three perspectives: Customer, Internal process and learning
& growth called leading indicators (Wisutteewong & Rompho, 2015). BSC had been
called the best innovation tool in 1997 after the launch of the book “The Balanced
Scorecard” in 1996 by Kaplan and Norton. The book contributed to theory and brought
practical improvements for the management and accounting field.
BSC integrates financial and non-financial measures that have a relationship in which
each perspective’s measure affects another perspective (Nørreklit, 2003). These affect in
this way as Learning and growth measures affect the measure of internal business
process which further affect customer perspective. Then the customer perspective
measures lead to impact the financial perspective (De Haas & Kleingeld, 1999).
BSC maintains a balance between financial and non-financial aspects of the firm.
Financial measures are the indicators of performance of the past that are important to
evaluate the performance/ efficiency and effectiveness of the firm (Kaplan & Norton,
29
1992, 1993, 1996). While the non-financial perspectives are important to measure
intangible assets (Wisutteewong & Rompho, 2015). BSC measures performance in four
aspects:
1. Financial- it considers shareholders, their profits and other measures from
shareholders point of view.
2. Customers- customer perspective focuses on how to appear for customers.
3. Internal- it measures the internal processes and systems that a firm should
consider.
4. Learning and growth- tells how a company can be consistent, excel, improve
and generate greater value (Kaplan & Norton, 1992, 1993, 1996).
The BSC has evolved in the past from the original Kaplan and Norton, (1992)
performance measurement tool to a management tool. BSC's first version has now led
into the third generation of scorecard with the contribution of authors that started in
2002. BSC adoption to make sure the alignment of goals with the organizational
strategy made it important for many companies all over the world. Organizations have
adapted BSC in accordance to their requirements due to different environments or
organizational cultures. Now BSC is also in use in public and nonprofit organizations
along with private firms. BSC first generation was focused more on four perspectives. It
also emphasized only the most important measures should be taken into the account
(Perkins et al., 2014).
The measures of BSC are strongly connected to the organizational strategy and also
dependent on it (Kaplan & Norton, 2004). The balance of the measure that all the
perspectives’ measures should be of the same number is also in the first generation
while the recent version of BSC does not demand it. After some years BSC has
transformed to have a more strategic approach. Companys’ overall goals are further
assessed in the form of strategic objectives that ultimately will lead to achieve the
business main goals and financial objectives. It helps to understand and implement
strategy. Moreover, BSC also supports the strategy to evolve due to change in
environments or market situations. It led to the second generation of BSC. Strategy map
was developed in this generation of BSC that determines the objectives of the company
in all the BSC perspectives in the form of a framework (Perkins et al., 2014).
30
BSC uses strategy map to understand and implement organizational strategy by
constructing measures (Olve et al., 2003). The second generation BSC creates the link
between corporate strategy and daily tasks. It also determines the link between all the
strategic objectives that helps the organization to recognize the useful changes to
achieve organization’s goals (Perkins et al., 2014).
Previously, strategy map converted only tangible assets into outcomes, whereas later on,
intangible assets were also introduced. These intangible assets are the information,
human capital and company capital. The strategic objective basis approach also shifted
from top-down to bottom-up. Practitioners introduced the third generation for the BSC
adoption in their own organizations according to their needs. It is used for achieving
determined goals that are included in the Destination Statement. These written
statements allow managers to create causality between measures, targets and goals
(Perkins et al., 2014).
Later in the new version of the third generation, the four perspectives were categorised
in two levels which are “activity” and “outcome.” Activity level includes internal and
learning and growth perspectives. Outcome level includes financial and customer
perspective. The destination statement eliminates the formality to understand the design
process and makes fast the achievement of strategic objectives. This increases the BSC
role in strategy development and enhances focus on the linkage model of strategy
(Ibid).
BSC implementation is done with the involvement of strategy by using a five principle
approach where strategy is the focus of the organization. Strategy alignment with
organizational objectives and implementation make the successful implementation of
BSC (Wisutteewong & Rompho, 2015).
Other than strategy, BSC perspectives are also based on the company mission and
objectives so these can be increased in numbers according to the company specific
strategy and purpose. For example some organizations have five or more perspectives in
their BSC (Olve and Sjöstrand, 2006).
3.2 RQ1- Role of BSC in Managing Strategy Risks
31
When it comes to talking about the roles that the BSC plays when it comes to managing
strategy risks different authors presented different points of view. According to some
authors BSC is helpful for risk assessment (Safitri and Pangeran, 2020; Renault et al.,
2020; Papalexandris et al., 2005; Wu & Hua, 2018), some believe BSC can be used as a
tool to control risks (Monica and Pangeran, 2020; Nugroho and Pangeran, 2021;
Gutama and Pujawan, 2019; Wang et al., 2010; Scholey, 2006), and the others believe
that BSC is a process that can be used by the management to gain the required
information needed to make decisions to tackle strategy risks (Cheng et al., 2018;
Beasley et al., 2006). Another notion to the role of BSC is also considered to be a
combination of both identification as well as controlling of risks, however emphasis is
also given on the fact to separate performance measures from the risk measures
(Calandro and Lane, 2006; Oliveira, 2014). In the next sections, roles of BSC; risk
assessment, risk controlling and as a medium to collect data for decision making will be
reviewed.
3.2.1 Risk Assessment
According to Safitri and Pangeran, (2020), RM is a process which is made up of three
categories. First is setting the context, second is the risk assessment process and then
finally risk treatment where an organization eliminates, mitigates, transfers or accepts
risks. It was noted by Safitri and Pangeran, (2020) BSC plays a role for risk assessment.
It was mentioned how important it is for an organization to categorize risks into the
BSC perspectives in order to plan and prepare solutions to provide the organization with
a culture where RM is realized at all business levels. While carrying out a risk
assessment in an organization, three sub processes are carried out (Safitri and Pangeran,
2020). These are risk identification, risk analysis and risk evaluation (Safitri and
Pangeran, 2020; Renault et al., 2020). As BSC primarily focuses on continuous
improvement and as a RM goal is protection of interests of stakeholder and to gain
financial excellence, an increased organizational goal can be achieved through use of
BSC as risk assessment as it is an extremely important factor in gaining overall business
excellency (Safitri & Pangeran, 2020). By going more in detail, Safitri and Pangeran,
(2020), described each of the three risk assessment sub-processes.
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3.2.1.1 Risk Identification
Risk identification is the process of finding the uncertain events that create hindrance
for the business to carry out its activities. It is essential for a company to recognize the
risky events and their source (Monica and Pangeran, 2020).
In this step an organization's vision, mission and strategy is understood and translated
into Key Performance Indicators (KPIs). These KPIs are then further segregated into the
financial, customer, internal business process and learning and growth perspectives of
the BSC (Safitri and Pangeran, 2020). Doing this will help the organizations in
identifying the risks as a poor performance indicated by KPI can raise the concern of
risk occurrence.
3.2.1.2 Risk Analysis
Here the aim is to analyze the impact of the risk and their likelihood which may cause
any problems for the organization in achieving its goals or future opportunities that may
come along the way for the enhancement of the business. Carrying out a risk analysis
through BSC can help an organization in carrying out a further risk evaluation as well as
aid in the decision making process in relation to the risks (Safitri and Pangeran, 2020).
3.2.1.3 Risk Evaluation
Risk evaluation helps in making the decision process further easier through the data
collected via risk analysis Safitri and Pangeran, (2020). This step is the preparation
stage for responding to the risks (Renault et al., 2020; Safitri and Pangeran, 2020).
People are also assigned tasks in relation to certain risk responses (Renault et al., 2020).
In this process it is determined which risks need to be treated and how to prioritize this
treatment based on the importance and urgency to deal with the risk (Safitri and
Pangeran, 2020). It eventually leads to development of measures to make sure the
effectiveness of the selected risk treatment action. These risk measures also help to
mitigate risks (Renault et al., 2020).
According to Papalexandris et al. (2005), risk assessment should be taken on at the
implementation stage of the BSC and is considered as a stage where risk and
33
uncertainties are identified, analyzed and prioritized, and are planned to manage the
risks.
3.2.2 Risk Control
A study conducted by Nugroho and Pangeran (2021), agreed and had a similar stance to
that of Safitri and Pangeran, (2020) in terms of the role of BSC which was limited only
uptil the extent of risk assessment. However, Nugroho and Pangeran, (2021) concluded
in their study by stating that through the use of the BSC four perspectives a more
extensive view on strategic planning as well as a more detailed and thorough view on
the risks that are probable to arise as well as the RM to enable the company in achieving
its mission. Monica and Pangeran, (2020), in their study discussed that the BSC was not
just limited to the role of risk assessment, they used it to perform the whole RM
process; risk assessment and risk treatment (controlling). In the risk assessment process
they further with the use of different tools along with the BSC got the desired objective.
Risk identification was done by including the events which were risky as well as
reasons for the risks caused. In Risk analysis two steps were carried out, likelihood and
impact criteria. To establish this it is important to be carried out at an early stage.
Through risk evaluation, the results were then transferred into a risk matrix which
helped in identifying the level of each risk event. Finally, all the data collected from the
risk evaluation, the next step consists of risk treatment which in other words can be
described as planning to control the risk. Here the objective is to reduce or mitigate the
risks that may cause hindrance for the company in achieving its overall goals (Monica
and Pangeran, 2020). Moreover, this process comprised of identification of different
alternatives to deal with the risks which were then identified as avoiding risks,
transferring of risk, mitigation of risk and acceptance of risk. Further steps within this
risk treatment process consisted of creation of an action plan for risk management to
reduce risk events. Planning and projecting of expected results in case the action plan is
implemented is the next step to do under risk treatment. Next the person who will be
accountable for the action plan to handle the risks is determined and finally it is
followed by monitoring of the risk expectations that may occur again (Monica and
Pangeran, 2020).
34
Gutama and Pujawan, (2019) in an attempt to put BSC in play, first introduced strategic
asset management for giving direction and path in the management of the assets of the
organization that may require a strategic approach. The strategic asset management plan
is then converted into management objectives internally as well as externally on the
basis of the four perspectives of the BSC. Going on more into the research it was
highlighted by the authors that currently BSC lacks a characteristic and that is of risk
identification but can be used in the mitigation of the prioritized risks (Gutama and
Pujawan 2019). Gutama and Pujawan (2019) in their conducted study try to identify risk
events as well as, the intensity of these risk events through the use of KPIs with the
BSC four perspectives as an underlying basis. Using these BSC perspectives, the origin
of the risk events are then identified.
Wang et al., (2010), proposes a RM process that can be used for identification of risk,
assessment of risk, risk response planning and controlling. They divided this RM
process into 8 steps:
1. To determine performance measures for an organization in terms of the BSC.
2. Determine the importance of organizational performance measures.
3. Determine specific performance measures for a project and the risks associated with
them.
4. Developing a relation matrix for overall organizations performance measures and
project specific performance measures.
5. To carry out a risk assessment for the performance measure of each project.
6. Prioritizing the risks.
7. Identification of sources that give rise to the risk and planning for measures to reduce
critical risks.
8. Monitoring and controlling of identified risks.
Step 1 to 6 constitutes risk assessment whereas 7 and 8 relate to “risk response planning
and risk monitoring and control, respectively” (Wang et al., 2010, p. 604).
Step 1: To determine performance measures for an organization in terms of the
BSC
35
Using the strategic goals of an organization, performance measures are developed by
applying the BSC. Using the business strategy, the planning process of a BSC starts by
putting together strategic indicators in order to create a BSC for the whole organization.
Following this, the organization wide BSC is then passed down into the business units
and other support departments. Doing this enables these business units or support
departments to develop their own BSC based on the four perspectives as this will enable
these units or departments to be closely connected with the overall business strategy
(Wang et al., 2010).
Step 2: Determine the importance of organizational performance measures
As the level of importance for every organizational performance measure may vary
from others, through this step it is identified what is the weight of each performance
measure in regards to its association with the company’s strategy. A higher “impact on
the upper tier performance measures will indicate a higher weight for the performance
measure at this level” (Wang et al., 2010, p.604). Also data collected from competitors
to compare with the company’s own performance measure is also common as the
organization may identify problems and get ideas for improvement. If through this,
enough data is collected it will aid in highlighting the strengths and weaknesses of the
company’s departments and prioritize them in accordance to the organizational
performance which may need to be improved. It is however, not to be taken lightly that
the data collected must be enough that is required to make the necessary comparisons
and evaluations (ibid, 2010).
Step 3: Determine specific performance measures for each stage of a project
In this step project performance measures are listed which make sure that the company
is able to achieve its performance measures. As discussed earlier, each department
makes a BSC of their own using the organization's overall performance measures hence
they are in complete alignment with the identified organizational performance
measures. Through this it can help in aligning the project specific performance
measures with strategic goals of the organization. Moreover, if the project specific
performance measures are many in comparison to the risk resources then the most
important and relevant measures can be selected (Wang et al., 2010).
36
Step 4: Developing a relation matrix for overall organizations and project
specific performance measures
As there are many different “degrees of correlation between the organizational
performance measures and the project performance measures, the significance of a
relationship is considered as: strong, medium, and weak” (p. 605). Through the use of a
matrix it can be checked if the identified project performance measures actually cover
all of the organizational performance measures (Wang et al., 2010).
Step 5: To carry out a risk assessment for the performance measurement of each
project
In this step risk assessment is carried out to analyze and evaluate all the risks that exist
in the organization and may hinder it from achieving its objectives (Wang et al., 2010).
Step 6: Prioritizing the risks
As it is known that carrying out extensive risk management requires huge effort.
Management may identify more risks than what they can actually manage. Therefore, in
situations like these it is efficient to prioritize risks for an effective RM based on their
effects on the company (Wang et al., 2010).
Step 7: Identification of sources that give rise to the risk and planning for
measures to reduce critical risks.
After determining and prioritizing the risks, the purpose of this step is to identify the
sources of risks and events that eventually impact the organization in a negative way
through analyzing past projects and any other relative factors. Moreover, this step helps
in developing planning measures for avoiding, transferring, mitigating and absorbing
risks (Wang et al., 2010).
Step 8: Monitoring and controlling of identified risks.
Monitoring and controlling of identified risks is a continuous ongoing process. In this
step existing risks are tried to be controlled whereas the management is also on the
lookout for any new risk affecting the organization (Wang et al., 2010).
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3.2.3 Collecting Data for Decision making for Strategy Risks
According to Cheng et al., (2018), BSC will enable managers to broaden their
knowledge and explore beyond the basic nature of strategy risks such as the probability
of their occurrence. Through this managers can become better aware about the meaning
of strategy risks. Moreover, through the use of the BSC, managers are assisted into
taking account of the qualitative nature of strategy risks while making strategic
assumptions. Through the use of a BSC, it becomes easier for the management in
making effective strategic assumptions as it has been noted in the past by Cheng et al.,
(2018) that when information is kept at the same place it reduces the fatigue for
management personnel to incorporate and integrate data from multiple sources. Cheng
et al., (2018) introduced risk information as an extra column within the BSC where each
risk is put adjacent to strategic objectives to which it relates. Through this BSC acts as a
tool where risk information is connected to the performance information. Moreover, the
design of BSC is also capable for managers to link the risks into their strategies while
making strategic assumptions (Cheng et al., 2018).
Beasley et al., (2006), in their study set out risk goals according to the four perspectives
of the BSC. In the learning and growth perspective, a problem can be faced that each
employee may have a different view on risk management. This can be due to the fact
that the employees’ idea of risk management may differ from that of the organization
due to different backgrounds. A goal here can be to ensure each employee holds the
same knowledge and definitions. Through this, objectives in regards to training and
development can be included in the context of risk management within this perspective.
It was further mentioned by Beasley et al. (2006), that risks do not only arise through
external forces but also via internal business processes, an example of such a process is
supply chain process. In the internal perspective of the BSC, risk objectives and their
measurement can easily be measured, objectives in regards to what range or variation of
risk is acceptable can be set as well as relative risk performance metrics can also be
incorporated in this perspective. Moving forwards, it has also been argued that “risks
related to strategy, markets, and reputation, all of which may affect or be affected by
customer satisfaction” (p.52) have often been ignored. Through the customer
38
perspective in the BSC, “an easy link to the management of risks related to strategy,
market, and reputation” (p.52) can be considered. Risk goals which relate to
“customers, markets, and reputation” can be easily included within this perspective.
Finally, the financial perspective of the BSC, helps in providing the cost/benefit
analysis of action required to respond to the risks. This is a vital part for any
organization in assessing the costs they might incur in comparison to the benefit they
might gain from trying to respond to the risks (Beasley et al., 2006).
According to Beasley et al. (2006), through the use of BSC, organizations and
individuals can get more knowledge about risk management objectives and therefore
realize the need to manage those risks hence, learning and growth is improved. This will
eventually lead towards aiding the internal business processes as risks will be dealt with
and tried to be eliminated or minimized which in return has a similar effect on the
customer satisfaction and eventually the overall financial performance (Beasley et al.,
2006).
Calandro and Lane, (2006) in their study introduced measures related to correspondence
in relevance to high level risks where they incorporated these measures into the BSC
four perspectives. The main context of their study was to show how risks can in an
efficient manner be “efficiently collected, organized and communicated” (p.35). Risks
were categorized into the Financial, Customer, Internal business process and learning
and growth perspective and were then linked and combined with the relative measures
needed to be set in place for efficient and smooth business development. According to
them, this process cannot be understood as a detailed risk analysis, rather it helps in
effective understanding of a cause and effect relationship developed from strategies and
its relative measures and to manage that relationship from a risk perspective. Calandro
and Lane, (2006) further emphasized on the BSC role by mentioning that it is a
framework where mission and strategies are communicated. This is done through
measurements to educate an organization's workforce about the factors that lead towards
formulation of risks. It is mentioned in their study that through BSC an organization
gets help in carrying out the risk assessment (Calandro and Lane, 2006).
39
3.3 Model and Hypotheses related to RQ1
What can be seen in the following model is based on the literature review presented by
the research team. The model shows the three different roles of BSC in managing
strategy risks. There is one independent variable and three dependent variables. BSC is
the independent variable that has an effect on different roles in RM. RM three roles
which are assessing, controlling and collecting data for decision making of strategy
risks are the three dependent variables. To prove these relations, hypotheses were
created as shown in the figure (2).
Figure 2: Model and hypotheses related to RQ1
3.4 RQ2- Managing Strategy Risks through Four Perspectives of BSC
3.4.1 Risks Categories
RM requires forecasting events, especially improbable ones that have never happened.
Because of RM's difficulty, some senior managers avoid or assign it and that may put
the firm in a vulnerable situation (Kaplan, 2009). Having an effective RM system
requires understanding the qualitative differences among types of risks a company may
face. Kaplan and Mikes' (2012) field study illustrated that risks come into one of three
categories. Each category's risks event can heavily impact a business's strategy and even
to its persistence.
40
Risk occurs in various forms and combinations. Some risks are known and avoidable,
which have been categorized as level three risks (Kaplan, 2009). These are internal risks
rising from within the company that are controllable and should be removed or avoided.
A few examples are “the risks from employees’ and managers’ unauthorized, illegal,
unethical, incorrect, or inappropriate actions and the risks from breakdowns in routine
operational processes” (Kaplan and Mikes, 2012, p.50).
Organizations need to accept some risks that may not have a huge impact on the
company and/or if the cost of managing those risks exceeds the effect that they may
have if left untouched. However, companies should eliminate level three risks since
they will not get any strategic interests from taking them on (Ibid). These can be
managed through internal audits, internal controls, and regular operating procedures
(Kaplan, 2009).
Strategy risks are intrinsic in the company’s strategy. The firm accepts these risks as
essential to pursue higher returns but tries to decrease their probability of happening or
mitigate them (Kaplan and Mikes, 2012; Kaplan, 2009). These are pretty different from
preventable risks because they are not intrinsically unacceptable. When a company has
a strategy with high expected returns, it usually needs to accept some considerable risks,
and managing those risks is critical in achieving the potential gains. For instance,
drilling several miles below the Gulf of Mexico's surface is highly risky, but the BP
company accepted those risks because it hoped to earn high profits from extracting the
oil and gas (Kaplan and Mikes, 2012). The strategy map implements a suitable
framework to identify strategy and critical operational risks and then controlled with
different risk indicator scorecards (Kaplan, 2009). This thesis will be studied on risks
included in level 2 which is strategy risks.
Finally, some risks have been categorized as level one risks which characterize as
uncontrollable and external events that can endanger the company’s existence. These
risks arise from circumstances outside the firm and are beyond its authority or control.
Origins of these risks involve natural and political disasters and significant
macroeconomic changes. Since firms cannot avoid or stop such events, their executives
must focus on identifying and reducing their impact (Kaplan and Mikes, 2012). These
41
risks are complicated to predict but can put the company in a very critical position if
they occur (Kaplan, 2009).
Kaplan (2009) presented risks’ examples of each level that the research team have
shown in the figure below. In this study, after collecting the risks related to the
petroleum equipment industry through reviewing the literature, the research group
intends to classify them into three levels and then focus on the level two risks.
Figure 3: Illustration of Kaplan’ (2009) three levels risks
3.4.2 Balanced Scorecard-Risk Management (BSC-RM)
Calandro and Lane (2006) classified and explained examples of high-level risks and
similar measures in the context of the BSC’s four-perspective framework. They have
decided to employ these four perspectives since these are well-known to performance
measurement practitioners and scholars also, these perspectives provide a suitable
method of classifying risks.
42
3.4.2.1 Financial Risk Perspective
Financial market risk can be described as volatility related to capital markets. An
example of this risk is the cost of capital. This can be measured through the Weighted
Average Cost of Capital (WACC) and Capital Asset Pricing Model (CAPM). Debt
financing can create solvency anxieties or the risk a company will not be able to satisfy
its financial commitments. This risk can be measured through the debt-to-equity ratio,
the cost of debt and Value-at-Risk (VaR). Another financial risk is the probability of
suboptimal tax planning, which can be measured by comparing the anticipated rate of
corporate effective tax to the actual effective rate over time (Calandro and Lane, 2006).
3.4.2.2 Customer Risk Perspective
The scope of a company’s life and value is a function of how fully it provides
customers’ needs over time, and hence the customer risk perspective is critical. An
example of risk is the risk of the company’s overall portfolio of customers. The risk of
missing these customers can be measured differently, such as the number of customer
complaints, random customer satisfaction questionnaires, and the variance of shopping
frequency from historical patterns (Calandro and Lane, 2006).
3.4.2.3 Internal Risk Perspective
Internal risks described as risks created by the company as it undertakes activities to
fulfil a business strategy. Four risks associated with this perspective were identified;
"Technological risk, Human Resources risk, Process risk, and Organizational risk."
The main concern related to technology is system security. To measure risks of poor
system security, the company can trace the number of system security violations over
time (Calandro and Lane, 2006).
Human Resource risk is the second internal risk. Calandro and Lane (2006, p.36) stated
that, “Having the right people with the right skills in the right place at the right time” is
crucial to victorious strategy execution. Unnecessary employee turnover, especially in
significant positions, makes the company incapable of executing its business strategy
successfully. This risk can be measured by “tracking employee turnover, employee
43
morale, employee satisfaction or the number (or percentage) of key personnel that leave
an enterprise during a given time frame” (ibid, p.36).
An example of process risk is that a company's processes and methods are not
adequately implemented. This risk can be traced through measures such as the “amount
and extent of unsatisfactory internal audit findings” (ibid, p.36).
The last internal perspective risk identified by Calandro and Lane (2006) is
organizational risk. This risk can be measured by tracking the number of administrative
complaints received overtime (ibid).
3.4.2.4 Learning and growth Risk Perspective
Learning risk derives from the likelihood that a firm’s educational incentives are not as
influential as possible and can be measured by tracking the productivity of employees
who have taken education and the percentage of employees sent for training promoted.
This perspective can significantly influence the results of other perspectives. For
instance, well-educated and trained employees do not deliberately run afoul of
regulations (Calandro and Lane, 2006).
44
Figure 4: An Example of RM-BSC model presented by Calandro and Lane (2006)
The figure (4) presented by Calandro and Lane (2006) as shown above demonstrates
major risks according to each perspective of the BSC and are then provided with their
relative measures.
45
Figure 5: BSC-enterprise logistics risks presented by Yongsheng and Li (2010)
Figure (5) indicates BSC-enterprise logistics risks presented by Yongsheng and Li
(2010). They studied how to prevent and minimize enterprise logistics risks. They
attempted to introduce an early warning indicators system (EWIS) for company
logistics risks based on BSC to maximize desired performance chances.
Infosys, the Indian IT services company, created risk discussions from the BSC. Its
executives reach zero percent risk related to business objectives defined in its corporate
scorecard. In building its BSC, Infosys had recognized “growing client relationships” as
a critical goal and chosen metrics for measuring growth, such as the “number of global
clients with annual billings over $50 million and the annual percentage increases in
revenues from large clients” (Kaplan and Mikes, 2012, p.55). By looking at the goal and
the performance metrics together, executives identified a new risk factor called client
default. When Infosys's business was based on various small customers, an individual
customer default would not endanger its strategy. However, a default by a customer
with annual billings over $50 million would face the company with difficulty. Infosys
started to control every large customer's credit default swap rate as an advance indicator
of default probability. When a customer's rate was raised, Infosys tried to quickly
recover money from its receivables or made requests for progression of payments to
avoid the risk of facing a default (ibid).
46
3.4.3 Types of potential strategy risks in IPIEM
As this research intends to find out what type of IPIEM strategy risks can be managed
by BSC four perspectives, it must first indicate risks related to the petroleum equipment
industry. This has been fulfilled through reviewing Persian and English literature. The
research team identified risks based on Kaplan's (2009) three risks’ levels for
distinguishing strategy risks from others. As it is shown in the table below, 31 potential
strategy risks have been identified.
Table 1: Own Illustration of identified strategy risks (level 2) in PEI
Label Reference
1 The need to provide Surety-bond with large
amounts for Participating in tenders and signing
contract
Naghizadeh et al., 2017
2 Lack of transparency in the rate of return on
investment
Naghizadeh et al., 2017
3 Limited funding for product development Wu, J., & Wu, Z. (2014);
Naghizadeh et al., 2017;
Askary et al., 2016
4 Risk of rising costs Askary et al., 2016
5 Financing risk Askary et al., 2016
6 Liquidity risk Askary et al., 2016
7 Provision of project funds Gharib and Ghodsypour, 2017
8 Rejection of the product after its release to the
market
Naghizadeh et al., 2017
9 Lack of enough knowledge of petroleum
companies of the existing capabilities in the
country
Naghizadeh et al., 2017
10 Changes in the demand for products requested Naghizadeh et al., 2017
11 Clients' opposition to pilot testing of the product Naghizadeh et al., 2017
12 Improper design of product at development stages Naghizadeh et al., 2017;
Gharib and Ghodsypour, 2017
13 Incorrect choice of ancillary items and Naghizadeh et al., 2017
47
complementary assets
14 Impossibility of accessing or delaying to access
the required equipment and machinery
Naghizadeh et al., 2017
15 Impossibility of accessing or delaying to access
manufacturing technologies
Keizer et al., 2005;
Naghizadeh et al., 2017
16 Improper selection of international partners Naghizadeh et al., 2017;
17 High bargaining power of one of the partner
organizations due to the monopoly of technical
knowledge
Ekanayake and Subramaniam
(2012); Naghizadeh et al.,
2017;
18 Lack of complementary and appropriate
infrastructure (e.g. in the drilling case: old pipes
and drilling rigs)
Naghizadeh et al., 2017;
19 The risk of meeting project assumptions Askary et al., 2016
20 proficiency risk and efficiency of the partners'
network
Askary et al., 2016
21 Risk of data validity and information resources Askary et al., 2016
22 Risk of the accuracy of computations and
estimates
Askary et al., 2016
23 Risk of inaccuracy in forecasting requirements Askary et al., 2016
24 Risk of changes in the scope of the project Askary et al., 2016
25 Coordination risk with partners Askary et al., 2016
26 Technology life cycle and fundamental
technology change
Wu, J., & Wu, Z. (2014);
Naghizadeh et al., 2017;
27 Not identifying alternative technologies/products Wu, J., & Wu, Z. (2014);
Naghizadeh et al., 2017;
28 Incorrectly evaluation and selection of possible
technology options
Wu, J., & Wu, Z. (2014);
Naghizadeh et al., 2017;
29 Not enough skilled and specialized human
resources
Naghizadeh et al., 2017;
Askary et al., 2016;
30 Not enough operational experience in similar
projects
Ekanayake and Subramaniam
(2012); Naghizadeh et al.,
2017;
31 Lack of knowledge of the manager about the
possible risks of the project
Naghizadeh et al., 2017;
Gharib and Ghodsypour, 2017
48
Level two (strategy risks) risks are the focus area to study. Studying all 31 strategy risks
is time-consuming and cannot be done given the limitation of time. It will also lengthen
the questions of the questionnaire, which is beyond the patience of the respondents.
Therefore, 8 strategy risks have been selected based on the degree of importance of risk
in the previous articles. These collected strategy risks are categorized based on BSC
four perspectives that are presented in the table (2). According to Kaplan and Mikes
(2012) the financial perspective represents revenue, price, and margin objectives; thus,
risks related to these objectives are considered in this category. The customer
perspective represents those objectives connected to the customer value proposition and
customer issues. “The internal process perspective has objectives for managing
operations, customers, innovation, and environmental, regulatory and social processes”
(ibid, p.50). Finally, the learning and growth perspective includes objectives for
technology and people. All risks were classified according to the definitions of each
perspective.
Based on Kaplan and Mikes’ (2012) categorization of risks for each BSC perspective,
the research team created the table presented below to show the categorization of
strategy risks selected for this study.
Table 2: Own Illustration of categorization of selected strategy risks in four perspectives of BSC
Risk Perspective Strategy risks
Financial Financing risk
Liquidity risk
Customer
Rejection of the product after its release to the market
Clients' opposition to pilot testing of the product
Internal Improper design of product at development stages
Improper selection of international partners
Learning & growth
Not enough operational experience in similar projects
Incorrect evaluation and selection of possible technology options
49
3.4.4 Conceptualization of strategy risks selected
3.4.4.1 Liquidity risk
Liquidity risk is the inability of an entity, whether an organization or individual, to
fulfill their financial obligation in the short run due to not being able to convert assets
into cash while facing a loss. It usually occurs when an entity is willing to sell an asset
in order to fulfil financial obligations but is unable to sell it at the market value.
Carleton and Siegel, (2021) identify a few reasons for its occurrence. Inefficient
markets can give rise to this risk as the asset may be unable to reflect their actual market
value. Having a limited cash flow can also affect the company’s ability to fulfill its
financial obligations. The structure of the market is also a very important factor as the
size of the market can have a direct impact on the selling of the asset (ibid). Type of
asset is another important factor, if it is a market asset it may not have much difficulties
in being sold however if it does not hold that characteristic it may take a longer period
of time to be converted into cash. The urgency level also impacts an entity's liquidity
profile as the more time it has before the obligation is due, the better the chances to
actually fulfill it. Finally market conditions can have a direct impact as a huge number
of sellers and few buyers can affect the ability of assets being sold (Carleton and Siegel,
2021).
3.4.4.2 Financing risk
Rhodes and Nanda, (2014) define this risk as the inability to find funding or investments
for future projects.
3.4.4.3 Rejection of the product after its release to the market
Non-acceptance of the product and non-use by petroleum exploitation companies is
another significant risk in the petroleum equipment industry. In many cases, even with
the development of technology, petroleum companies are not willing to buy and use
these products for various reasons, including reluctance to accept the risk of using
50
domestic products and the habit of consuming foreign products (Naghizadeh et al.,
2017).
3.4.4.4 Clients' opposition to pilot testing of the product
The next significant risk is the reluctance of operating companies to conduct pilot tests
of the product. One of the essential points in developing technologies related to
equipment types is testing them in natural environments to address the shortcomings
and problems before the final release of the product. This is not possible for equipment
companies to test in natural environments such as in oil and gas fields in many cases
(Naghizadeh et al., 2017).
3.4.4.5 Improper design of product at development stages
This is a risk which is common to be faced by any manufacturing or construction
company. It arises due to many different factors such as improper selection of materials
(Ishak et al., 2007), high interval time between market research or even lacking
personnel having enough knowledge and skills.
3.4.4.6 Improper selection of international partners
Another significant risk is the inappropriate selection of international partners to
develop the technology. Domestic manufacturers should evaluate their partners in terms
of technical and professional capacity. Secondly, the partner's goals of this technical
cooperation and the foreign partner's previous projects and resume should be evaluated.
In many cases, selecting an unsuitable partner has led to the failure of equipment
technology development projects in Iran (Naghizadeh et al., 2017).
3.4.4.7 Not enough operational experience in similar projects
Operational experience refers to all the steps going on behind the picture in order to
create and deliver a good experience for the customers. This is something that is paid
very little attention to and if not taken seriously can disrupt the organizations and its
operations in a harmful manner (Upton, N/A). Hence, not having enough operational
experience in similar projects may prove to be harmful for the company.
51
3.4.4.8 Incorrect evaluation and selection of possible technology options
Firstly, the choice of a certain technology is dependent on the available resources and
market size. Right selection of technology is important for minimizing costs and
increasing profits (Zhou, 2019).
The right choice of technology means choosing the best option from the available
technologies. The adopted technology should be in accordance with the company
requirement. It should also be related to the new products. (Shehabuddeen et al., 2006).
According to Lamb & Gregory, 1997 “Technology selection involves gathering
information from various sources about the alternatives, and the evaluation of
alternatives against each other or some set of criteria”. “They suggest that evaluation
of technology is concerned with ‘the notions of cost, benefit, and risk”
(Shehabuddeen et al., 2006).
3.5 Model and Hypotheses related to RQ2
What can be seen in the following model is based on the literature review presented by
the research team. The model indicates that through the use of four perspectives of BSC
strategy risks can be managed. Later, the risks selected by the research team for this
paper are shown and categorized in accordance with the BSC perspectives. The four
perspectives have been identified as independent variables whereas the strategy risks
have been identified as dependent variables. To prove these relations, hypotheses were
created as shown in the figure (6).
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Figure 6: Model and hypotheses related to RQ2
53
4 Empirical Study
The fourth chapter presents the empirical data gathered from the participants. It also
highlighted non response of survey which is of much importance in the research.
Furthermore, validity and reliability tests are done to analyze the quality of this study.
In the last, empirical data is analyzed by doing linear regression in SPSS. The SPSS
output is provided in the form of graphs and tables with their explanation that concludes
the final decisions respective of each hypothesis.
4.1 Pretest of Questionnaire
As mentioned by Bell et al. (2019), before conducting any research it is important to
make sure that the measures should be reflecting the original meaning behind the
question. This can be done by taking feedback from people within the relative field or
having knowledge about it. To ensure validity for this study, the research team sent the
questionnaire to two professors having vast experience and knowledge in this area and
gained their feedback. After considering their recommendations and suggestions the
new version of questionnaire was sent to four companies from the IPIEM. It is also
made sure that these responses were not included into the final count of respondents i.e.
30 (Bell et al., 2019). Feedbacks were recorded by the team via emails, phone calls and
online meetings and the following suggestions were made:
Consider using graphics to illustrate important concepts.
Focus on the respondents company instead of any company as they can only
speak about their organization.
Questions should be written in a precise manner.
Number of questions should be reduced.
Avoid writing complicated questions which confuse the respondents.
After analysing all the feedback, necessary adaptations were made and included into the
questionnaire. The following adaptations were made:
Graphic models were added for respondents' understanding.
Focus was only made on the respondent’s company.
Long questions were rewritten.
54
Number of questions reduced from 56 to 40.
The complete questionnaire guide can be found in appendix 1.
4.2 Data Collection
Questionnaires were distributed with the help of Sharif fund. The online link to the
questionnaire with a request message was distributed by Sharif fund for 270 companies
separately and in two WhatsApp groups consisting of one member from each of the
companies within SIPIEM. These groups had head counts of 300 and 500 plus
individuals who bore different job titles such as General Manager, Member of the board
of directors, Risk manager etc. Post circulation of the questionnaire, daily reminders
were sent out by the research team to the Sharif fund and then by them to their groups
and separately for each company as well. The following table (3) represents the more
detail information about questionnaire distribution and the number of responses:
Table 3: Result of data collection
Number of questionnaire distributed 270
Required sample size 262
The number of times the questionnaire was viewed 208
Number of responses after the first request 12
Number of responses after the first reminder 5
Number of responses after the second reminder 14
Number of responses after the third reminder 4
Number of responses after the fourth reminder 6
Total number of responses 41
The number of responses who were not aware of RM process in their
company (11)
Total number of responses can be used in this study 30
55
4.2.1 Non-response
Non-response occurs if a part of the sample does not participate in answering the
survey. This can be minimized with some simple techniques (Bell et al., 2019). For
example, following up or sending reminders for the participants help to get more
responses (Baruch and Holtom, 2008; Bell et al., 2019). For this study, reminders were
sent out every other day in order to increase the possibility of attaining a high response
rate.
It is in reality true to have non-responses. The non-respondents cannot be categorized
together with the target population as they consist of a population that did not or were
not willing to be a part of the research and due to this reason the results gathered may
have the potential of being biased which in other terms is known as the non-response
bias (Saunders et al., 2019).
According to Rogelberg and Stanton (2007), due to a lower response rate the
generalizability of the collected data may also come into question as it has been noted
that in situations where non-response bias has been factor, the data collected may often
lead to conclusions that are not entirely accurate and cannot be generalized to the
complete population.
Throughout the years the response rate of the organizational level surveys has witnessed
a fall. It can be seen through the work of Baruch and Holtom (2008) that the response
rate at the time was about 35 to 40% with a standard deviation of 18.2%. It was then in
2009 noted by Shih and Fan (2009) that the response rate decreased even more and fell
around 33% with 22% standard deviation. This downward trend in the response rate
continued until in 2017 it reached 10%. According to Mol (2017), it is common for web
surveys to have a response rate of 10% or even lower than that. It is also noted that the
response rate for web surveys usually tends to be lesser compared to the other methods
used to conduct a survey. Mol (2017) also mentioned that with the response rate of
lower than 10% the results can still however be deemed as reliable if the researcher
makes sure about the quality of the response.
Web based questionnaires are used for this study. Web based surveys possess many
characteristics in terms of presentation of the survey (Bell et al., 2019). As discussed by
56
Bell et al., (2019), surveys are able to get less response than interviews. This thesis’
survey also faced limitations in getting high number of responses.
The research team received 30 responses with a response rate of 11.4%. This rate is
considered acceptable for conducting the further analysis as according to different
authors (Mol, 2017; Saunders et al., 2019) it is decreasing day by day and it can be as
low as up to 10%.
4.3 Reliability Test
Reliability analysis is done to measure the internal consistency of variables with each
other and with the concept that is measured. Reliability is determined by Cronbach’s
Alpha values. The range of values is from 0 to 1. 0 is for minimum reliability whereas 1
indicates highest internal reliability (Bell et al., 2019). The high value of Cronbach
Alpha indicates the questions or concepts are reliable. Alpha coefficient Value of 0.7 or
above is acceptable (Bell et al., 2019; Pallant, 2016). While Pallant (2016) argues, 0.6-
0.7 is also admissible value for a concept to be reliable.
For this study's questionnaire 11 areas were tested. All the 11 parts are measuring
different concepts related to particular hypotheses. Each group of questions was
analyzed separately by using the Alpha test in SPSS. Each group of concepts has
multiple questions using various measures. To ensure the coherence of each measure
with the concept, reliability coefficients are analyzed (Bell et al., 2019). The reliability
test was only done for the questions using Likert scale so it does not include background
questions that have different options like frequency or binary format.
Table 4: Result of Reliability test
Label
Reliabilities Coefficients
Result N of
Items Cronbach’s
Alpha
Risk Assessment 6 0.863 Reliable
Risk Controlling 3 0.484 Not reliable
Data Collection for Decision Making on Risk 4 0.484 Not reliable
57
Liquidity risk 3 0.699 Reliable
Financing risk 3 0.659 Reliable
Rejection of Product after its release to the
market 3 0.264 Not reliable
Clients’ opposition to pilot testing of the
product 2 0.758 Reliable
Improper product design at development stages 3 0.484 Not reliable
Incorrect selection of international partners 3 0.761 Reliable
Not enough operational experience in previous
similar projects 2 0.801 Reliable
Incorrect evaluation and selection of possible
technology options 3 0.888 Reliable
The table above (4) states the reliability of each group of concepts. N of items represent
the total number of questions in one group for each hypothesis.
By doing Alpha test, most of the concepts are indicated to be reliable as their values are
above 0.6; except for four concepts that are named as risk controlling, data collection
for decision making on risk, rejection of product after its release to the market and
improper product design at development stages. Due to the fact that high number of
responses were not collected and the results can then seem to be biased which may not
allow the results to be replicated if the study is conducted again at a different time or by
another researcher. This does not however mean that the results provided are not true
(Saunders et al., 2019).
4.4 Descriptive Analysis
Descriptive analysis was done based on the data collected from background questions 2
to 5 in the questionnaire. The following tables show the results.
58
Table 5: Companies' experience in PEI (year)
The table (5) presented above shows that out of a total of 30 respondent companies,
3.3% of companies have been in the industry for 1 to 5 years, 3.3% for 6 to 10 years,
20% for 11 to 15 years, 33.3% for 16 to 20 years and 40% of companies for more than
20 years.
Table 6: Subsidiary of another foreign company
As far as the ownership of the company is concerned as shown in table (6), 96.7% of the
respondents are not a subsidiary of another foreign company whereas 3.3% of the
companies are a subsidiary of a foreign company.
Table 7: Job title of respondents
59
Table (7) shows that 6.7% of the respondents are risk managers, 13.3% are general
managers, 23.3% are members of the board of directors and 56.7% selected others for
their job title within their company.
Table 8: Companies using BSC for managing strategy risks
In regards to the question of companies using BSC for managing strategic risks, it can
be seen according to table (8) that 26.7% of the respondents stated Yes and 73.3% of
the respondents chose No.
4.5 Testing Assumptions
The assumptions considered for this study are normality, homoscedasticity, and
linearity tests as the research needs to make sure if linear regression analysis can be
used. All the graphs and tables related to the assumptions testing for each hypothesis
can be found in Appendix (2) to (34).
4.5.1 Normality Tests
Normality shows the distribution of data for a single measure variable. It considers the
normal distribution as a standard approximation. The distribution of actual data is
compared with the normal distribution. If there is a high difference in both, then
statistics tests F and t are not valid. Non-Normality is determined by two factors: high
variance in the data distribution from normal distribution and size of the sample (Hair et
al., 2014).
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4.5.1.1 Histogram
The normality of data values is usually tested by using a histogram graph (Hair et al.,
2014). If the data forms a bell-like shape when compared with the histogram standard
curve it indicates data is normal (Douglas et al., 2013) and assumption of regression is
met.
After analyzing the normality assumption through histogram, the graphs show that data
is finely distributed on both sides of the center. In all graphs, the data forms a normal
curve compared to the standard curve showing like a bell shaped look so according to
Douglas et al. (2013) all the graphs have normal distribution as the data is equally
divided in both halves of the histogram. So, it is concluded for all the hypotheses that
the data is normal.
4.5.1.2 Normal P-P Plot
While in the case of small data size, the Normal Probability (NP-P) Plot is a suitable
method to compare the cumulative distribution of variable values with that of normal
distribution. Normal distribution in the Probability Plot is the diagonal line while actual
values of the data lie near (above or below) the straight line and few values also come
exactly on the straight line. If the plotted values are distantly far from the normal line, it
shows the non-normal distribution. The more data values close to the normal confirm
data normality (Hair et al., 2014).
Normal P-P Plot shows the data in the form of outliers. As in the graphs, the outliers are
not very far from the straight line that shows the distribution of data is normal (Hair et
al., 2014). For all hypotheses the data distribution in N P-P Plot is normal. With testing
this assumption, both tests of normality are done to conclude data is normal.
61
4.5.2 Homoscedasticity Test
Hair et al. (2014, p.33) stated that “When the variance of the error terms (e) appears
constant over a range of predictor variables, the data are said to be homoscedastic.” The
best way for testing homoscedasticity is an analysis of residuals. When the residuals are
distributed equally and do not tend to bunch together at some values and scattered
distances at other values, data is homoscedastic. If the data is scattered randomly like a
shotgun blast, it can be considered homoscedastic data (statisticssolutions, 2021).
For this study, all the variables were tested separately. Each graph shows a random
distribution of data and does not seem to tend to bunch together. Thus, data is
homoscedastic.
4.5.3 Linearity Test
Linearity is based on correlation of the variables which show a linear relationship
(Bryman and Cramer, 2011; Hair et al., 2014). By checking the value of deviation from
linearity in the ANOVA table, we can check whether there is a linear relationship
between dependent and independent variables. If the value of deviation from linearity is
higher than 0.05, there is a linear relationship between dependent and independent
variables.
By testing linearity for dependent and independent variables for each hypothesis, the
results indicate that from 11 hypotheses, 9 of them have the value of deviation from
linearity higher than 0.05 which means there is a linear relationship between dependent
and independent variables. However variables of 2 hypotheses HD1 and HF1 related to
“financing risk” and “rejection of product after release to the market” don’t show the
linear relationship since their values of deviation from linearity are less than 0.05. Since
HD1 and HF1 have normal distribution and homoscedastic data, the research team
decided to include them in regression analysis.
62
4.6 Linear Regression Analysis
In this part, each hypothesis was analyzed separately through simple linear regression
analysis. Since the research team intends to test only the significant relationship
between dependent and independent variables, not to predict changes in the dependent
variable based on changes in the independent variable, the p-value in the ANOVAa table
from the outputs of linear regression analysis was checked. In the following, the
ANOVAa table for each hypothesis is presented with the analysis. Also, the rest of
outputs are available in the appendices (35-45).
4.6.1 Testing hypotheses related to RQ1
4.6.1.1 Testing Hypothesis A1
HA0: BSC has no effect on assessing strategy risks in IPIEM.
HA1: BSC has a significant effect on assessing strategy risks in IPIEM.
Table 9: Linear regression output for HA1
The p-value (Sig) associated with this F value indicates whether there is a linear
relationship between dependent and independent variables. For checking this, the p-
value is compared to the alpha level (typically 0.05). If the p-value is lower than the
alpha level, there is a linear relationship between dependent and independent variables
or the independent variable can be utilized to explain the dependent variable. If the p-
value is greater than 0.05, it means that there is no statistically significant relationship
between dependent and independent variables or the independent variable does not
reliably explain the dependent variable (Bryman and Cramer, 2011).
63
In the above table (9) the p-value related to F is 0.001 which is lower than 0.05. Thus,
the null hypothesis HA0: BSC has no effect on assessing strategy risks is rejected and
HA1: BSC has a significant effect on assessing strategy risks is accepted.
4.6.1.2 Testing Hypothesis B1
HB0: BSC has no effect on controlling strategy risks in IPIEM.
HB1: BSC has a significant effect on controlling strategy risks in IPIEM.
Table 10: Linear regression output for HB1
In the above table (10) the p-value related to F is 0.204, which is higher than 0.05. It
means there is not a significant relationship between variables. Thus, the null hypothesis
HB0: BSC has no effect on controlling strategy risks in IPIEM is accepted and HB1:
BSC has a significant effect on controlling strategy risks in IPIEM is rejected.
4.6.1.3 Testing Hypothesis C1
HC0: BSC has no effect on collecting data required for making decisions for strategy
risks in IPIEM.
HC1: BSC has a significant effect on collecting data required for making decisions for
strategy risks in IPIEM.
64
Table 11: Linear regression output for HC1
In the table (11) the p-value related to F is 0.178, which is higher than 0.05. Thus, the
null hypothesis HC0: BSC has no effect on collecting data required for making
decisions for strategy risks in IPIEM is accepted and HC1: BSC has a significant effect
on collecting data required for making decisions for strategy risks in IPIEM is rejected.
4.6.2 Testing hypotheses related to RQ2
4.6.2.1 Testing Hypothesis D1
HD0: BSC financial perspective has no effect on managing financing risk
HD1: BSC financial perspective has significant effect on managing financing risk
Table 12: Linear regression output for HD1
In the above table (12) the p-value related to F is 0.103, which is higher than 0.05. Thus,
the null hypothesis HD0: BSC financial perspective has no effect on managing
financing risk is accepted and HD1: BSC financial perspective has significant effect on
managing financing risk is rejected.
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4.6.2.2 Testing Hypothesis E1
HE0: BSC financial perspective has no effect on managing liquidity risk
HE1: BSC financial perspective has significant effect on managing liquidity risk
Table 13: Linear regression output for HE1
In the table (13) the p-value related to F is 0.016 < 0.05. Thus, the null hypothesis HE0:
BSC financial perspective has no effect on managing liquidity risk is rejected and HE1:
BSC financial perspective has significant effect on managing liquidity risk is accepted.
4.6.2.3 Testing Hypothesis F1
HF0: BSC customer perspective has no effect on managing risk of the rejection of the
product after its release to the market
HF1: BSC customer perspective has significant effect on managing risk of the rejection
of the product after its release to the market
Table 14: Linear regression output for HF1
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In the above table (14) the p-value related to F is 0.339, which is higher than 0.05. Thus,
the null hypothesis HF0: BSC customer perspective has no effect on managing risk of
the rejection of the product after its release to the market is accepted and HF1: BSC
customer perspective has significant effect on managing risk of the rejection of the
product after its release to the market is rejected
4.6.2.4 Testing Hypothesis G1
HG0: BSC customer perspective has no effect on managing risk of clients' opposition to
pilot testing of the product.
HG1: BSC customer perspective has significant effect on managing risk of clients'
opposition to pilot testing of the product
Table 15: Linear regression output for HG1
In the above table (15) the p-value related to F is 0.001 < 0.05. Thus, the null hypothesis
HG0: BSC customer perspective has no effect on managing risk of clients' opposition to
pilot testing of the products is rejected and HG1: BSC customer perspective has
significant effect on managing risk of clients' opposition to pilot testing of the products
risk is accepted.
4.6.2.5 Testing Hypothesis H1
HH0: BSC internal perspective has no effect on managing risk of improper design of
product at development stages.
HH1: BSC internal perspective has significant effect on managing risk of improper
design of product at development stages.
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Table 16: Linear regression output for HH1
In the above table (16) the p-value related to F is 0.001 < 0.05. Thus, the null hypothesis
HH0: BSC internal perspective has no effect on managing risk of improper design of
product at development stages is rejected and HH1: BSC internal perspective has
significant effect on managing risk of improper design of product at development stages
is accepted.
4.6.2.6 Testing Hypothesis I1
HI0: BSC internal perspective has no effect on managing risk of improper selection of
international partners.
HI1: BSC internal perspective has significant effect on managing risk of improper
selection of international partners.
Table 17: Linear regression output for HI1
In the above table (17) the p-value related to F is 0.003 < 0.05. Thus, the null hypothesis
HI0: BSC internal perspective has no effect on managing risk of improper selection of
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international partners is rejected and HI1: BSC internal perspective has significant
effect on managing risk of improper selection of international partners is accepted.
4.6.2.7 Testing Hypothesis J1
HJ0: BSC learning and growth perspective has no effect on managing risk of not
enough operational experience in previous similar projects.
HJ1: BSC learning and growth perspective has significant effect on managing risk of
not enough operational experience in previous similar projects.
Table 18: Linear regression output for HJ1
In the above table (18) the p-value related to F is 0.001 < 0.05. Thus, the null hypothesis
HJ0: BSC learning and growth perspective has no effect on managing risk of not
enough operational experience in previous similar projects is rejected and HJ1: BSC
learning and growth perspective has significant effect on managing risk of not enough
operational experience in previous similar projects, is accepted.
4.6.2.8 Testing Hypothesis K1
HK0: BSC learning and growth perspective has no effect on managing risk of incorrect
evaluation and selection of possible technology options.
HK1: BSC learning and growth perspective has significant effect on managing risk of
incorrect evaluation and selection of possible technology options.
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Table 19: Linear regression output for HK1
In the above table (19) the p-value related to F is 0.001 < 0.05. Thus, the null hypothesis
HK0: BSC learning and growth perspective has no effect on managing risk of incorrect
evaluation and selection of possible technology options is rejected and HK1: BSC
learning and growth perspective has a significant effect on managing risk of incorrect
evaluation and selection of possible technology options is accepted.
4.6.3 Summary of Hypotheses Test Results
The following table (20) presents the summary of the test results of each hypothesis by
stating acceptance or rejection.
Table 20: Summary of hypotheses test result
Variable Hypothesis Result
Risk Assessment HA1: BSC has a significant effect on assessing
strategy risks in IPIEM. Accepted
Risk controlling HB1: BSC has a significant effect on controlling
strategy risks in IPIEM. Rejected
Collecting Data
for Decision
making
HC1: BSC has a significant effect on collecting data
required for making decisions for strategy risks in
IPIEM
Rejected
Financial
Perspective
HD1:BSC financial perspective has significant effect
on managing financing risk Rejected
HE1:BSC financial perspective has significant effect
on managing liquidity risk Accepted
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Customer
Perspective
HF1: BSC customer perspective has significant effect
on managing risk of the rejection of the product after
its release to the market
Rejected
HG1: BSC customer perspective has significant effect
on managing risk of clients' opposition to pilot testing
of domestic products
Accepted
Internal
Perspective
HH1: BSC internal perspective has significant effect
on managing risk of improper design of product at
development stages.
Accepted
HI1: BSC internal perspective has significant effect on
managing risk of improper selection of international
partners.
Accepted
Learning and
Growth
Perspective
HJ1: BSC learning and growth perspective has
significant effect on managing risk of not enough
operational experience in previous similar projects.
Accepted
HK1: BSC learning and growth perspective has a
significant effect on managing risk of incorrect
evaluation and selection of possible technology
options.
Accepted
5 Conclusion
The chapter aims to present and discuss the results gathered in the previous chapter and
explain what they mean by the findings. It starts off with discussion for RQ1 and then
moves onto RQ2. At the end of discussion, the RM-BSC model for this study which
was created by the research team is presented. This is followed by a conclusion of the
entire research work and talks about how the findings affect the conclusion made by the
authors. Limitations faced by the research team throughout the whole research work are
then presented which is then followed by suggestions for further study.
5.1 Discussion
Based on the results derived from the survey conducted in the IPIEM for RQ1 to find
out what roles does a BSC play in managing strategy risks, it became inevitable that
BSC plays an important role of risk assessment within the organizations in IPIEM. This
was confirmed by running the linear regression through which it was identified that
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hypotheses related to risk assessment got accepted. This means that in IPIEM, BSC can
be a useful tool for assessing strategy risks and plays a role of an acting factor in
achieving overall business excellency. As previously discussed risk assessment
comprises risk identification, risk analysis and risk evaluation (Safitri and Pangeran,
2020).Through risk identification it is possible to identify any possible uncertainties that
may arise as well as identifying its source. Moreover, when the vision, mission and
strategies of the organizations in the IPIEM is converted into KPIs and further
segregated into the four perspectives of the BSC it will make the risk identification
process much easier. With the sub role of risk analysis, organizations within IPIEM can
through the use of BSC, analyze the impact of the risks and their probability.
Furthermore, through the use of BSC, risk evaluation can be carried out where data
analysed will be further evaluated. This process comprises preparation of response to
risks. Relevant personnel are assigned with tasks in relation to risk response,
prioritization of risks as well as developing measures (Renault et al., 2020; Safitri and
Pangeran, 2020).
Moving on to risk controlling, the hypotheses related to this role got rejected and hence
it can also be said that the BSC does not play a role in controlling the strategy risks in
the IPIEM. This means that in IPIEM, companies cannot gain benefit from the use of
BSC if their aim is to control strategy risks by avoiding, transferring, mitigation and
acceptance of risk as was stated otherwise by Monica and Pangeran (2020). This also
implies that a BSC cannot help the organizations operating in the IPIEM in making an
action plan that is required to reduce risk events.
Regarding the role of BSC for collection of data for decision making for risks, this role
also had to face a similar fate as risk control where its hypothesis got rejected which
means in the IPIEM, BSC is not considered to be a suitable tool for collecting data for
taking decisions regarding the strategy risks. It can also be deduced from this point that
in the IPIEM this role of BSC may not be considered to be very effective for its said
purpose and other tools are preferred or are being utilized.
The results of the ANOVAa table through regression analysis conducted for RQ2 shows
that out of the two hypotheses for the financial perspective of the BSC only one
regarding liquidity risk got accepted whereas hypothesis in relation to financing risk had
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to face the fate of rejection. The reason for rejection of this hypothesis can be that the
companies may gain finances from third party financial service providers such as Sharif
fund. Doing this will not have any effect on the company's credit rating or credit history
and thus if a situation ever arises to take a loan from a bank they could get so.
The findings show that through the use of the financial perspective, liquidity risk can be
managed within the IPIEM. Having this said, it is now inevitable that liquidity risk can
be managed by measuring two main indicators; current ratio and quick ratio.
For the customer perspective, a similar situation was observed where one out of two
hypotheses was accepted. “BSC customer perspective has a significant effect on
managing risk of the rejection of the product after its release to the market” got rejected.
A reason for the rejection of this hypothesis may be because some companies do not
face this risk at all. For instance, if they produce the product based on the customers'
order, they will not face the risk of rejection of the product after its release to the
market.
The second hypothesis HG1 related to customer perspective got accepted. Thus it can
be concluded that through the customer perspective of the BSC the risk of clients'
opposition to pilot testing of products can be managed in the IPIEM using the indicator
“number of contracts that did not decide or mention any clause for pilot testing.”
Moving on towards the internal perspective of the BSC both hypotheses got accepted.
This means IPIEM can apply BSC as a tool for managing internal perspective strategy
risks by defining relevant indicators. These results show that two types of strategy risks
in IPIEM can be managed through the internal perspective of BSC. These are; improper
design of the product at development stages and improper selection of international
partners. Indicators for measuring these strategy risks were also identified; "High
intervals of time between market research, low percentage of personnel with relevant
knowledge and skills in the product development team for the former, and low
percentage of similarity of previous projects done by the partner and low percentage of
the partner's success in accomplishing previous international projects" for the latter
mentioned risk. By periodically measuring these indicators, IPIEM can manage these
two strategy risks.
73
For the final BSC perspective, learning and growth, it was noted that both hypotheses
similar to those of internal perspective got accepted. This means that two types of
strategy risks in relation to learning and growth can be managed through the learning
and growth perspective of BSC. These strategy risks are; not enough operational
experience in previous similar projects and incorrect evaluation and selection of
possible technology options. For both the hypotheses getting accepted it means that the
companies in IPIEM can use the measures that were identified to in managing the
learning and growth perspective strategy risks. For the former, "high number of staff
without having experience in previous similar projects was identified" and for the latter,
"low percentage of companies using a specific technology for a previous similar
project" as well as a "low percentage of positive reviews on a specific technology that
the company may want to use", were identified.
The following table (21) is an RM-BSC model for this study made by the research team
inspired by Calandro and Lane’s (2006) RM-BSC model.
Table 21: RM-BSC model for this study inspired by Calandro and Lane’s (2006) RM-BSC model
74
The table (21) is based on the findings of this study. It proves what Calandro and Lane’s
(2006) and Kaplan and Mike (2012) stated about using BSC as an RM tool. They
believed companies can be managed by categorizing their strategy risks based on the
four perspectives of BSC. Table (21) indicates strategy risks in IPIEM and indicators
related to these risks to measure them.
Previous research has not explained in detail the use of a BSC as a tool for the entire
RM process. This study showed that the BSC alone cannot be relied on for RM in the
organization. It is now clear that BSC can perform one of the three RM processes; risk
assessment. This may be a reason that some case studies, for example Nugroho and
Pangeran (2021) have used a combination of BSC with another method like ISO 31000
for managing risk in the company.
5.2 Conclusion
This study was aimed at managing strategy risks through the use of a BSC. To achieve
this aim it was first decided to investigate the roles that the BSC plays in managing
strategy risks (RQ1) and then investigating the types of strategy risks that can be
managed through four perspectives of BSC (RQ2). For RQ1, through vast literature
research it was noted by the research team that there are three roles identified by other
researchers as a part of the RM process. These are as follows; risk assessment, risk
control and collecting data for decision making of risk. It was then through detailed
literature review discussed the point of views of previous authors on the relative
matters. However, these studies were mostly theoretical research as well as case
studies.
Secondly, for the statement related to RQ2 it was first identified what are the kinds of
risks that can be managed. Going through Kaplan’s (2009) article it became clear that
level two risk; strategy risks are quantifiable and controllable.
As for the types of strategy risks that can be managed through four BSC perspectives, a
total of 31 risks were identified from the literature which then through careful selection
by the research team and based on the importance of the risks a total of 8 strategy risks
were selected to be evaluated further. All of this was decided to be investigated in the
75
IPIEM given the importance of the petroleum industry in the Iranian economy. A
quantitative method was decided to be the most suitable option to go with conducting
this research. To aid this form of method, a web based questionnaire was perceived as
the appropriate choice as it complements the type of study decided by the authors
perceived to be fit. These results were then used in SPSS and a linear regression
analysis was performed.
A questionnaire consisting of questions related to general information, roles of BSC and
types of strategy risks that can be managed through four BSC perspectives was
formulated. This questionnaire was pre-tested by professionals having relative
knowledge and experience as well as managers of four companies within IPIEM for the
validity test. Based on the questionnaire answers, a regression analysis was carried out
in SPSS and the findings showed that among the three roles (risk assessment, risk
controlling and data collection for decision making) to manage strategy risk; BSC can
only be used to carry out the role of risk assessment within the IPIEM. As for the other
two roles, it was noted that they cannot serve their said purpose in the IPIEM through
the use of BSC. So for RQ1 it is concluded that BSC can only perform the assessment
of strategy risks in IPIEM rather than the complete RM process.
As far as the types of strategy risks that can be managed is concerned, the analysis and
findings of hypotheses determine that out of 8 strategy risks, 6 can be assessed with
each of the four perspectives of BSC. These strategy risks are liquidity risk, risk of
clients’ opposition to pilot testing of the product, risk of improper design of product at
development stages, risk of improper selection of international partners, risk of incorrect
evaluation & selection of technology options and lastly the risk of not enough
operational experience in similar previous projects.
Based on the results of empirical analysis and literature review it can now be derived on
the basis of RQ1 that BSC plays a role of assessing the strategy risk. It is not wrong to
state that through making this conclusion, its prospective effect can also be adjusted into
the RQ2 where the term “managing risks'' can now be converted into “assessing risks”.
Hence, the research concludes that BSC can now be used to assess the earlier mentioned
6 strategy risks in IPIEM. This study helps in contributing to the existing knowledge
76
and theories as well as more importantly to the practical implementation of the
knowledge.
5.3 Limitations
While the research team collected the required data from relevant respondents to fulfill
the purpose of this research; the major drawback faced is the smaller number of
respondents. This study results are limited due to the less response rate. Because of the
time constraints, the research team was not able to collect all the responses from
previously calculated sample size.
Another reason was the lockdown that is currently being held in Iran due to the current
pandemic of COVID-19 and also the month of Ramadan which makes the working
hours shorter in the country. Since most of the organizations are not observing the usual
office routine, it makes it hard for the researchers to approach the respondents and ask
for their participation. We believe the number of responses would have been much
higher if these factors would not have existed. Furthermore, this study is based upon
one industry in one specific country and the results achieved are limited to IPIEM only.
5.4 Suggestions for Further Study
This research has been conducted by studying only IPIEM. It would be interesting to
conduct research in other industries to see if BSC can be used there in the assessment of
strategy risks that would increase the validity of this study.
As the research team was able to get only 11.4% responses out of the total sample size,
this limitation of lesser responses suggests a future study with the high response rate to
increase the generalizability of the findings in IPIEM.
Another suggestion for further research is the study of the same subject with the
international context. Since this research was done with the Iranian petroleum industry
and the findings may not apply for the other countries, companies from different
countries can be considered for future research. These future studies would not only
help to increase generalization of thesis findings but also would show if different
77
industries in various countries differentiate in their opinion and practice when it comes
to adopting the BSC as an RM tool.
78
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Appendix 1: Questionnaire Guide
Appendix 1: Questionnaire Guide
Section Questions Purpose
Controlling
question
Are you aware of the process of risk
management in your company?
To check if the respondents
are aware of the RM process
in their company.
Background questions
How many years has your company been
working in the petroleum equipment
industry?
To know the worth of the
given answers
Is your company a subsidiary of another
foreign company?
If it is purely an Iranian
company
What is your job title in the company? To know which positions in
the companies are responsible
for the RM process
Is your company using Balanced
Scorecard (BSC) for managing strategy
risks?
To see if any company are
using BSC as a RM tool
Assessing strategy
risks
The company uses a specific approach
such as KPIs/ KRIs for identification of
strategy risks.
Identification of strategy risks
The company analyzes the probability of
occurrence of strategy risks.
Analysis of strategy risks
The company analyzes the future impacts
of strategy risks.
Through the use of prioritization process
(based on the importance and their
impacts) the company can evaluate
strategy risks effectively.
Strategy risk evaluation
88
Based on the company’s strategic goals, it
can identify strategy risks related to these
goals.
Use of BSC for assessing
strategy risks
Using KRIs/KPIs can help the company in
assessment of strategy risks.
Controlling strategy
risks
Identification of different alternatives to
treat risks such as avoiding, transferring,
mitigation and acceptance of risks helps
the company to make better strategies to
reach its objectives.
To control the strategy risks
Creation and execution of an action plan
for risk treatment helps the company to
reach its objectives.
Continuous monitoring of risk
expectations probable to occur again is a
step that the company carries out in
controlling strategy risks through risk
indicators.
Use of BSC for Controlling
strategy risk
Data collection for
decision making for
strategy risks
The company employs a process to convey
risk information to the relevant personnel
for making decisions about strategy risks.
To collect relevant data
required for the decision
making for strategy risks
The company employs a process which
helps managers to link the risks into their
strategies while making strategic
assumptions.
Taking the qualitative nature of strategy
risks in consideration can help the
management in formulating effective
strategic assumptions to reach its
objectives.
Use of BSC for data collection
for decision making for
strategy risks
89
Having all the required information at the
same place to incorporate and integrate
data from multiple sources makes it easier
for management to make effective
strategic assumptions.
Financial perspective
Liquidity risk Decrease in Current ratio (Current
Assets/Current Liability) is an indicator
which shows the company may face
Liquidity risk.
Measuring liquidity risk
Decrease in Quick ratio (Cash & Cash
Equivalent/Current Liabilities) is an
indicator which shows the company may
face liquidity risk.
Measuring relevant financial indicators
(e.g. Current ratio and Quick ratio) helps
the company to manage the liquidity risk.
Measuring Liquidity risk by
using BSC
Financing risk High amount of short term and long term
loans already taken, is an indicator which
shows that the company may face
financing risk.
Measuring financing risk
Low monetary % of assets in comparison
to the finance required is an indicator
which shows that the company may face
financing risk.
Measuring relevant financial indicators
(e.g. short term and long term loans
already taken and monetary percentage of
assets in comparison to the finance
required) help the company to manage the
financing risk.
Measuring financing risk by
using BSC
90
Customer perspective
Risk of rejection of
product after its
release to the
market
Low % of customers integrated into the
innovation process is an indicator which
shows that the company may face the risk
of rejection of a product after its release to
the market.
Measuring risk of rejection of
product after its release to the
market
Low rate of product compliance with
customer preferences and requirements is
an indicator which shows that the
company may face the risk of rejection of
a product after its release to the market.
Measuring relevant customer indicators
(e.g. percentage of customers integrated
into the innovation process and the
percentage of product compliance with
customer preferences and requirements)
helps the company to manage the risk of
rejection of product after its release to the
market
Using BSC to Measure risk of
rejection of product after its
release to the market
Risk of clients
opposition to pilot
testing of the
product
High number of contracts signed between
client and the company that did not decide
or mention any clause for pilot testing is
an indicator which shows that the
company may face the risk of clients
opposition to pilot testing of the product.
Measuring risk of clients
opposition to pilot testing of
the product
Measuring relevant customer indicators
(e.g. the number of contracts that did not
decide or mention any clause for pilot
testing) helps the company to manage the
clients opposition to pilot testing.
Using BSC for measuring risk
of clients opposition to pilot
testing of the product
Internal perspective
91
Risk of improper
design of product at
development stages
High intervals of time between market
research conducted is an indicator which
shows that the company may face the risk
of improper design of product at
development stages.
Measuring risk of improper
design of product at
development stages
Low % of personnel with relevant
knowledge and skills in the product
development team is an indicator which
shows that the company may face the risk
of improper design of product at
development stages.
Measuring relevant internal indicators (e.g.
intervals of time between market research
conducted and % of personnel with
relevant knowledge and skills) help the
company to manage the risk of improper
design of product at development stages.
Using BSC for measuring risk
of improper design of product
at development stages
Risk of improper
selection of
international partner
Low % of similarity of previous projects
done by the partner is an indicator which
shows that the company may face the risk
of improper selection of international
partner.
Measuring risk of improper
selection of international
partner
Low % of the partner's success in
accomplishing previous international
projects is an indicator which shows that a
company may face the risk of improper
selection of international partner.
Measuring relevant internal indicators (e.g.
% of similarity of previous projects done
by the partner and partners’ success rate)
help the company to manage the risk of
improper selection of international partner.
Using BSC for measuring risk
of improper selection of
international partner
92
Learning and growth
Risk of not enough
operational
experience in
similar projects
High number of staff without having
experience in previous similar projects is
an indicator which shows that the
company may face the risk of not enough
operational experience in similar projects.
Measuring risk of not enough
operational experience in
similar projects
Measuring relevant learning and growth
indicators (e.g. number of staff without
having experience in previous similar
projects) helps the company to manage the
risk of not enough operational experience
in similar projects.
Using BSC for measuring risk
of not enough operational
experience in similar projects
Risk of incorrect
evaluation and
selection of possible
technology options
Low % of companies using a specific
technology for a previous similar project is
an indicator which shows that the
company may face the risk of incorrect
evaluation and selection of possible
technology options.
Measuring risk of incorrect
evaluation and selection of
possible technology options.
Low % of positive reviews on a specific
technology that the company may want to
use is an indicator which shows that the
company may face the risk of incorrect
evaluation and selection of possible
technology options.
Measuring relevant learning and growth
indicators (e.g. % of companies using a
specific technology for a similar project
and the % of positive reviews on a specific
technology) help the company to manage
the risk of incorrect evaluation and
selection of possible technology options.
Using BSC for measuring risk
of incorrect evaluation and
selection of possible
technology options.
93
Appendix 2: Normality test for HA1
Appendix 3: Homoscedasticity test for HA1
Appendix 4: Linearity test for HA1
94
Appendix 5: Normality test for HB1
Appendix 6: Homoscedasticity test for HB1
Appendix 7: Linearity test for HB1
95
Appendix 8: Normality test for HC1
Appendix 9: Homoscedasticity test for HC1
Appendix 10: Linearity test for HC1
96
Appendix 11: Normality test for HD1
Appendix 12: Homoscedasticity test for HD1
Appendix 13: Linearity test for HD1
97
Appendix 14: Normality test for HE1
Appendix 15: Homoscedasticity test for HE1
Appendix 16: Linearity test for HE1
98
Appendix 17: Normality test for HF1
Appendix 18: Homoscedasticity test for HF1
Appendix 19: Linearity test for HF1
99
Appendix 20: Normality test for HG1
Appendix 21: Homoscedasticity test for HG1
Appendix 22: Linearity test for HG1
100
Appendix 23: Normality test for HH1
Appendix 24: Homoscedasticity test for HH1
Appendix 25: Linearity test for HH1
101
Appendix 26: Normality test for HI1
Appendix 27: Homoscedasticity test for HI1
Appendix 28: Linearity test for HI1
102
Appendix 29: Normality test for HJ1
Appendix 30: Homoscedasticity test for HJ1
Appendix 31: Linearity test for HJ1
103
Appendix 32: Normality test for HK1
Appendix 33: Homoscedasticity test for HK1
Appendix 34: Linearity test for HK1
104
Appendix 35: Regression analysis outputs related to HA1
105
Appendix 36: Regression analysis outputs related to HB1
Descriptive Statistics
Mean Std.
Deviation N
Mean Risk Controlling 5.9333 0.76263 30
Continuous monitoring of risk expectations probable to occur again is a step that the company carries out in controlling strategy risks through risk indicators
5.67 1.295 30
106
Appendix 37: Regression analysis outputs related to HC1
107
Appendix 38: Regression analysis outputs related to HD1
108
Appendix 39: Regression analysis outputs related to HE1
109
110
Appendix 40: Regression analysis outputs related to HF1
111
Appendix 41: Regression analysis outputs related to HG1
112
113
Appendix 42: Regression analysis outputs related to HH1
114
Appendix 43: Regression analysis outputs related to HI1
115
Appendix 44: Regression analysis outputs related to HJ1
116
117
Appendix 45: Regression analysis outputs related to HK1
118