Performance measures in CEO bonus
contracts
Author: Ree, Martin van der Student number: 11426071 Thesis supervisor: Mr. M. Schabus MSc
Date: 26-7-2017 Word count: 10.066
MSc Accountancy & Control, variant Control
2 Statement of Originality
This document is written by student Martin van der Ree, who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3 Table of contents
1 Introduction
1.1 Organizational strategy…...……….………...4
1.2 Non-Financial performance measures………..…..……….4,5
1.3 Research question……….………...………..5
1.4 Motivation for this research………....……….……..6
2 Literature review
2.1 Organizational strategy………..7
2.1.1 The Adaptive Cycle………..……….8,9 2.2 Types of organizational strategy…..………..………9,10,11 2.2.1. Defender strategy………..……..………….……….9,10
2.2.2. Prospector strategy………..…..…………...………...10,11 2.2.3 Use of defender-prospector theory in prior research………11
2.3 Non-financial performance measurement….………..……….12,13
2.4 Customer satisfaction………...……….……….………..…13
2.5 Hypothesis……….………..…...…….14
3 Methodology
3.1 Sample selection………15 3.2 Measurement of the independent variable……….……….…...15 3.3 Measurement of the dependent variable………15 3.4 Empirical model………15,16 3.5 Control variables……….………...16 4 Analysis 4.1 Descriptive statistics…..………17,18 4.2 Bivariate statistics………...………...…19,20 4.2.1 Multivariate analysis on H1………20,21,22 4.2.2 Multivariate analysis on H2………22,23,24 5 Summary and Conclusion
5.1 Summary………...24,25 5.2 Discussion……….………26
4 1. Introduction
1.1 Organizational strategy
Every organization has certain goals that the organization wants to achieve. In order to do so, the organization must take actions to reach these goals. This process of actions by managers and employees to attain the organizational goals, is known as the organizational strategy (Barney and Griffin, 1992). According to Miles and Snow (2005), strategy is ‘the general direction set for the company and its various components to achieve a desired state in the future’. The strategy gives the employees guidelines for their actions and can create a shared view on the long-term development of a company. These guidelines are necessary to avoid self-interested behaviour by employees (Adnan & Mutlu, 2014). To stimulate appropriate behaviour, firms can develop special performance measurement systems. At the end of such measurement period, the employees will be rewarded on the outcomes of the performance measures. Therefore, it is in their personal best interest to act according to the measures.
According to prior research (e.g. Govindarajan and Gupta 1985, Simons 1987), the performance measures that are used in annual bonus compensation are linked to the strategy. This link is made to ensure that the incentives of the managers are aligned with the goals of the organization (Ittner et al, 1997). Important research of Miles and Snow (1978), as well a study of Michael Porter (1980), indicates that competitive strategy can be seen as a continuum between two main strategic orientations. The first strategy is linked to the prospector companies, which are characterized by a differentiation strategy. The differentiation strategy is used by companies in a dynamic environment with innovative products. These companies are continuously identifying new products or services and bring them to the market, but are also quick in adapting to changes in the environment of the company and follow a “first to market strategy” (Ittner et al, 1997).
The second strategy is called defender strategy and pursued by companies that are characterized by a cost leader strategy. This strategy is focused on maintaining and defending the current position of a company in the market. (Miles and Snow, 2005). The defender strategy tries to accomplish this by improving the current efficiency in operations in order to obtain lower costs. Furthermore, the defenders try to provide a stable set of products and services to the market. Together with competitive pricing and raising the standard quality of their products, the defenders try to make it hardly impossible for new competitors to enter these markets.
1.2 Non-financial performance measures
Non-financial performance measures are a part of the performance management system of a company. This system is developed to attain the company’s goals and targets, and consist most of
5 the times of both, financial and non-financial performance measures. A good combination of financial and non-financial measures has several advantages, like goal congruence, sensitivity for changes in the organization and informativeness for the management. Financial performance measures are provided to measure the results of a firm’s strategy and operations in monetary units. Examples of this are sales or profit numbers. Non-financial performance measures can be described as any quantitative measure of a performance, individual or as an entity, that is not expressed in monetary units. Examples of non-financial performance measures are customer satisfaction and the market share of the company (Perera and Harrison, 1997).
1.3 Research question
Since the most important goal of defender firms is to increase their operating efficiency, they have different control mechanisms than prospector companies. For example, the performance measures of defender firms are more backward looking (Miles and Snow, 2005), in order to see if the efficiency improves compared to the previous period. In general, financial performance measures, and especially accounting measures, are more backward looking than non-financial performance measures (Govindarajan and Gupta, 1985). The management uses financial measures like cost control, return on investment ratio’s and the operating profit.
In contrast, the prospector firms use other performance measures to see if their market share has increased or if there are new product developments. These new product developments involve more time than short term developments, which means that it takes more time before these results are visible in short term performance measures. Short term performance measures like operating profit are therefore not informative enough in prospector companies to evaluate the manager’s actions and performance.
Govindarajan and Gupta (1985) found out that this provides an incentive for firms to make more use of non-financial performance measures. Although this study suggest that strategy of a firm influences the choice of performance measures, this was only tested for business unit managers and not for CEOs. In this study, this relationship is examined on a corporate level by investigating if firms which put more weight on the prospector strategy, make more use of non-financial performance measures in the annual CEO bonus compensation. In total, there will be two relationships examined. First, I hypothesize that prospectors make more use of non-financial performance measures in annual CEO bonus compensation than defenders. Second, I hypothesize that prospectors make more use of customer satisfaction performance measures in the annual CEO bonus compensation than defenders.
6 1.4 Motivation
The aim of the research is to create a clearer view on the importance of non-financial measures in annual CEO bonus compensation and if or how the strategy of a company influences this. On the one hand, there is expected that strategy will influence this compensation, because research of Ittner et al in 1997 proved this hypothesis. In this study, Ittner et al (1997) concluded that companies increase the use of non-financial performance measures, when these firms follow an innovation-oriented strategy. So, although this kind of research had been conducted in the past, the outcomes could still be different.
First of all, because the business environment has changed since the late nineties. The globalization and technological developments have had a huge impact on, for example, the firm’s production process. These developments could have an influence on the way strategy is used and how this is related to the use of non-financial performance measures. This is one of the most important motivations for the replication of this research.
Second, the aim is to extend the research of the relationship between strategy and the non-financial performance measures, by examining the relationship between strategy and the use of customer satisfaction performance measures. Until this moment, there is no research done with the influence of strategy on the use customer satisfaction in annual CEO bonus compensation. Therefore, this part could extend the research by getting a more in-depth view of the use of a non-financial performance measure. a moderating variable to the research.
Prior research of Down and Raposo (2005) examined the influence of shareholders on CEO compensation, and whether this could influence the organizational strategy. However, the relationship between strategy and compensation that they studied was in a different direction, by investigating the influence on CEO compensation on strategy. The approach of Down and Raposo differs from this research, because this research investigates the influence of strategy on CEO compensation.
Furthermore, Abdel-Maksoud (2005) did research on the influence of strategy on CEO compensation, but their research was more general and specified on production companies. This research will have a broader and more general scope, and can reflect their results on more firms than only the production firms. Thus, this research contributes to the existing knowledge, because prior research on this topic was conducted a long time ago and the relationship between strategy and customer satisfaction was not statistically analysed before. This makes it interesting to conduct the research and in this way this research can contribute to the existing knowledge.
7 2. Literature Review
2.1 Organizational strategy
In order to obtain the goals of an organization, the organization must take actions to reach these goals. This process of actions by managers and employees to attain the organizational goals, is called strategy. (Barney and Griffin, 1992) According to Miles and Snow (2005), strategy is ‘the general direction set for the company and its various components to achieve a desired state in the future’. The strategy gives the employees guidelines for their actions and can create a shared view on the long-term development of a company (Kalkan et al, 2014). In the innumerous different options, companies should choose their perfect strategy, Miles and Snow (2005) distinguish three strategic types of organizations; Defenders, analysers and prospectors. Each of the three types have their own strategy, depending on their own environment and situation. In addition to the three main types, they mention also a fourth strategic type of organizations: The reactor. In this research, only the defenders and prospectors will be discussed. The reason for this, is that these types are the most contradictory. This increases the chance of significant different results between the companies that use these two strategies. In one of the next sections, this defender and prospector strategy will be discussed more in depth.
2.1.1 The Adaptive Cycle
Miles and Snow (2005) have developed a general model that expands the ideas that are consistent with the strategic choices of the organization. This model is called ‘The Adaptive Cycle’. The essence of this model is that the organizational behaviour is not entirely reserved by the environmental conditions of the organization and that the top manager’s decisions the determinants are for this organizational structure. These choices are very diverse and complex, but they can be summarized in three main ‘problems’ of organizational adaptation: The entrepreneurial problem, the engineering problem and the administrative problem. The adaptive cycle can be used in all organizations, and can be recognized mostly in rapidly growing organizations. This could for example be a new organization or one that survived a crisis and starts growing again (Miles and Snow, 2005).
The Entrepreneurial Problem
The entrepreneurial problem is different for new organizations than for already existing organization. For a new organization, it is important that the entrepreneurial insight of the organization should be defined in a concrete definition of an organizational domain, such as a specific good or service in a target market or market segment (Yun Sun and Pan, 2011). For ongoing organizations, this has an extra dimension, because there are already many processes,
8 routines and solutions for other problems in the organization. This makes it harder to change the definition of the entrepreneurial insight within the organization (Miles and Snow, 2005).
The solution to this problem is the same for both new and already existing companies. The key to this problem is the acceptance of the manager of a certain product or market domain. This acceptance is especially important when the management allocates the resources to a specific domain and states objectives that must be achieved (Matos Marques Simoes and Esposito, 2014). Managers should at that point be committed to their targets and objectives. This commitment should not only be internal, but external as well (Miles and Snow, 2005).
The Engineering Problem
The engineering problem is the situation in which the organization must create a system that enables the solution of the management to the entrepreneurial problem. This system requires the management to make two important decisions. The first decision is to find an appropriate technology for producing and distributing the chosen products in the target markets (Miles and Snow, 2005). The second decision is to find a way to spread new information and communication throughout the organization (Lies, 2012). This can also be found by adjusting the existing linkages of the organization. This way of communication is important to make sure that the chosen technology can operate in the right way. The solution to this problem will be dependent of the company and its culture (Miles and Snow, 2005).
The Administrative Problem
When the management has solved the engineering problem, the next problem is the implementation of the administrative system. The administrative system is likely to change, because the configuration of the organization will not remain the same after solving the engineering problem. During this implementation phase will become clearer what the structure of the organization is going to be, because in this phase the management will more focused on the internal operations rather than on the external environment (Century and Cassata, 2016). The administrative problem can be described as rationalizing or stabilizing the activities which solved the problems in the two previous phases of this adaptive cycle (Miles and Snow, 2005). This involves more than just eliminating the uncertainty within this organizational system. It is also about focussing on the processes that can enable the organization to be continuously innovative in the future (Yun Sun and Pan, 2011). Under perfect circumstances, the management would be able to create an administrative system that monitors the organizations operations, without risking new future innovations not to appear. This requires the system to be not only lagging, which means that it rationalizes the strategic decisions made in previous points in the administrative processes, but
9 also leading. The leading part of this system enables the organization’s future capacity to proceed in being innovative (Miles and Snow, 2005). These three main problems will be discussed per type of organizational strategy in the next section, which should provide a better view on this adaptive cycle.
2.2 Types of organizational strategy 2.2.1 Defender strategy
The first strategy of the typology of Miles and Snow (2005) is the defender strategy. This strategy is focussed on maintaining and defending the current position of a company in a market. A key concept in this strategy is stability, which can be accomplished by producing a limited set of products in a specific segment of a market. This segment is only a small piece of the total market (Miles and Snow, 2005).
To defend their position best, defenders try to withhold new competitors from the market, by competitive pricing or by raising the quality of their products that much, that it is hardly impossible for new companies to enter the small market without being inefficient (Williams and Narendran, 2000). To be very competitive, the company invest initially in single core technology that is cost-efficient. Later in time, they only extend this technology if necessary. The research and development expenditures are therefore only high on the short-term (Miles and Snow, 2005).
The defender strategy also carries risks. The most important one is the ineffectiveness. This means that the company is not able to match their products to the customers in their specific environment. So, when the company refuses to fulfil the demands of their target consumer, they might lose market share on very short notice (Miles and Snow, 2005).
When applying the adaptive cycle to the defender typology, the entrepreneurial problem for the defender is about how to shield a part of the market to maintain a stable set of customers. According to the typology, they do this by aggressively focus on only their domain, and ignore all the developments outside this domain. Furthermore, they try to gain market share through market penetrations. Product development is mostly conducted on existing goods, so there is little attention for innovations of new product lines. The engineering problem for defenders is about how to be the most efficient in the production process and with the distribution of the goods. The defender’s solution is to focus on cost-efficiency technologies and on the improvements in this field of technology. Also, these firms have a tendency towards vertical integration, to profit from economies of scale. The administrative problem for defender firms is about how to keep the control over the organization in order to pursue the optimal efficiency. An important solution is to create a good support level within the organization. This can be accomplished by forming a strong coalition of people that are internally accepted by other employees. The strong coalition will
10 have credits from the employees, and are their decisions are more likely to be accepted than from people outside the organization. Besides this, also performance measures could provide the control over the organization (Miles and Snow, 2005).
2.2.2 Prospector strategy
The second strategy is the prospector strategy. This strategy is considered the opposite from the defenders. It is used by companies in a dynamic environment with innovative products. To compete in this environment, a good prospector strategy must result in efficiently finding opportunities in the market and producing new products (Miles and Snow, 2005). But, besides the efficiency, also the reputation of being an innovative player in the market is very important for the company. To keep a good reputation, the company must release new products in a certain period. To locate these new areas for opportunities, it is important that the company develops a system to observe new trends, environmental conditions or events. Therefore, the companies invest in special departments who are trained to recognise these activities. When new opportunities are recognised, it is important that the company can act towards new developments easily (Alpkan and Gemici, 2016). That is why flexibility is vital for these organisations. This flexibility is not only for the technology, but also for the administration and the employees, which must be open for change (Miles and Snow, 2005).
The risk of losing the reputation as an innovative company, also brings costs with it. Because when the company have to develop and release new products over a certain period, this causes also the inevitable ‘market failure rate’. This rate shows how many of the new products eventually survive or fails in the market. Therefore, it is more difficult to attain their profit levels than with the defender strategy. Also, having multiple technologies make it impossible to be complete efficient in the cost of this technologies, because they are never completely efficient at the same time. However, the efficiency gained by being able to suit to the demands of the world of tomorrow is considered more valuable by this companies (Miles and Snow, 2005).
The adaptive cycle can also be applied to the prospector strategy. The defender entrepreneurial problem is how to find and exploit new products and new opportunities or markets. The prospector tries to solve this by monitoring the developments of their own domain, by investing in equipment to improve their products. However, they also keep an eye on the domain of other products and goods, by monitoring environmental conditions and events around the world. In this way, prospectors continuously try to find new market opportunities. Their engineering problem is how to avoid a commitment for the long term to a single technological process. Being tied to a single process is not desirable for a prospector, due to their nature to
11 change. They try to avoid this, by using multiple flexible technologies instead of the fixed technological processes, and try to embed the knowledge of the technology in the people. In this way, it is easier to change for the employees, because their knowledge is sufficient to change from technology. The administrative problem is about how to facilitate and control all these different developments and operations. The prospector tries to keep control over the organization by investing in research and development, and marketing. These departments are the most dominant of the organization, because of their differentiated nature. To get control over the processes, complex coordination mechanisms are installed and people are not only rewarded on their own performance, but the performance is rated relative to the performance of important competitors (Miles and Snow, 2005).
The adaptive cycle of the prospector, relative to the adaptive cycle of the defender, indicates that this adaptive cycle can be applied to many types of organizations and that they can be very different along these firms.
2.2.3 Prior research on defender-prospector theory
To see if the defender-prospector typology is accepted by other researchers or the research field in general, this paragraph will examine if this typology is used in other research.
Over the years, the defender-prospector theory is used many times in different types of research. For example, the study of Bedford, Malmi and Sandelin (2016), examines different management control combinations that are effective in several different strategic contexts. One of the most important strategic contexts is the prospector-defender, of which they state that this is a good and clear strategic context to determine clear and concise controls in order to be effective. Lin, Tsai and Wu (2013), used in their research the typology to conduct a framework for strategic decision making. Another study, conducted by Walker (2013), used the typology to identify strategic management and performance in public organizations. Moreover, there is research about the defender-prospector typology, on which firms have to adjust their supply chain risk management, in order to mitigate the risks regarding the supply chain (Mishra et al, 2016). In the research of Moore (2005), the applicability of the typology is examined in domestic retail organizations. This research indicated that the defender and prospector typology are both applicable and present in this industry.
These studies provide evidence that the typology of organizations can be used in different industries, but also is applicable for research on different topics at different organizational levels. Therefore, there can be concluded that the typology of Miles and Snow (2005) is accepted in the research field of organizational theory, and is useful theory to employ in this thesis.
12 2.3 Non-financial performance measures
Non-financial performance measures are a part of the performance management system of a company. This system is developed to attain the company’s goals and targets, and consist mostly both financial and non-financial performance measures. Financial performance measures are made to measure the results of a firm’s strategy and operations in monetary units. Examples of this are sales or profit numbers. Non-financial performance measures can be described as any quantitative measure of a performance, individual or as an entity, that is not expressed in monetary units. Examples of this way of performance measurement are customer satisfaction and market share (Perera and Harrison, 1997).
Non-financial performance measures were originally introduced as a supplement to the financial performance measures, because people assumed that these financial measures were not able to evaluate the total performance. This application of non-financial performance measures has several advantages.
One of the main advantages of the combination of financial and non-financial performance measures, is that financial performance measures are backward looking and the non-financial performance measures more forward looking. This means that when a firm has a balanced performance management system, both the performance of the past as well of the future is considered (Behn and Riley, 1999). Second, non-financial performance measures are known to be better able to predict future performance, because of their forward-looking nature (de Waal and Kourtit, 2013).
Third, the non-financial performance measures can enhance the implementation of the firm’s strategy. They contribute to this with three mechanisms. First, the variety of performance measures creates a better understanding between the strategic priorities of the firm. Second, non-financial performance measures are used for goal congruence. This means that these performance measures are able to influence the employees’ behaviour and actions in order to be in line with the organizational goals, on both the short and long term. The last mechanism is that non-financial performance measures enable a more efficient allocation of the resources in the company, because the priorities for the resources is more or less embedded in the measures (Dossi and Patelli, 2010). Furthermore, non-financial performance measures are sensitive. This means that it is possible for an employee to influence his performance measures, because these measures are sensitive for changes in behaviour or performance. Last of all, the non-financial performance measures are informative, which implies that they give more information than just the numbers. (Behn and Riley, 1999).
13 A limitation of the non-financial performance measures is that some of them could be relative easy to manipulate, for example if the measures are subjective or subjected to public verification (Ittner et al, 1997). When non-financial measures are based on surveys, data could be biased because of the standard survey problems. Examples of the survey problems are: only respondents who are extremely satisfied or dissatisfied, the outcomes could be susceptible to interpretation and it is hard to conduct good questions that are understandable for every respondent (Anderson and Sullivan, 1993).
2.4 Customer Satisfaction
The concept of customer satisfaction is one that seems to speak for itself. The company measures the customers’ state of mind, whether they are satisfied with the product or service delivered by the company. This satisfaction can lead to loyalty and repurchasing products of that company. To keep the customer satisfaction high, companies have to put continuous effort in the process of listening to customers and improving their products or services. This is the only way to maintain high satisfaction levels (O’Sullivan and McCallig, 2012). Especially when markets get more crowded and competitive, customer satisfaction can be the difference between surviving in such environment or going down. Unfortunately, this is not an easy process. Companies need to understand how to measure the customer satisfaction, and to quantify it. This is a difficult process, because the measurement is not always exact and consists a gap between the expectations of a customer and the perceived perception of the quality of the product. This differs for every customer (Anderson and Sullivan, 1993).
The measurement of customer satisfaction can be a struggle for companies. Most of them cover it by conducting surveys. In these surveys, customers can judge several aspects of the process of purchasing, like the speed of delivery, quality of service or product quality (Marsh, 1979). However, conducting a good survey may not as easy as it seems. According to Anderson and Sullivan (1993), there are three main problems. In the first place, it is difficult to produce good questions, which are not susceptible to individual interpretation and do not have to be supplemented by other questions. Also, it is hard to formulate the questions without suggesting to an outcome. Some companies avoid this by asking the customers to give a rate from 1 to 6 on a certain statement (1 is fully agree and 6 fully disagree). Finally, it is difficult to get a response from all the customers. Most of the times, only very satisfied or very dissatisfied customers are motivated to respond. Companies try to solve this by rewarding customers with a discount when they fill in the survey (Anderson and Sullivan, 1993).
14 2.5 Hypothesis
Theory and prior literature suggests that the prospector strategy is mostly used by innovative companies in dynamic environments (Miles and Snow, 2005). This implies that these companies should invest in research and development, to be able to discover new developments. These investments are likely to have a negative impact on the revenue and profit number in the short term, because the benefits derived from them are uncertain and are hard to measure. Therefore, it intuitively makes sense that companies with this strategy tend to focus more on non-financial performance measures. Especially in the competitive, innovative environments, non-financial performance measures can contribute to the process of surviving in this environment. On the other hand, according to the theory of Miles and Snow (2005), the defender strategy is more used in narrow markets with products that do not differentiate much. The expenditures on research and development of new technologies are lower and more efficient, because the company is focussed on a specific part of the market.
The differences between these strategies, suggest that it is likely that a prospector company uses more non-financial performance measures for CEO compensation than a defender company. This leads to the first hypothesis:
-H1: Prospectors make more use of non-financial performance measures in the CEO compensation than defenders.
Building on this first hypothesis, the second hypothesis is more detailed. Especially in the before mentioned competitive and innovative environments, customer satisfaction could be a specific non-financial performance measure which could contribute to the process of surviving in this environment. With this measure, companies can create a clearer view about how customers think about the company and their products or services.
In the same way that the theory suggests that it is more likely that the prospector company will use non-financial performance measures in general, is it in this case more likely that prospector companies will use customer satisfaction more often. Therefore, this leads to the second hypothesis:
-H2: Prospectors make more use of customer satisfaction performance measures in the annual CEO bonus compensation than defenders.
15 3. Methodology
3.1 Sample selection
The total sample of this research consists out of U.S.-listed companies. This sample is a result of the forty largest firms on revenues, according to the Fortune 500 list of 2016. The reason for using this list, is that the firms in this list have publicly available data online in their annual report. From these forty companies, the firms of the financial industry are excluded, because this industry is too complex and different from the other industry. There were three financial industry firms in the top-forty, which brings the total sample on thirty-seven companies.
I hand collect data on performance measures from firms’ annual reports and download the remaining data from Compustat. The researched period is from 2009 until 2014. Within this period, the research has 222 observations (thirty-seven companies, times six years).
3.2 Measurement of the independent variable
The operationalization of the different variables is an important part of the methodology. The independent variable is the strategy (STRATEGY). Prospectors are typically companies with high research and development (R&D) expenditures. The degree of how much a firm is a prospector or defender is therefore dependent of the R&D expenditures. To determine at what level a firm is a prospector, there is a median split used. The median for R&D is zero. Therefore, a value higher than zero of R&D indicates a prospector, and a value of zero being a defender.
3.3 Measurement of the dependent variable
The use of non-financial performance measures (NFPM) in annual bonus compensation is measured with a value of 1 if a firm uses the non-financial performance measures in a year, and 0 otherwise. In hypothesis 2, the independent variable is the customer satisfaction (CUST_SATISF). The use of the customer satisfaction is also measured with a value of 1 if a firm uses customer satisfaction in the annual CEO bonus plan for a year, and 0 otherwise.
3.4 Empirical model
As Ittner et al (1997) showed in their previous research that firms that put relative more weight on a prospector strategy, make more use of non-financial performance measures in their annual bonus compensation. Although this research was conducted a long time ago, the expectation is still that there is a positive relationship between these variables. Therefore, to measures the effect of strategy on this use of non-financial performance measures, the following empirical model is estimated:
16 H1 : NFPM = B0 + B1 * STRATEGY + B2 * FIRMSIZE
+ B3 * LEVERAGE
+ B4 * MTB + Year-FEs + error
To support H1, I expect a positive correlation between strategy and the use of non-financial performance measures, so a significant positive Beta 1.
As Anderson and Sullivan (1993) state in their research, a loyal customer base can be the difference between surviving in a competitive environment or not. This competitive environment is a characteristic from the prospector, which implies a positive relationship between these two variables. Furthermore, a positive relationship is expected between the prospector strategy and the use of non-financial performance measures in hypothesis 1, of which the customer satisfaction is a measure. Therefore, the following empirical model is used to test this hypothesis:
H2 : CUST_SATISF = B0 + B1 * STRATEGY + B2 * FIRMSIZE + B3 * LEVERAGE
+ B4 * MTB + Year-FEs + error
To support H2, I expect a positive correlation between strategy and the use of customer satisfaction, so a significant positive Beta 1.
3.5 Control variables
There are several control variables used in this research, to test if there is a significant causal relationship between the variables that is not caused by other variables. The first control variable is the size of a company (FIRMSIZE). The variable is measured by taking the logarithm of the total assets of the company. The logarithm is used to rule out the big differences. This control variable is relevant because it might be possible that companies with a high revenue tend to use non-financial measures more than companies with a lower revenue, because it is more expensive to have a more diversified performance measurement system. Another control variable is leverage (LEVERAGE). This variable is measured by calculating the leverage ratio. This is the ratio of the total leverage, as a percentage of the total assets. The third control variable is the market-to-book ratio (MTB). This ratio is calculated by dividing the market value of the company with the book value per share. After that, the logarithm is calculated. The last control variable is the year-fixed effects (YFE). This control variable tries to rule out that an effect is caused by another year than the year that the event actually took place.
17 4. Analysis
4.1 Descriptive Statistics
In this subchapter, the descriptive statistics for the variables used in the hypotheses, are presented. The descriptive statistics show information about the number of observations and the spread of the variables. Table 1 gives an overview over the descriptive statistics of all the variables used in the analysis. All the continuous variables are winsorized at the first and ninety-ninth percentile. The winsorization limits the extreme values in the data, which reduces the effect of possible outliers.
In total, there are 222 observations. However, some of the variables have missing values, which leaves a smaller number of observations. For example, not all the data concerning the non-financial performance measures and customer satisfaction was publicly available, or sometimes there was no annual report available for 2009. However, 206 is still a representative number for this variable. Also, some observations are missing for the control variables, which are most of the times caused by the lack of data collected out of the publicly available databases.
a. Dependent variables: NFPM is the variable on-financial performance measures, measured by checking the annual reports of the selected firms out of the sample. CUST_SATISF is the variable customer satisfaction, also measured by checking the annual reports of the selected firms out of the sample.
b. Independent variable: STRATEGY is the strategy variable, measured by looking at the R&D expenses. c. Control variables: FIRMSIZE is the variable firm size, measured as the logarithm of the total assets of the firms.
LEVERAGE is the variable leverage, measured by calculating the ratio of the leverage respective to the total assets. MTB is the variable market-to-book ratio, which is calculated by dividing the market value of the company by the
book value per share.
d. Year-fixed effects (YFE) are included in this model. Due to their little significance in these descriptive statistics, they are excluded from the table.
Variables N SD Q1 Mean Median Q3
NFPM 206 0.489 0 0.388 0 1 CUST_SATISF 206 0.389 0 0.184 0 0 STRATEGY 221 0.497 0 0.434 0 1 FIRMSIZE 222 1.349 10.685 11.693 11.608 12.334 LEVERAGE 221 0.105 .088 0.160 .139 .223 MTB 217 2.482 7.278 7.591 8.137 9.086 TABLE 1
Descriptive statistics for the dependent variables, the independent variable, and the control variables.
18 The data of all the variables, except for the MTB, are skewed to the right, due to the higher mean than the median. In general, the standard deviation of all the variables show that there is not much spread in the data. This is confirmed by the little differences in the Q1 and Q3 for the data. For the dummy variables this makes sense, because there are only two options for this variable. When looking at the control variables FIRMSIZE and MTB, this can easily be explained because these numbers are logarithms. Regarding the control variable LEVERAGE, it seems that the firms do not differ much in leverage percentages to finance their activities. However, these differences are probably bigger when the leverage would be measured as an absolute number.
Almost 39% of the examined companies use non-financial performance measures in their annual CEO bonus contracts. Furthermore, 18,4 percent uses customer satisfaction in order to evaluate the CEO in the annual bonus contract. This means that only about half of the companies
that use non-financial performance measures, also use customer satisfaction as a measure to evaluate their CEO. Out of the 206 strategic observations, 96 companies (43,4%) are indicated as a prospector, and 125 companies (56,6%) are indicated as a defender. This is a good distribution for the variable. The logarithmic values of the FIRMSIZE and MTB have caused that their data does not vary as much as these numbers do in the real world. However, for the usefulness and comparability of this data, it is crucial that these numbers are the logarithm. The average leverage ratio of the firms in the sample is 16 percent, while the firm with the highest leverage has a ratio of 45%.
Regarding the median of the variables, it makes sense that those of the dummy variables are zero. The variables non-financial performance measures and strategy are labelled less than fifty, but more than twenty-five percent of the observations with the value of 1, which is why their median is zero and their value for Q3 is 1. For the variable customer satisfaction, the value 1 is used less than twenty-five percent, which is why the Q3 value of this variable is zero. For the variables FIRMSIZE and MTB it is hard to state anything about their mean, because of their logarithmic values.
Overall, the descriptive statistics indicate that the sample consists out of both types of strategic firms, and that the data about those firms have a balanced distribution of the variables.
19 Bivariate statistics
In addition to the descriptive statistics in the section before, a correlation table is discussed in this section. The relationships between every pair of the variables and the significance of this
relationship is displayed in Table 2.
***, **, * Statistically significant correlation at the 1 percent, 5 percent, and 10 percent levels (two tailed).
This table indicates that the correlation between strategy and non-financial performance measurement is 0.2779. The positive number indicates that those variables have a positive relationship. The level significance of the relationships is displayed with the stars behind the correlation, and when the correlation coefficient is bold. The more stars, the more significant the relationship is, as explained in the comment under the table.
The most important relationships to examine are those for the two hypotheses. The first relationship is those between the non-financial performance measures and the strategy (0.2279). This relationship is indicated as positive and significant, which indicates that there is a real possibility that the hypothesis will be confirmed, when other variables are added to this relationship.
Customer satisfaction and strategy are also positively related (0.3733), even stronger than the non-financial performance measurement and the strategy. This relationship is significant as well, considering the very low p-value. Customer satisfaction and non-financial performance measurement have a striking high correlation. This is not a surprise, considering that customer satisfaction is a non-financial performance measure and therefore only can appear when a company uses these non-financial performance measures. However, it is interesting to see that not every company that uses the non-financial performance measures, automatically include a customer satisfaction measure in the system. In the multivariate analysis, this could be something to examine with a closer look at the data.
1 2 3 4 5 6 1. NFPM 1.0000 2. CUST_SATISF 0.5969*** 1.0000 3. STRATEGY 0.2779*** 0.3733*** 1.0000 4. FIRMSIZE 0.0754 -0.0973 0.0508 1.0000 5. LEVERAGE -0.1794*** -0.0339 -0.0918 -0.0659 1.0000 6. MTB -0.0674 0.0618 0.3854*** -0.0117 0.1626** 1.0000 TABLE 2 Bivariate statistics
20 Furthermore, there are other relationships significant, like the leverage and the use of non-financial performance measure. This relationship is negative (-0.1794), which indicates that when the amount of leverage goes up, the use of non-financial performance measures goes down. Also, the positive relationship (0.3854) between the Market-to-book ratio and the strategy is significant. This indicates that the Market-to-book ratio is correlated with being a prospector.
Although some of the relationships are already significant, this table is just presenting the results of the correlations between two variables. The relationships for the hypotheses and their significance could change when there are more variables involved. This influence of other variables is examined in the next paragraph.
4.2.1 Multivariate analysis on H1
In this section of the analysis, the empirical models for both hypotheses are tested. The first regression is conducted for the first hypothesis, which was: H1 = Prospectors make more use of non-financial performance measures in the CEO compensation than defenders.
The research hypothesis is tested by using a basic regression model. The difference with the bivariate model is, that in this model the relationship is checked for other variables. The results are displayed in table 3.
a. ***, **, * Statistically significant correlation at the 1 percent, 5 percent, and 10 percent levels (two tailed). b Year-fixed effects (YFE) are included in this model, but excluded from the table.
Variables
Coefficient
t-stat.
p-value
STRATEGY
0,349***
4,71
0,000
FIRMSIZE
0,024
0,99
0,324
MTB
-0,040***
-2,68
0,008
LEVERAGE
-0,415
-1,21
0,229
Intercept
0,226
0,72
0,473
N
205
F-value
3,53
(Pr. > F)
0,0004
Adjusted R-squared
0,1004
OLS regression with the dependent variable: Non-financial
performance measurement (NFPM).
21 First, the analysis demonstrates some general results about the model in the table. The F-test tries to test the null hypothesis, under the assumption that there are no correlations. In other words: in the situation that all the coefficients of the independent variables equal zero. The result of the F-test is a coefficient of 0.0004. This indicates that the null hypothesis can be rejected with high confidence, even above a percentage of 99,99%. Furthermore, the adjusted R-squared is 0,1004, which indicates that 10,04% of the variance of the model is explained by the variables. In quantitative research, this is a proper level for a model.
The first significant relationship is to examine is the influence of the independent variable (STRATEGY) on the dependent variable (NFPM). The statistics display that the strategy has a significant relationship with non-financial performance measurement. This is consistent with prior research that has found a positive relation that firms engaging in a prospector strategy rely more on non-financial performance measures in annual CEO bonus contracts (Ittner et al, 1997). Moreover, this confirms the expectations regarding the characteristics of being a prospector out of the literature review. Because the firms are working with differentiated products in a competitive environment, the firms have to be more differentiated in their performance evaluation as well.
Furthermore, the negative coefficient of the market-to-book ratio is significant. This indicates that a higher market-to-book ratio, will cause that firms are less likely to use non-financial performance measurement in annual CEO bonus contracts.
In the observations of this research, Exxon Mobil Corporation and the McKesson Corporation are examples of firms that adopted the non-financial performance measures even before 2009. However, not all the firms used the non-financial performance measures throughout the whole research period. Comcast is an example of a firm that adopted the non-financial performance measures on evaluating their CEO in 2011. The reason, stated in their annual report of 2011, was that the non-financial performance measures expands the evaluations to secure not only the short-term, but also the long-term strategic goals for the firm. A misfit with this hypothesis is Apple. The high-tech company is eminently a prospector with one of the highest R&D expenditures of this dataset, but still evaluates its CEO on only financial performance measures. In their annual report, it looks like Apple is very confident that their products match their customers demand and therefore assumes that if the financial performance measures are good, the customer satisfaction will also be positive. Another company that is considered a prospector, General Motors, does also evaluate their CEO on only financial performance measures. This may indicate that not all the firms are convinced of the advantages of the non-financial performance measures, or that the firms do not see the added value of evaluating their CEO on it, next to the non-financial performance measures on which they evaluate their ‘regular’ employees.
22 Overall, the results in the analysis show a positive relationship between the strategy and the use of non-financial performance measures in annual CEO bonus compensation. This confirms the hypothesis that prospectors make more use of non-financial performance measures in their annual CEO bonus compensation. This is also in line with the prior research regarding this topic. This could be a coincidence, because the sample of this research is small and only a small subset of firms is examined. However, the results affirm also the development in performance management in the last decades. In this period, the performance measurement systems are extended and improved with more diverse and varied performance measures. Besides the financial performance measures, non-financial performance measures became increasingly important. Moreover, studies of Dossi and Patelli (2010), de Waal and Kourtit (2013) and Behn and Riley (1999) showed that the advantages of the non-financial performance measures were recognised by firms and especially used in the competitive environments. All these aspects together support the results of the first regression. Therefore, the first hypothesis is accepted.
4.2.2 Multivariate analysis on H2
The second regression is conducted on the second hypothesis, which was: H2 = Prospectors make more use of customer satisfaction performance measures in annual CEO bonus contracts than defenders. The results are presented in table 4.
a. ***, **, * Statistically significant correlation at the 1 percent, 5 percent, and 10 percent levels (two tailed). b. Year-fixed effects (YFE) are included in this model, but excluded from the table.
Variables Coefficient t-stat. p-value
STRATEGY 0,344*** 5,91 0,000 FIRMSIZE -0,027 -1,39 0,167 MTB -0,021* -1,78 0,077 LEVERAGE 0,190 0,7 0,482 Intercept 0,469* 1,9 0,059 N 205 F-value 4,31 (Pr. > F) 0,0000 Adjusted R-squared 0,1273 TABLE 4
OLS regression with the dependent variable: Customer Satisfaction (CUST_SATISF).
23 The general results concerning this second model are predominantly positive. The F-test is again positive, and even more significant than in the first model. The significance of this F-test (0.00000) indicates that the null hypothesis can be rejected with high confidence, above the percentage of 99,99%. This model explains the variance also better than the first model, with an adjusted R-squared of 12,73 percent.
The intercept has a high coefficient, which is the intercept for this model when other variables are zero. Although this relationship is not significant for the highest level, it is below the 10 percent level. Therefore, this relationship is significant. The main relationship for the second hypothesis, between the strategy and the use of customer satisfaction as a performance measure, has a positive correlation. This relationship is highly significant, due to the significance level of 0.000. This indicates that being a prospector increases the chance that a firm will evaluate the CEO in his annual bonus compensation plan on customer satisfaction.
The only control variable that is significant, is the market-to-book variable. The significance is, same as for the intercept, not very high, but still below the 10 percent level. The relation of this variable is negative, which would mean that a higher market-to-book ratio will cause less use of customer satisfaction as a performance measure. This is consistent with the relationship of the market-to-book ratio and the use of non-financial performance measures.
The outcome of the relationship between the strategy and the use of customer satisfaction performance measures, confirms the study of Anderson and Sullivan (1993). Their research concluded that firms have a higher chance on surviving a competitive environment, when the firms have a loyal customer base. This implies that the firm puts a lot of weight on the opinion of their customers and that the firm evaluates the employees on the satisfaction of the customers. Other research from O’Sullivan and McCallig (2012) confirms also that good customer satisfaction can lead to higher revenues and a higher firm value, which can be a reason for firms to adopt this measure over the years.
Boeing Company and Microsoft are typically companies out of this database that use the customer satisfaction performance measures throughout the whole examined period. This is not the case for all the companies that used the customer satisfaction in the observations. Some firms adopted the customer satisfaction performance measures at a later stage. For example, Ford, introduced the customer satisfaction performance measures in 2013, at the same time when they introduced their non-financial performance measures for evaluating their CEO. Implementing customer satisfaction performance measures, when a firm is about to implement their non-financial performance measures, may seem a sensible combination. However, some companies like the Citigroup or the Valero Energy Corporation, are using different non-financial performance
24 measures. These companies do not state specific reasons for this, but regarding their industry it makes sense that the Valero Energy Corporation evaluates their CEO on a non-financial performance measure that involve environmental and employee health factors. Comcast, that adopted the non-financial performance measures in 2010, also did not include a customer satisfaction performance measure in its new evaluation system, but focused more on employee satisfaction and health measures.
Overall, the results in the analysis show a positive relationship between the strategy and the use of customer satisfaction performance measures in annual CEO bonus compensation. This confirms the hypothesis that prospectors make more use of customer satisfaction performance measures in annual CEO bonus compensation than defenders. Besides the results of this regression, the outcome is in line with prior research of Anderson and Sullivan (1993) and O’Sullivan and McCallig (2012). The developments in technique and communication provide a new environment for businesses, and in this environment the competition is stronger than ever. Nowadays, people can order via internet almost every product from any place in the world. This increased competition makes it important for companies to distinguish themselves from the competitors. Building a good customer relationship is an expensive, but very effective one. The rationale behind this, is that loyal customers always will come back to buy products at the company they trust (Anderson and Sullivan, 1993). Although apparently not all the prospectors agree to this theory, there is still a significant part of the companies that does. Big companies like Boeing and Microsoft are good examples of firms that believe in the power of the customer satisfaction performance measure. All these facts together support the results of this regression. Therefore, the second hypothesis is accepted.
5. Summary and Conclusion 5.1 Summary
This paper examines the influence of the organizational strategy on the use of non-financial performance measures in annual CEO bonus contracts. This strategy describes desirable actions for their employees, to attain the goals that the organization wants to reach. This process of actions is known as the organizational strategy. In this study, two main typologies of the organizational strategy are used: the prospector and the defender. The prospector is a firm that is characterized by a differentiation strategy in a highly competitive environment. The defender is a firm that is characterized by a cost leader strategy and the focus for defending their current market position.
25 To stimulate the appropriate behaviour, firms can develop special performance measurement systems. At the end of the performance measurement period, the employees will be rewarded on the outcomes of the performance measures. Most of these systems consist out of financial performance measures, but the use of non-financial performance measures is rising. One of the main non-financial performance measures is the customer satisfaction. This measure seems to become increasingly important in the world of today, due to developments like globalisation, which makes it easier for customers to buy somewhere else. However, measuring the customer satisfaction can be a problem for companies, due to survey problems like vague questions, incomplete responses, and receiving only the responses from the very satisfied or very dissatisfied customers.
Prior research suggests a positive relationship between strategy and the use of performance measures used in the annual bonus compensation of CEOs. However, most of this research is older than twenty years, which raises the question if this is still applicable. Furthermore, prior research examined that a loyal customer base is crucial for companies to survive in highly competitive environments.
In this research, two relationships are hypothesized. The first hypothesis states that prospectors make more use of non-financial performance measures in annual CEO bonus compensation than defenders. The second hypothesis states that prospectors make more use of customer satisfaction performance measures in the annual CEO bonus compensation than defenders.
To conduct this research, thirty-seven companies out of the Fortune 500 list of 2016 were selected for the sample. Data about these companies was partly hand-collected and partly collected from large databases like Compustat. The researched period is a six-year period, from 2009-2014, which creates 222 observations for the analysis. The independent variable, strategy, was determined by valuing the firms with R&D expenses as a prospector, and the firms without as a defender. The dependent variable, non-financial performance measures, was determined by valuing the firms who used these measures with the value one, and the firms without these measures with the value zero. This same method holds for the other dependent variable, the customer satisfaction. Furthermore, the control variables firm size, market-to-book ratio and leverage were used to see if any causal relationship between the variables not was caused by other variables. Finally, the regression was controlled for year-fixed effects.
26 5.2 Discussion
The results of the analysis indicate that the strategy and the use of non-financial performance measures have a strong and significant relationship. Furthermore, the results indicate that firms that use a prospector strategy make more use of customer satisfaction performance measures, than firms that use a defender strategy. These results support the expectation beforehand that firms try to align their annual CEO bonus compensation with the organizational strategy. The prior literature shows that prospectors have to use the non-financial performance measures, to distinguish themselves from their competitors, in order to survive in this highly competitive environment. Differentiated products and good customer service are therefore very important for these firms. On the other hand, defenders are focused on their cost leader strategy. Their customers do not care much for customer service in the first place, but mainly about the price of the products. This is how these companies can distinguish themselves from their competitors. Therefore, it makes sense that these companies care less about non-financial performance measures for their annual CEO bonus compensation, because it yields not that much as investing in cost saving processes.
Off course, this study has also some limitations. First, the sample could be bigger to create a higher legitimacy of the results. Although there are 37 firms out of different industries used, it is possible that the sample is not completely representative. the sample consists out of the biggest companies in the United States. However, the descriptive statistics show that the variance of the observations are balanced and only little skewed. This indicates that not a particular industry of segment is over-represented in the sample.
In addition to the first limitation, a possible limitation could be that the results do not apply for every industry. This is a limitation that creates room for additional research in the future. Industry specific research may be different and can have a significant influence on the results. For example, in the high-tech gadget industry there will probably nothing but prospectors, or in the low-budget supermarket segment there will mostly be defenders. This distribution of strategy could therefore influence the results in such a way, that the outcomes will deviate from the results of this research.
The positive correlation, combined with the confirming literature that was already published, makes that I accept the main hypothesis of this research: prospectors make more use of non-financial performance measures in annual CEO bonus compensation than defenders. Moreover, the analysis shows that also the second hypothesis is accepted. According to the results of the second regression, prospectors make more use of customer satisfaction performance measures in annual CEO bonus compensation than defenders. Although there are some limitations for this research, I consider the results as significant and a true representation of this research topic.
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