University of Groningen Faculty of Economics
Master International Economics and Business
The Impact of Employee Motivation on Firm innovation: a
regression study and an exploratory case study of BASF.
Student: Ya Qing Chou Student ID: S1413074
Title: The Impact of Employee Motivation on Firm innovation: a regression study and an exploratory case study of BASF
Month and year: September 2007 Final project ID: IE&B_0607_1413074 Supervisor: G. de Jong
ABSTRACT
Employee’s motivation plays an important role by enhancing employee’s performance. Since knowledge is partially transferable due to its tacit and codified nature, firms need to know how to stimulate employees effectively in order to extract the full benefits of knowledge exploration, which may contribute to firms’ innovative performance. Therefore, a multiple regression model is tested to investigate the relationships between employees’ motivation and innovations. In addition, an exploratory case study has been included to discover other potential effects of employees in the innovative process of firms. Data is derived from CIS-4 survey of the period 2002 – 2004 and the participating company BASF.
Keywords: innovation, motivation, creativity, employees
Acknowledgements:
TABLE OF CONTENTS
Pages
1. Introduction 4
2. Literature Review of Innovation and Motivation 7
2.1 Innovations: how de we interpret these phenomena? 7
2.2 Factors that influence innovation 8
2.3 Motivation: should it be taken into consideration? 12
3. Part I: The Regression Study 14
3.1 Literature Review 14
3.1.1 Innovation Resources made available by Firms 14
3.1.2 Organizational Innovations 14
3.1.3 Collaborative Partnerships 15
3.1.4 Government Support 16
3.2 Research Questions and Hypotheses 17
3.3 Theoretical Model 19
3.4 Methods and Data 21
3.4.1 Data Collection 21 3.4.2 Measures 21 3.4.3 Methods 23 3.5 Empirical Results 25 3.5.1 Results 25 3.5.2 Robustness Analysis 29
3.6 Conclusions, Limitations and Future Expansions 30
3.6.1 Limitations 30
3.6.2 Conclusions and Future Expansions 33
4. Part II: The Case Study BASF 35
4.1 Why a Case Study? 35
4.2 Literature Review 35
4.3 Research Questions 36
4.4 Research Methods 37
4.5.1 Company Information 38
4.5.2 Amar’s Motivation Theory 39
4.6 Conclusions 47
5. Reflections & Implications for Future Research 49
References 51
Appendices 55
1. INTRODUCTION
It has been widely acknowledged that innovations are a requisite to a firm’s survival in the competitive and uncertain business environments of nowadays (Shalley et al., 2000; Gilson et al., 2005; Prajogo & Ahmed, 2006). Successful companies do not longer use the generic strategies of low cost leadership or quality differentiator, but rather focus on innovations to rank themselves in a unique selling proposition in the market (van Ark et al., 2003). There are many different definitions given to explain innovations. Innovations are seen as the renewal and the enlargement of the offered products and/or services; the realization of new production, supply and distribution methods; the introduction of changes in the organisation (CBS, 2004: p. 24). I.e., innovations are new combinations of inputs and the creation of new ideas leading to innovations in products, services and processes (van Ark et al., 2003).
Success in innovations depends on many environmental and contextual factors that affect the organization (Cobbenhagen et al., 2001; Prajogo & Ahmed, 2006). The most important stream of research areas focus on the technological aspects and the human aspects. The literature pays much more attention on the technological aspect, while the human aspect remains rather unexposed. It should be identified that the human dimension is a necessary requirement too when achieving the desired performance level (Bax, 2003). Both are necessary and complement each other. While technological developments give an opportunity to the firm to innovate, the human aspect should exploit the benefits that are obtained from the technological innovation (Adner & Levinthal, 2001; Prajogo & Ahmed, 2006).
Despite the fact that the human component plays an essential role in the innovative potential of firms, many managers see motivating employees one of the biggest challenges to deal with (Shalley et al. 2000). Motivation actually has positive influences on performance, creativity, and growth of firms (Miner, Smith & Bracker, 1994; Amabile, 1997; Amar, 2004; Baum & Locke, 2004). Low motivation may result in work disengagement, low productivity, and illness due to job dissatisfaction. These consequences may have disastrous impacts on firm performance in the current dynamic business environments (Shalley et al., 2000).
evolving towards a more strategic nature. Second, the increased globalization forces change economies gradually towards a knowledge-based character. The knowledge-based economies are increasingly experiencing a process of de-materialization. In other words, the cost structure of goods are getting gradually more dominated by intangible components (den Hertog et al., 1997). The third issue is that many innovation researchers (den Hertog et al., 1997; van Ark et al., 2003) recognize that innovations stem not only from technology advancements, but also arise from non-technological aspects too. The soft side of innovations is often neglected (den Hertog et al., 1997; Cobbenhagen et al., 2001). This refers to the fact that people play an important role in generating innovations in services and goods. Knowledge exists in the human mind, which cannot be easily manipulated. However, knowledge can be stimulated by exciting the deepest parts of the employee’s mind (Bax, 2003; Amar, 2004). The upshot of this is that employees need to be motivated in order to keep up the innovative and creative potential of firms (cf. Amabile, 1997).
The purpose of this study is to investigate the innovative potential of firms by focusing specifically on the role of the human factor; i.e., the employees. This study will consist of two separate studies. First, there will be a regression study by deriving the data of the Community Innovation Surveys (CIS) of the years 2002 – 20041. The second part will focus on an exploratory case study of the firm BASF where two interviews are conducted with the HR-manager and the Research HR-manager.
Two different research methods are conducted. Namely, a regression study and an exploratory case study of the firm BASF. The regression study tests hypotheses quantitatively. The focus is on breadth, rather than depth. It can capture a population’s characteristics by making inferences from a sample’s characteristics (Cooper & Schindler, 2003). Unfortunately, data is hard to obtain concerning this subject matter. The CIS-4 survey of StatLine Databank was used where data was available at sectoral level. The results were unsatisfactory due to sampling problems.
Therefore, an additional case study has been added in order to elaborate the human influences on firm innovations. Case studies place more emphasis on a full contextual analysis of fewer events or conditions and their interrelations. However, the reliance on qualitative data makes support or rejection more difficult, because it allows evidence to be verified and avoids missing data (Cooper & Schindler, 2003). It is subjective to people’s perceptions, and the researcher requires experience to find useful pieces of information.
Moreover, findings cannot be generalized. On the other hand, innovations are more complex than linear models would suggest. Cobbenhagen et al. (2001) argue that innovations are achieved through iterative and interactive processes. These all depend on the specific internal and external context of firms that could influence employee’s motivation on firm innovations. Therefore, a case study could give better insights on the subject.
2. LITERATURE REVIEW OF INNOVATION AND MOTIVATION
The first part of the literature review will focus on the definition of innovations and the different innovation factors will be discussed accordingly. Then, the focus is on the motivation of employees by focusing on the question why motivation should be taken into consideration. This section will be general, since there will be separate literature reviews for the two different studies. Both research methods focus on different elements that are going to be examined.
2.1 Innovations: how do we interpret these phenomena?
Many explanations exist to explain innovations. The definition given by the European Commission defines innovation as the “renewal and enlargement of the offered products and services and with related markets; the realization of new production, supply, and distribution methods; the introduction of changes in management, organization of work and the labour conditions and skills of the employees” (CBS, 2004: p. 24). Innovations are new combinations of inputs and the creation of new ideas leading to innovations in products, services, and processes (van Ark et al., 2003).
For a long time, innovations were seen as the result of technology inventions that were put onto the market by an entrepreneur (Utterback & Abernathy, 1975; Cobbenhagen et al., 2001; Verspagen & Werker, 2003). Now, innovations are often seen as a problem-solving process in commercial companies where the government and public research institutions play a supporting role. Thus, innovations are interactive processes where formal and informal relations between firms and other parties play an important role. Further, innovations are learning processes, where learning follows from usage, application and sharing (CBS, 2004: p. 24). Utterback & Abernathy (1975) distinguish between product and process innovations.
Product innovation is a new technology or combination of technologies introduced commercially to meet a user or market. Product innovation evolves from one dominant strategy to another with time (Utterback & Abernathy, 1975).
Product innovation aims primarily at improving product performance. As the product design stabilizes, process innovation becomes important in order to lower costs of producing products (cf. Adner & Levinthal, 2001).
Summarizing, innovation is deliberately renewing products, services or processes plus the renewal of organisation forms or marketing strategies. In real business life, innovations are the result of the combination of different types of knowledge to something new (CBS, 2004: p. 83). This leads to the exploration of the most important innovation factors that allow innovations to occur in the following paragraph.
2.2 Factors that influence innovations
Cobbenhagen et al. (2001) state that innovations are influenced by many intervening variables. Therefore, most important factors influencing innovations are explained below. A high-level distinction will be made between technological and non-technological factors.
In general, technology is considered as an important input for innovative success (Cobbenhagen et al., 2001), by facilitating the innovative capital goods that are available in firms to employees to exploit the knowledge that employees possess. It has been proven that ICT advancements increase the innovative performance when looking specifically at productivity (Hempell, 2002). Moreover, incremental or radical shifts in technologies result in various implications as technology is embedded within the designs of products as well as the production or operating systems of firms (Henderson & Clark, 1990; Prajogo & Ahmed, 2006).
Human capital is one of the non-technological factors that influence innovations2. The levels of education of the labour force and the expenses on training by firms can influence innovations significantly. Glynn (1996) states that the personal IQ of individuals contributes to the innovative potential of organizations. The individual intelligence is a person’s own capability to process, interpret, encode, manipulate, and access information in order to acquire, retain and apply knowledge quickly and successfully to meet external challenges or solve problems in a particular domain or context (Glynn, 1996). CBS (2004) points out that the development of knowledge-based economies depends heavily on the level of education
and the provision of education. Knowledge is an important input when realizing innovations. The role of human individuals comes into play.
However, knowledge comes in two forms: codified and tacit. Codified knowledge can be put onto paper or can be stored in electronic forms, while tacit knowledge consists of skills that are in the minds of human beings. The last one has a silent and unspoken nature (CBS, 2004: p. 29). Innovations in firms can be realized when combining different types of information into something new, and it should be noticed that innovations are learning processes. The human importance as an antecedent of innovation deals with how to manage the knowledge and turn this into successful innovations. These all depend on the absorptive capacity of a firm, which is the ability of firms to recognize the value of new external information and knowledge, assimilate, and apply them. This ability is important in the innovative output (Prajogo & Ahmed, 2006: p. 502). Research & Development (R&D) is an important indicator when generating knowledge, although, expenditures of this calibre are often of technological nature (RENESER, 2006). There are various factors influencing innovations in different sectors3. Firms have the opportunity to develop knowledge themselves through own research facilities or via external links as research institutes, universities, and competitors.
Last aspects are factors that stimulate innovations by facilitating the connection of the input and output factors are throughput factors. Van Ark et al. (2003) distinguish between three types of throughput indicators: organizational capital, marketing capital, and social capital. These indicators might enhance the innovation process and are typically non-technological.
Organizational capital can be explained as how the organization is organized in order to allow sufficient space for the individuals involved in the organization to innovate accordingly. Following van Ark et al. (2003) this form can be measured as changes in organisation structures, management styles, and new company strategies. The structure of organizations may involve a significant role in the innovation process, because when organizing an ‘innovative’ organization as such that new ideas and knowledge could flow from the appropriate sources to the right destinations of the organization (Thompson, 1965; Noe et al., 2003). Thompson (1965) explains that when there is an inability to legitimize conflicts it will depress creativity. The argument is that conflict diffuses ideas, the less
bureaucratized organizations, the more conflicts and uncertainties there will be and this will result in more innovations. Glynn (1996) emphasizes that innovation is “facilitated by an environment that provides a cognitive basis for creative efforts through structures encouraging the creation of systematic understandings and ongoing exploration of alternative points of view”. Prajogo & Ahmed (2006) underline top management support, people and culture when nurturing the environment that allows for successful innovations.
Marketing capital and social capital is referred to new marketing concepts and partnerships, respectively (van Ark et al., 2003). New marketing concepts influence the innovation process through exploring the market needs and try to adapt the firm to the environmental conditions in order to remain competitive (Cobbenhagen et al., 2001). It is a stimulus that keeps the firm sharply adjusted to the highly dynamic business environments. Social capital is an important throughput for the innovation process, because some knowledge is very expensive to create by firms on their own, and therefore when gauging into partnerships, firms are able to broaden their knowledge fields and might positively impact the learning process. This may result in the competitive advantage of speeding up the reaction process of firms in the fast changing business world. Hence, when firms expose themselves to others and share resources or knowledge it might increase the potential to innovate (Cloodt; Hagedoorn & Kranenburg, 2006).
11 Figure 1 Factors influencing innovations
Source: Services Innovation, Performance and Policy: A Review by van Ark et al. (2003).
Innovations
Knowledge – tacit
or codified
Social Capital
Organisational Capital
Marketing Capital
Hard Capital
Human Capital
2.3 Motivation: should it be taken into consideration?
It has been emphasized that people and social practices represent one of the basic ingredients of innovations in organizations. Consequently, efforts should be made towards managing people by creating and maintaining an environment that supports innovations by motivating them and giving them opportunities to develop these innovations (Bax, 2003; Prajogo & Ahmed, 2006). Motivation is associated with the forces acting on a person causing him to act in a certain way (Amar, 2004). Amabile (1997) states that motivating creativity4 in organizations is the first step in the innovation process, and she distinguishes between intrinsic and extrinsic motivation. The first one refers to motivation to work on something because it is interesting, involving, exciting, satisfying, or personally challenging. This type of motivation resides in a person’s own personality. Whereas extrinsic motivation refers to expected evaluation, surveillance, competition with peers, dictates from superior, and rewards.
Top management should support and make commitments to realize innovations. The role of resources and power comes into picture where the organizational culture should nurture innovations (Thompson, 1965; Amabile, 1997; Prajogo & Ahmed, 2006). Though, many managers see motivating employees one of the biggest challenges (Shalley et al., 2000). Major parts of knowledge exist in human minds, and cannot be manipulated easily. To manage knowledge of employees by motivating employees in a way that the deepest parts of the human get excited is quite daring (Amar, 2004). For organizations it is important to know how to motivate employees effectively, due to the changing motivating environment factors that surround the employees. In other words, assume that generation X was motivated by working hard in order to climb the corporate ladder successfully, while the latter generation Y is not stimulated by achieving the top, but rather focus on self-regard. Thus, it is necessary for management to provide a quality of working life for its own employees that serves their needs in terms of overall wellbeing, skills development, and career paths (Amar; 2004; Prajogo & Ahmed, 2006). Referring to firm practices that focus on empowering employees and allow them to get more involved in the organization. Empowerment is related to the decentralization of the organization.
Organizations that create a working environment for employees where creativity is encouraged together with limiting the constraints can result in job satisfaction and significant benefits can be achieved when fostering the innovative potential of employees (Thompson, 1965; Amabile, 1997; Shalley et al., 2000; Hauschild, 2001; Stokols et al., 2002; Amar, 2004).
Capabilities, skills and even attitudes of employees contribute significantly to innovations. Previous studies establish the important finding that people need to be excited to come up with new creative ideas (Miner et al., 1994; Shalley et al., 2000; Baum & Locke, 2004; Gilson et al., 2005). Environmental or contextual factors can help to stimulate the creativity of the individuals. The organisation structure, colleagues, and resources available to workers can influence the motivation level of employees. For instance, it has been found that autonomy positively impacts the level of creativity (Baum & Locke, 2004). Good working relationships with colleagues as in working teams can influence creativity in a positive manner as well when employees communicate certain practices and working methods among each other, may increase employees’ knowledge fields (Smith et al., 2005).
3. PART I: THE REGRESSION STUDY 3.1 Literature Review
The previous section was the general literature review that establishes the findings of researchers (Miner et al., 1994; Amabile, 1997; Shalley et al., 2000; Amar, 2004; Langfred & Moye, 2004) that motivation should be taken into consideration when looking at employee’s performance in organizations. This section is the first part of the study that focuses on the regression study to test for effects of four motivational variables, which will be explained below. A multiple regression model is tested by looking what the effects of the four variables are by using LS-methods. The main aim of this research part is to investigate if the four selected motivation variables influence the dependent variable firm innovations and how these influence the dependent variable.
3.1.1 Innovation Resources made available by Firms
Thompson (1965) argues that for organizations to be innovative, a bureaucratic organization structure is not the most viable option. However, just organizing the organization to diffuse ideas in an effective way is not sufficient. Thompson (1965) stresses the importance of the resources for innovation, like money, time, skills and goodwill. The availability of ICT-facilities, the R&D expenditures and many other facilities that allow employees to develop their knowledge further is a motivating stimulus provided by the firm. An environment that provides employees the necessary tools to develop successful innovations is a requisite, it may motivate employees to use these tools effectively and it will result in innovations (Prajogo & Ahmed, 2006). In addition, the improvements in ICT-facilities increased productivity (Hempell, 2002). Therefore, the expenditures that the firms make in order to invest in their employees can be a motivator for employees to exploit the advantages and the disadvantages to use them.
3.1.2 Organizational Innovation
Amabile, 1997; Baum & Locke, 2004; Prajogo & Ahmed, 2006) point out that autonomy and empowerment are important predictors of innovations.
Autonomy is a job characteristic that determines the motivating potential of a job. It leads to the critical psychological state of “experienced responsibility for outcomes of the work,” which in turn leads to outcomes as high work effectiveness and high internal work motivation (Langfred & Moye, 2004). By empowering employees, employees take responsibility and authority to make decisions, they share the resulting rewards and losses, which results in higher dedication to their work (Noe et al., 2003). The use of rules, checks, and controls will constrain the way a job is done and influence creativity negatively (Shalley et al., 2000). Informality provides an inducement for individuals to exercise greater individual discretion and has been associated with greater motivation and commitment (McGrath, 2001). Research indicates that employees need organizational systems and procedures to support and encourage their creative efforts (Shalley et al., 2000). A supportive work environment complements the creativity requirements of the jobs5.
This aspect of organizational innovation is the dedication of management to organize the organization in a way that it provides the right directions to their employees to exchange their creative ideas and can be seen as a motivator for employees. The firm is constantly trying to structure and restructure its organization in the most helpful way to allow employees to innovate as effective as possible (CBS, 2004; 2006).
3.1.3 Collaborative Partnerships
Knowledge is important in order to realize innovations and firms have many options to obtain knowledge, for example, through own internal sources6 or via other external routes. Environmental challenges have led to the increased strategic alliances where two or more firms agree to pool their resources to pursue specific market opportunities (Gulati, 1995; Cloodt et al., 2006). Organizations often learn by collaborating with other organizations when sharing knowledge and those alliances can generate rents by leveraging the complementary resource endowments of an alliance
partner (Dyer & Singh, 1998). Other ways of partnering are via universities or research institutions. The advantage of collaborative partnerships can increase creativity of the collaborating firm. Diversity is brought into the organization and this can lead to idea exchanges on the work-floor (Noe et al., 2003). The employees of the involved partners work together and both hold different types of information, where the cognitive conflict is likely to increase which can lead to more productive exchanges and greater attempts to combine information and knowledge in an effort to reduce conflict (Smith et al., 2005). When individuals in an organization hold the same stock of knowledge, creativity may be dampened, because members of the organization will be less likely to perceive value in the exchange and combination process (Smith et al., 2005). Thus, collaborative partnerships can be seen as a way to motivate employees to innovate properly.
3.1.4 Government Support
Policies are necessary when market failures are present (Navaretti & Venables, 2004) in order to allow for the correction of market failures. Many firms do not perform innovation activities due to its public-good characteristics7 and innovations are subject to uncertainty (Verspagen & Werker, 2003). Therefore, some firms are careful when investing in innovation activities, because the rents earned back in the future are usually disappointing, due to spillover effects. Klette et al. (2000) show that subsidies have had a positive effect on the performance of the recipients. However, Segerstrom (2000) show that there are positive and negative effects of government subsidies. The main argument is that when there is a permanent increase in the R&D-subsidy rate, firms immediately respond by increasing R&D-expenditures. Firms will devote more resources to R&D, but the technological complexity also increases rapidly. Researchers will exhaust the supply of simpler problems more quickly and find themselves struggling with simpler problems. The consequence is that innovation rates will drop.
Davidson & Segerstrom (1998) show that more recipient firms will lead to less technical changes. There must be a distinction between innovative R&D and imitative R&D. The former produces higher quality products, whereas the latter imitates other firms’ products. Both types create new knowledge, but only innovative R&D leads to
genuine innovations. Subsidies make monopoly profits earned from successful innovations short-lived, due to imitative R&D. As a consequence, it decreases technical change, and results in slower economic growth.
Subsidies have the intention to encourage firms to perform innovative activities, but the explanation of Segerstrom (2000) shows that recipients can exhibit both positive and negative impacts on the innovative performance of firms.
3.2 Research Questions and Hypotheses
The detection that innovations do not stem solely from technology advances, sketches the reality that innovations can occur from multiple sources. People are important driving forces of innovations (Bax, 2003; Amar, 2004). Many researchers (cf. Utterback & Abernathy, 1975) explain innovations by concentrating on the technological factors, while the non-technological factors are being neglected (RENESER, 2006). Cobbenhagen et al. (2001) mention that is difficult to make a clear distinction between these non-technological and technological factors, because of the interactions between these two. Therefore, the non-technological parts should be taken into account too, to make the relationship between the factors that contribute to innovations more complete. The non-technological components stem to a large extent from the human factor. Explicitly, knowledge recombination leads to innovations and the major part of knowledge can be found in the minds of the individuals. Unfortunately, ideas just don’t come up as a machine that is producing products at a continuous rate. That is, people need to be stimulated to generate the recombining of knowledge flows that show the ways to innovate successfully. This leads to the main research question of the regression study:
Does employees’ motivation lead to more innovation?
Innovation Resources - Facilities to innovate can be motivating for employees to
innovate. The hard side of innovations can be a motivator for workers to develop themselves in their working methods and increased usage can deepen the knowledge area in which they are active (Hempell, 2002). When firms put tools and facilities at the disposal of their employees, it gives their employees the opportunity to innovate (Bax, 2003). Without the necessary facilities it can be difficult for employees to innovate, because they simply do not possess the right equipment. This points to the first hypothesis:
Hypothesis 1: There exists a positive relationship between innovation resources and innovation.
Organizational Innovations - Organizational structures do influence motivation of
employees. By empowering employees, employees take responsibility and authority to make decisions; they share the resulting rewards and losses, which result in a higher dedication to their work (Noe et al., 2003). McGrath (2001) argues that greater autonomy allows for innovations. Van Ark et al. (2003) state that organizational capital facilitates the innovation process. The way in how the organization is organized can stimulate or decline employees’ motivation (Thompson, 1965). The second hypothesis is:
Hypothesis 2: There is a positive relation between organizational innovation and innovation.
Interfirm Partnerships - Contextual and environmental factors influence the organization
Hypothesis 3: There is a positive relationship between the number of innovative partnerships and innovation.
Government Support - The final aspect is the contribution of government support to
companies. Many firms receive subsidies or other forms of government support in order to solve market failures in the underinvestment by some firms in innovative activities (Navaretti & Venables, 2004; Verspagen & Werker, 2003). Segerstrom (2000) shows that firms innovate less when the R&D subsidiy rate increases. Firms devote more resources to R&D, but technological complexity increases too. The employees will focus more on the complex issues and exhaust the simpler problems.
Therefore, it could be argued that subsidies encourage firms to devote more resources to innovative activities. Klette et al. (2000) show that subsidies have positive effects on firm performance. On the contrary, the more firms receive government support or the increase in subsidy rate can negatively influence the innovative performance of firms (Davidson & Segerstrom, 1998; Segerstrom, 2000). Shortly said, subsidies lead to more firm innovations, but after a point where subsidies become too high the effects become negatively. This leads to the fourth hypothesis:
Hypothesis 4: There exists a quadratic relationship (an inverted U-curve) between government support and firms’ innovations.
3.3 Theoretical Model
motivation variables that could explain the changes in the dependent variable. Such as organizational innovations and innovative expenditures.
A multiple regression model will be used to test the impacts of employees’ motivation. The following regression model will be tested, including the control variables. The abbreviations of the variables are given in Table 1.
The research model:
INNOV = 0 + 1*INNEXP + 2*ORGIN + 3*PARTN + 4*GOVSUP + 5*GOVSUP2
+ 1*SECSIZ + 2*INNPAT + 3*MARKINN + 4*OBSTAC + 1 (1)
Table 1. The explanations of the variables in the research model.
Variables Abbreviations Explanations
Dependent INNOV Product and process
innovations in the number of firms in each sector.
Independent INNEXP Innovation expenditures by
all the firms in each sector.
ORGIN Organizational innovations
by the firms in each sector.
PARTN Collaborative partnerships
between firms in each sector.
GOVSUP Government support received
by the firms in each sector.
Control SECSIZ Size of the sector, the
number of firms in each sector.
INNPAT The number of innovators
that possesses intellectual property rights (patents, trademarks, copyrights, and designs).
MARKINN The number of firms in each
sector that experienced marketing innovations.
OBSTAC The number of firms that
3.4 Methods and Data 3.4.1 Data Collection
The model will be tested with data derived from the CIS-4 survey of the period 2002 to 2004. Data is online available at the website of the Dutch CBS, in the StatLine databank. The total sample population covers 57.509 firms that are distinguished between innovators and non-innovators by CBS. For a firm to be included in the survey it has to (1) have more than ten employees, and (2) be based in the Netherlands. The level of analysis is sectoral8. The CIS-surveys focus on different innovation elements each period. Therefore, the focus is on the Netherlands solely. The CIS-surveys mainly focus on technological aspects of innovations. Small firms tend to invest less or do not invest at all in innovative firms than large firms (CBS, 2004). That is why firms with less than ten employees are excluded in the CIS-surveys.
3.4.2 Measures
Independent Variables
Innovation Resources - The variable innovation resources will be measured as the total
innovative expenditures per sector in millions of euros. This variable is defined as total expenditures for one or more activities9 to realize the renewal or improvement of
products, services or processes technologically. It is subject to all expenses in the last year of the considering year, excluding depreciation, but including the direct personnel costs and investment expenditures (StatLine, 2006).
Organizational Innovations - Organizational innovations are defined in StatLine (2006)
as a renewal of or an important change in the company’s structure or management methods with the aim of improving the efficiency of the firms’ processes and/or the quality of goods and services. StatLine (2006) shows how many firms did have organizational innovations in the considered period in each sector.
Interfirm Partnerships - Collaborative partnerships can influence the collaborating
companies. This can be together with universities, firms, and institutions. Partnerships will be measured as the number of firms in each sector that is pursuing innovation projects together with a partner (StatLine, 2006).
Government Support – Government is measured as the number of firms in each sector
that receive government support in forms as subsidies or fiscal arrangements. Segerstrom (2000) argues that there are positive and negative effects of government subsidies on firms’ innovativions.
Dependent Variable
Innovations - The dependent variable, innovations are measured as the total number of
firms of each sector experiencing product and process innovations in the period 2002 – 2004 (StatLine, 2006).
Control Variables
Size of sectors - Size of the sectors should be included in the model due to the fact that
size does play a role in the innovativeness of firms (CBS, 2006). More firms in sectors may constitute more competition, where innovations are important for firms’ survival in the competitive business environment. Therefore, the number of firms in all the sectors in manufacturing and services are included to control for this effect on innovations. This control variable is measured as the number of firms in each sector (StatLine, 2006).
Innovators with patents - Patents are instruments that can be used to remedy a situation of
underinvestment in R&D due to the public good character of technology. By creating a (legal) barrier to copy the knowledge created by an inventor, by granting the inventor the monopoly to use the knowledge in the market (Verspagen & Werker, 2003).
characteristics10 and knowledge is subject to uncertainty. Patents are necessary, because they provide incentives to individuals by offering them recognition for their creativity and material reward for their marketable inventions. These incentives encourage innovation, which assures that the quality of human life is continuously enhanced. Patents enhance creativity and innovation (www.wipo.int/pct/en/).
Therefore, this control variable is included, because it might influence the dependent variable innovations. This variable will be measured as the number of innovators that possess intellectual property rights in the form of patents, industrial designs, trademarks, and copyrights (StatLine, 2006).
Marketing Innovations - Marketing innovation is the implementation of new or strong
improved product designs/concepts or selling methods in order to make the goods and services more attractive to conquer new markets (StatLine, 2006). Cobbenhagen et al. (2001) and van Ark et al. (2003) mention that new marketing concepts keep firms sharp in its innovations when continuously adapting to the turbulent environments. This control variable should be included to make the model more complete, and will be measured as the number of firms in each sector that experienced marketing innovations in the period 2002 – 2004.
Obstacles - This control variable is measured as the experienced obstacles in the
considered period that resulted in the delay, premature stops and/or never-started innovation projects. This could influence the dependent variable in a negative way. The Databank gives several reasons why firms experienced these obstacles and influenced innovations negatively11.
3.4.3 Methods
The instruments that are used for the operationalization of the multiple regression model is the Internet for deriving the data of the CIS-4 survey via the StatLine databank
10 Public good characteristics are non-excludability and non-rivalry (Verspagen et al., 2003)
that is available on the CBS-website, Eviews 5.1 for running the LS-regressions and testing the assumptions of the model.
Data is derived from StatLine databank. The data was converted into the number of firms when it was given in percentages. The data was available at sectoral level, therefore the sample consists only of 30 sectors.
Eviews was used to run the LS-method by estimating the results. Additional specification tests were performed to test the assumptions of the research model12.
3.5 Empirical Results 3.5.1 Results
The Eviews output given in Table 2 shows that there are 30 sectors containing 57.509 firms. The intercept parameter is negative. When holding all the other variables constant, the number of innovations would be negative, which sounds rather extraordinary. Though, it should be included to allow for mathematical completeness and to improve the models’ predictive ability (Hill et al., 2001). In addition, it is insignificant when looking at the p-value of the correlation coefficient.
The first explanatory variable innovation expenditures of each sector shows a negative correlation coefficient with the dependent variable innovations in each sector. However, the effect could almost be neglected since the impact is rather small. In addition, the p-value is 0.3499; this indicates that it is insignificant. Hypothesis 1 is not supported. Table 2 shows a negative correlation coefficient. By testing H0: 2 = 0 H1: 2 0, it can be concluded that the null is accepted, because the critical value is smaller than
the t-statistic of this correlation coefficient assuming confidence levels of 10%, 5% and 1%.
The sign of the second explanatory variable is negative, which is contradictory of what was forecasted in the hypothesis. Thus, there are negative influences of organizational innovations on the dependent variable innovations. The p-value of the coefficient is 0.3960, indicating that it is insignificant too. Hypothesis 2 is not supported.
H0: 3 = 0 H1: 3 0. The t-statistic -0.867 is larger than the critical values -1.701 (10%),
-2.048 (5%) and -2.763 (1%). Therefore, the null cannot be rejected, resulting in the rejection of hypothesis 2.
The third explanatory variable interfirm partnerships has a positive influence on the dependent variable. The p-value is insignificant, when assuming of 0.05. When assuming confidence levels of 5% and 10%, hypothesis 3 is supported. The critical values are smaller than the t-statistic of 1.945, and the null hypothesis is rejected; H0: 4 = 0 H1: 4 > 0. When assuming 1% confidence interval, hypothesis 3 is rejected. The critical
value is 2.467, which is larger than the t-statistic.
quadratic relationship with an inverted U-curve. The correlation coefficients c(5) and c(6) are both negative, especially c(6) that influence the shape of the U-curve is too small. Both correlation coefficients are insignificant when assuming of 0.05. Hypothesis 4 is not supported when assuming of 0.05 and 0.01. When testing for H0: 5 = 0 H1: 5 0,
the t-statistic of -1.941 is larger than the critical values of -2.048 and -2.763. Thus, accept the null. H0: 6 = 0 H1: 6 0. The t-statistic is -1.027, and this is larger than the critical
values of -2.048 and -2.763. Accepting the null hypothesis, together with the rejection of the influence of 5 and 6, results in the rejection of hypothesis 4 that government support
influences the dependent variable. When testing at the confidence level of 10%, 5 is still
rejected, but 6 is accepted. Hypothesis 4 is rejected at 1% and 5% confidence interval,
and partially supported by the acceptation of 6 with the 10% level.
The control variable sector size has a positive influence on the dependent variable and it is significant. For the control variable, innovators with intellectual property rights, the coefficient is positive and significant. However, for the control variable marketing innovations the coefficient is negative and insignificant. The last control variable is significant and has a remarkable influence on the dependent variable.
Table 2. The E-Views output of the research model. Dependent Variable: INNOV
Method: Least Squares Date: 07/05/07 Time: 13:51 Sample: 1 30 Included observations: 30 INNOV=C(1)+C(2)*INNOEXP+C(3)*ORGIN+C(4)*PARTN+C(5) *GOVSUP+C(6)*(GOVSUP^2)+C(7)*SECSIZ+C(8)*INNOPAT +C(9)*MARKIN+C(10)*OBSTAC Coefficien
t Std. Error t-Statistic Prob.
C(1) -14.16016 22.59500 -0.626694 0.5379 C(2) -3.24E-08 3.38E-08 -0.957266 0.3499 C(3) -0.103972 0.119868 -0.867382 0.3960 C(4) 0.846510 0.435199 1.945110 0.0660 C(5) -0.939868 0.484186 -1.941130 0.0665 C(6) -0.000526 0.000512 -1.027299 0.3165 C(7) 0.040970 0.014884 2.752700 0.0123 C(8) 1.033214 0.447554 2.308581 0.0318 C(9) -0.414677 0.280258 -1.479623 0.1546 C(10) 2.364354 0.415062 5.696385 0.0000
R-squared 0.990393 Mean dependent var 458.9667 Adjusted R-squared 0.986070 S.D. dependent var 457.2455 S.E. of regression 53.96748 Akaike info criterion 11.07584 Sum squared resid 58249.77 Schwarz criterion 11.54291 Log likelihood -156.1376 Durbin-Watson stat 1.880160
The assumptions13 of the model are tested by running specification tests. It follows that assumption 1 is satisfied. The F-statistic is 423.157 with a probability of
0.00014. Implying that at least one of the correlation coefficients does influence the dependent variable.
Assumption 2 asserts that the average of all omitted variables, and any other errors made when specifying the model is zero. Implying that the model is on average correct (Hill et al., 2001). The RESET-test is performed to detect omitted variables15. When the p-values are lower than 0.05, it is suggested that the model is inadequate. With one fitted term, the F-statistic is 2.503 with probability of 0.130. The probability exceeds the level of significance, and this implies that the model is adequate. Even with two fitted terms, the p-value exceeds the level of significance and the hypothesis of the inadequate model has been rejected. Shortly said, it is adequate.
Assumption 3 refers to the detection of any heteroskedasticity. Heteroskedasticity exists when variances for all observations are not the same, thus the aim is var (yt)=var(et)= 2. To test for heteroskedasticity, the residuals test with White
Heteroskedasticity (no cross terms) test is performed16. The F-statistic is 1.137 with probability 0.413. The p-value is larger than 0.05. Therefore, the null hypothesis will be rejected. Meaning that the variance is constant over time and it is homoskedastic.
Assumption 4 refers to the detection of any autocorrelation. This assumption has been satisfied. The Durbin-Watson statistic is 1.88017, where the upper- and lower bound
is 0.926 and 2.034. The Durbin-Watson statistic falls within the range of the upper- and lower bound, therefore, the null hypothesis is not rejected and implies that autocorrelation does not exist in this model.
Assumption 5 says that the values of xtk are not random and are not exact linear
functions of the other explanatory variables. It is assumed that the explanatory variables are known prior to observing the values of the dependent variable and no variable is redundant. This is partially true for this assumption, because all the included variables in this model are not random. However, all explanatory variables are tested for collinearity. It appears that certain pairs of variables show high correlation coefficients among each
14 See Methodology paper.
15 See Methodology paper for the results of the RESET-test.
other18. Thus, all the values of the explanatory variables are partially linear functions of the other explanatory variables.
If the assumptions 1 to 5 hold and the sample size T is sufficiently large, then the least squares estimators are the Best Linear Unbiased Estimators (BLUE) of the parameters in a multiple regression model. This is not the case, since assumption 5 has been violated by multicollinearity and the sample size is rather small.
Assumption 6 states that the random errors have normal probability distributions and the values of the dependent variable is normally distributed about their mean. The Jarque-Bera test is performed to test for normal distributed errors19. The Jarque-Bera test is 0.1033. 0.1033 > 0.05, the null hypothesis is not rejected and this indicates that the errors are normally distributed. In other words, assumption 6 holds.
3.5.2 Robustness Analysis
A robustness analysis is performed to see if the support for the hypotheses improves. The variable partnerships has been excluded20. In Table 3, it can be seen that the variable obstacles and sector size are significant and positive. It is remarkable that the correlation coefficient of obstacles is rather high. Other variables are all insignificant, and the sign of innovation expenditures on innovations still remains negative. However, it became less negative than when the variable of partnerships were included. The variable of organizational innovations is positive. Though, it should be noted that it is still insignificant. The correlation coefficients of government support still remains negative and insignificant. The control variable sector size remains significant and positive. However, the control variable patents requested by the number of firms in each sector is still positive, but became insignificant in this analysis. For marketing innovations, the correlation coefficient became less negative, but remains insignificant.
Shortly said, the exclusion of the variable partnerships did not offer support for the hypotheses21.
18 See Methodology paper for the correlation matrix.
19 See Methodology paper for the results of the Jarque Bera Test.
Table 3. The E-Views output without the variable partnerships. Dependent Variable: INNOV
Method: Least Squares Date: 07/20/07 Time: 14:16 Sample: 1 30 Included observations: 30 INNOV=C(1)+C(2)*INNOEXP+C(3)*ORGIN+C(4)*GOVSUP+C(5) *GOVSUP^2+C(6)*SECSIZ+C(7)*INNOPAT+C(8)*MARKIN+C(9) *OBSTAC
3.6 Conclusions, Limitations and Future Expansions 3.6.1 Limitations
The empirical results show that what has been hypothesized is not totally verified by the hypotheses tests. Moreover, most of the included variables are insignificant. Common explanations are a) aggregation of data, b) collinearity, and c) sample size.
The aggregation of the data into sectors could create problems in the analysis. Since the separate effects of individual firms cannot be detected in the innovative process of firms by their own employees.
Second, the fifth assumption of the model has been violated, because the variables are correlated among each other. Therefore, the separate effects of the variables are hard to determine, and the estimators are not BLUE.
Third, the sample size of only 30 sectors is too small (Hill et al., 2003). Hill et al. (2003) state that for a multiple regression model a sample size of 50 would be appropriate.
Several explanations will be given for the high collinearity between the variables. First, the variable partnerships. This variable shows high correlations with three other explanatory variables. This variable has been defined as the number of innovators that have pursued innovation projects together with other firms or institutions (partners). These partners are competitors, suppliers, government, universities, etc. One of the reasons why this variable has high correlation coefficients with organizational innovations is that the variable organizational innovations has been defined by the StatLine (2006) as the renewal of or an important change in the company structure or management methods with the aim of exploring knowledge whereby efficiency of the firms and/or the quality of goods and services improve. These are changes in knowledge management systems, significant changes in company structures, and changes in relationships with third parties. Changes in relationships with third parties are referring to changes in the partnerships between firms and its collaborators. This might the reason why the correlation coefficient between these two explanatory variables is so high, because the definitions and the measures tend to overlap.
potential partner. Therefore, these two variables tend to move together in systematic ways. The high correlation between sector size and partnerships may be explained by the fact that when a sector has more firms, partnerships are more likely to occur. Reasons for firms would be that the more competition, they might see partnerships as a strategic move to obtain competitive advantages. The smaller the number of firms in the sector, the number of firms engaging in partnerships is less. This might be that the number of firms to collaborate with others is less, which results in less partnerships (Lee & Lim, 2006).
The high correlation coefficient between partnerships and the innovators that requested patents in the considered period might be explained by the fact that patents are requested in order to protect intellectual property rights of firms. When collaborating with third parties and there are significant breakthroughs, it might be wise to request patents in order to protect the obtained knowledge. Van Ark et al. (2003) find that there are more partnerships established in services when patent application goes easier.
There is a high correlation coefficient between partnerships and the number of marketing innovations. Marketing innovations are defined as the implementation of new or strongly improved product designs or selling methods to deliver goods and services in a more attractive way to conquer new markets (CBS, 2006). Marketing innovations are changes in product designs and the distribution of goods and services. It might be that partners have important roles in realizing these marketing innovations. This might explain the high correlation coefficient between these two explanatory variables.
For obstacles and partnerships, it might be that when there are more difficulties for firms to innovate, they have reasons to share resources with third parties (Lee & Lim, 2006).
improvements in the distribution activities and product designs of marketing innovations. Explaining for obstacles and organizational innovations, it might be that when firms in sectors dealing with more obstacles tend to pursue more organizational innovations in order to realize more innovation projects (CBS, 2004; 2006).
For government support and patents requested by firms, it might be explained by the fact that when more firms receive support of governments, they would be more likely to request patents in order to clearly indicate who the ‘real’ owner of the invention is. Government support and obstacles are highly correlated; imperfect markets explain this that firms tend to under-invest in innovation projects due to knowledge spillovers (Verspagen et al., 2003). It might be that when more obstacles experienced in the sector it can be explained by more government support by the Dutch government (Cobbenhagen et al., 2001).
Marketing innovations and innovators with intellectual property rights show high correlation coefficients too. Changes in product designs and in distribution methods may require protection methods in order to prevent competitors from imitating (Verspagen et al., 2003).
Innovators with patents and obstacles explores a high correlation coefficient, the explanation might be that when more obstacles are present, the innovators are more likely to request patents in order to prevent from knowledge spillovers that might influence the benefits versus the costs of the innovation projects (Verspagen et al., 2003).
Marketing innovations and obstacles are highly correlated. When there are more obstacles for firms in each sector, marketing innovations might be necessary to overcome the difficulties by improving distribution methods or product designs (CBS, 2004; 2006).
3.6.2 Conclusions and Future Expansions
The recognition that it is influenced by human motivation (Prajogo & Ahmed, 2006) is not empirically supported by problems in the data. A larger sample size with the focus on micro-level data, and with the appropriate measures in the appropriate forms is required. Briefly said, better data collection is needed. Further, the data should be given per year and not once per three years. Hopefully, more datasets will be created in order to overcome the data collection problem concerning this subject.
4. PART II: THE CASE STUDY BASF 4.1 Why a Case Study?
Cobbenhagen et al. (2001) mention that many factors influence innovations. There is not one perfect path to follow when it comes to innovations. Innovations depend on many factors that are available inside and outside the firm. Every situation that occurs to a firm is unique; the interaction between all the different factors can influence employees’ behaviour in the innovative process.
The regression study does not empirically support the subject matter, due to aggregated data and collinearity. A different approach is required to reveal employee’s effects on innovations.
Third, a highly competitive and successful firm in the chemicals industry where innovations are a requisite was willing to participate. Thus, the case firm is a perfect candidate when revealing the reasons why some firms are able to innovate so successfully.
4.2 Literature Review
Amar’s theory of Motivation Drivers and Motivation Antecedents Model
Amar (2004) notifies that motivation is dynamic due to the constantly evolving and changing characteristics of the living organization. Therefore, to motivate employees in a consistent manner it is important to develop a working understanding of the human mind and behaviour of their own practices based on the results of the dynamics of the specific motivation systems. Amar’s (2004) theory recognizes five motivation drivers: sociological, psychological, generational, knowledge work, and cultural.
circumstances, capabilities and views shape employees differently. Fourth, the knowledge
work driver is related to technology. When firms acquire new technologies, the provision
to employees with the equipment motivates them to use it (Hempell, 2002). Last, the
cultural driver has to do with the different backgrounds of employees, and the enabling
of communication technologies that allows the exposure of different cultural working colleagues (Amar, 2004).
Amar (2004) distinguishes between three motivation antecedents: job antecedents, outcomes antecedents, and organisational system antecedents. These antecedents are sources within the firm that managers can use to motivate workers.
The first one refers to the characteristics in the job that excites the employee to have pleasure in his/her job (Amabile, 1997; Shalley et al., 2000). These include the job content and the environmental factors. The second antecedent is the outcome that refers to rewards and benefits that employees receive. The last antecedent has to do with the organisation of the firm such as policies, practices, controls, and security (Thompson, 1965; Shalley et al., 2000; Langfred & Moye, 2000; McGrath, 2001). These antecedents are not equal to motivation contents, because these focus on the needs of workers. The antecedents actually jolt psyches of the workers, buoying employees’ creativity and releasing the energy to work creatively (Amar, 2004).
All these motivation drivers and antecedents are given in Figure 2. It is the innovation motivation antecedents model of Amar (2004). According to this model, to motivate workers successfully, organizations should make effort in studying the drivers of the employee’s motivating behaviour. Organizations should respond to changes in these motivation drivers by devising motivation antecedents, and turn these antecedents into major sources from where motivation can emanate. Thus, constant update and revision is required to continue employee’s excitement when keeping organizations innovative.
4.3 Research Questions
Does employee’s motivation lead to more firm innovations and how does employee’s motivation influence these innovations?
Figure 2. The Innovation Motivation Antecedents Model of Amar (2004).
Source: Motivating knowledge workers to innovate: a model integrating motivation dynamics and antecedents by Amar (2004).
4.4 Research Methods
The case study is performed in the form of a semi-structured questionnaire22. The Human Resources (HR)-manager of BASF Resins BV has been interviewed, and the Research Manager of BASF Corp. has filled in the questionnaire due to distance problems. The questionnaire is based on the framework of Amar’s motivation theory. Five motivation drivers and motivation sources were investigated with the aim of exploring if and how employee’s motivation influence firm innovations.
The interview with the HR-manager was recorded on a voice-recorder and notes were written down on paper. Whereas the Research Manager filled in the questionnaire and send it back by e-mail. All the results were returned to the managers for verification and approval. Additional information is obtained via company websites and annual reports that were available at the Nijehaske subsidiary.
4.5 Results
4.5.1 Company Information
The HR-manager of the company BASF Resins BV in Nijehaske has been interviewed. This company was formerly known as Johnson Polymer BV and was taken over by BASF Nederland BV on July 1st 2006. The Research Manager of BASF Headquarters in Ludwigshafen also participated in this study.
The headquarters of BASF is established in Ludwigshafen in Germany with more than 100 large sites over the world and operating in more than 170 customer countries (www.basf.com). The company has more than 95.000 employees worldwide. It is active in five business segments: (1) chemicals, (2) plastics, (3) performance products, (4) agricultural products and nutrition, and (5) oil and gas.
BASF Resins BV in Nijehaske is of origin Johnson Polymer BV, an American family company that develops, produces and sells a broad range of resins, polymers, and high solid oligomers. The products are semi-finished products for the business-to-business market. The focus is on combining the best product quality together with environmental friendly techniques. Moreover, the company produces products for specific applications, which are based on different technologies. Much attention has been paid to markets focusing on R&D by revealing the new needs of the market, to allow for continuous improvements of products and technologies. It is now part of the Business Unit BASF Resins and this is also a component of the larger division of Performance Chemicals of BASF – The Chemical Company in Ludwigshafen. The company exists of 156 employees with the majority consisting of Dutch nationalities. The main reason to acquire Johnson Polymer BV is the prestigious performance of the company in the area of polymers, resins, and oligomers. For BASF, it is a method of enhancing the knowledge in this business segment.
4.5.2 Amar’s Motivation Theory23
Motivation Drivers
Sociology - Johnson Polymer BV used to have and still has a working environment that is
characterized by a diverse cultural workforce. By paying respect for each other based on the different nationalities it was able to grow successfully by understanding each other and working with each other as best as possible. All the different departments within this plant work together in multifunctional teams. The recent acquisition did not lead to massive organisational changes. Besides, the period is too short to experience any long-term impacts of the take-over.
The Research Manager clearly sees changes in sociology that influences employee’s motivation in innovations. Employees focus less on sustainability where researchers strive more for quick wins and results. However, demographic changes did not influence this aspect. BASF always focuses on “thinking out of the box” and “looking from different perspectives”. According to the Research-manager diversity in research is not an effect of demography rather an intrinsic effect of humans. BASF has become a trans-national company. The difficulty is trying to integrate without losing the firm culture. The possibility is that it increases diversity. However, integrating firms into BASF did not increase overall motivation of the firm’s employees.
This aspect does play an important role in the motivation of employees in the search of innovations. Though, demographic influences remains unclear, because it depends more on the current firm culture and vision in how it supports employee’s initiatives.
Psychological - Salaries are important incentives to keep employees stimulated to work.
BASF Resins BV uses training on the job where employees have to work together in multifunctional teams. It is important to give employees chances to develop themselves. Employees get formal conversations about their evaluations and functioning over the previous period. The raises in salaries are based on the functioning of the employees. Thus, individual performance is taken into account when looking at salaries. In addition,
there is a profit arrangement for the whole company. The more profits the firm makes, the more each employee can get of the profits. These all depend on the level of the function each employee exhibits, the number of years working for the company (i.e., loyalty), and individual bonuses linked to the revenues of the total firm.
BASF Corp. uses current praise and gives increased project responsibility to stimulate employees. It believes that incentives keep employee’s motivation on a high level. However, the Research Manager does not believe that incentives are drivers for innovations. He believes that challenging work would be more appreciated. For example, being a patent owner, which pays off in money and getting the recognition for the patent is more challenging.
BASF Resins BV gives employees incentives to develop themselves as best as possible. By using an evaluation scheme that focuses on the five core competences: (1) customer focus, (2) innovative capability, (3) performance targeted, (4) drive for results, and (5) team player. The evaluations will be judged on these five core competences to allow the employee to grow within the firm. After the acquisition, BASF Resins BV made some changes in its evaluation schemes. It replaced the American evaluation scheme HE-job for the Strata-system. These job evaluation schemes are focused on the job classification system, which determines the fixed salaries. The reward policy did not change. The job classification system Strata looks at different competences than compared to the previous American evaluation schemes. According to the HR-manager, the HE-job system focuses more on output of the employees and is more flexible than compared to the Strata-system. He concludes that it is more important to look at what the capabilities of the employees are and how valuable these are for the company rather than looking at the degree that the employee has obtained. In other words, firms should look at what the main competitive advantages are and try to bundle these with the competences of the employees (cf. Noe et al., 2001; Bax, 2003).
BASF Corp. gives employees the chance too to develop within the company, by authorizing more responsibility, increasing employee’s self-confidence, and further qualification for future use within the firm.
increase each other’s understanding in their functions. The ambience in the company can be more comfortable among employees; this will stimulate the motivation of the employees. One of the employees confirms that salaries matter, but what matters more to this employee is the fact that the job is challenging. This results in getting responsibility. In addition, this person loves the customer contact. The job satisfies this person’s needs in what he is looking for in the job. Not only the monetary rewards are important, but also the aspects that influence this employee’s self-regard.
Generational - The company in Nijehaske exists of employees of different ages. In the
social annual report it can be seen that the majority of the males fall in the categories 35 to 44 years and 45 to 54 years. For females, the category 35 to 44 years old dominates. In Ludwigshafen, the average age of the employees is approximately 41 years old. Amar (2006) argues that differences between generations can influence the level of motivation among employees. On the contrary, this is not totally verified by the HR-manager and the Research manager. The fact is that each employee is unique. Recently, the HR-manager hired a 60-year old employee, because it is about the person’s characteristics and competences rather than the age. Again, the focus is on core competences of the employee. The firm trains the employees by coaches that focus specifically on employees’ competences. In other words, age is not as important as the person’s characteristics and its capabilities. Whereas the Research manager says that motivation is not about the age of employees. He notices that older employees think and act on their experience. This leads to more efficiency, but it blocks breakthrough innovations.
Knowledge Work - Knowledge work is important for the firm’s innovativeness. The
