The Effects of Project Management Mechanisms on Innovation Performance in Hi-Tech Firms: Mediation of Teamwork Processes and Moderating Effects of Different Team Members’ Cultural Values

The Effects of Project Management Mechanisms on Innovation Performance in Hi-Tech Firms: Mediation of Teamwork Processes and Moderating Effects of Different Team Members’ Cultural Values Dissertation zur Erlangung des Grades eines Doktors der Wirtschaftswissenschaften der Rechtsund Wirtschaftswissenschaftlichen Fakultät der Universität Bayreuth Vorgelegt von Aim-Orn Imcharoen aus Lampang, Thailand Dekan: Prof. Dr. Markus Möstl Erstberichterstatter: Prof. Dr. Ricarda Bouncken Zweitberichterstatter: Prof. Dr. Reinhard Meckl Tag der mündlichen Prüfung: 12.10.2011 Page | i Abstract High tech firms increasingly form innovation projects composed of team members with different cultural backgrounds to respond to their customers’ needs. Prior studies have regarded these cross cultural innovation projects as an important instrument for developing innovative products, yet little effort has been investigated on the issue of the effect of project management mechanisms (autonomy and control) on these projects and the impacts of team members’ cultural backgrounds on different project management mechanisms. Moreover, prior studies have neglected to bridge the gap between the effect of these project management mechanisms on communication and coordination of teamwork processes. Therefore, this study aims to fulfill the gaps in project management and cross cultural study by exploring the effects of different project management mechanisms on several types of innovation performance. In particular, it examines the relationships of these project management mechanisms on innovation performance mediated by the teamwork processes and moderated by the different backgrounds of team members represented by their cultural values. Structural equation modelling was used to test all hypotheses from 434 new product development project team members. The results indicated that control mechanisms had stronger effects on innovation performance than providing autonomy. Additionally, the study showed that all project management mechanisms (autonomy and control mechanisms) had indirect effects on radical innovation and project efficiency through communication and coordination. However, these control mechanisms had indirect impacts on incremental innovation only through coordination but not communication. Importantly, this study revealed that control mechanisms could apply to the team members with different cultural backgrounds in encouraging higher innovation performance. In order to enhance higher innovation performance, the suggestions to apply the appropriate project management mechanisms to their team members with different cultural backgrounds are provided. Keywords: Project management, NPD projects/innovation projects, Teamwork Processes in communication and coordination, Project management in cross-cultural study, Individualism, and Power Distance, and Innovation Performance Page | ii Acknowledgement I would like to express my sincere appreciation and gratitude to my supervisor, Prof. Dr. Ricarda B. Bouncken, Chair of ABWL & Organisation, Personal and Innovation Management, University of Greifswald and Chair of Strategic Management and Organization, University of Bayreuth, Germany for her support and advisement during the last five years. I also would like to convey my gratitude to my committee members, Prof. Dr. Torsten Kühlmann and Prof. Dr. Reinhard Meckl for their valuable time and helpful comments on my work. I would like to thank to colleagues at Greifswald University and Bayreuth University who have helped and supported me during difficult times in my studies. I would like to thank to my colleagues and friends in Thailand who assisted me in the collection of data and proof reading of this study, especially, my best friend Usapan Swasdio and Aswin Sangpikul, who always inspire and encourage me. Last but most importantly, this dissertation would not have been completed without my parents and family who have encouraged me all my life. Page | iii Table of Contents Abstract .......................................................................................................................... i Acknowledgement ......................................................................................................... ii Table of Contents ......................................................................................................... iii Table of Figure ............................................................................................................. vi List of Tables .............................................................................................................. viii List of Abbreviations .................................................................................................... ix Chapter 1: Introduction ............................................................................................ 10 1.1 Introduction ........................................................................................................ 10 1.2 Research Questions .......................................................................................... 13 1.3 Objectives .......................................................................................................... 14 1.4 Research Scope ................................................................................................ 14 1.5 Contributions ...................................................................................................... 15 1.6 Outline of this Study ........................................................................................... 15 Chapter 2: Theory ..................................................................................................... 18 2.1 National Culture Theories .................................................................................. 18 2.1.1 What is Culture? ......................................................................................... 18 2.1.2 Cultural Dimensions .................................................................................... 19 2.1.2.1 Hall & Hall’s Cultural dimensions ........................................................ 19 2.1.2.2 Trompenaars’s Cultural Dimensions ................................................... 21 2.1.2.3 Hofstede’ s Cultural Dimensions ........................................................ 23 2.1.3 Linkage between Cultural dimensions and Individual Behaviors .................. 24 2.2 Innovation Definitions and Typology of Innovation ............................................. 26 2.2.1 Innovation Definition .................................................................................... 26 2.2.2 Typologies of Innovation ............................................................................. 28 2.3 Measuring Innovation Performance ................................................................... 36 2.4 Project and Project Management in Organizations ............................................. 40 2.4.1 Definition of Project ..................................................................................... 40 2.4.2 Project Structures and the NPD Team ........................................................ 41 2.4.2.1 Project Structures for Developing Innovation ...................................... 41 2.4.1.2 NPD Team ......................................................................................... 44 2.4.3 The Global NPD Project & Cross Cultural Innovation Project ...................... 45 2.4.4 NPD Process and Project Management ...................................................... 47 2.4.5 Project Management Mechanisms of NPD Projects .................................... 50 Page | iv 2.4.5.1 Autonomy and Innovation Performance ............................................. 56 2.4.5.2 Monitoring Progress and Innovation Performance .............................. 57 2.4.5.3 Process Control and Innovation Performance .................................... 58 2.4.5.4 Output Control and Innovation Performance ...................................... 60 2.4.5.5 Comparing the Effects of Control Mechanisms on Innovation Performance ...................................................................................... 61 2.5 Teamwork Processes ........................................................................................ 61 2.5.1 Communication as a Mediator ..................................................................... 63 2.5.2 Coordination as a Mediator ......................................................................... 66 2.6 Project Management Mechanisms and Cultural Dimensions .............................. 68 2.6.1 High Individualism and Autonomy ............................................................... 73 2.6.2 Power Distance and Autonomy ................................................................... 74 2.6.3 Individualism and Monitoring Progress ........................................................ 76 2.6.4 Power Distance and Monitoring progress .................................................... 77 2.6.5 Individualism and Process Control .............................................................. 78 2.6.6 Power Distance and Process Control .......................................................... 79 2.6.7 Individualism and Output Control ................................................................ 80 2.6.8 Power distance and Output Control ............................................................. 81 2.7 Summary of this Chapter ................................................................................... 82 Chapter 3: Methodology ........................................................................................... 84 3.1 Sample .............................................................................................................. 84 3.2 Questionnaire and Measurement Development ................................................. 84 3.2.1 Predictor Variables ...................................................................................... 84 3.2.2 Dependent Variables: Innovation Performance ........................................... 86 3.2.3 Moderating Variables .................................................................................. 87 3.3 Pre–test ............................................................................................................. 87 3.4 Data Collection .................................................................................................. 88 3.5 Statistical Analysis ............................................................................................. 88 Chapter 4: Analysis and Results ............................................................................. 90 4.1 Basic Concept of Structure Equation Model (SEM) ............................................ 90 4.1.1 SEM Approach and Measurement Model .................................................... 91 4.1.1.1 Global Fit Indices ............................................................................... 92 4.1.1.2 Local Fit Indices ................................................................................. 93 4.2 Assessment of Measures ................................................................................... 95 4.3 Structural Model ................................................................................................. 98 4.4 Descriptive Analysis ........................................................................................... 99 Page | v 4.4.1 Characteristics of Firms and Respondents .................................................. 99 4.4.2 Descriptive Statistics ................................................................................. 102 4.4.3 Descriptive Analysis of Separated Groups ................................................ 103 4.5 Hypotheses Testing and Results ...................................................................... 108 4.5.1 Direct Effects of Project Management Mechanisms .................................. 108 4.5.2 Mediating Test and Effects ........................................................................ 113 4.5.2.1 Mediating Test Procedures ............................................................... 113 4.5.2.2 Mediating Effects of Communication and Coordination .................... 114 4.5.3 Moderator Effects ...................................................................................... 131 4.5.3.1 High Individualism and Low Individualism ........................................ 132 4.5.3.2 High Power Distance and Low Power Distance ................................ 139 4.6 Summary of this Chapter ................................................................................. 146 Chapter 5: Discussion ............................................................................................ 148 5.1 Direct and Indirect Effects of Project Management Mechanisms ...................... 148 5.1.1 Autonomy on Innovation Performance ...................................................... 148 5.1.2 Monitoring Progress on Innovation Performance ....................................... 151 5.1.3 Process Control on Innovation Performance ............................................. 153 5.1.4 Output Control on Innovation Performance ............................................... 155 5.2 Moderating Effects of Different Cultural Values ................................................ 158 5.2.1 Autonomy and Innovation Performance .................................................... 158 5.2.2 Monitoring Progress and Innovation Performance ..................................... 161 5.2.3 Process Control and Innovation Performance ........................................... 163 5.2.4 Output Control and Innovation Performance ............................................. 165 Chapter 6: Conclusion and Recommendations .................................................... 168 6.1 Conclusion ....................................................................................................... 168 6.1.1 Direct Effects ............................................................................................. 168 6.1.2 Indirect Effects .......................................................................................... 169 6.1.3 Moderating Effects of Individualism & Power Distance .............................. 170 6.2 Contributions and Management Implications .................................................... 173 6.2.1 Theoretical and Academic Implications ..................................................... 173 6.2.2 Practical Implications ................................................................................ 174 6.3 Limitation and Future Research ....................................................................... 176 References ................................................................................................................. 178 Appendix 1: Questionnaire ....................................................................................... 197 Page | vi Table of Figure Figure 1-1: Structure of This Dissertation ....................................................................................... 17 Figure 2-1: Typology of Innovation ................................................................................................. 29 Figure 2-2: S-Curves ...................................................................................................................... 33 Figure 2-3: Project Structure in Organization ................................................................................. 42 Figure 2-4: Project Organization and Its Effectiveness .................................................................. 44 Figure 2-5: Cross-Cultural Project Team ........................................................................................ 46 Figure 2-6: An Overview of a Stage-Gate System (Cooper, 1990) ................................................ 48 Figure 2-7: Stage of Project Life Cycle ........................................................................................... 49 Figure 2-8: First Conceptual Framework ........................................................................................ 55 Figure 2-9: Second Conceptual Framework ................................................................................... 63 Figure 2-10: Third Conceptual Framework ..................................................................................... 73 Figure 2-11: Overall Conceptual Framework ................................................................................. 83 Figure 4-1: Structure Equation Model (Backhaus et al., 2006) ...................................................... 91 Figure 4-2: Measurement of Internal Fit Indices ............................................................................. 94 Figure 4-3: Measurement Model and Structure Equation Modeling ............................................... 99 Figure 4-4: Respondents’ Profile by Industry ............................................................................... 101 Figure 4-5: Respondents’ Profile by Gender and Age.................................................................. 101 Figure 4-6: Respondents’ Profile by Gender and Industries ........................................................ 102 Figure 4-7: Path Coefficients of PMMs and Innovation Performance .......................................... 109 Figure 4-8: Path Coefficients of Control Mechanisms on Radical Innovation .............................. 111 Figure 4-9: Path Coefficients of Control Mechanisms on Incremental Innovation ....................... 112 Figure 4-10: Path Coefficients of Control Mechanisms on Project Efficiency .............................. 112 Figure 4-11: Mediation Testing by Baron and Kenny ................................................................... 113 Figure 4-12: Effects of Autonomy and Communication on Innovation Performance ................... 116 Figure 4-13: Effects of Monitoring Progress and Communication on Innovation Performance ... 118 Figure 4-14: Effects of Process Control and Communication on Innovation Performance .......... 120 Figure 4-15: Effects of Output Control and Communication on Innovation Performance ............ 122 Figure 4-16: Effects of Autonomy and Coordination on Innovation Performance ........................ 124 Figure 4-17: Effects of Monitoring Progress and Coordination on Innovation Performance ........ 126 Figure 4-18: Effects of Process Control and Coordination on Innovation Performance .............. 128 Figure 4-19: Effects of Output Control and Coordination on Innovation Performance ................. 130 Figure 4-20: Path Coefficients Comparing between High and Low Individualists ........................ 137 Figure 4-21: Differences of PMMs on Innovation Performance (High and Low Individualists) .... 138 Figure 4-22: Path Coefficients Comparing Path Coefficients (High and Low PD) ....................... 144 Figure 4-23: Differences of PMMs on Innovation Performance (High and Low PD) .................... 145 Figure 5-1: Autonomy and Teamwork Processes on Innovation Performance ............................ 150 Figure 5-2: Monitoring Progress and Teamwork Processes on Innovation Performance ............ 152 Page | vii Figure 5-3: Process Control and Teamwork Processes on Innovation Performance .................. 155 Figure 5-4: Output Control and Teamwork Processes on Innovation Performance .................... 157 Figure 5-5: Impact of Cultural Values on the Relationship between Autonomy and Innovation Performance ............................................................................................................... 159 Figure 5-6: Impact of Cultural Values on the Relationship between Monitoring Progress and Innovation Performance ............................................................................................. 162 Figure 5-7: Impact of Cultural Values on the Relationship between Process Control on Innovation Performance ............................................................................................................... 163 Figure 5-8: Impact of Cultural Values on the Relationships between Output Control and Innovation Performance ............................................................................................................... 166 Page | 14 formation of innovation teams with members from different counties. The individuals' differences associated with their national cultural may cause them to react differently to various project mechanisms. This leads to the third research question: "how well these project management mechanisms in autonomy and controls increase innovation performance given the different cultural backgrounds of project team members?”. Within the context of contingency theory, the answer to this question may help project managers to better understand the optimal way to organize and manage teams across different geographic and cultural environments. It would also help project managers select the most effective project management approaches for an individual’s cultural background for a given project type, in order to achieve optimal innovation performance. 1.3 Objectives Regarding the above research questions, this study aims to fulfil the gaps with specific objectives as follows:  To examine the direct effects of project management mechanisms on innovation performance  To investigate indirect effects of project management mechanisms on innovation performance using communication and coordination as mediators  To examine the direct effects of project management mechanisms on innovation performance given differences in the cultural backgrounds of team members  To determine whether these project management mechanisms in autonomy and control have different effects on innovation performance for NPD team members with different cultural backgrounds 1.4 Research Scope In this study, different project management mechanisms regarding autonomy and control are examined. To explore the different effects of these project management mechanisms on innovation performance, on communication and coordination within project teams, and on different cultural groups of team members, NPD projects and innovation projects are scope in this study. These innovation projects create radical innovation products/services or develop low radical innovation products/services (incremental innovation products); both types are included in this study. The effects of project management mechanisms (PMMs) applied to those projects were collected from project Page | 15 managers and team members of NPD projects or innovation projects in high technology industries in various countries . 1.5 Contributions With regard to the problem statement, research questions, and objectives as mentioned above, this research contributes to the NPD literature in several aspects. First, there is limited research on the effects of project management mechanisms of autonomy and different control on various types of innovation performance including radical, incremental and project efficiency. Therefore, the examination of this topic increases the understanding of project management mechanisms and their influence on innovation performance. In addition, this study will demonstrate the relationships between different types of innovation performance and various levels of project management mechanisms (high autonomy to low autonomy). Secondly, the results of this study are expected to provide a better understanding of the issue of communication and coordination as intervening variables between project management mechanisms and innovation performance. Thirdly, the results would expand the utilization the different project management mechanisms with different cultural backgrounds of team members as contingent variables/situations. This may yield additional knowledge on cross-cultural project management with respect to the application of project management mechanisms to team members with different cultural backgrounds. In practice, the results of this study may help project managers and senior managers to apply these project management mechanisms with different types of projects, and select the appropriate control mechanisms to maximize the innovation performance of their team. A successful project enhances a firm’s opportunities to meets its objectives and optimize its profit. Within its varying contexts, the results of this study may help project managers, senior managers, and executives to understand the optimal way to organize and manage people in different geographic and cultural environments. 1.6 Outline of this Study This dissertation has been structured as follows: Chapter one provides an overview of this study. Page | 16 Chapter two reviews relevant literature providing the theoretical background and identification of national culture theories, cultural dimension and linkage between cultural dimensions and individual behaviors. It also includes innovation typology, measurements of innovation performance, project definition, project structure and NPD team, NPD process and project management, a description of the project management mechanisms. The relationships between project management mechanisms and teamwork processes with respect to communication and coordination are reviewed. In addition, the effects of project management mechanisms on innovation performance given differing cultural backgrounds of team members are discussed. From the literature review, hypotheses based on previous studies are presented. Chapter three introduces the research methodology used to collect data, sample, and measurement development, and pre-test of measurement. This is followed by the selection of the statistical method to test all hypotheses. Chapter four provides an assessment of measurement steps, descriptive analysis, and testing of the hypotheses. The results of hypotheses testing are presented, as are the findings from the empirical analysis. Chapter five presents a discussion of the results and findings pursuant to the hypotheses of this study. Chapter six provides the conclusion and contributions, limitations and future research. Page | 17 Figure 1-1: Structure of This Dissertation 2.1.  National Culture Theories  Cultural Dimensions,  Linkage to Individual Behavior Chapter 1: Introduction 1.5 Structure in this Dissertation 1.1 Introduction and Research Problems Chapter 2: Theory and Literature Review 2.4  Project,  Project Structure and NPD Team,  Cross Cultural Innovation Project  PMMs 2.5  PMMs and team processes of communication and coordination 2.2 & 2.3  Innovation Definition,  Typology of Innovation  Measuring Innovation Performance 1.2 & 1.3 Research Questions And Objectives 1.4 Research Scope 1.5 Contributions 4.1.  SEM approach Chapter 4: Hypotheses Testing and Results 4.3  Hypotheses testing and Results (Direct effects, mediating effects 4.2  Descriptive analysis 4.4  Hypotheses testing and Results (Moderating effects) 5.1.  Discussion Chapter 5 Discussion Chapter 6 : Conclusion, Contributions and Limitations 6.1.  Conclusions 6.3 Limitation and Future Research 6.2  Contributions 3.1.  Sample Chapter 3 Research Methodology and instrument development 3.3  Pre-test 3.2  Questionnaire and Measurement Development 3.4  Data collection 3.5 Statistic Analysis 2.6 PMMs and Cultural Dimensions/Values 2.7 Summary of this chapter Page | 18 Chapter 2: Theory This chapter reviews the theoretical approaches related to and previous studies regarding, national culture and project management. The chapter will be divided into four sub-sections. First, this chapter addresses national culture, cultural values, and linkage between cultural dimensions and individual behaviors. In addition, innovation and innovation typologies are described as an essential source of competitive advantage for high-tech firms. Second, this chapter reviews project definition, project structure, measurements of innovation performance, project structure and team, NPD process and project management and project management mechanisms. The section also discusses the relationships between project management mechanisms (in term of autonomy and different kinds of control) and innovation performance. Third, teamwork processes are introduced in the context of the relationship between project management mechanisms and innovation performance. These communication and coordination teamwork processes are utilized as mediators between project management mechanisms and innovation performance. Fourth, this chapter reviews project management mechanisms and cultural values. Two cultural values are selected as moderators of the relationship between project management mechanisms and innovation performance. 2.1 National Culture Theories 2.1.1 What is Culture? The origin of the word ‘culture’ is from the Latin word ‘cultura’ and the verb ‘colere’, which means tending or cultivating (Kroeber and Kluckhohn, 1952, p.86). Scholars define culture from several perspectives. Kluckhorn (1951, p.86) articulates that “culture consists in pattern ways of thinking, feeling, and reacting, acquired and transmitted mainly symbols, constituting the distinctive achievement of human groups, including their embodiment in artifacts; the essential core of culture consists of traditional ideas and especially their attached values”. Similarly, Kroeber and Parsons (1958, p. 583) refer to culture as the “transmitted and created content and patterns of values, ideas, and other symbolic meaningful systems as factors in the shaping of human behaviors”. Hall and Hall (1990) define culture as a system for creating, sending, storing, and processing information. The most prominent scholar in cross cultural study, Hofstede (1980), defines culture as “the collective program of the mind that distinguishes the member of one human group from another”. Hosftede (1991) further explains culture as the set of Page | 19 collective believes and values that distinguishes people of one nationality from those of another. He adds that culture might be defined as the interactive aggregate of common characteristics that influences a group’s response to its environments. More importantly, these scholars share the belief that cultures could be collective values shaped and transmitted to be core values through social learning processes and observation reflecting to individuals’ attitudes, individuals’ thinking, individuals’ behaviors, and individuals’ actions (Bandura, 1986; Erez and Gati, 2004). Therefore, people live in different parts of the world with diversified environments and geographies; they may have different cultures, values, norms, and behaviors according to the place they live. People in one nation are expected to behave differently from another nation. In order to demonstrate the cultural differences among nations, many scholars developed their framework, conceptualized, and categorized national culture into various dimensions and attempted to measure these dimensions/values for various nations (Child, 1981; Newman and Nollen, 1996). For example, Hall (1977) and Hall and Hall (1990) specify their cultural dimensions based on context (communication), time, and space orientation. Trompenaars (1993) describes seven cultural dimensions: Universalism versus Particularism; Individualism versus Collectivism; Affective versus Neutral; Specific versus Diffuse; Achievement versus Ascription; Orientation toward time; and orientation toward the environment. Among these scholars, Hofstede (1980, 1990) categorized cultural dimensions based on work related values into four dimensions and he revealed cultural differences exist among nations. Hence, these differences in national cultural dimensions/values may help people in one nation to better understand why people in different nations behave, expect and react differently to the same circumstances (e.g., management practices or leadership styles). The differences in individuals’ behaviors might be rooted from cultural values that differ across the world (Hofstede, 1980, 1991; Trompenaars, 1994). The national cultural dimension concept findings of three prominent scholars are further explained in detail in the next section. 2.1.2 Cultural Dimensions 2.1.2.1 Hall & Hall’s Cultural dimensions In order to understand cultural differences, Hall (1976, 1983), Hall and Hall (1990) distinguished culture into three concepts of cultural dimension. The first, cultural dimension is identified based on the ways of information is transmitted and communicated: High-Context (HC) or Low-Context (LC). According to Hall (1976), HC communication involves the use of implicit and indirect messages (e.g., facial Page | 20 expressions, tone of voice and gestures) in which meanings are embedded in the person or in the socio-cultural context. Gudykunst, Ting-Toomey (1988) summarize that HC communication is indirect, ambiguous, harmonious, reserved, and understated. On the other hand, Hall (1976) further explains that LC communication involves the use of explicit and direct messages in which meanings are contained mainly in the transmitted messages. Therefore, communication in LC cultures is expected to be clear and direct, explicit, and easily understood, with the information accessible to everyone (Schneider and Barsoux, 2003). According to Hall and Hall (1990), the Arab countries as well as France are HC cultures. On the other hand, he describes the USA, the UK and Germany as LC cultures. The second cultural dimension described by Hall and Hall (1990) identifies the method in which activities are organized by individuals with regarding to time: Polychronic or Monochronic. According to Hall and Hall (1990, p. 15), people belonging to Monochronic societies tend to do one task/activity at a time, plan, adhere to schedules, and fully commit to the job. People from monochronic societies are LC and need to search for more information to support their decision making (Morden, 1999). In addition, they tend to be concerned with privacy, respect private property and be accustomed to short term relationships. On the other hand, people belonging to Polychronic societies tend to do many tasks/activities at the same time. Their emphasis is on human transactions rather than holding to schedule, and they change plan often and easily. Furthermore, in term of relationships, they are concerned with people who are closely related (e.g., family and close friends) and tend to build lifetime relationships. The third cultural dimension described by Hall and Hall (1990) identifies culture based on space in terms of territory, physical and personal space. Degree of space can refer to levels of power and control, relationships to people (Hall and Hall, 1990), or the degree of involvement with others (Schneider and Barsoux, 2003). People from different cultural backgrounds require different levels of space between themselves and others. For example, Hall and Hall (1990) noted that American and German supervisors tend to establish their own territory (e.g., offices) separate from others. On the other hand, French supervisors prefer to occupy a space in the middle of an office surrounded by their sub-ordinates in order to control them (p.11). In terms of physical and personal space, people from colder climates (Germany, Scandinavia, England) use a larger physical distance when they communication. People from warm climates (French, Italy, and Greek) prefer close distances (Hall and Hall, 1990; Reisinger and Turner, 2003). Page | 21 Hall’s three cultural dimensions (1990) are beneficial in identifying the cultural differences among nations in terms of communication, use of time, and space. The differences in national cultures vary depending on the cultural orientation of people in a nation. However, cultural dimensions defined by Hall are somehow not clear (e.g., space), and they may be difficult to apply to the measurement of cultural differences in various countries. This argument is supported by Dahl (2004) who noted that one side of Monochromic/Polychronic time cultural dimension and the HC/LC context is extremely useful, but the other side is ambiguous. He further stated that the ambiguity makes it difficult to apply the concept within the framework of analytical approach (e.g., comparing culture). 2.1.2.2 Trompenaars’s Cultural Dimensions Trompenaars (1993) and Trompenaars and Hampden-Turner (2002) categorized a set of cultures into seven cultural dimensions based on human relationships, time and nature. Each concept is summarized below. Universalism versus Particularism. Trompenaars and Hampden–Turner (2002) specified this cultural dimension based on human relationships. Universalist societies tend to feel that general rules and obligations are a strong source of moral. Universalists tend to follow the rules and look for “one best way” of dealing equally and fairly with all cases (Trompenaars, 1996, p. 52). People in universalist societies tend to focus on rules more than relationships. They assume that the standards they hold are the “right” ones and they attempt to change the attitudes of others to match. On the other hand, particularist societies are those where “particular” circumstances are more important that rules (Trompenaars, 1996, p. 53). He further explained that in particularist societies, relationships (e.g., family or close friends) are stronger than rules and the response may change according to circumstances and people involved. Individualism versus Communitarianism. Trompenaars and Hampden–Turner (2002) specified this cultural dimension based on how people relate to each other. Parsons (1955) describes individualism as “a prime orientation to the self” and collectivism as “a prime orientation to common goals and objectives (as cited in Trompenaars, 1996). People in Individualism cultures tend to focus on “I”, and prefer the individual’s responsibility and achievement. In contrast, people in communitarianism (collectivism) cultures prefer joint responsibility and the group’s achievement. This cultural dimension is similar to Hofstede’s Individualism-Collectivism cultural dimension. Page | 22 Affective versus Neutral. Trompenaars and Hampden–Turner (2002) categorized this cultural dimension based on the relationships between people with respect to reasons and emotion. People in Affective societies tend to show their feeling openly through both verbal and non-verbal communication (e.g., laughing, smiling, and expressions on their faces of worrying or disgust). In contrast, people in Neutral societies do not reveal their thinking or feeling in public. They control their feelings carefully and keep them to themselves (Trompenaars and Hampden -Turner, 2002). Specific versus Diffuse. This cultural dimension also emphasizes the relationships of people with others. According to Trompenaars and Hampden –Turner (2002), people in Specific societies are characterized by their direct and precise communication as well as their clear distinction between work life and private life. In contrast, people in diffuse societies are characterized by indirect and evasive communication as well as a combination between work life and private life. Achievement versus Ascription. Trompenaars and Hampden–Turner (2002) identified this cultural dimension based on a societies definition of status. Status of people in Achievement societies is based on their recorded accomplishments, job performance, and their knowledge. In contrast, status of people in ascription societies is based on their birth, education, age, family, social position, and connections. Persons in ascription cultures use their titles extensively and respect their superiors in terms of hierarchy and age. Time orientation. Trompenaars and Hampden –Turner (2002) divided this cultural dimension into the importance of past, present and future time orientation as well as the management of time (Sequential versus Synchronous). People in past orientation cultures view everything in the context of tradition or history and they tend to have great respect for ancestors and older people. People in present orientation cultures enjoy their current activities and tend to be most interested in present relationships. People in future orientation cultures talk of aspiration and future achievement. In addition, people in Sequential time cultures tend to do only one activity at a time and stick to their plan and schedules. On the other hand, people in Synchronous time cultures tend to do many activities at the same time. For them, schedules are less important than relationships. Relationship to nature. Trompenaars (1996) and Trompenaars and Hampden–Turner (2002) specified this cultural dimension based on controlling nature (environments). People in inner-directness cultures focus their actions toward others and believe that Page | 23 they can control environments and outcomes. On the other hand, people in outerdirectness cultures believe that environments control their actions. 2.1.2.3 Hofstede’s Cultural Dimensions Within the cultural dimension field of study, the most prominent work in cross–cultural studies has been performed by Hofstede (1980). He examined how culture varies based on work related values by using a standard survey to collect data from 116,000 IBM employees from 66 countries between 1967 and 1973. Hofstede (1980) found that cultural differences among nations can be categorized into four cultural dimensions. These dimensions are: (1) Power Distance; (2) Uncertainty Avoidance; (3) IndividualismCollectivism; and (4) Masculinity–Femininity. These four cultural values are viewed differently across countries. Additionally, Hofstede and Bond (1988) added a fifth dimension, the Confucian dynamic or Long-term relationship. The five cultural dimensions are described below. Individualism-Collectivism is defined as pertaining “to societies in which the ties between individuals are loose : everyone is expected to look after himself or herself or his or her immediate family” and collectivism is defined as pertaining “to societies in which people from birth onwards are integrated into strong, cohesive in groups, which throughout people’s lifetime continue to protect them in exchange for unquestioning loyalty” (Hofstede, 1991, p. 51). In organizations, people of individualist societies define the self as an autonomous entity, independent of groups, prioritize personal goals/interests over group goals/interests, with their behaviors driven by their own beliefs, values, and attitudes, and orientation toward task achievement (Kim, Triandis, Kagitcibasi, Choi, and Yoon, 1994; Markus and Kitayama, 1991; Triandis, 1995). In contrast, collectivists define the self in terms of its connectedness to others in various ingroups, focusing on collective goals/targets, with behaviors driven by social norms, duties, and obligations, and orientation toward harmonized relationship rather than tasks achievement (Markus and Kitayama, 1991; Triandis, 1995). Therefore, people from Individualist countries (e.g., U.S.A) tend to be self-directed and emphasize personal achievement toward their work and collectivists prefer having smooth and harmonic relationships with their in-group (relationship orientation). Power Distance is defined as “the extent to which less powerful members of organizations and institutions accept and expect that power is distributed unequally” (Hofstede, 1991, p. 27). High power distance societies show great reliance on centralization and formalization of authority, and have great tolerance for the lack of Page | 30 (2) Henderson and Clark (1990) categorize innovation based on product level with respect to the links between core concept (architectural knowledge) and components. According to Henderson and Clark’s concept (1990, p.2), core concept (architectural knowledge) is knowledge in the ways in which the components are integrated and linked together into a coherent whole. A component is defined as a physically distinct portion of the product that embodies a core design concept. These types of innovation are; (1) Radical innovation; (2) Architecture innovation; (3) Modular innovation; and (4) Incremental innovation, as shown in Figure 2-1 (2). Radical innovation involves creating a dominant new design (component) that incorporates a link with new architecture (core concept). Architecture innovation is a reconfiguration of an established system to link components in a new way. In this type of innovation, the core concept (architecture knowledge) remains the same but new interaction and linkages between components are introduced. Modular innovations involve in replacing one or more core concepts (architecture knowledge) without changing the linkages between components of a product. Incremental innovation refines and extends individual components or the linkages between components under core concept (e.g. core established design). (3) Tushman, Anderson, and O’ Reilly (1997) differentiate types of innovation based on technology life cycle (R&D) and the impact of these types on the market. As shown in Figure 2-1 (3), typologies of innovation are classified into four types including; (1) Major product service innovation; (2) Architecture innovation; (3) Major process innovation; and (4) Incremental product service innovation. Major product service innovation is developed due to radical technological change and high competition. This forces the development of a new dominant design and creates a new market (e.g., from Analog to Digital). Architecture innovation is created based on incremental improvements in technology (e.g., reconfiguration of technology) and is sold in a new market. Major process innovation is developed based on radical technology which is applied to producing a product in an existing market (Tushman and Anderson, 1986). Incremental product, process, service innovation is based on incremental improvements in technology (e.g., in sub systems) with an emphasis on an existing market (e.g., Sony walkman). (4) Chandy and Tellis (1998) categorize innovation into four typologies based on the degree of newness of the technology and degree of newness to markets. Newness of technology refers to the extent to which the technology involved in a new product is new or different from prior technologies. Newness to market indicates that the extent to which the new product fulfills key customers’ needs better than existing products. Their concept Page | 31 is illustrated in Figure 2-1 (4) and classified into four typologies of innovation; (1) Radical innovation; (2) Technological breakthrough innovation; (3) Market breakthrough innovation; and (4) Incremental innovation. A radical innovation product is developed based on high newness of technology and significantly fulfills customers’ needs. A technological breakthrough innovation product is created by employing high newness of technology but with a low achievement in fulfillment of customers’ needs per dollar. In contrast, a Market breakthrough innovation product is created based on low level of newness of technology, but it provides high level of customers’ fulfillment per dollar. An Incremental innovation product is built based on low-level of newness of technology and it provides a low level of fulfillment of customers’ need per dollar as well. (5) Bootz-Allen and Hamilton (1982) categorize innovativeness based on Newness to the market and Newness to the company, resulting in six product types ranking from low to high on each dimension. These six product types are: (1) cost reductions; (2) improvements in existing products; (3) repositioned products; (4) additions to existing product lines; (5) new product lines; and (6) new-to-the-world as shown in Figure 2-1 (5). For cost reductions, a new product is developed to provide similar performance at lower cost. Repositioning of product focuses on existing products that are target to new market segments. For improvement and revision to existing products, a new product provides improved performance or greater perceived value replace existing products of a firm. Addition to existing product line, a new product is developed to supplement a firm’s established product lines. For new product line, a new product is developed to allow a firm to enter established market at the first time. Lastly, new to the world product, a new product is developed to create new entirely new market for a firm. (6) Wheelwright and Clark (1992b) classify innovation in terms of the degree to which in-house projects changed the firm’ s product portfolio. Their typologies of innovation included: (1) Derivative project; (2) Platform project; (3) Breakthrough project; and (4) R&D projects as shown in Figure 2-1 (6). First, derivative project refers to enhancements of an existing product process. The examples of derivative projects can be: (1) improved reliability, or minor change in material used; (2) new packaging or new feature with little or no manufacturing process change; and (3) design changes. Second, platform projects involve greater product or process changes than derivatives projects do. This type of project offers fundamental improvement in cost, quality, and performance. Third, breakthrough projects involve significant changes to existing products and processes that fundamentally differ from previous ones, namely, a completely new product category Page | 32 with a new market. Last, Research and Development projects involve the combination of know-how and know-why of new materials and new technologies. According to the different typologies of innovation mentioned above, innovation can be categorized based on various aspects, for example, newness of technology, newness to market, newness to customers or even newness to a firm. Radical innovations have been described in many ways, but seldom precisely defined (Green, Gavin, and AimanSmith, 1995). In addition, the different aspects of radical innovation have led to the creation of many different labels/terms, for example, discontinuous innovation (Anderson and Tushman, 1990), architectural innovation (Abernathy and Clark, 1985), radical innovation (Chandy and Tellis, 1998), new to-the world product (Booz and Hamilton, 1982), major product innovation, (Tushman et al., 1997) or technology or marketing breakthrough innovation (Zhou, Yim, and Tse, 2005). Due to the varied aspects of innovation, and the terms used to describe innovation, it is a complex process to group typologies. Additionally, grouping typologies may lead to conflicting results (Jordan and Segelod, 2006). In order to avoid conflicting of typologies, this study differentiates innovation according to Chandy and Tellis’s concept (1998) into three general typologies: (1) breakthrough innovation; (2) radical innovation; and (3) incremental innovation. The three general typologies are described below.  Breakthrough innovation is categorized based on S-curves of technology and benefits per dollar with respect to market breakthrough and technological breakthrough according to Chandy and Tellis (1998, 2000) and as shown in Figure 2-2. A technological breakthrough product adopts a substantially different technology than existing products. Firms develop new products based on state-of the art of technology which replaces the existing technology; however, these new products may not satisfy customers. This may be because the state of the art technology used for breakthrough innovation, while superior to the existing technology, may be complex for customers (Rogers, 1983). Customers may have no experience with the technology underlying these products and consequently they have to learn how to use this new product (Lee and O'Connor, 2003; Veryzer, 1998b). New products, developed by using state of the art technology, offer distinguished benefits to customers, however they are slightly imperfect in terms of compatibilities (e.g., product functions) because the technology may not be mature. Additionally, not every technology breakthrough product becomes a radical product innovation. While developing a market breakthrough product innovation, firms employ existing Page | 33 technologies, improve them for new products, and then sell them in a new market. Some scholars argue that developing a product under market breakthrough offers lower risk on technological development due to using existing technology, but it is still high risk on market side because the market lacks customers (Christensen and Bower, 1996). Therefore, in developing market breakthrough innovation products, firms require knowledge related to customer’s needs (Song and Parry, 1999). Figure 2-2: S-Curves Source: Adopted from Chandy and Tellis (1998, 2000)  Radical Innovation is based on a different set of engineering and scientific principles creating a new core concept for a firm (Henderson and Clark, 1990). By developing a new technical core concept with a different set of linking components, a radical innovation product offers high profits and makes existing products obsolete. Additionally, the radical innovation concept of Chandy and Tellis (1998) involves many new technologies and provides significantly greater customer benefit, relative to an existing product. These two criteria distinguish radical innovation products from technological breakthrough innovation products. Since the radical innovation product is a further developmental step from technical breakthrough, a firm faces high risks, high uncertainty in terms of technological and market feasibility, a high possibility of failure, high investment (Song and Parry, 1999; Veryzer, 1998a; Wind and Mahajan, 1997; Zhou et al., 2005) and a long term development period for R&D (McDermott and O'Connor, 2002). Even with high risk, uncertainty and investment, radical innovation products tend to contribute significantly to a firm’s growth and profitability (Veryzer, 1998a). Time Benefit/ per dollar Technological Breakthrough a b c Existing Technology (T1) New Technology (T2) Market B reakth rough Radical Innovation Page | 34  Incremental innovation is the opposite of radical innovation. Based on Chandy and Tellis’ definition (1998), incremental innovation involves relatively minor changes in technology and provides relatively low incremental customer benefit. Incremental innovation involves developing individual components, which underlies a core concept/core system (Anderson and Clark, 1990). Incremental innovation product development is focused on existing product improvements, or line extensions that minimally improve the existing performance (Zhou et al., 2005). In term of markets, incremental innovation products are developed for an existing market. Although innovation can be categorized into three main types; in general, firms may use the results/outputs of technological breakthrough projects for the development of radical innovation products/projects. Thus, only radical and incremental innovation product/projects are mentioned in this study. These two types of innovation are important for firms (De Brentani, 2001). Firms develop products based on state of the art technology (radical innovation) in order to achieve long term competitiveness in the market by unveiling new to the world product. On the other hand, development of incremental innovation may provide a better response to customers’ needs and differentiate their products from their competitors in the current market. These differences lead to different requirements for managing these kinds of products in terms of the structure of a firm, resources, and the skills and related knowledge of project team members (Lee and O'Connor, 2003; Song and Parry, 1999; Veryzer, 1998a). Stamm (2003) summarized differences between incremental and radical innovation according to nine aspects as shown in Table 2-1 (as cited in Popadiuk and Choo, 2006). From Table 2-1, it can be seen that developing radical innovation products based on discontinuous technology takes a long-term development time and involves a high degree of uncertainty with respect to failure. In contrast, developing incremental innovation products based on existing knowledge and step-by-step processes, takes a short-term development time and low level of uncertainty. Stamm (2003) further describes the different processes, structures, players, resources and skills that these two different types of innovation require. Page | 35 Table 2-1: Difference between Incremental and Radical Innovation Focus Incremental Radical Time frame Short term—6 to 24 months Long term—usually 10 year plus Development trajectory Step after step from conception to commercialization, low levels of certainty Discontinuous, iterative, set-backs, high levels of uncertainty Idea generation and opportunity recognition Continuous stream of incremental improvement; critical events large anticipated Ideas often pop up unexpectedly, and from unexpected sources, slack tends to be required; focus and purpose might change over the course of the development Process Formal, established, generally with stages and gates A formal, structured process might hinder Business case A complete business case can be produced at the outset, customer reaction can be anticipated The business case evolves throughout the development, and might change; predicting customer reaction is difficult Players Can be assigned to a crossfunctional team with clearly assigned and understood roles; skill emphasis is on making things happen Skill areas required; key players may come and go; finding the right skills often relies on informal networks; flexibility, persistence and willingness to experiment are required Development structure Typically, a cross-functional team operates within an existing business unit Tends to originate in R&D; tends to be driven by the determination of one individual who pursues it wherever he or she is Resource and skill requirements All skills and competences necessary tend to be within the project team; resource allocation follows a standardized process It is difficult to predict skill and competence requirements; additional expertise from outside might be required; informal networks; flexibility is required Operating unit involvement Operating units are involved from the beginning Involving operating units too early can again lead to great ideas becoming small Source: Adopted from Stamm (2003) Due to the different characteristics of radical and incremental innovation products/projects, it is interesting to examine which management mechanism can best manage radical innovation or incremental innovation. Various targets/goals in product development (e.g., innovation project or routine project) may require different project structure/management mechanisms to motivate/promote/support and contribute to innovation performance. The different project goals/targets have different measures of success or performance. These measures are discussed in the next session. Page | 36 2.3 Measuring Innovation Performance A number of researchers agree that measuring project performance is important to everyone involved in the project, including project managers, customers, and other stakeholders (Cleland, 1986; Shenhar, Levy, and Dvir, 1997). Firms need to know their performance whether productivity of technology have been reached to reward and motivate their performance, to identify area of improvement, and to inform their stakeholders (Behn, 2003; Cordero, 1990). Development of different types of innovation projects/purposes results in different outputs or performance, which require different measures. Prior studies have investigated various aspects of project performance. Measurements of innovation performance based on three aspects, i.e. project efficiency, project effectiveness, and achievement of project goals or project performance are summarized in Table 2-2. Project efficiency is measured based on the degree to which the project is completed on time and within schedule, whereas project effectiveness is the completion of the project within budget. Kerzner (2009) noted that some scholars pooled project efficiency and effectiveness with proper performance, calling this “project success” or “project performance”. Cleland (1986, p.8) argued that project success is meaningful if it is measured based on project technical performance and the contribution of the project to the strategic mission (as cited in Shenhar et al., 1997). Some scholars measure project success based on project performance (Kerzner, 2009). However, a project may be implemented successfully but it may fail in terms of customer satisfaction (Pinto and Slevin, 1988). Shenhar et al., (1997) support that these measurements in terms of effectiveness and efficiency may indicate a well-managed project, but may not indicate success in the long-term nor benefit to customers. Many scholars e.g., Shenhar et al.,(1997) and Kerzner (2009), suggest that a measurement of project performance or project success should be composed of multiple dimensions in order to cover all aspects including completion within allocated time and budget, proper performance, and the level of acceptance by customers/users. In order to cover all aspects, recent studies measure project performance based on technical performance, and customers’ satisfaction. For example, Hoegl and Germuenden (2001) measured a software team’s performance in terms of effectiveness and efficiency. Efficiency was measured according to adherence to schedule and budget. Effectiveness was measured based on the technical quality of the software solution, including the satisfaction with the software solution from the perspectives of both customers and team members. Another study by Lewis et al., (2002) measured the Page | 37 project performance based on technical knowledge built, achievement of commercial success, and adherence to schedule and budget. Apart from measuring project performance in terms of achieving project effectiveness, efficiency, and accomplishment of goals, there have been a number of studies focusing on the degree of product innovativeness as a measure of new product performance as shown in Table 2-2. Product innovativeness has been measured as an independent variable, a dependent variable, or a moderator in the previous studies (Danneels and Kleinschmidt, 2001). Measuring innovativeness (e.g., product superiority) is important for high tech firms as it helps to indicate their performance in developing new products (Griffin and Page, 1993) in terms of new product success, financial success, product profitability and market share (Cooper and Kleinschmidt, 1987). Since product innovativeness increases a firms’ competitive advantage (Brown, 1992; Goldenberg, Lehmann, and Mazusky, 2001), it creates additional incentives for firms to invest in innovation in order to compete in high-tech markets (Lee and O'Connor, 2003). From the literature review, there are many aspects for measuring product/project innovativeness. The innovativeness of a project can be measured in terms of: (1) product advantages; (2) technological newness; (3) product newness to the firm or to industry/market; and (4) financial performance. Product advantage can be measured from new product characteristics in perceiving superiority or uniqueness of product benefits (in quality, benefit, and functionality), product performance compared to competitors, and scope of newness (e.g., new platforms), or providing new modules (Jordan and Segelod, 2006; Kleinschmidt and Cooper, 1991). These characteristics of product advantages provide a more concrete picture of a firm’s ability to meet customer needs (Li and Calantone, 1998). Additionally, product innovativeness can be measured by applying the state of the art of technology that has never used before in developing new product, which is technological newness (Song and Parry, 1997). Applying technology newness in developing new product may increase product superiority or uniqueness as well. In relation to innovativeness, prior study has examined product newness in two aspects; that are “newness to the firm” and “newness to customers/competitors” (Atuahene-Gima, 1995; Cooper and de Brentani, 1991; Lee and O'Connor, 2003). The product, which is new to the firm, may not be new to the market. Under this perspective, product newness to the firm refers to the degree of similarity between the new product and the products already marketed by the firm, ranging from incremental products (product improvement and modifications) to radical products (new product lines and new to the world products) Page | 38 (Atuahene-Gima, 1995). Product newness to customers refers to the extent to which the new product is compatible with the experiences and consumption of customers (Atuahene-Gima, 1995). It can be measured based on the level of difficulty customers face in adopting the product, e.g., whether or not product requires new knowledge (Atuahene-Gima, 1995; Lee and O'Connor, 2003). Table 2-2: Measurements of Innovation Performance Outputs/Outcomes Constructs/Measures 1. Project efficiency & effectiveness & Project performance Efficiency & Effectiveness: (Bonner et al., 2002; Cleland, 1986; Hoegl and Gemuenden, 2001; Kerzner, 2009; Lewis et al., 2002; Salomo et al., 2007; Shenhar et al., 1997; Song, Thieme, and Xie, 1998)  Within schedule & within budget  Meeting objectives/ goals of project Project’s Performance: (Hoegl and Gemuenden, 2001; Kerzner, 2009; Shenhar et al., 1997)  Technical quality of the software solutions  Proper performance at specific level  Meeting design goals in operational specifications, technical specifications Customers satisfactions/ impact on customers: (Hoegl and Gemuenden, 2001; Kerzner, 2009; Shenhar et al., 1997)  Customers satisfactions  Fulfilling customer needs/Actually used by customers 2.Product innovativeness at project level Product Advantage : (Ali, Krapfel, and Labahn, 1995; Atuahene-Gima, 1995; Cooper, 1979; Cooper and de Brentani, 1991; Cooper and Kleinschmidt, 1995; Kleinschmidt and Cooper, 1991; Li and Calantone, 1998; Song and Xie, 1996)  Uniqueness of product benefit (e.g., unique features)  Superior performance comparing to competitors (e.g., faster and higher performance)  Scope of newness (e.g., offering new product platform, or new module for an existing product) Technological Newness: (Brentani, 2001; Song and Parry, 1997)  Providing advantage by relying on technology never used before Product newness: (AtuaheneGima, 1996; Brentani, 2001; Cooper and de Brentani, 1991; Kleinschmidt and Cooper, 1991; Lee and O'Connor, 2003; Song and Parry, 1997)  Product newness to the firms (e.g., exploit technology totally new to the firm  Product newness to the market/industry (e.g., the first product in the market or repositioning of an existing product)  Market newness to the firm (e.g., new customers)  Product newness to customers/ adoption difficulty to the customer (Lee and O'Connor, 2003) (e.g., customers needed to learn how to use this new product). Financial performance: (Cooper, 1979; Cooper and Kleinschmidt, 1995; Kleinschmidt and Cooper, 1991; Salomo et al., 2007; Song and Parry, 1996)  Profit  Sales vs. objectives (Sale attained relative to objectives)  Market share Source: Adopted and adapted from Jordan and Segelod (2006) Page | 39 Furthermore, product newness can be measured by market newness to the firm, for example, whether a new product serves new customer needs or new customers for firm (Kleinschmidt and Cooper, 1991). Finally, product innovativeness can be correlated positively with the product’s market performance, i.e. the level of its financial and competitive outcomes in the market (Li and Calantone, 1998). Therefore, financial measures are used to measure outcomes of a new product (at the firm level) in terms of sales, profits and market share in many previous researches (Cooper, 1979; Cooper and Kleinschmidt, 1995; Griffin, 1993; Kleinschmidt and Cooper, 1991; Salomo et al., 2007; Song, Souder, and Dyer, 1997). Even though performance of a product/project can be measured from various aspects, most studies combined several aspects together and measured performance as “innovation performance”, “product competitive advantages” (Song and Montoya-Weiss, 2001), “product success” (Akgun and Lynn, 2002), “new product performance” (Song et al., 1997) or “NPD project performance” (Bonner et al., 2002). This study will measure innovation performance using project efficiency and product innovativeness, either radical innovation or incremental innovation. Project efficiency measures the adherence of schedules, budgets, and degree to which rework is required. The other two measurements in this study are radical and incremental innovation, which demonstrate different level of developed product innovativeness. These two outputs for NPD projects reflect the level of development of the product in terms of newness to firm, to industry, and to customers. Outputs from radical innovation are created by using state of the art technology and generate unique product features to customers and into the market. On the other hand, outputs from incremental innovation projects may result in reduced costs for existing products, improved performance of existing products, or an extension of a line of products within a firm. In order to achieve the firm’s objectives in developing different innovation products, firms require project structure, team members, and management mechanisms employed by project managers to motivate their team members. Hence, project management mechanisms are essential to project performance/success. The next section will discuss the project definition, project structures, and team members. The development of projects will be described in terms of NPD projects and cross cultural projects. The relationship between project management mechanisms and innovation performance will be discussed. Page | 46 a virtual project team (Jarvenpaa and Leidner, 1999), or a disperse project team (Boutellier, Gassmann, Macho, and Roux, 1998) in developing new products. According to Maznevski and Athanassiou (2006, p.632) a “global project team” is an internationally distributed group of people, identified by its members and the organization as a team unit, with a specific mandate to make or implement decisions that are international in scope. In the similar vein, McDonough, Kahn and Barczak (2001) define the “global NPD project team” as one comprised of individuals who work and live in different countries and are culturally diverse. McDonough, Kahn and Barczak (2001) summarize that a global NPD project team is both geographically dispersed and culturally diverse. In the same way, Jarvenpaar and Leidner (1999) describe “a global virtual project team” as cultural diverse with team members spanning the globe. Another type of project team e.g., cross-cultural project team is established for developing a new product based on the diverse cultural backgrounds of team members. Figure 2-5 depicts a project team composes of many members with different cultural backgrounds and responsibilities who work together on a project. Even though rarely defined as a cross-cultural project team in the literature, a global team composed of members from different nations could be a cross-cultural NPD project team as well, since diverse cultural backgrounds may bring new ideas for the development of new products (Bouncken, 2004). Some research evidence has shown that a diverse project team contributes significantly to innovation in product and system development (Eriksson et al., 2002; Wheatley and Wilemon, 1999). Cox and Blake (1991) also claim from their study that people of different genders, nationalities, and racioethnic groups hold different attitudes and perspectives on issues; therefore cultural diversity should increase team creativity and innovation. Figure 2-5: Cross-Cultural Project Team R&D:Indian (Collectivism) Process and Supply chain design: German (Individualism & high uncertainty Avoidance) R&D: American (Individualism) Manufacturing: Taiwanese (Collectivism) Design: Chinese (Collectivism & High P ower D istance ) Marketing: Austrian (High Masculinity & Uncertainty) Page | 47 However, the cross-cultural innovation team may face complexities and difficulties in terms of communication, geographical disparity, and cultural differences that influence team members’ behaviors (McDonough et al., 2001). Cultural values stemming from different backgrounds influence team members’ behaviors related to how members work together in a team and respond to management mechanisms provided by a project manager. Management of the cross-cultural innovation team is challenging with respect to fostering the development of new ideas and stimulating coordination among team members. All of these issues can influence the level of innovativeness of project. Research on cross-cultural NPD project teams has rarely investigated the use of project management mechanisms to enhance innovation performance under different team members’ cultural backgrounds (Shore and Cross, 2005). Therefore, one objective of this study is to shed more light on cross-cultural innovation project management. In the next section, the relation between NPD process and project management, project management mechanisms and innovation performance will be discussed. 2.4.4 NPD Process and Project Management When NPD projects are formed to develop innovation within firms and their own structures and teams are established, these projects and teams must be managed by project managers in order to achieve the project’s goal. It is important to clarify the idea to be developed by NPD team before launching into the market; these teams have to go through the process of idea generation into production and launching a new product (from the idea) into the market (Aleixo and Tenera, 2009; Garcia and Calantone, 2002). This is called NPD process or innovation process. Johne (1984) suggests that NPD process may consist of two phases; (1) initiation (idea generation, screening, and concept testing); and (2) implementation (product development, test marketing, and product launch)(as cited in Nakata and Sivakumar, 1996). However, several scholars view NPD process within different perspectives, for example, six Stage Gate of Cooper (1990) or two phases of innovation process of Johne (1984). Most of the perspectives of innovation/NPD process have more than two stages/phases which were based on their NPD process according to the state of product during its development (Aleixo and Tenera, 2009). This NPD process may increase the success of the development of a new product (Cooper and Kleinschmidt, 1986). It might also facilitate matching customers’ needs to a new product. Regarding the NPD process, for example, the StageGate-system (Cooper, 1990), the project manager drives the NPD project from phase to phase and organizes the team to meet the requirements (specified deliverables) of each Page | 48 phases. Senior managers act as gate keepers to approve needed resources, review output quality and approve an action plan for the next phase as shown in Figure 2-6. Figure 2-6: An Overview of a Stage-Gate System (Cooper, 1990) As already mentioned the project manager drives their team to pass all needs of each phase and thereby achieving the project’s goals. However, during project execution, characteristics of NPD process (e.g., goals and requirements) are always change (De Maio, Verganti, and Corso, 1994) due to technical uncertainty or changing customers’ needs. In order to pass all deliverables of each phase and achieving project goals, project managers require some tools to help them with their management tasks. Project management is found to be an important role in product development in terms of competency integration, logical planning, emphasis on anticipation of constraints, and the control of critical areas execution (De Maio et al., 1994). It is vital for project managers in having the necessary management tools to motivate their team to achieve those project’s goals. Project management has typically been defined as including planning, monitoring, organizing, and control processes. For example, the UK Association of Project Management (1995) provides a definition of project management as “The planning organization, monitoring and control of all aspects of projects and the motivation of all involved to achieve the project objectives safely within agree time, cost, and performance criteria.” Similarly, Kerzner (2009, p.4) further defines “project management” as “The planning, organizing, directing, and controlling of companies resources for a relatively short term objective that has been established to complete specific goals and objectives.” Page | 49 Kerzner further commented that there are five principles underlying project management: planning, organizing, staffing, controlling, and directing which are important processes related to the development of new products. Munns and Bjeirmi (1996) refer to project management as the process of controlling the achievement of the project objectives by applying a collection of tools and techniques. They further describe project management using project life cycle and explain that project management covers only stages 2 to stage 4, i.e. planning, production, and handover as shown in Figure 2-7. Munns and Bjeirmi’s definition of project management can be applied to all types of projects (e.g., R&D projects, product development projects or customized projects for customers). Therefore, it can be concluded that project management composing of planning, production and handover relates to managing NPD project in identifying customers’ needs, keeping a project on schedule and meeting milestones and rapidly producing a product (McDonough et al., 2001). Figure 2-7: Stage of Project Life Cycle In this current study, the above definitions of project management will be used as the foundation of project management. These definitions clearly state that project management is control of a project to keep a team focused on tasks in order to achieve a project’s goals. Although the above project management definitions emphasize project control through planning and monitoring of a project, NPD projects need another management mechanism such as granting autonomy or participation in management as well. This is supported by Kessler and Chakabarti (1999) who revealed that both granting autonomy and control through monitoring of milestones had an impact on a radical Time 1 2 3 4 5 6 Project Project management 1 Conception Client, Users, Third parties 2 Planning Client, Project Team, Third parties 3 Production Client, Project Team, Producer, Third parties 4 Handover Client, Project Team, Producer, Third parties 5 Utilization Client, Users, Third parties 6 Closedown Client, Users, Third parties Page | 50 innovation project, however, granting autonomy had a higher effect on the radical innovation project than monitoring of milestones. In order to monitor, organize, and control NPD projects, the project manager requires management mechanisms/tools for the project and team. The next section discusses the relationship between project management mechanisms and innovation performance in further detail. 2.4.5 Project Management Mechanisms of NPD Projects In general, project management is the planning, monitoring, and evaluating of a project and team in order to achieve project goals. During project implementation, project managers require mechanisms/tools/techniques to help them identify customers’ needs, keep projects on schedule, and integrate and coordinate tasks. These mechanisms/tools/techniques may include an informal leadership style, procedure/planning diagrams, Gantt charts and Work Breakdown Structure (WBS) (McDonough and Leifer, 1986; Sicotte and Langley, 2000). Mechanisms such as, the Gantt chart or WBS help the project manager to plan, monitor, and evaluate innovative tasks according to project milestones. These mechanisms also help the project manager to control the project to reduce the risks of technology and market uncertainty, but may limit team members’ creativity (Bonner et al., 2002) which is needed for developing new products. Therefore, contrasting mechanisms in both autonomy and control are necessary for NPD projects. Feldman (1989, p.83) supported that both autonomy and control mechanisms are always necessary in organizations and neither can exist without the other: “not only are autonomy and control needed in organizational innovation, but they cannot be understood separately, because autonomy is dependent both structurally and managerially on a context of control”. Feldman analyzed the relationship between autonomy and control and developed four conclusions: 1. Autonomy and innovation always depends on a context of control for their relevance to an organization. 2. Under conditions in which innovation is required and autonomous behavior is important, general management control is needed. 3. When control and autonomy are not balanced, a vicious cycle can develop. 4. Innovation within an organization requires participants to have a highly developed sense of the legitimate possibilities of autonomy. Page | 51 Due to the above mentioned conclusions, a project, as a temporary organization, requires both autonomy and control mechanisms. In order to provide a framework of project management mechanisms, it is necessary to review the relevant literature regarding project management mechanisms (autonomy and control) and their relationship to innovation performance as summarized in Table 2-4. According to the literature review, scholars use different statements to describe project management mechanisms such as project management styles (Lewis et al., 2002), project management method (Tatikonda and Rosenthal, 2000), project leadership style (McDonough and Barczak, 1991), project management characteristics (Thieme, Song, and Shin, 2003), and control mechanisms (Bonner et al., 2002) to measure the relationship between project management mechanisms and innovation performance or NPD success. It should also be noted that each study included a different level of analysis, and applied different measurements of both project management mechanisms and innovation performance. However, it is worth to observe the relationships between each project management mechanism and innovation performance. Table 2-4: Related Studies on Project Management Mechanisms Topics Authors Samples Results Autonomy Amabile and Gryskiewicz (1987) R&D scientists From their interviews with R&D scientists, they found that lacking of operational autonomy or freedom over one's work or ideas inhibited creativity. Thamhain (1990) 934 Professionals participating R&D project teams Autonomy (freedom) did not significantly correlate with innovative R&D team performance. McDonough and Barczak (1991) 30 NPD projects in 12 British companies Granting autonomy to project team members significantly increased the speed of development of NPD projects. Barzack and Wilemon (1992) Project teams Technical professionals desired a high degree of autonomy to control their activities and to make their own decisions about their roles and how to solve specific problems Bart (1991; 1993) 57 sub-ordinate managers in 10 large companies Granting autonomy to subordinates in making decisions (as one of informal control), appeared to support both exponential new product projects and incremental new product projects. Kim and Lee (1995) 103 R&D Project teams in Korea Autonomy climate was negatively associated with team performance in Korea. Page | 52 Table 2-4: Related Studies on Project Management Mechanisms (Continued) Topics Authors Samples Results Gerwin and Moffat (1997a) 53 Cross-functional product development team Withdrawing autonomy was negatively correlated with both task and process aspects of team performance. Olson, Walker, and Ruekert (1995) 45 projects from 12 firms They noted that a high level of autonomy within the firm was an advantageous for radical product innovation. Kessler and Chakrabarti (1999) 75 New product development projects from 10 firms Empowerment team had a positive effect on the speed of development of a radical project. Empowerment team had no impact on incremental project. Tatikonda and Rosenthal (2000) 120 project managers in the execution phase (individual level) Autonomy positively was associated with project execution and success in terms of technical performance, unit cost, and time to market Lewis et al., (2002) Project managers in 80 projects Participative control reflecting team autonomy had a positive effect on commercial objectives (reasonable manufacturing cost & market share) Bourgault Drouin and Hamel (2008) 149 project managers of technical projects Granting team autonomy had positive effect on teamwork effectiveness for both moderated and highly dispersed teams. Monitoring Progress Eisenhard and Tabrizi (1995) 72 product development projects drawn from European, Asian, and U.S. computer firms. Faster product development was associated with frequent milestones. Kessler and Chakrabarti (1999) 75 New product development projects from 10 firms Frequent milestones were associated with faster development of radical project. Frequent milestones had no effect on the development of an incremental project. Lewis, Dehler, and Green. (2002) project managers in 80 projects Monitoring milestones and progress, one project management style, negatively impacted technical knowledge. Salomo, Weise and Gemünden (2007) 132 Project managers of NPD projects Process formality (milestones monitoring) had a non-significant effect on innovation success. Process Control Bart (1991) 57 sub-ordinate managers in 10 large companies (firm level) Less reliance on firms’ formal systems (i.e. formal screening/evaluation) supported both exponential new product projects and incremental new product projects. Itter and Larcker (1997) Consulting company survey of organizational practices covering the automobile and computer industries in four countries Process improvement tool was associated with enhanced performance of industries’ profitability Page | 53 Table 2-4: Related Studies on Project Management Mechanisms (Continued) Topics Authors Samples Results Tatikonda (1999) 108 new product development projects Project management formality was significantly correlated with derivative projects (extensions to an existing product family). Tatikonda and Rosenthal (2000) 120 project managers in the execution phase Formal processes were positively associated with project execution success (technical performance, unit cost, and time to market). Cardinal (2001) 148 participants related to SBU in 57 Pharmaceutical firms Process control (centralization and formalization) enhanced new drug development (radical innovation). Bonner et al., (2002) 95 NPD Projects Process control had a significantly negative effect on NPD project performance. Benner and Tushman (2002) Two large-sample, longitudinal studies of the photography and paint industries Process management activities (e.g., ISO)in firms were associated with an increase in both explore and exploit innovation (radical and incremental innovation) Li et al., (2006) 194 participant of high technology firms Process control increased radical innovation, but decreased incremental innovation in Chinese technology firms. Bourgault , Drouin and Hamel (2008) 149 project managers of technical projects (team level) Formalized processes had a strong effect on teamwork effectiveness only for highly distributed teams. Output Control Bonner et al., (2002) 95 Projects across a variety of industry Output control had no effect on NPD project performance. Cardinal (2001) 148 participants related to SBU in 57 Pharmaceutical firms Output control had a positive effect on radical innovation and incremental innovation. Li et al., (2006) 194 participant of high technology firms Output control increased incremental innovation, but decreased radical innovation in Chinese technology firms. In relation to granting autonomy, West (1997) found that creative people are selfconfident, need autonomy, make independent judgments, and thrive on risk (as cited in McAdam and McClelland, 2002). Amabile and Gryskiewicz (1987) further stated that autonomy is one factor that encourages new ideas. The creation of ideas is assumed to be related to innovation among R&D scientists. Most studies of NPD projects and R&D projects found that freedom/autonomy granted to project members enhanced innovativeness of the project and led to faster project development (Kessler and Chakrabarti, 1999; Lewis et al., 2002; McDonough and Barczak, 1991; Tatikonda and Page | 54 Rosenthal, 2000). Granting autonomy to a team may be important for NPD projects in generating ideas for new products at initiation stage, as well as in solving technical problems occurring during implementation stage. Granting autonomy to a team allows individual team members to make decisions and select flexible ways of developing products in uncertainty situations. However, some scholars found that autonomy had a negative effect on team performance in Korea (Kim and Lee, 1995) and that autonomy did not correlate with R&D team performance (Thamhain, 1990). Project management control mechanisms are the opposite of autonomy. Research covering different types of control was reviewed in the literature. Ouchi (1977) categorized organizational control into output and behavior control. Jaworski (1988) identified control of marketing activities as formal control (e.g., input, process and output) and informal control (e.g., self, social, and culture). Snell (1992) used human resource management control in input, behavior (process) and output control to investigate the relationship between management control and standard of desirable performance. Several studies pooled control mechanisms together and referred to formal management control. For example, Chow, Kato, and Merchant (1996) examined the management control consisting of new income target, discretionary program expense, headcount control, procedure control and directives given at meetings at the profit center level of large U.S. and Japanese firms. However, only a few studies (e.g., Bonner et al., 2002; Cardinal, 2001) have investigated various control mechanisms applied in NPD projects. From the literature review (Table 2-4), some studies revealed that control mechanisms are associated with the success of NPD projects. Thus, this study emphasizes different types of control mechanisms including: (1) monitoring progress (i.e., the extent to which innovative tasks are monitored and controlled according to project milestones); (2) process control/behavior control (i.e., the extent to which tasks are monitored according to pre-defined procedures); and (3) output control or outcomes control (i.e., the extent to which performance goals are utilized as a control). According to the literature review, these control mechanisms have both negative and positive influence on innovation performance. For example, formalization, an organizational structure utilized by a software group, had a strong influence on technical innovation (Zmud, 1982). One study indicated that formal processes and output control might enhance both radical and incremental innovation within R&D projects at pharmaceutical firms (Cardinal, 2001). Bonner et al., (2002) found that process control had a negative effect on NPD project performance and output control had no effect on NPD project performance. The literature review as well indicates that most studies investigated some variables (e.g., control Page | 55 mechanisms) effect on single innovation performance (e.g., team performance or NPD project performance). There are few studies (e.g., Tatikonda and Rosenthal, 2000) that examine both autonomy and control mechanisms concurrently. Therefore, this study explores mechanisms including: (1) autonomy; (2) monitoring progress; (3) process control; and (4) output control, and investigates whether they contribute differently to innovation performance in radical innovation, incremental innovation, and project efficiency. The first conceptual framework of this study is shown in Figure 2-8. Figure 2-8: First Conceptual Framework This conceptual framework demonstrates the relationship between project management mechanisms and innovation performance. Project management mechanisms are considered independent variables. The four independent variables demonstrate both autonomy and control mechanisms. These mechanisms are: (1) autonomy; (2) monitoring progress; (3) process control; and (4) output control. Autonomy represents the empowerment/freedom given to project team members in exploring, discussing, and making decisions about problems and how to solve those problems related to their tasks. The other three mechanisms represent different control mechanisms of work on the NPD/innovative project. Project management mechanisms of autonomy and control are selected for this study because they represent the range of levels of project management mechanisms from granting high autonomy to granting low autonomy (control) that are essential for developing NPD projects. From a review of the previous literature, it has been determined that these four project management mechanisms help to improve innovation performance as a dependent variable consisting of: (1) radical innovation; (2) Process Control Output Control Monitoring Progress Autonomy Radical Innovation Incremental Innovation Project Efficiency H1 (+) H2 (P1) (+) H3 (P2) (+) H4 (P3) (+) H5 (P2>P3) and (P2>P1) Innovation Performance Project Management Mechanisms Page | 62 from different functions/department or different cultural backgrounds requires teamwork processes that facilitate task accomplishment through task related collaborative behaviors e.g., coordination and cooperation (Rousseau, Aube, and Savoie, 2006). Communication and coordination help a team exchange information, share knowledge, and coordinate tasks. In a team setting, members combine their diverse knowledge and experiences to create new products or new services (Leenders, van Engelen, and Kratzer, 2003). To capture the complex nature of innovation project teamwork, Hoegl and Gemuenden (2001) conceptualized and empirically validated a teamwork quality concept composed of six facets. The six facets of teamwork quality are composed of: (1) communication; (2) coordination; (3) balance of member contributions; (4) mutual support; (5) effort; and (6) cohesion, as indicated in both task execution and social interaction within a team. This study focuses on two of the facets of teamwork quality: communication and coordination. Communication and coordination have been shown to be important for developing NPD projects. For example, Brown and Eisenhardt (1995) revealed that communication is an essential component of the NPD processes. Frequent communication leads to clear roles, responsibilities, and cooperation of team members (Barczak and Wilemon, 2001). In addition, most innovative tasks are interrelated; therefore, task coordination enhances task accomplishment (Rousseau et al., 2006) and project performance (Gerwin and Moffat, 1997b; Souder and Moenaert, 1992). Project management mechanisms provided by project managers may influence the communication and coordination of team members. For example, Stewart and Barrick (2000) noted that a decentralization structure within a firm (where a high level of autonomy is granted to employees) might improve communication. Control mechanisms, namely monitoring tasks, could motivate and promote coordination and communication among different parts of the development team (Eisenhardt and Tabrizi, 1995), thereby increasing innovation performance. Most previous studies investigated the coordination and communication of team members as antecedents of innovation performance (e.g., Hoegl and Gemuenden (2001). There are few investigations, which focused on communication and coordination as mediators between project management mechanisms and innovation performance. Therefore, coordination and communication of teamwork quality are selected as mediators of the relationship between project management mechanisms and innovation performance in this study. This leads to the second conceptual framework of this study Page | 63 as shown in Figure 2-9. This framework explains the effects of the project management mechanisms composed of: (1) autonomy; (2) monitoring progress; (3) process control; and (4) output control on communication and coordination teamwork processes. The communication and coordination variables further affect innovation performance in radical innovation, incremental innovation, and project efficiency. The details of each teamwork process are explained below. Figure 2-9: Second Conceptual Framework 2.5.1 Communication as a Mediator According to White (1992), communication is the ‘nervous system’ that makes organizations and organizational units cohered, permits their members to coordinate all work, affects as well as creates the social environment, and stimulates the creative performance of employees. Nobel and Birkinshaw (1998) define communication as the exchange of information through various media including face-to-face contact, telephone, letter, and electronic mail. In cross-functional teams, communication is the vehicle through which personnel from multiple functional areas share information and therefore it is critical to the successful implementation of a project (Pinto and Pinto, 1990). Previous studies revealed that communication is essential to innovation projects, because communication aids the dissemination of knowledge and ideas (e.g., generating new knowledge and insights) (Kratzer, Leenders, and van Engelen, 2004). Communication is also essential to the timely availability of information required by innovation team members (Leenders et al., 2003). Communication within a team promotes a better understanding of sub-task coordination as well. It has been widely Coordination H7 a,b,c,d H6 a,b,c,d Project Efficiency Teamwork Processes Communication Project management Mechanisms Innovation P erformance Radical Innovation Incremental Innovation Autonomy Monitoring progress Process Control Output Control Page | 64 investigated that communication is an essential component of the NPD process (Brown and Eisenhardt, 1995). In addition, communication between team members will maximize performance (Allen, 1977; Katz, 1982; Katz and Tushmann, 1981; Keller, 2001). Research on R&D projects suggests that intra-project communication is positively related to performance (Barczak and Wilemon, 1991; Faris, 1973; Pelz and Andrews, 1966; Rubenstein, Chakrabarti, O’Keefe, Souder, and Young, 1976). Although many studies explored communication as an antecedent of projects’ success, they rarely investigate it as a mediator between project management mechanisms and innovation performance. With respect to the relationship between autonomy and communication, granting autonomy promotes communication of project team members. Empowerment granted to individual team members in decision making on their role of the project allows them to search for new information and to share new ideas and information freely with other team members. Prior scholars supported that autonomy (decentralization) encouraged open communication within groups (Stewart and Barrick, 2000). The development of complex new products can be initiated from a vague idea. The stronger the communication within a team, the more ideas are shared and the roles as well as responsibilities are clarified, leading to increased innovation performance. Therefore, autonomy enhances communication and thereby increases innovation performance of projects. Monitoring progress and communication. Innovative project tasks are ambiguous and difficult to define. A work break down structure (WBS) and established milestones help team members to clarify and prioritize tasks. A WBS also identifies the interrelated subtasks, personnel, duration, and completion date for each task. When innovative tasks deviate from schedule or milestones are missed, progress monitoring by project managers allows the team to discuss how to solve problems or speed up projects. Due to complex and interrelated tasks, arising problems may force team to frequently communicate with the others to fine-tune tasks and provide feedback, which are essential to innovation project performance (Hoegl, Weinkauf, and Gemuenden, 2004). In addition, innovative projects are implemented within a limited timeframe and always face uncertainties (Gratton and Erickson, 2007). In order to complete tasks effectively, team members must search for information related to their tasks, which leads to frequent communication with the others. Therefore, monitoring progress may encourage communication among the project manager and team members. The combined effects of monitoring progress and communication lead to improved innovation performance. Page | 65 With regard to process control and innovation performance, predefined processes or procedures encourage communication of team members. Some scholars found that Quality Function Deployment (QFD), a type of process control, enhances communication levels and sharing of information within the core-team (Griffin and Hauser, 1992). This type of process control focuses on sequencing processes, which allow teams to bring the requirements of customers together and manage all the elements needed to define, design and deliver a product to meet or exceed customer needs (Cooper, 1990; Griffin and Hauser, 1992). Another study by Johnson, LaFrance, Meyer, Speyer, and Cox (1998) found that specific procedures had indirect effects on organizational innovativeness through communication. Process control may encourage communication among team members because standardized processes and procedures may not fit with the development of complex NPD projects. Because of this reason, project team members must communicate to make adjustments and changes to the processes and procedures to meet the complex needs of their NPD projects. Therefore, applying process control with a NPD team stimulates communication and thereby enhances innovation performance. Output control and communication. Specific goals for individual team members may also promote communication within a team. In innovative projects, performance goals for team members may be ambiguous (e.g., developing breakthrough products) and difficult to achieve. Therefore, team members with ambiguous goals need to communicate with their project managers and team members. It is found that output control enhanced the interaction of a NPD team with its customers (Bonner, 2005). Based on prior studies of output control, it can be assumed that output control may encourage communication within a team. Team members also need to discuss with other team members as to how they should proceed with their innovative tasks in order to achieve performance goals. These circumstances push team members to communicate with each other, thereby increasing innovation performance. Thus, considering the results of previous studies, project management mechanisms support team communication and enhance higher innovation performance. It can be assumed that communication, which is one of the teamwork quality dimensions, may be a mediator of the relationship between project management mechanisms and innovation performance. This relationship leads to a series of hypotheses as follows: Hypothesis 6a: Communication mediates the relationship between autonomy and innovation performance (radical innovation, incremental innovation, and project efficiency). Page | 66 Hypothesis 6b: Communication mediates the relationship between monitoring progress and innovation performance (radical innovation, incremental innovation, and project efficiency). Hypothesis 6c: Communication mediates the relationship between process control and innovation performance (radical innovation, incremental innovation, and project efficiency). Hypothesis 6d: Communication mediates the relationship between output control and innovation performance (radical innovation, incremental innovation, and project efficiency). 2.5.2 Coordination as a Mediator According to Cannon-Bowers, Tannenbaum, Salas, and Volpe (1995) “coordination” refer to integration of team members’ activities to ensure task accomplishment within established temporal constraints. NPD project’s tasks are interdependent and complex. Coordination ensures that team members’ tasks are sequenced, synchronized, integrated, and completed within established temporal constraints without double work or wasting efforts (Cannon-Bowers et al., 1995; Rousseau et al., 2006; Spreitzer, Cohen, and Ledford, 1999). Thompson (1967) suggests that the greater the number of interdependent tasks, the greater the cooperation effort required. According to Hoegl and Gemuenden’s study (2001), coordination is the interrelatedness and current status of individual contributions depending on delegated tasks to individual members working on parallel sub tasks. They further add that team members need to agree on a common WBS, schedule, budget, and deliverables to coordinate tasks effectively and efficiently. Prior studies revealed that coordination of team members fosters innovation and project performance. For example, a study by Ancona and Caldwell (1992) demonstrated that task coordination increased innovation of NPD teams. Another study by Hoegl, Weinkauf, and Gemuenden (2004) revealed that coordination helps increase overall team performance when interrelated innovative tasks are high. Although many studies examined coordination as an antecedent of projects’ success or team performance, they rarely investigated it as a mediator between project management mechanisms and innovation performance. The relationship between each project management mechanism and coordination is discussed below. Regarding the relationship between autonomy and coordination, granting autonomy to individual team members in making decisions may affect innovation performance Page | 67 indirectly by generating coordination among team members. In general, granting autonomy to project team members for decision making on their project tasks promotes individuals’ motivation (Hackman and Oldham, 1976) and responsibility for outputs and outcomes of work (Kirkman and Rosen, 1997). At the same time, they realize their responsibility and commitment to accomplish their innovative tasks on time. NPD projects with technical interrelated systems and components could not be accomplished without coordinating with others. Autonomy increases authority and decision making of how to coordinate with the others to achieve technical tasks within schedule. This may push the team members to coordinate with the others to complete their assigned tasks, thereby enhancing innovation performance (McDonough, 2000). Monitoring progress and coordination. In addition, monitoring progress of innovative tasks may promote coordination among different parts of a NPD team because of the interrelation of tasks and timeframes (Eisenhardt and Tabrizi, 1995). The complex systems and sub components of innovative tasks force team members to coordinate integrating complex tasks. Marks and Panzer (2004) revealed that computer-mediated team monitoring improves coordination and feedback processes, which in turn improves team performance (as cited in Chiocchio, 2007). Monitoring progress may push the team members to coordinate with the others to complete their assigned tasks within schedule, thereby enhancing innovation performance. Process control (standardized processes and procedures) normally encourages coordination of team members. Pre-defined processes and monitoring of how well team members follow these processes push team members to follow procedure. Sometimes, standardized processes or specific sub-process may not be applicable to the customers’ needs, customers’ problems, or senior managers’ requirements. For example, project managers and team members may develop new sub-components to increase more functions into a new product development according to customers’ needs. Coordination among a team or between the team and customer facilitates testing whether these subcomponents are compatible with the whole system and which sub-components need to be reworked (Crowston, 1997). Imai, Ikujiro, and Takeuchi (1985) found that the process management approach eases the coordination of a cross-functional team. Similarly, Pinto, Pinto and Prescott (1993) indicated that formalized rules and procedures have significant direct and/or indirect effects on project outcomes by influencing crossfunctional cooperation. Based on the above discussion, process control may force team members to coordinate based on interdependent tasks, thereby increasing innovation performance. Page | 68 Output control and coordination. Output control may also enhance coordination among team members through the establishment of specific performance goals. Specific goals without guidelines from project manager are not only challenge for team members but also motivate them to coordinate with the others as to how to proceed with their related tasks. This coordination helps the team to complete their tasks, thereby promoting innovation performance. With regard to the above discussion and the results of previous studies, project management mechanisms support team coordination and enhance higher innovation performance. Hence, it can be assumed that coordination, which is one of the teamwork quality dimensions, may be a mediator of the relationship between project management mechanisms and innovation performance. This relationship leads to a series of hypotheses as follows: Hypothesis 7a: Coordination mediates the relationship between autonomy and innovation performance (radical innovation, incremental innovation, and project efficiency). Hypothesis 7b: Coordination mediates the relationship between monitoring progress and innovation performance (radical innovation, incremental innovation, and project efficiency). Hypothesis 7c: Coordination mediates the relationship between process control and innovation performance (radical innovation, incremental innovation, and project efficiency). Hypothesis 7d: Coordination mediates the relationship between output control and innovative performance (radical innovation, incremental innovation, and project efficiency). 2.6 Project Management Mechanisms and Cultural Dimensions Due to increasing diversity in the workforce, a shift in the scope of the work environment from local to international markets, the increasing numbers of mergers and acquisitions between different countries, and high competition in the global market (Gibson, 1995), there has been an increase in the form of NPD project teams consisting of members with different cultural backgrounds. Additionally, many firms have been using a diverse, multinational innovation project team for a new product development. It is claimed that this type of team may increase innovativeness (e.g., developing more alternatives to problems) and provide a better response to customers’ needs due to the diverse of Page | 69 cultural backgrounds and geographic distribution of team members (McDonough et al., 2001). This type of team also encounters communication problems and differing working behaviors possibly due to varying cultural backgrounds of team members (Gudykunst et al., 1996). Individuals’ behaviors based on their cultural backgrounds may affect the way they work together in a NPD project team. Additionally, some members of this NPD project may react differently to project management mechanisms (autonomy and control) applied by project managers. To support this claim, Table 2-5 summarizes the studies on the impact of cultural values on different management practices. Most of these multinational research studies have examined different kinds of control at organizational levels, only a few studies have investigated project management mechanisms (autonomy and control) concurrently and their effects on innovation performance given differing cultural backgrounds of team members. However, these findings suggest that cultural values of individualism and power distance tend to relate to management practices, which are implemented by their supervisors/project managers. For example, evidence from two case studies by Shore and Cross (2005) suggest that national cultures play an important role in how team members think, behave, and how they make decisions. However, there is a little literature addressing culture’s role in project management (Shore and Cross, 2005). Thus, these two cultural dimensions of Hofstede (1980), individualism and power distance, are applied in this study to demonstrate how different individual team members with their different cultural backgrounds react to given project management mechanisms. These prior findings are used to discuss how different project management mechanisms influence on different cultural backgrounds of team members in the next section. Page | 70 Table 2-5: Summary of Research on the Role of Cultural Values in Different Management Practices Authors Values Impact of Values Methodology Results Agarwal, DeCarlo, and Vyas (1993)  Low Power Distance/Individualism  High Power Distance/Low Individualism Independent Variables: Job Codification, Rule Observation (Close supervision), Role Ambiguity, Role Conflict, Organization commitment. Dependent Variables: Work Alienation  Quantitative analysis: Regression analysis  Respondents: 184 American and 178 Indian salespersons  Low PD/High Indiv . : U.S. sample reacted more negatively to organizational formalization (Rule observation).  High PD/ Low Indiv.: Indian salespersons reacted positively to organizational formalization (Rule observation). Chow, Kato and Shield (1996)  Individualism  Power Distance  Uncertainty  Masculinity Variables:  Net income target  Discretionary program expense targets  Headcount control  Procedure control  Directive given at meeting  Quantitative analysis: Analysis of variance  Respondents: U.S. and Japanese profit center managers  Japanese profit center managers were subject to tighter procedural controls and controls via directives given at meetings.  Japanese managers were subject to significantly tighter controls overall than were the U.S. managers. DeCarlo and Agarwal (1999)  Low Power Distance/Individualism  High Power Distance/Low Individualism Independent Variables:  Autonomy  Management consideration Dependent Variables: Job satisfaction  Quantitative analy sis: Regression analysis  Respondents: Indian, American and Austrian salespeople  High PD/ Low Indiv.: Managerial consideration behavior had a positive influence on salespersons’ job satisfaction in India, but has no influence on salespersons’ job satisfaction in either the United States or Australia.  Low PD: Autonomy had positive influence on job satisfaction of salespeople in United States, Australia and India. Kirkman and Shapiro (1997)  Power Distance  Individualism  Being oriented  Determinism  Perception of the fairness of team pay  Perceived congruence of values between agent and target Independent Variables: resistance to selfmanagement, resistance to team, resistance to selfmanagement working team Dependent Variables: Global self-management working team effectiveness  Literature review and propositions  High PD/Low PD. Individuals from high power distance culture will resist a high level of self-management more than individuals from low power distance cultures.  High Indiv.: Individuals from individualistic cultures will resist teams more than individuals from collectivistic cultures. Page | 71 Table 2-5: Summary of Research on the Role of Cultural Values in Different Management Practices (Continued) Authors Values Impact of Values Methodology Results Eylon and Au (1999)  High and Low Power Distance Independent Variables:  Empowered (information availability, active belief and perceived responsibility) Dependent Variables:  Job Satisfaction  Job Performance  Quantitative analysis: Analysis of ANOVA  Respondents: 135 MBAs students  Individuals from high power distance cultures did not perform well when empowered.  Participants from low power distance cultures performed similarly when empowered, controlled, and disempowered. Murphy (2003)  Individualism  Power Distance  Uncertainty  Masculinity Variables:  Formal rules  Top-down planning process/participative planning  Relative evaluation  Team-based rewards  Quantitative analysis: Analysis of variance  Respondents: final-year or senior-level students in the business school of two universities in Mexico City and in the Los Angeles area.  No differences were found in preferences for organizational formality (formal vs. informal rules and procedures) or participative planning (topdown vs. participative budgeting).  American students exhibited stronger preferences for personal evaluations and individual rewards rather than team-based rewards preferred by the Mexican respondents. Shore and Cross (2005)  Individualism-Collectivism  Power Distance  Future Orientation  Performance Orientation  Human Treatment Variables:  Management structure and style  Geographic work distribution  Budgetary commitment  Family and education  Pay and equity  Case analysis Interview: team members of two large science projects. Team members (Japan, USA, France, China)  Collectivists: Research group in Japan tended to favor a strong central team, worked closely with their Home Team, and was comfortable with strong central control and clearly defined structure.  Individualists: Research group in the U.S. tended to work more independently and preferred a decentralized structure.  High PD: The French preferred to have top people involved.  Low PD: The low power distance members preferred a structure that is more decentralized with greater autonomy. Page | 78 Hypothesis 11: Monitoring progress is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for both high and low power distance members. 2.6.5 Individualism and Process Control Process control is implemented in high technology firms. Generally, the process control concept, for example, the stage-gate process, is a method used to manage the NPD process to increase the probability of a new product success (Cooper and Kleinschmidt, 1991). Previous studies have shown that process control improved both radical and incremental innovation (Benner and Tushman, 2003) but decreased NPD project performance (Bonner et al., 2002). When process control is applied to high individualists, who prefer freedom and self-achievement, it may be perceived as a reduction in autonomy and an increase in direct control. Highly individualistic team members with a preference for autonomy may not prefer this kind of control because a specific process is dictated to them. This argument is partially supported by Forrester (2000) who found that in American firms (an individualist society), innovations are conducted without adherence to formal process, while in Japanese firms (a collectivism society), innovations are developed using predefined processes. Similarly, managers in western countries value their experience more than rules and procedures (Smith, Peterson, and Wang, 1996). On the contrary, low individualist (collectivist) team members, who prefer group decisionmaking, may appreciate this kind of control because it provides a guideline and a sense of consensus with regard to project goals. At the same time it provides a sense of the supervisor's concern, level of care, and support (Atuahene-Gima and Li, 2002); sentiments which are highly valued in collectivist countries. Process control may reduce risks from unexpected outcomes, and avoids task related conflicts with others team members (Atuahene-Gima and Li, 2002). In addition, process control requires the attention of project managers and senior managers during every stage of the NPD project. A study of innovation projects in Japan (a collectivist society) by Herbig and Palumbo (1996) found that Japanese firms focused on process innovation to improve new products with new applications, reduce time to market (speed), and reduce costs. Consequently, it could be assumed that process control would increase innovation performance for low individualists. Based on the above literature, it may be hypothesized that: Page | 79 Hypotheses 12: Process control is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for low individualist team members rather than high individualist team members. 2.6.6 Power Distance and Process Control People in high power distance societies accept that the power in institutions is unequally distributed, and depend on their managers to lead their innovative projects (Adler, 1997; Kirkman and Shapiro, 1997). Process control, which provides written guidelines and monitoring following these guidelines of team, may be suitable for high power distance team members because senior managers and project managers pay close attention to. Process control also sends a sign of concern, care, and support to team members who are developing new products (Atuahene-Gima and Li, 2002). Evidence revealed that process control is positively associated with radical innovation in Chinese (high power distance country) technology firms (Li et al., 2006). Similarly, a study by Agarwal, DeCarlo and Vyas (1993) revealed that Indian sales people (high power distance) reacted positively to organizational formalization, whereas American sales people (low power distance) reacted negatively to organizational formalization. Thus, if provided with process control (guidelines and structure), high power distance team members will be enthusiastic to take risks and responsibilities for developing radical innovation and incremental innovation products, thereby promoting innovation performance. In contrast, people in low power distance societies expect their project leaders to consult with them and to discuss related tasks (Begley, Lee, Fang, and Li, 2002; Lam et al., 2002). Elenkov (1998) found that leadership in the United States, a low power distance country, promoted subordinates' participation in managers' decisions. Process control, which emphasizes the process and monitors whether team members follow the specified process, may not fit with low power distance members’ preferences because this approach directs the way they must complete their tasks therefor limiting participation in decision making. Hence, high power distance people tend to accept process control more than low power distance people. Based upon the above related literature, the following hypothesis was developed: Hypothesis 13: Process control is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for high power distance team members than for low power distance team members. Page | 80 2.6.7 Individualism and Output Control In literature review, output control implemented by project managers emphasis on results when monitoring, evaluating and rewarding (Anderson and Oliver, 1987) and tends to give autonomy to team members without specifying means/processes to achieve the expected results. Several studies revealed that output control increased new drug innovation and drug enhancement innovation in the pharmaceutical industry in the United States (Cardinal, 2001) and influences job performance in strategic business units (SBUs) (Jaworski, Stathakopoulos, and Krishnan, 1993). When output control is employed to individualists, who value autonomy and prefer to work individually to achieve their personal goals, they are allowed to determine their own methods in developing innovation products. Application of output control, which is a “hands off” approach, may motivate individualists to create alternative solutions for new products (Atuahene-Gima and Li, 2002). However, output control requires a crystallized standard of desirable performance and clear performance goals at an early stage of NPD projects (Bonner et al., 2002; Snell, 1992). This is supported by Fang, Evans, and Zou (2005) who revealed that highly specific output control increased the performance of American sales persons who are individualists. Hence, it is assumed that output control with clear and specific performance goals would increase radical innovation, incremental innovation, and project efficiency for individualist team members. On the other hand, when output control is employed to collectivist team members who value group goals and team members' decisions, they will ignore the autonomy of output control in determining their own methods and search for an agreement and opinions among team members to fulfill their tasks. Collectivists do not only require the team’s opinion to implement their innovation tasks, but also desire their supervisors to show concern for them by telling how to do their tasks (Atuahene-Gima and Li, 2002). Implementing specified goals of output control may be ambiguous and difficult for the team to implement. Without support of a project manager during project execution, output control may not result in the achievement of innovation performance for collectivist team members. Therefore, with the above arguments, output control implemented by project managers allows high individualist team members to manage their own innovative tasks. For individualist team members, the higher the use of output control, the greater the project performance. It can be hypothesized that: Page | 81 Hypothesis 14: Output control is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for high individualist team members rather than low individualist team members. 2.6.8 Power distance and Output Control Output control employed by project managers can imply that there are no means/procedures/guidelines on how to achieve a project objective, thereby providing some autonomy. Output control provides team members with the freedom to create and select their own means of implementation; however, they need to be responsible for the project’s outputs such as technical specifications for a new product. Normally, it is difficult to predict the outcomes of a NPD project, therefore output control used as a performance measurement, can cause high risk for high power distance team members. The reason is that they have to decide on their own on the method to implement and are responsible for achieved or unachieved outcomes. In addition, outputs/outcomes of project may be affected by environmental and firm’ factors beyond their control (Atuahene-Gima and Li, 2002; Oliver and Anderson, 1994). Therefore, high power distance team members may feel unsecure. Including selecting their own methods to implement without any participation from their supervisor, this may increase their insecurity because they are used to a hierarchy in the organization, where senior managers are the decision makers (Sagie and Koslowsky, 2000). Thus, it is expected that output control applied to high power distance team members may decrease their innovation performance. On the other hand, people in low power distance societies assume that the status between project managers and team members is equal. As such they believe they have an equal right to participate in decisions that concern them (Sagie and Aycan, 2003). Output control seems to fit low power distance team members well, as they are selfdetermined and prefer having some control over the achievement of tasks’ outcomes (Lam et al., 2002). Output control implemented by project managers allows low power distance team members to manage their innovative tasks on their own. In accordance with the above arguments, it can be hypothesized that: Hypothesis 15: Output control is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency for low power distance members rather than high power distance members. Page | 82 2.7 Summary of this Chapter This conceptual framework is developed from a literature review of NPD project management/ project styles/project control e.g., Lewis et al., (2002), Bonner et al., (2002), Cardinal (2001), Tatikonda and Rosenthal (2000), which have direct influence on innovation performance. This relationship is assumed to be mediated by communication and coordination of teamwork processes. Furthermore, this relationship is assumed to have different effects on innovation performance based on team members’ cultural backgrounds as illustrated in Figure 2-11. The relationship between 11 constructs can be divided into three parts of the conceptual framework as described below. First, innovation performance is described according to project managers’ and team members’ perception of various aspects. Innovation performance is composed of radical innovation, incremental, and project efficiency. Radical innovation and incremental innovation indicate different levels of newness of products, which depends on the newness of the technology used in developing the product and provides many new features to customers. Project efficiency indicates how well these development projects are managed (within time, budget, and specific performance). Second, the four project management mechanisms investigated include autonomy, monitoring progress, process control, and output control. These mechanisms demonstrate the different levels of project management ranging from high level of granting autonomy to low level of granting autonomy (control). Each of these managerial controls may have a different effect on innovation performance. Third, the teamwork processes investigated include communication and coordination. Generally, communication and coordination facilitate teamwork and innovation performance. It is assumed that project management mechanisms (both autonomy and control) employed by project managers could foster communication or coordination in teamwork processes, thereby increasing innovation performance. Therefore, communication and coordination are expected to be mediators between the four project management mechanisms and innovation performance. Fourth, in order to measure the effects of project management mechanisms on innovation performance given different groups of team members, cultural values of the members in terms of Individualism and Power Distance are employed in this study. It can be assumed that the different cultural backgrounds of team members will cause Page | 83 them to react differently to project management mechanisms applied by project managers as shown in Figure 2-11. Figure 2-11: Overall Conceptual Framework Autonomy (Freedom) Monitoring Progress Process Control Output Control Radical Innovation Incremental Innovation Project Efficiency Communication Coordination H1 - H4 H6 a,b,c,d (+) H7 a,b,c,d (+) Teamwork Process es H1 (+) H2 (P1) (+) H3 (P2) (+) H4 (P3) (+) H5 (P2>P3) and (P2>P1) Moderators: (H 8 – H1 5 ) Individualism/ Power Distance Project Management Mechanisms Innovation Performance Page | 84 Chapter 3: Methodology This chapter describes sample and data collection, the research instrument, measurement development, and pre-test. It also includes a description of the statistical analysis to be used in this study. 3.1 Sample The population in this study included project managers and team members who work on the development of innovation projects (e.g., developing high radical innovation products or low radical innovation products for customers) for international high technology companies. In actual work environments, it is difficult to find such a population. Therefore, this study used the purposive selecting method to find representative participants for this study by drawing the representatives from high-tech firms. High–tech industries, namely foods, electronics, semiconductor, and software, were selected because these industries often form project teams to develop new products. 3.2 Questionnaire and Measurement Development In order to collect data, a questionnaire was developed from relevant literature. The questionnaire included several constructs to be measured including project management mechanisms, teamwork processes, and innovation performance. The questionnaire was designed based on the Likert-scale ranking from ‘strongly disagree’ (1) to ‘strongly agree’ (5). 3.2.1 Predictor Variables In this study, project management mechanisms and teamwork processes are the independent variables. The four project management mechanisms adopted from Lewis et al., (2002) and Bonner et al., (2002) include autonomy, monitoring progress, process control, and output control. The two teamwork processes adopted from Hoegl and Gemuenden (2001) include communication and coordination. These mechanisms are discussed below. Autonomy reflects the delegation of decision-making and problem solving to team members by the project manager (McDonough and Barczak, 1991). Autonomy was measured from three items adopted from McDonough and Barczak (1991). Under the Page | 85 autonomy construct, the three items that were measured are: “I have freedom to explore, discuss, and challenge ideas on my own”, “I have freedom to make my own decisions about what problems need to be solved”, and “I have freedom to run my part of project”. Monitoring Progress is assessed based on frequent monitoring according to NPD project milestones and schedule (Lewis et al., 2002). This construct was measured using with three items adopted from Lewis et al., (2002). Under the monitoring progress construct, the three items that were measured are: “To what degree is reaching milestones controlled in the project?”, “To what degree is tracking process about being on schedule implemented in the project?”, and “To what degree is progress about "hard data" (e. g., test results) controlled in the project”. Process control refers to setting procedures used to perform the tasks, and monitoring how well they follow specific innovation processes (Bonner et al., 2002; Jaworski and Kohli, 1993). This construct was measured with three items adopted from Bonner et al., (2002). Under the process control construct, the three items that were measured are: “Management monitors the extent to which I follow established procedures”, “Management evaluates the procedures I use to accomplish a given task“, and “Management modifies my procedures when desired results are not obtained.” Output control indicates the degree to which managers set specific outcome goals for NPD team members, quality standards, and specific goals for NPD team members. This construct was measured using three items adapted from (Bonner et al., 2002). Under the output control construct, the three items that were measured are: “Specific performance goals are established for my job”, “Management monitors the extent to which I attain my performance goals.”, and “I receive feedback from management concerning the extent to which I achieve my goals”. Coordination reflects the degree of common understanding regarding the interrelatedness and current status of individual contributions under sub-tasks (Hoegl and Gemuenden, 2001). This construct was measured with three items adapted from Hoegl and Gemuenden (2001). Under the coordination construct, the three items that were measured are: “The work done on subtasks is closely harmonized”, “There are clear and fully comprehended goals for subtasks within our team”, and “The goals for subtasks are accepted by the team members”. Communication refers to the exchange of information among team members based on mutual support. This construct was measured with three items adapted from Hoegl and Page | 86 Gemuenden (2001) Under the communication construct, the three items that were measured are: “Discussions and controversies are conducted constructively in the team”, “Suggestions and contributions of team members are respected in the team”, and “Suggestions and contributions of team members are discussed and further developed”. 3.2.2 Dependent Variables: Innovation Performance This study utilizes multiple perspectives of innovation performance as judged by project managers and team members of NPD projects. This innovation performance measured in terms of radical innovation, incremental innovation and project efficiency are discussed below. Radical Innovation refers to the degree to which the product of the NPD project is developed on based new technology, offers superior features to customers, and creates a new product for the market. This construct was measured with four items adapted from Lee and O’Connor (2003) and Song and Parry (1997). Under the radical innovation construct, the four items that were measured are: “Product/service/software features were novel/unique to customers”, “This product/software/service introduced many completely new features for product/software/service into the market”, “This product/software/service was highly innovative/totally new to the market”, and “The product/software/service from this project relied on technology never used in the industry before”. Incremental Innovation reflects the development of a product built on existing knowledge and technology to present an updated version of the product, and/or improve the product’s existing performance. This construct was measured with three items adapted from Lee and O’Connor (2003) and Gatignon, Tushman, Smith, and Anderson (2002). Under the incremental innovation construct, the three items that were measured are: “This product/software/service was an updated version of existing products/services/ software solutions”, “The product/software/service was redeveloped to improve performance of existing products/service/SW.”, and “This product/software/service was customized based on existing knowledge and technology within firms”. Project Efficiency represents the degree to which efficiency and effectiveness was achieved in tasks in the development project. This construct was measured with three items adapted from Hoegl and Gemuenden (2001). Under the project efficiency construct, the three items that were measured are: “This project was within schedule”, “This project was within budget” and “This project required little rework. Page | 87 3.2.3 Moderating Variables Individualism considers an individual’s preference with the degree to which individuals are integrated into groups. The ties between individuals are loose in individualistic societies, the emphasis is on individual’ goals, and individual achievement is higher. This construct was measured with four items adapted from Triandis, McCusker and Hui (1990), and Shulruf, Hattie and Dixon (2003). Under the individualism construct, the three items that were measured are: “I do work better alone than in groups”, “I prefer to be self-reliant rather than depend on others”, and “It is important to me that I perform better than others on a task”. Power Distance refers to an individual’s preference related to an unequal distribution or power between supervisors and subordinates in the work place. This construct was measured with three items adapted from Maznevski, Gomez, DiStefano, Noorderhaven, and Wu (2002), Adler (2002), and Shulfruf et al., (2003). Under this construct, the three items that were measured are: “Lower levels in the hierarchy should carry out the requests of senior people without question”, “The supervisor is always right because he or she is the boss” and “You should be quiet when you don't agree with your boss”. 3.3 Pre–test As this is a cross-cultural study, all respondents need to understand the meaning of the constructs and associated measurement questions. Hence the questionnaire was pretested in two stages. First, some parts of questionnaire (e.g., related to culture values) were tested using MBA students from four countries: Thailand, Poland, Germany and Syria, to obtain feedback on clarity and appropriateness of questions. The questions asked about their behaviour related to cultural values. Based on the first stage, some items of cultural constructs were modified to ensure respondents could understand the questions and choose appropriate answer. Some items were dropped from the questionnaire. Furthermore, statistical methods, such as Cronbach-Alpha and factor analysis, were used to analyze the data and to select the best and most reliable items for this study. All constructs were then put into a questionnaire was pre-tested again (second stage) before final data collection using several groups of project managers and team members. The tests were conducted in Thailand and Germany. After the second pre-test, the questionnaire with some minor changes, such as wording and the addition of some information, was completed and carried out. The final questionnaire is presented in Appendix 1. Page | 94 validity of the measures linked with individual constructs; and (3) discriminant validity (Hulland, 1999) as shown in Figure 4-2. These local fit indices are briefly described in the following sections. Figure 4-2: Measurement of Internal Fit Indices A. Individual Item Reliability Individual item reliability is assessed by examining the loadings (or simple correlations) of the measures with their respective construct. Items that score less than 0.4 should be dropped (Hulland, 1999). Moreover, it is necessary that the loading are significantly related with their respective underlying constructs (t-value >2.0; p<0.05). The significant level of factor loadings provides support for the convergent validity of the respective scales (Anderson and Gerbing, 1988). B. Convergent Validity Convergent validity assumes that the items in the specific construct should share a high proportion of variance in common (Hair et al., 2006). For measuring convergent validity, three testing instruments were used: a) Cronbach’s Alpha (α), b) construct reliability, and c) average variance extracted (Fornell and Larcker, 1981). Nunnally and Bernstein (1994) suggest 0.7 as a benchmark of high quality Cronbach’s Alpha. Composite Reliability (CR) assesses the internal consistency of a measure and is analogous to the coefficient Cronbach’s Alpha. CR was calculated using procedures suggested by Fornell and Larcker (1981). The formula is ( ) ( ) ( ) ∑∑ ∑ + = ii i vc ελ λ ρ η 2 2 a) Items reliability c) Discriminant Validity  Factor Loading  Cronbach Alpha  Composite Reliability (CR)  Average Variance Exact (AVE)  Fornell-Larcker Ratio Internal Fit Indices b) Convergent Validity Page | 95 Where i λ is the standardized loading for each observed variable, i ε is the error variance associated with each observed variable and η ρ is the measure of construct reliability (Fornell and Larcker, 1981). CR value greater than 0.6 indicates a very good fit (Bagozzi and Yi, 1988). Lastly, Average Variance Extracted (AVE) is the average variance shared between a construct and its measure. Variance extracted can be computed from model estimates using this formula: ( ) ∑ ∑ ∑ + = ii i vc ελ λ ρ η 2 2 Where i λ is the standardized loading for each observed variable, i ε is the error variance associated with each observed variable and η ρ is the measure of construct reliability (Fornell and Larcker, 1981). Therefore, a value of AVE that is equal to or greater than 0.50 indicates evidence of internal consistency (Fornell and Larcker, 1981). C. Discriminant Validity Discriminant validity is determined by demonstrating that a measure does not correlate very highly with another measure from which it should differ (Campbell, 1960) or is the extent to which measures of a given construct differ from measures of other constructs in the same model (Hulland, 1999). The procedures for testing discriminant validity among the constructs suggest that the average variance exacted of one construct (i.e., the average variance shared between a construct and its measures) should be greater than the variance shared between the construct and other constructs in the model (the squared correlation between two constructs) (Fornell and Larcker, 1981). If the value of the Fornell & Larcker ratio is smaller than 1, it indicates good discriminant validity, that is, the given construct differs from the other constructs in the same model (Fornell and Larcker, 1981). The Fornell-Larcker ratio can be computed from this formula. ( ) ( ) ( ) 2 , Fornell-Larcker-Ratio 1 i i i i r AVE ξ ξ ξξ = < 4.2 Assessment of Measures As shown in Table 4-1, an overall measurement model with 9 constructs and 28 items (without individualism and power distance constructs) was analyzed. The global fit Page | 96 indices of measurement model indicate a good fit (X 2 = 446.051, DF = 247, X 2 /DF= 1.806, CFI = 0.949, and RMSEA = 0.043) as shown in the bottom of Table 4-1. Table 4-1 presents the internal fit indices which compose of the factor loading and individual item reliability of all items used in each construct, including Cronbach’s Alpha, Composite Reliability (CR), Average variance exact (AVE), and discriminant validity of each of the constructs in the SEM measurement model. All standard factor loadings were significant (p < 0.01), ranking from 0.545 to 0.891 indicating that each item was strongly related to its underlying construct including Cronbach’s Alpha, for which greater than 0.70 indicates satisfactory reliability. Moreover, the composite reliability (CR) values are higher than the necessary condition of 0.6 (Bagozzi and Yi, 1988), indicating high internal consistency. In most cases, values of average variance extracted (AVE) are greater than 0.5 except in individualism and power distance constructs. These two constructs are not included in the structure model. Instead they are used to split data into two groups (e.g., high individualism and low individualism) for further multi-group analysis. Thus, most items and constructs had adequate reliability and convergent validity. In addition, Fornell-Larcker’s values for testing discriminant validity are less than one indicating discriminant validity of constructs. In summary, the measurement model demonstrated adequate reliability, convergent validity, and discriminate validity as shown in Table 4-1. Table 4-1: Internal Fit Indices of Measurement Model Constructs Items Standard factor loadings a Individual. indicator reliability α CR AVE FornellLarcker Autonomy Freedom in running my part of the project. 0.681 0.464 0.77 0.85 0.65 0.54 Freedom to explore, discuss and challenge ideas. 0.744 0.554 Freedom to make own decisions about problems need to be solved. 0.750 0.562 Monitoring Progress Reaching milestones were controlled in the project. 0.842 0.709 0.88 0.88 0.71 0.49 Tracking process about being on schedule implemented in the project 0.871 0.758 Progress about "hard data" (e. g. test results) controlled in the project? 0.818 0.669 Page | 97 Table 4-1: Internal Fit Indices of Measurement Model (Continued) Constructs Items Standard factor loadings a Individual. indicator reliability α CR AVE FornellLarcker Process Control Management monitors the extent to which I follow established procedures. 0.789 0.622 0.86 0.84 0.65 0.54 Management evaluates the procedures I use to accomplish a given task. 0.891 0.793 Management modifies my procedures when desired results are not obtained. 0.726 0.527 Output Control I received feedback on how I accomplish my performance. 0.741 0.549 0.77 0.78 0.54 0.65 Management monitors the extent to which I attain my performance goals. 0.666 0.444 I receive feedback from management concerning the extent to which I achieve my goals. 0.792 0.627 Coordination The work done on subtasks was closely harmonized. 0.709 0.502 0.82 0.86 0.67 0.52 There were clear and fully comprehended goals for subtasks within our team. 0.867 0.752 The goals for subtasks were accepted by the team members. 0.806 0.502 Communication Discussions and controversies have been conducted constructively 0.736 0.542 0.84 0.86 0.67 0.52 Suggestions and contributions of team members have been respected 0.898 0.806 Suggestions/contributions of team members have been discussed and further developed 0.822 0.675 Individualism I do work better alone than in groups 0.608 0.369 0.57 0.57 0.31 0.42 I prefer to be self-reliant rather than depend on others 0.575 0.331 It is important to me that I perform better than others on a task”. 0.485 0.235 Power Distance Lower levels in the hierarchy should carry out the requests of senior people without question 0.633 0.401 0.71 0.72 0.46 0.28 The supervisor is always right because he or she is the boss” 0.801 0.641 You should be quiet when you don't agree with your boss 0.609 0.370 Page | 98 Table 4-1: Internal Fit Indices of Measurement Model (Continued) Constructs Items Standard factor loadings a Individual. indicator reliability α CR AVE FornellLarcker Radical Innovation Offered new/unique features to customers. 0.701 0.491 0.82 0.84 0.57 0.62 Introduced many completely new features into the market. 0.833 0.693 Relied on technology never used in the industry before. 0.678 0.460 Highly innovativetotally new to the market. 0.787 0.619 Incremental Innovation Updated version of existing products/services/ software solutions. 0.701 0.492 0.74 0.79 0.56 0.63 Redeveloped product to improve the performance of existing products 0.874 0.763 Customization product The based on existing knowledge and technology within firms. 0.636 0.405 Project Efficiency Within schedule 0.897 0.836 0.78 0.79 0.57 0.61 Within budget 0.777 0.583 Required a little rework. 0.545 0.289 Note: a All factor loadings are significant (t > 2.0), Global fit of the measurement model (without individualism and power distance constructs): X 2 =446.051, DF = 247, X 2 /DF= 1.806, CFI = 0.949, and RMSEA = 0.043. 4.3 Structural Model After the baseline model (measurement model) which is based on assumptions and theories was tested in two stages, the final measurement model composed of all items and constructs from Table 4-1 was postulated into the structural model as shown in Figure 4-3. This measurement model is composed of four exogenous (independent) constructs, which are autonomy, monitoring progress, process control, output control, and six endogenous (dependent) constructs, which are communication, coordination, radical innovation, incremental innovation, and project efficiency. This proposed structural model is estimated via maximum likelihood by using AMOS 16 in the next step for testing hypotheses. Page | 99 λ 19 β 3 β 2 β 1 λ 3 λ 2 λ 1 Autonomy X1 δ 1 X2 δ 2 X3 δ 3 λ 6 λ 5 λ 4 X4 δ 4 X5 δ 5 X6 δ 6 λ 9 λ 8 Process Control X7 δ 7 X8 δ 8 X9 δ 9 λ 12 λ 11 λ 10 Output Control X10 δ 10 X11 X12 Measurement Model Measurement Model λ 16 λ 17 λ 18 Y5 Y6 ε 5 ε 6 Y4 ε 4 λ 13 λ 14 λ 15 Y1 ε 1 Y2 ε 2 Y3 ε 3 ζ 1 λ 28 λ 27 Efficiency Y14 ε 14 Y15 ε 15 Y16 ε 16 λ 26 λ 25 λ 24 Incremental Y11 ε 11 Y12 ε 12 Y13 ε 13 λ 23 λ 20 Radical Y7 ε 7 Y8 ε 8 Y9 ε 9 ζ 3 ζ 4 ζ 5 ζ 2 β 5 β 4 β 6 γ 11 γ 12 γ 13 γ 14 γ 15 γ 6 γ 7 γ 8 γ 9 γ 10 γ 16 γ 17 γ 18 γ 19 γ 20 γ 1 γ 2 γ 3 γ 4 γ 5 Structure Model Y10 ε 10 λ 7 λ 21 λ 22 Coordination δ 11 δ 12 Monitoring Progress Communication Figure 4-3: Measurement Model and Structure Equation Modeling 4.4 Descriptive Analysis 4.4.1 Characteristics of Firms and Respondents There were four hundred and thirty three members in this study. The size of the firms in this study ranged from five to more than one thousand employees. Members were dominantly male. Approximately sixty-five percent (65.2%) of respondents are male and thirty-two percent (32.5%) female. The majority of the respondents’ ages were between 31 to 40 years old (45.9%). Approximately forty-one percent (40.6%) of respondents had a college level (bachelor degree). Twenty-one percent (21.2%) had a German education system (diploma level) or master degree education. Only three percent (3%) of members had education at postgraduate (PhD.) and around percent (2.3%) had an education at technical/vocational school. The nationalities of the members were Thai (29.3%), German (18.20%), Austrian (8.53%), British (7.14%), American (4.14%), Dutch (3.69%), French (3%), Brazilian (2.07%), Italian (1.84%), Filipino (1.38%), Japanese (1.38%), Korean (1.38%), Russian (1.38%), Turkish (1.38%), Argentinean (1.15%), Indian Page | 100 (1.15%), Taiwanese (1.15%), Polish (0.92%), Irish (0.69%), Swiss (0.69%), Belgium (0.46%), and Spanish (0.46%). Table 4-2: Charateristics of Respondents and Industries Percent (%) Gender Male 283 (65.2%) Female 141 (32.5) Missing 10 (2.3%) Age 20-30 years 113 (26%) 31-40 years 186 (42.9%) 41-50 years 86 (19.5%) 51 and above 13 (4.8%) Missing 25 (5.8%) Education Vocational/technical school 10 (2.3%) College (Bachelor Degree) 176 (40.6%) Master (Magister) 92 (21.2%) PhD 13 (3 %) Others (Apprenticeship) 8 (1.8%) Missing 135 (31.1%) Nationalities Thai 126 (29.03%) German 79 (18.20%) Austrian 37 (8.53%) British 31 (7.14%) American 18 (4.14%) Dutch 16 (3.69%) French 13 (3%) Brazilian 9 (2.07%) Italian 8 (1.84%) Filipino, Japanese, Korean, Russian, Turkish 30 (6.9%) (6 people/country) Argentinean, Indian, Taiwanese 15 (3.45%) (5 people/ country) Polish 4 (0.92%) Irish and Swiss 6 (1.38%) (3 people/ country) Belgium, Spanish 4 (0.92%) (2 people/ country) Chinese, Chinese (HK), Colombian, Danish, Dominican, El Sawadorian, Indonesia, Kenyan, Malaysian, Pakistani, Romania, South African, Srilangian, Venezuelan, Zimbabwean 15 (3.45%) (1 person/ country) Missing 23 (5.30%) Position on the project Project manager 156 (35.9%) Team member 199 (45.85%) Missing 79 (18.20%) The study was conducted across-industries. The members consisted of project managers (35.9%) and team members (45.8%) working on projects including new Page | 101 product/service development, existing product/service improvement, and development of technology (R&D) in four main industries. These industries are food & consumer’s products, semiconductor, three sub-industries of Technology e.g., Software & IT services, Hardware and Electronics (e.g., computer and peripherals), and other industries, e.g., telecommunication as shown in Figure 4-4. Food & Bevarge & Personal Care, 163, 39% Software , 140, 33% Hardware electronic, 27, 6% Others (Telecomunication) , 17, 4% Semiconductor, 75, 18% Figure 4-4: Respondents’ Profile by Industry As shown in Figure 4-5, the proportion of male members between the ages of 21-30 years, between 31-40 years, between 41-50 years, and 51 years and above is higher than female. Of the members between the ages of 20 and 30 years, fifty-seven percent (56.6%) are male members and forty–three percent (43.4%) are female. Approximately sixty-five percent (65.2%) of members in the age between 31 to 40 years are male and thirty-five percent (34.8%) are female. Seventy–seven percent (77.1%) of members in the age between 41 and 50 years are male and twenty-three percent (22.9%) are female. Around eighty-eight percent (87.5%) of the members age 51 and older are male, while only thirteen percent (12.5%) are female. 21 (87.5%) 64 (77.1%) 120 (65.2%) 64 (56.6%) 3 (12.5%) 19 (22.9%) 64 (34.8%) 49 (43.4%) 0 20 40 60 80 100 120 140 20-30 years 31-40 years 41-50 years 51 and above Male Female Figure 4-5: Respondents’ Profile by Gender and Age Page | 102 When the gender of members are divided based on their industries, the proportion of male in software industry, hardware & electronics industry, semiconductor industry, and other industries is higher than female. As shown in Figure 4-6, around seventy-five percent (75.4%) of respondents, working in software firms are male and twenty–five percent (24.6%) are female. Eighty-five percent (85.2%) of members working in hardware and electronic peripherals firms are male and fifteen percent (14.8%) are female. Approximately fifty percent (49.7%) of respondents working for food and consumers’ products firms are male and half (50.3%) are female. Of the respondents in the semiconductor industry, eighty-one percent (81.4%) are male and approximately nineteen percent (18.6%) are female. Of the respondents in other industries e.g., telecommunication, eighty-eight percent (87.5%) are male and thirteen percent (12.5%) are female. 14 (87.5%) 81 (49.7%) 23 (85.2%) 104 (75.4%) 57 (81.4%) 13 (18.6%) 82 (50.3%) 4 (14.8%) 34 (24.6%) 2 (12.5%) 0 20 40 60 80 100 120 Software Hardware & Electronic Food & Beverage & Consumers ' products Semiconductor Others (e.g. telecommunication) Male Female Figure 4-6: Respondents’ Profile by Gender and Industries 4.4.2 Descriptive Statistics Table 4-3 presents the descriptive statistics for all constructs measured in this study including means, and standard deviation and correlation coefficients among the constructs. The inter-correlations among the constructs revealed that there is significant correlation among control mechanisms. Monitoring progress significantly correlates to process control (r = 0.29), and output control (r = 0.38) at a 0.01 significance level as expected. The teamwork processes of communication and coordination also highly correlate to each other (r = 0.65) at a 0.01 significance level. Three constructs under innovation performance have a low correlation to each other. Radical innovation correlates with incremental innovation (r = 0.15) and with project efficiency (r = 0.16) at a 0.05 significance level. Page | 103 The project management mechanisms have a correlation with communication and coordination and innovation performance. Different project management mechanisms demonstrate different relationships with innovation performance. Autonomy correlates with both communication (r = 0.38) and coordination (r = 0.29) at a 0.01 significance level but it significantly correlates only to project efficiency (r = 0.10) at a 0.05 significance level. Monitoring progress is significantly correlated with communication (r = 0.36), coordination (r = 0.40), and project efficiency (r = 0.34) at a 0.01 significance level. Process control is correlated with communication (r = 0.20) and coordination (r = 0.24) at a 0.01 significance level. Process control shows a slight relationship with radical innovation (r = 0.17), and incremental innovation (r = 0.16) at a 0.05 significance level. Output control is correlated with communication (r = 0.21) and coordination (r = 0.24) at a 0.01 significance level. Additionally, output control is correlated with incremental innovation (r = 0.17) and project efficiency (r = 0.22) at 0.05 and 0.01 significance levels. Furthermore, communication and coordination demonstrate a positive relationship with innovation performance. Communication positively correlates with radical innovation (r = 0.15), incremental innovation (r = 0.20), and project efficiency (r = 0.24) at a 0.05 significance level. In addition, coordination correlates with radical innovation (r = 0.265), incremental innovation (r = 0.217), and project efficiency (r = 0.304) at a 0.01 significance level. In relation to cultural values, both individualism and power distance have negative relationships with project management mechanisms (autonomy and control). Individualism has a negative correlation with monitoring progress (r = -0.16), process control (r = -0.14) and output control (r = -0.18) at a 0.01 significance level. In addition, individualism has a negative correlation with communication (r= -0.17), coordination (r = - 0.19), and project efficiency (r = -0.10) at 0.01 and 0.05 significance levels. Power Distance has a negative relationship with autonomy (r = -0.15), monitoring progress (r = - 0.15), and communication (r = -0.15) at a 0.01 significant level. Power distance also had a positive relationship with process control (r = 0.15) at a 0.01 significant level. Nevertheless, there was no relationship between individualism or power distance and innovation performance with regard to radical innovation and incremental innovation. 4.4.3 Descriptive Analysis of Separated Groups In order to provide further information related to different groups of respondents, the correlation among all constructs in each group were further analyzed as shown in Table 4-4 and Table 4-5. Among high individualist team members, autonomy significantly Page | 110 Hypothesis 1 is stated that autonomy increases innovation performance (radical innovation, incremental innovation, and project efficiency). The results show that granting autonomy does not increase innovation performance as shown in Table 4-6 and Figure 4-7. Giving autonomy to team members is not significantly related to radical innovation with value of 0.064 (t-value = 1.189, p > 0.10), nor is it related to incremental innovation with -0.059 (t-value= -0.979, p>.10), nor to project efficiency with the value of 0.073 (tvalue = 1.487, p > 0.10). Thus, hypothesis 1 can be rejected. Hypothesis 2 is stated that monitoring progress increases innovation performance (radical innovation, incremental innovation, and project efficiency). As shown in Table 4-6 and Figure 4-7, the results show that monitoring progress increases radical innovation with 0.106 (t-value = 1.974, p <0.10) and project efficiency with 0.345 (t-value = 7.010, p <0.01). However, monitoring progress has no significant impact on incremental innovation with 0.062 (t-value = 1.019, p>0.10). Therefore, hypothesis 2 is partially supported. It could be said that monitoring progress enhances only radical innovation and project efficiency. Hypothesis 3 is stated that process control increases innovation performance (radical innovation, incremental innovation, and project efficiency). The results illustrate that process control increases only radical innovation with 0.225 (t-value = 4.171, p<0.0001) and incremental innovation with 0.144 (t-value = 2.332, p<0.05). However, there is no significant relationship between process control and project efficiency as shown in Table 4-6 and Figure 4-7. The results suggest that increasing process control promoted both radical innovation and incremental innovation. Thus, hypothesis 3 is partially supported. Hypothesis 4 is stated that output control increases innovation performance in terms of radical, incremental innovation, and project efficiency. The results illustrate that output control had no significant impact on radical innovation with 0.049 (t-value = 0.918, p>0.10). However, output control increases incremental innovation with 0.197 (t-value= 3.102, p<0.05), and project efficiency with 0.165 (t-value= 3.359, p<0.0001) as shown in Table 4-6 and Figure 4-7. With all project members, output control increased only incremental innovation and project efficiency. Therefore, hypothesis 4 is as well partially supported. Hypothesis 5 is stated that process control has a stronger effect on innovation performance (radical innovation, incremental innovation, and project efficiency) than the other two mechanisms (output control and monitoring progress). Regarding the effects of control mechanisms on radical innovation, process control and monitoring progress Page | 111 increase radical innovation with 0.225 (t-value = 4.171, p<0.0001) and 0.11 (t-value = 1.974, p<0.10) respectively. However, output control has no effect on radical innovation. In order to confirm these different effects, the chi-square difference test was performed. The values of chi-square difference test of two paths are 17.15 (p<0.0001) and 17.29 (p<0.0001) respectively which are higher than the critical value of 3.84 (at 5% level) indicating rejection of the null hypothesis (two path coefficients are equal). Therefore, process control has a stronger effect on radical innovation than output control and monitoring progress as shown in Table 4-6 and Figure 4-8. Figure 4-8: Path Coefficients of Control Mechanisms on Radical Innovation Notes: n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 † p<0.10 Regarding the effects of control mechanisms on incremental innovation, process control and output control increase incremental innovation with 0.14 (t-value = 2.332, p<0.05) and 0.20 (t-value = 3.102, p<0.05) respectively, but process control has a weaker effect than output control. However, monitoring progress has no effect on incremental innovation. The value of chi-square difference test between process control and output control is 14.10 (p<0.0001) and between process control and monitoring progress is 45.12 (p<0.0001) indicating the different effects between two paths. Therefore, output control has a stronger effect on incremental innovation than process control and monitoring progress as shown in Table 4-6 and Figure 4-9. Autonomy (Freedo m ) Radical Innovation Process Control Output Control Monitoring Progress n.s. +0.11 † +0.23*** n.s. Process Control (P3) >Output control (P4) = ∆χ2 = 17.15. (p<0.0001) Process Control (P3) > Monitoring Progress (P2) = ∆χ2 = 17.29 (p<0.0001) (P2) (P3) (P4) Page | 112 Figure 4-9: Path Coefficients of Control Mechanisms on Incremental Innovation Notes: n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 † p<0.10 In relation to control mechanisms and project efficiency, process control has no effect on project efficiency; only monitoring progress and output control have an effect on project efficiency with 0.34 (t-value = 7.010, p<0.0001) and 0.17 (t-value = 3.359, p<0.005) respectively. The chi-square difference test between process control and output control and between process control and monitoring progress are 6.104 (p<0.0001) and 7.026 (p<0.0001) respectively indicating unequal effects of path coefficients. Additionally, the chi-square difference test between the effect of monitoring progress on project efficiency and output control on project efficiency is 46.536 (p<0.0001) demonstrating unequal effect. Consequently, monitoring progress has a stronger effect on project efficiency than output control and process control respectively as shown in Table 4-6 and Figure 4-10. Figure 4-10: Path Coefficients of Control Mechanisms on Project Efficiency Notes: n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 and † p<0.10. Incremental Innovation Process Control Output Control Autonomy (Freedom) Monitoring Progress n.s. +0.14* n.s. +0.20* Process Control (P3) >Output control (P4) = ∆χ2 = 14.10. (p<0.0001) Process Control (P3) > Monitoring Progress (P2) = ∆χ2 = 45.12 (p<0.0001) (P4) (P3) (P2) Project Efficiency Process Control Output Control Autonomy (Freedom) Monitoring Progress n.s. +0.34*** n.s. +0.17 † Process Control (P3) >Output Control (P4) = ∆χ2 = 6.104 (p<0.0001) Process Control (P3) > Monitoring Progress (P2) = ∆χ2 = 7.062(p<0.0001) Monitoring Progress (P2)>Output Control (P4) = ∆χ2 = 46.536(p<0.0001) (P4) (P3) (P2) Page | 113 In summary, process control has a stronger effect on radical innovation than output control and monitoring progress. Output control has a stronger effect on incremental innovation than process control and monitoring progress. In addition, monitoring progress has a higher effect on project efficiency than output control and process control. Consequently, hypothesis 5 is partially supported as shown in Table 4-6 and Figure 4-8, Figure 4-9, and Figure 4-10. 4.5.2 Mediating Test and Effects 4.5.2.1 Mediating Test Procedures Regarding hypotheses 6 a, b, c, d and hypothesis 7 a, b, c, and d, this study examines whether coordination and communication mediate the relationships between project management mechanisms (autonomy, monitoring progress, process control, and output control) and innovation performance regarding radical innovation, incremental innovation, and project efficiency. As shown in Figure 4-11, the procedures for testing mediation recommended by Baron and Kenny (1986) indicate that full mediation is present when the path from independent variable (c’) to the dependent variable is non-significant but the remaining paths are significant. Partial mediation is present when all paths are significant (1) from independent variables to dependent variables; (2) from independent variable to mediator; and (3) from mediator to dependent variable. Figure 4-11: Mediation Testing by Baron and Kenny According to Baron and Kenny (1986), to test the mediating effects of the constructs., the four step of testing mediators are pursued using AMOS. First, a direct path from independent variables (autonomy, monitoring progress, process control, and output control) to dependent variables (innovation performance e.g., radical innovation, incremental innovation, and project efficiency) is established. Second, the direct path from independent variable (autonomy, monitoring progress, process control, and output control) to the mediators is established (communication, coordination). Third, the path from mediator variable (communication or coordination) to dependent variable (e.g., radical, incremental, and project efficiency) is established. The last step is testing the Page | 114 path from independent variable to dependent variable. This path must be significantly reduced (in step 3) when the mediator is added (communication or communication) in the model. Importantly, Sobel’s test is conducted to confirm the effects of mediation (Baron and Kenny, 1986). This study employed the Sobel’ s test because it confirms the results of a mediation effect with large samples. This study calculates Sobel’s test by using the interactive program calculation developed by Preacher and Leonardelli 3 . In order to explain mediating effects of communication and coordination, Table 4-7 – Table 4-14 reports step, paths, standardized estimate (path coefficient), results of mediating test, and Sobel’ test. T-value, higher than 1.96, is indicate statistic significant at 5% level. Additionally, Figure 4-12 – Figure 4-19 are developed to show the mediation effect of communication/coordination by using standard path coefficient at different significant level. 4.5.2.2 Mediating Effects of Communication and Coordination Hypothesis 6a is stated that communication mediates the relationship between autonomy and innovation performance (radical and incremental innovation, and project efficiency). As shown in Table 4-7 step 1-1, autonomy is not significantly associated with radical innovation with 0.063 (t-value = 1.133, p>0.10). In step 1-2, autonomy is significantly associated with communication with 0.360 (t-value = 8.009, p<0.001). When communication is added in step 1-3, the results show that communication is significantly associated with radical innovation with 0.170 (t-value = 2.898, p<0.05). In addition, the path coefficient from autonomy to radical innovation significantly reduced from 0.063 (tvalue = 1.133, p>0.10) in step 1-1 to 0.003 (t-value = 0.059, p>0.10) in step 1-3, suggesting full mediation of communication. Sobel’s test supported that the reduction is statistically significant with Z-value = 2.747 (p< 0.005). Consequently, it could be said that there is an indirect effect of autonomy on radical innovation via communication as shown in Figure 4-12. It could be explained that autonomy may enhance individual creativity and individual innovative thinking. But developing radically innovative products requires not only team members’ creativity in their tasks but also integration of many complex sub-components/systems. Therefore, to faster radical innovation, granting only autonomy to individual team may not be enough; it requires integration of each innovative sub components/system through communication among team members. 3 http://www.people.ku.edu/~preacher/sobel/sobel.htm Page | 115 In step 2-1 and 2-2 the direct path from autonomy to incremental innovation is nonsignificant with the value of -0.070 (t-value = -1.128, p>0.10). The path from autonomy to communication is significant with the value of 0.360 (t-value = 8.009, p<0.00). When communication is added into step 2-3, the path from autonomy to incremental innovation has a stronger ranking from -0.070 (t-value = -1.128, p >0.10) to -0.117 (t-value = -1.748, p<0.10). In addition, in step 2-3 the path from communication to incremental innovation is significant with 0.138 (t-value = 2.067, p<0.05). Sobel’s test confirms the effect of communication as a mediator in step 2-3 with a significant with Z-value = 1.998 (p<0.05). All path coefficients are significant indicating partial mediation of communication. Therefore, autonomy can cause a negative direct effect on incremental innovation and can have an indirect effect on incremental innovation via communication as shown in Table 4-7 and Figure 4-12. The negative effect of autonomy on incremental innovation could result from the low degree of autonomy required for the development of incremental innovative products (small improvements of sub-components) rather than the development of radical innovative products. The positive indirect effect of autonomy on developing incremental innovative products through communication is stronger. As a result, given high autonomy facilitates communication of the team, which in turn promotes development of incremental innovation products. It could be concluded that communication partially mediates the relationship between autonomy and incremental innovation. In addition, the direct path from autonomy to project efficiency is insignificant with 0.054 (t-value = 1.006, p>0.10) in step 3-1, whereas the path from autonomy to communication is significant with the value of 0.360 (t-value = 8.009, p<0.0001) in step 3-2. When communication is added in step 3-3, the path from autonomy to project efficiency reduces but is still insignificant with -0.038 (t-value = -0.674, p>0.10). The path from communication to project efficiency is significant with 0.282 (t-value = 4.952, p<0.0001) demonstrating full mediation of communication as shown in Table 4-7 and Figure 4-12. This full mediation of communication is confirmed by the significant reduction of Sobel’s test with Z-value of 4.210 (p<0.0001). Therefore, the indirect effect of autonomy on project efficiency via communication is significant. In summary, hypothesis 6a is supported. Communication fully mediates the relationship between autonomy and radical innovation and project efficiency. However, it partially mediates the relationship between autonomy and incremental innovation as shown in Table 4-7 and Figure 4-12. These results conclude that autonomy has indirect effects on radical innovation, incremental innovation, and project efficiency through communication. Page | 116 Figure 4-12: Effects of Autonomy and Communication on Innovation Performance Notes: Numbers are standardized regression weights after the mediator (communication) has been entered in the model. n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 and † p<0.10. Table 4-7: Mediation Effects of Communication on the Relationship between Autonomy and Innovation Performance Step Path Standardized Estimate T-value Results 1 Sobel’s test = 2.747 (p<0.05) 1-1 Autonomy Radical Innov. 0.063 (n.s) 1.133 Full mediation 1-2 Autonomy Communication 0.360*** 8.009 1-3 AutonomyRadical Innov. Communication Radical Innov. 0.003 (n.s) 0.170* 0.059 2.898 2 Sobel’ test = 1.998 (p<0.05) 2-1 Autonomy Incremental Innov. -0.070 (n.s) -1.128 Partial mediation 2-2 Autonomy Communication 0.360*** 8.009 2-3 AutonomyIncremental Innov. CommunicationIncremental Innov. -0.117 0.138* -1.748 2.067 3 Sobel’s test = 4.210 (p<0.00001) 3-1 Autonomy Project Efficiency 0.054 (n.s) 1.006 Full mediation 3-2 Autonomy Communication 0.360*** 8.009 3-3 Autonomy Project Efficiency Communication Project Efficiency -0.038 (n.s) 0.282*** -0.674 4.952 Notes: n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 and † p<0.10. Innov. is innovation. Hypothesis 6b is predicted that communication mediates of the relationship between monitoring progress and innovation performance (radical innovation, incremental innovation, and project efficiency. As presented in Table 4-8, step 1-1 and 1-2 indicate that the path from monitoring progress to radical innovation is significant with 0.110 (tvalue = 1.993, p<0.05). In addition, the path from monitoring progress to communication is significant with 0.304 (t-value = 6.496, p<0.0001). When communication is added in step 1-3, the effect of direct path coefficient of monitoring progress on radical innovation is reduced from 0.110 (t-value = 1.993, p<0.05) to 0.067 (t-value = 1.162, p>0.10), indicating full mediation by communication. Sobel’s test confirmed the significant 0.36*** Communication Autonomy (Freedom) Project Efficiency 0.00 (n.s) 0.17* 0. 14 * 0.28*** - 0.0 4 (n.s) Incremental Innovation Radical Innovation - 0.12 † Page | 117 reduction effect with a Z-value of 2.537 (p<0.05). Consequently, monitoring progress has no direct effect on radical innovation but it has an indirect effect on radical innovation through communication as shown in Figure 4-13. While the effect of the path coefficient from monitoring progress to incremental innovation is non-significant with 0.071 (t-value = 1.147, p>0.10) in step 2-1 as shown in Table 4-8, the path from monitoring progress to communication to incremental innovation is significant with 0.304 (t-value = 6.496, p<0.0001) in step 2-2. When communication is added into the model in step 2-3, and the effect of monitoring progress on incremental innovation reduces from 0.071 (t-value = 1.147, p>0.10) to 0.044 (t-value = 0.681, p>0.10), and the path from communication to incremental innovation is not significant with 0.09 (t-value = 1.336, p>0.10). In this regard, the results suggest that monitoring progress has either no direct effect on incremental innovation or no indirect effect on incremental innovation via communication. This is confirmed by a non-significant Z-value of Sobel’s test of 1.335 (p>0.10). Hence, communication is not a mediator between monitoring progress and incremental innovation as shown in Figure 4-13. In relation to monitoring progress and project efficiency in Table 4-8, the path from monitoring progress to project efficiency and to communication in step 3-1 and 3-2, are significant with 0.355 (t-value = 6.928, p<0.0001) and 0.304 (t-value = 6.496, p<0.0001), respectively. When communication is added in step 3-3, the path from monitoring progress to project efficiency reduces from 0.355 (t-value = 6.928, p<0.0001) to 0.309 (tvalue = 5.757, p<0.0001) and it is still significant demonstrating partial mediation of communication. These results are confirmed with significant reduction of Sobel’s test with Z-value = 3.047 (p<0.005). Hence, monitoring progress has both a direct effect on project efficiency and an indirect effect on project efficiency through communication. It could be said that communication partially mediates the relationship between monitoring progress and project efficiency as shown in Figure 4-13. In summary, hypothesis 6b is partially supported as shown in Table 4-8 and Figure 4-13. Communication fully mediates the relationship between monitoring progress and radical innovation, but it partially mediates the relationship between monitoring progress and project efficiency. However, communication is not a mediator of the relationship between monitoring progress and incremental innovation. Monitoring progress facilitates communication of team members, but communication among team members does not improve performance of developing incremental innovation. Page | 118 Figure 4-13: Effects of Monitoring Progress and Communication on Innovation Performance Notes: Numbers are standardized regression weights after the mediator (communication) has been entered in the model. n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 and † p<0.10 Table 4-8: Mediating Effects of Communication on the Relationship between Monitoring Progress and Innovation Performance Step Path Standardized Estimate T-value Results 1 Sobel’s test = 2.537 (p<0.01) 1-1 Monitoring Progress  Radical Innov. 0.110* 1.993 Full mediation 1-2 Monitoring progress Communication 0.304*** 6.496 1-3 Monitoring progress Radical Innov. Communication Radical Innov. 0.067(n.s) 0.153* 1.162 2.684 2 Sobel’s test = 1.335 (p>0.10) 2-1 Monitoring progress Incremental Innov. 0.071(n.s) 1.147 No mediation 2-2 Monitoring progress Communication 0.304*** 6.496 2-3 Monitoring progress Incremental Innov Communication Incremental Innov. 0.044(ns) 0.086(ns) 0.681 1.336 3 Sobel’s test =3.047 (p<0.005) 3-1 Monitoring progress  Project Efficiency 0.355*** 6.928 Partial Mediation 3-2 Monitoring progress Communication 0.304*** 6.496 3-3 Monitoring progress  Project Efficiency Communication Project Efficiency 0.309*** 0.176*** 5.757 3.333 Note: S.E. is Standardized path coefficient significant at ***p<0.0001, **p<0.001, *p<0.05 and † p<0.10. Innov. is innovation. Hypothesis 6c is stated that communication mediates of the relationship between process control and innovation performance (radical innovation, incremental innovation, and project efficiency. In Table 4-9, step 1-1 shows that process control is significantly associated with radical innovation with 0.229 (t-value = 4.220, p<0.0001). In step 1-2, process control is significantly associated with communication with 0.134 (t-value = 2.733, p<0.05). In step 1-3, when communication is entered into the model, both paths from process control to radical innovation and from communication to radical innovation are significant with 0.208 (t-value = 3.823, p<0.0001) and 0.139 (t-value = 2.583, p<0.05). The relationship between process control and radical innovation decreases from 0.30*** Communication Monitoring Progress Project Efficiency 0.07 (n.s) 0.15* 0.0 9 (n.s) 0.18*** 0.3 1 *** Incremental Innovation Radical Innovation 0.04 (n .s) Page | 119 0.23 (t-value = 4.220, p<0.0001) in step1-1 to 0.21 (t-value = 3.823, p<0.0001) in step 13 and it is still significant demonstrating partial mediation of communication. Sobel’s test confirms the small reduction with significant Z-value = 1.857 (p < 0.10). Therefore, process control has both a direct effect on radical innovation and an indirect effect on radical innovation through communication as shown in Figure 4-14. In addition, Table 4-9, shows that communication does not mediate the relationship between process control and incremental innovation. Step 2-1 and 2-2 reveal that the path from process control to incremental innovation and to communication are significant with 0.163 (t-value = 2.570, p<0.01) and 0.134 (t-value = 2.733, p<0.05), respectively. In step 2-3, when communication is added into the model, the direct path of process control is significantly associated with incremental innovation with 0.151 (t-value = 2.381, p<0.05). Furthermore, the path from communication to incremental innovation is not significant with 0.080 (t-value = 1.296, p>0.10). This fails to meet mediation’s criteria. Sobel’s test also indicates that the mediated effect is not statistically significant with Zvalue = 1.175 (p>0.10). Therefore, communication is not mediator between process control and incremental innovation because of the strong effect of process control and incremental innovation as shown in Figure 4-14. In addition, Table 4-9 shows that communication mediates the relationship between process control and project efficiency. In step 3-1 and 3-2, the direct path from process control to project efficiency is non-significant with -0.03 (t-value = -0.610, p>0.10), but the path from process control to communication is significant with the value of 0.134 (t-value = 2.733, p<0.05). In step 3-3, when communication is entered into the model, the effect of process control on project efficiency is still non-significant with -0.06 (t-value = -1.151, p>0.10), but the effect of communication on project efficiency is significant with 0.273 (tvalue= 5.169, p<0.0001) indicating full mediation of communication. When Sobel’s test is conducted, the results show that the effect of process control on project efficiency when communication is entered is significant with 2.415 (p<0.05). Regarding mediation’s criteria, process control has no direct effect on project efficiency, but it has indirect effect through communication as shown in Figure 4-14. Therefore, hypothesis 6c is partially supported. Communication partially mediates the relationship between process control and radical innovation, but fully mediates the relationship between process control and project efficiency. However, communication does not mediate the relationship between process control and incremental innovation since there is a direct effect of process control on incremental innovation as shown in Figure 4-14. Page | 126 In summary, hypothesis 7b is supported. Coordination fully mediates the relationship between monitoring progress and radical innovation and incremental innovation. Even monitoring progress does not directly affect radical innovation and incremental innovation, but it affects coordination and finally contributes to radical and incremental innovation. However, it partially mediates the relationship between monitoring progress and project efficiency since monitoring progress has both direct and indirect effects on project efficiency. Therefore, it could be concluded that monitoring progress indirectly affects radical innovation and incremental innovation through coordination. Additionally, monitoring progress may either increase project efficiency directly or increase project efficiency indirectly through coordination. Figure 4-17: Effects of Monitoring Progress and Coordination on Innovation Performance Notes: Numbers are standardized regression weights after the mediator (communication) has been entered in the model. n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 and † p<0.10. Table 4-12: Mediating Effects of Coordination on the Relationship between Monitoring Progress and Innovation Performance Step Path Standar dized Estimate T-value Results 1 Sobel’s test = 3.016 (p<0.005) 1-1 Monitoring progress Radical Innov. 0.110* 1.993 Full Mediation 1-2 Monitoring progressCoordination 0.339*** 7.345 1-3 Monitoring progress Radical Innov. Coordination Radical Innov. 0.053(n.s) 0.192*** 0.910 3.333 2 Sobel’l test = 1.996 (p<0.05) 2-1 Monitoring progress  Incremental Innov. 0.071(n.s) 1.147 Full Mediation 2-2 Monitoring progress Coordination 0.339*** 7.345 2-3 Monitoring progress Incremental Innov. Coordination Incremental Innov. 0.027(n.s) 0.139* 0.408 2.101 3 Sobel’s test =3.783 (p<0.0001) 3-1 Monitoring progress  Project Efficiency 0.355*** 6.928 Partial Mediation 3-2 Monitoring progress Coordination 0.339*** 7.345 3-3 Monitoring progress  Project Efficiency Coordination  Project Efficiency 0.285*** 0.242*** 5.245 4.501 Note: Note: S.E. is Standardized path coefficient significant at ***p<0.0001, **p<0.001, *p<0.05 and † p<0.10. Innov. is innovation. 0.34*** Coordination Monitoring Progress Project Efficiency 0.05 (n.s) 0.19*** 0.14* 0.24*** 0.29*** Incremental Innovation Radical Innovation 0.03 (n.s) Page | 127 Hypothesis 7c is stated that coordination mediates the relationship between process control and innovation performance (radical innovation, incremental innovation, and project efficiency). Table 4-13 shows that the coordination partially mediates the relationship between process control and radical innovation. In step 1-1 and 1-2, all paths from process control to radical innovation and to coordination are significant with values of 0.229 (t-value= 4.220, p<0.0001) and 0.165 (t-value= 3.400, p<0.0001), respectively. When the mediator (coordination) is added into the model (step1-3), all paths from process control to radical innovation and from coordination to incremental innovation are significant with 0.198 (t-value = 3.641, p<0.0001) and 0.169 (t-value = 3.138, p<0.05) achieving partial mediation. Sobel’s test was performed and a Z value of 2.308 (p<0.05) revealed a significant reduction in the direct path from 0.229 (t-value = 4.220, p<0.0001) in step 1-1 to 0.198 (t-value = 3.641, p<0.0001) in step 1-3. Based on the criteria, these findings reveal the partial mediation of coordination. Therefore, it could be concluded that process control has both direct and indirect effects on radical innovation through coordination as shown in Figure 4-18. With reference to Table 4-13 in step 2-1, the direct effect of process control on incremental innovation is found with the value of 0.163 (t-value= 2.570, p<0.01), and path from process control to coordination is significant as well with 0.165 (t-value=3.400, p<0.0001). When coordination is entered in step 2-3, the effect of process control on incremental innovation decreases from 0.163 (t-value = 2.570, p<0.01) in step 2-1 to 0.142 (t-value = 2.229, p<0.05) in step 2-3. The path from coordination to incremental innovation is significant with 0.122 (t-value = 1.945, p<0.10). Sobel’s test reveals that this reduction is statistically significant with Z-value = 1.699 (p<0.10). Based on the criteria, coordination is a mediator the relationship between process control and incremental innovation. Therefore, process control has both a direct effect and indirect effect on incremental innovation through coordination of team members as shown in Figure 4-18. In addition, coordination full mediates the relationship between process control and project efficiency. As shown in Table 4-13 and Figure 4-18, in step 3-1, process control has a non-significant effect on project efficiency with -0.032 (t-value = -0.610, p>0.10). In step 3-2, path from process control to coordination is significant with 0.165 (t-value = 3.400, p<0.0001). When coordination is entered in step 3-3, the effect of process control on project efficiency decreases from step 1 and is still not significant with -0.076 (t-value = -1.434, p>0.10) but the path from coordination to project efficiency is significant with 0.352 (t-value = 6.535, p<0.0001). Sobel’s test confirms the significant reduction in direct effect with a Z-value of 3.038 (p<0.005). This finding indicates full mediation of Page | 128 coordination based on mediation’s criteria and Sobel’s test. Consequently, process control has only an indirect effect on project efficiency through coordination of team members. In summary, hypothesis 7c is supported. The results reveal that process control increases both radical innovation and incremental innovation directly. Process control also directly encourages coordination of team members, thereby contributing to radical innovation and incremental innovation. However, in order to enhance project efficiency, process control needs to be implemented together with encouraging coordination of team members. Figure 4-18: Effects of Process Control and Coordination on Innovation Performance Notes: Numbers are standardized regression weights after the mediator (communication) has been entered in the model. n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 respectively † p<0.10. Table 4-13: Mediating Effects of Coordination on the relationship between Process Control and Innovation Performance Step Path Standardized Estimate T-value Results 1 Sobel’s test = 2.308 (p<0.05) 1-1 Process Control Radical Innov. 0.229*** 4.220 Partial mediation 1-2 Process Control Coordination 0.165*** 3.400 1-3 Process Control Radical Innov. Coordination Radical Innov. 0.198*** 0.169* 3.641 3.138 2 Sobel’l test = 1.699 (p<0.10) 2-1 Process Control  Incremental Innov. 0.163** 2.570 Partial mediation 2-2 Process Control Coordination 0.165*** 3.400 2-3 Process Control Incremental Innov. CoordinationIncremental Innov. 0.142* 0.122 † 2.229 1.945 3 Sobel’s test =3.038 (p<0.005) 3-1 Process Control  Project Efficiency -0.032(ns) -0.610 Full mediation 3-2 Process Control Coordination 0.165*** 3.400 3-3 Process Control  Project Efficiency Coordination Project Efficiency -0.076(ns) 0.352*** -1.434 6.535 Note: S.E. is Standardized path coefficient significant at ***p<0.0001, **p<0.001, *p<0.05 and † p<0.10. Innov. is innovation. 0. 17** * Coordination Process Control Project Efficiency 0. 20*** 0. 17* 0. 12 † 0. 35 *** - 0 . 08(n.s) Incremental Innovation Radical Innovation 0. 14* Page | 129 Hypothesis 7d is predicted that coordination mediates the relationship between output control and innovation performance (radical innovation, incremental innovation, and project efficiency). As shown in Table 4-14 step 1-1 and 1-2 the path from output control to radical innovation is not significant with 0.057 (t-value = 1.038, p>0.10) but the path from coordination to radical innovation is significant with 0.131 (t-value = 2.681, p<0.05). In addition, when coordination is entered in step 1-3, the effect of output control to radical innovation reduces from 0.057 (t-value = 1.038, p>0.10) in step 1-1 to 0.034 (t-value = 0.614, p>0.10) in step 1-3, and path from coordination to radical innovation is significant with 0.205 (t-value= 3.737, p<0.0001). Sobel’s test was performed to test whether the effect of output control on radical innovation decreases and the Z-value revealed a significant reduction with 2.169 (p<0.05). Regarding, mediation criteria, only indirect effects of path coefficients are significant indicating full mediation of coordination. Therefore, these results suggest that output control has an indirect effect on radical innovation through coordination of team members as summarized in Figure 4-19. Table 4-14 presents the significant paths from output control to incremental innovation, and from coordination to incremental innovation with 0.211 (t-value = 3.274, p<0.001) and 0.131 (t-value = 2.681, p<0.05) in steps 2-1 and 2-2. When coordination is added in step 2-3, the path coefficient of output control on incremental innovation decreases from step 2-1 but it is still significant with 0.192 (t-value = 3.006, p<0.05). The path from coordination to incremental innovation is significant with 0.118 (t-value = 1.925, p<0.10) as well. Sobel’s test was performed to test whether the effect of output control on incremental innovation decreases and the Z-value revealed an insignificant reduction of the direct effect of coordination with 1.564 (p>0.10). Even though all path coefficients are significant, the direct path coefficient of output control on incremental innovation is found to be stronger than the indirect effect through coordination. Hence, coordination does not mediate the relationship between output control and incremental innovation. In other words, output control has stronger direct effect on incremental innovation than the indirect effect on incremental innovation via coordination as shown in Figure 4-19. In steps 3-1 and 3-2, as shown in Table 4-14, the paths from output control to project efficiency and output control to coordination are significant with 0.195 (t-value = 3.793, p<0.0001) and 0.131 (t-value = 2.681, p<0.05) respectively. When coordination is added in step 3-3, the effect of the direct path from output control to project efficiency reduces from 0.195 (t-value = 3.793, p< 0.0001) in step 3-1 to 0.153 (t-value = 2.917, p<0.05) in step 3-3 but this path is still significant. In addition, the path from coordination to project efficiency is significant with 0.314 (t-value = 5.917, p<0.0001). With regard to the Page | 130 mediation criteria, this suggests a partial mediation of coordination. Therefore, output control has direct impact and an indirect impact through coordination on project efficiency as shown in Figure 4-19. In summary, hypothesis 7d is supported. Coordination fully mediates the relationship between output control and radical innovation, but it partially mediates the relationship between output control and incremental innovation and project efficiency. It could be said that output control has an indirect effect on radical innovation, incremental innovation, and project efficiency through coordination. Output control increases directly incremental innovation and project efficiency as well. Figure 4-19: Effects of Output Control and Coordination on Innovation Performance Notes: Numbers are standardized regression weights after the mediator (communication) has been entered in the model. n.s. is non-significant. Standardized path coefficient is significant at ***p<0.0001 **p<0.001 *p<0.05 and † p<0.10 Table 4-14: Mediating Effects of Coordination on the Relationship between Output Control and Innovation Performance Step Path Standardized Estimate T-value Results 1 Sobel’s test =2.169 (p<0.05) 1-1 Output Control Radical Innov. 0.057(ns) 1.038 Full mediation 1-2 Output Control Coordination 0.131* 2.681 1-3 Output Control Radical Innov. Coordination Radical Innov. 0.034(ns) 0.205*** 0.614 3.737 2 Sobel’s test = 1.564 (p>0.10) 2-1 Output Control  Incremental Innov. 0.211** 3.274 Partial mediation 2-2 Output Control Coordination 0.131* 2.681 2-3 Output Control Incremental Innov. Coordination Incremental Innov. 0.192* 0.118 † 3.006 1.925 0.13* Coordination Output Control Project Efficiency 0.03 (n.s) 0. 21*** 0. 12 † 0.31*** 0. 15* Incremental Innovation Radical Innovation 0.19* Page | 131 Table 4-14: Mediating Effects of Coordination on the Relationship between Output Control and Innovation Performance (Continued) Step Path Standa rdized Estimate T-value Results 3 Sobel’s test =2.440 (p<0.01) 3-1 Output Control  Project Efficiency 0.195*** 3.793 Partial mediation 3-2 Output Control Coordination 0.131* 2.681 3-3 Output Control  Project Efficiency Coordination Project Efficiency 0.153* 0.314*** 2.917 5.917 Note: Note: S.E. is Standardized path coefficient significant at ***p<0.0001, **p<0.001, *p<0.05 and † p<0.10. Innov. is innovation. 4.5.3 Moderator Effects To test the moderating effects of hypothesis 8 through hypothesis 15, a multi-group analysis was performed which compares the difference or similarly of path coefficients for two groups. Splitting group process: Four hundred thirty five members (434) were divided into two groups based on members’ score on individualism by using a median split (Arnold, 1982). Invariance Testing Procedures: As there are different sub-groups especially in a crossnational context, items and scales may have unequal values. Because this is a crosscultural study, the validity of the structure models and scales developed in one group have to be examined and supported in other groups as well. In that, the instrument of measurement has to work in the same way (Byrne, 2004; Drasgow and Kansfer, 1985). Therefore, before conducting a multi-group analysis, the measurement of items, constructs, and path coefficients have to be invariant. This time, testing invariance of the constructs was performed simultaneously across the two groups; in which all parameters were estimated for two groups at the same time. Invariance testing of the measurement model across two groups by AMOS was applied as described by Byrne (2004). First, the baseline model with free estimation was performed simultaneously across two groups at the same time. Then, all measurement parameters were constrained to be equal in both groups via maximum likelihood. To test the invariance between two groups, the chi-square was checked by comparison to the baseline model, in which all constructs were freely estimated against another model (constrained structure parameters constructs). By doing so, the structural paths are equal across groups, yielding a chi-square value for the “constrained model” (Tabachnick and Fidell, 1996). If the constrained model is worse than the unconstrained model by Page | 132 showing significant p-value in the comparison between unconstrained and constrained model, this means that there are some unequal parameters (Robert, Probst, Martocchio, Drasgrow, and Lawler, 2000). 4.5.3.1 High Individualism and Low Individualism After splitting all respondents into two groups based on their score on individualism, these two groups were compared. Project team members were classified based on a score of individualism. The first group consisted of high individualism team members with individualism score ranking from 2.33 - 5.00. The second group was low individualism team members with individualism scores ranking from 1.00 - 2.33. Before comparing the differences of path coefficients between these high and low individualism groups of members, testing of invariance approach and criteria across two groups by AMOS as described by Byrne (2004), were conducted. To check whether the factor loadings were invariant across two groups, the unconstrained model was compared to constrained model in which the factor loading were specific invariance across two groups. Table 4-15 shows the comparison between an unconstrained model, a fully constrained model and a best fit model and their fit indices. The baseline model/unconstrained model (model1) is freely estimated for all parameters. The fully constrained model (model2) is employed to control invariance of all parameters across two groups (high and low individualism). According to the results of invariance testing as shown in Table 4-15, the chi-square value, degree of freedom (DF), CFI, and RMSEA of the fully constrained model are worse than unconstrained model. As reported in Table 4-15, X 2 changes from 207.141 to 220.813, and degree of freedom (DF) changed from 126 to 133, and CFI decreases from 0.914 to 0.907. Importantly, the Pvalue is significant (p = 0.057) indicating a large chi-square difference between the two groups. Thus, in order to find the best fit model and invariance of all parameters across the two groups, another step was conducted. Byrne (1998) and Marsh (1994) propose the least restrictive model that does not require any of the parameters estimates to be the same in different groups. Some fixed parameters and freed some parameters are employed. With the above procedures of free and fixed parameters, the best-fit model with a small improvement on goodness of fit indices was found as shown in Table 4-15 model 3 (Best model). Importantly, the results are confirmed by the non-significance of the P-value (p = 0.151). Therefore, the measurement model is invariant between high and low individualism members. For this reason, the path-coefficients of the structural model across these two groups could be compared and interpreted. Page | 133 Table 4-15: Comparison of Unconstrained, Fully Constrained, and Best Fit Model Model X 2 DF X2/DF CFI RMSEA   X 2   DF P-Value 1 Unconstrained 207.141 126 1.644 0.914 0.039 2 Fully Constrained 220.813 133 1.660 0.907 0.039 13.672 a 7 a 0.057 3 Best Model 216.567 132 1.641 0.910 0.039 9.426 b 6 0.151 Notes: a comparing between Model 2 and Model 1, b comparing between Model 3 and Model 1 After the invariance model (the best fit model) is developed, then the model can be estimated simultaneously across two groups at the same time. Therefore, paths between two groups can be compared. Table 4-16 summarizes the measured paths (column 1), the standard estimation (path coefficient) and critical ration (t-value) of each group (columns 2 and 3), and a comparison of the differences in specific path coefficient between two groups of respondents (column 4). T-values higher than 1.96 are regarded as significant at a 5% level and p-values less than 0.05 indicate a significant difference in specific path across two groups. Figure 4-20 illustrates the effects of all path coefficients between high and low individualist team members and Figure 4-21 depicts clearly the statistical differences of each path coefficient on innovation performance between high and low individualist team members. Table 4-16: Path Coefficients Comparing between High and Low Indiv. Members Paths 1) High Indiv 2) Low Indiv Chi - square test (Measuring of Difference of Path) S.E t-value S.E t-value Hypothesis 8: High Indiv. > Low Indiv. a: Autonomy Radical Innov. 0.126 † 1.694 0.019 0.238 X 2 = 0.848 (p =.357) b: Autonomy Incremental Innov. 0.022 0.258 -0.142 -1.644 X 2 = 1.864 (p =.172) c: AutonomyProject Efficiency 0.075 1.020 0.035 0.457 X 2 = 0.079 (p =.779) Hypothesis 10: High Indiv. > Low Indiv a: Monitoring progressRadical Innov. 0.113 1.532 0.117 1.483 X 2 = 0.000 (p =.992) b: Monitoring progress Incremental Innov. 0.127 1.502 -0.008 -0.101 X 2 = 1.337 (p =.249) c: Monitoring progressProject Efficiency 0.408*** 6.481 0.235** 3.105**  X 2 = 4.294 (p =.043) Hypothesis 12: Low Indiv > High Indiv. a: Process control Radical Innov. 0.311*** 3.983 0.149 † 1.875 X 2 = 3.697 (p =.055) b: Process control Incremental Innov 0.115 1.360 0.166 † 1.924 X 2 = 0.023 (p =.879) c: Process controlProject Efficiency 0.047 0.738 -0.138 † -1.828 X 2 = 3.108 (p =.078) Hypothesis 14: High Indiv > Low Indiv. a: Output control  Radical Innov. 0.124 † 1.682 -0.039 -0.494 X 2 = 2.306 (p =.129) b: Output control Incremental Innov. 0.182* 2.132 0.222* 2.511 X 2 = 0.051 (p =.821) c: Output controlProject Efficiency 0.235*** 3.743 0.121 1.613 X 2 = 1.734 (p =.188) Note: S.E. is Standardized path coefficient significant at ***p<.0001, **p<.001, *p<0.05 and † p<0.10. Indiv. is individualist team members and Innov. is innovation. Page | 134 Hypothesis 8 is stated that autonomy is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for high individualist team members rather than low individualist (collectivist) team members. As shown in Table 4-16, autonomy increases radical innovation with 0.126 (t-value = 1.694, p<0.10) for high individualist team members. The same path from autonomy to radical innovation is non-significant for low individualist team members with 0.019 (t-value = 0.238, p>0.10). In addition, the effect of path from autonomy to incremental innovation is nonsignificant with 0.022 (t-value = 0.258, p>0.10) for high individualist team members and with -0.142 (t-value = 1.644, p>0.10) for low individualist team members. The path from autonomy to project efficiency is non-significant as well in both high and low individualist team members with 0.075 (t-value = 1.020, p>0.10) and with 0.035 (t-value = 0.457, p>0.10) respectively. Furthermore, the chi–square, computed for testing the difference between the two groups, shows that all paths from autonomy to radical innovation (X 2 = 0.848, p>0.10), to incremental innovation (X 2 = 1.864, p>0.10), and to project efficiency (X 2 = 0.079, p>0.10) are not significantly different indicating no difference between these two groups. Therefore, hypothesis 8 is partially supported. It could be concluded that autonomy increases radical innovation performance only for high individualist team members; however, granting autonomy does not enhance innovation performance for low individualist team members as shown in Figure 4-20 and Figure 4-21. Hypothesis 10 is stated that monitoring progress is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for high individualists rather than low individualists. In Table 4-16, the results show that monitoring progress increases only project efficiency with 0.408 (t-value = 6.481, p<0.0001) for high individualists and with 0.235 (t-value = 3.105, p<0.001) for low individualist team members. The effect of monitoring progress on radical innovation is not significant with 0.113 (t-value = 1.532, p>0.10) for high individualists and with 0.117 (t-value = 1.483, p>0.10) for low individualist team members respectively. In addition, the effect of monitoring progress on incremental innovation is not significant for high and low individualist team members with 0.127 (t-value = 1.502, p>0.10) and -0.008 (t-value = - 0.101, p>0.10) respectively. Additionally, the chi-square test shows that paths from monitoring progress to radical innovation (X 2 = 0.000, p>0.10) and to incremental innovation (X 2 = 1.337, p>0.10) are not significantly different between these two groups. The results in Table 4-16 show that monitoring progress increases project efficiency for both high and low individualist team members, and the chi-square test reveals that the effect of monitoring progress on project efficiency is stronger for high individualists rather Page | 135 than for low individualist team members (X 2 = 4.294, p<0.05) as shown in Figure 4-20 and Figure 4-21. Therefore, hypothesis 10 partially confirmed. The findings revealed the positive effect of monitoring progress on project efficiency in both high and low individualist team members; however, the effect of monitoring progress is stronger for high individualist team members. Hypothesis 12 is stated that process control is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for low individualist team members rather than high individualist team members. The results in Table 4-16 show that the path coefficient of process control increases radical innovation with 0.311 (t-value = 3.983, p<0.0001) for high individualist team members and with 0.149 (t-value = 1.875, p<0.10) for low individualist team members. In addition, the chi-square different test shows a significant p-value (X 2 = 3.697, p<0.10). This indicates that the effect of process control on radical innovation is different between high and low individualism team members. The effect of process control on radical innovation is stronger for high individualists as shown in Figure 4-21 in the first panel. With regard to incremental innovation, the results expose that the effect of the path from process control to incremental innovation is significant with 0.166 (t-value = 1.924, p<0.10) for low individualist team members, but is non-significant with 0.115 (t-value = 1.360, p>0.10) for high individualist team members. The chi-square different test of path coefficients from process control to incremental innovation shows a non-significant p-value (X 2 = 0.023, p>0.10) demonstrating no differences between the two groups. In addition, the effect of process control on project efficiency is not significant with 0.047 (t-value = 0.738, p>0.10) for high individualists but it is negatively significant with -0.138 (t-value = -1.828, p<0.10) for low individualist team members. The chi-square difference test of this path coefficient shows a significant p-value (X 2 = 3.108, p<0.10) indicating a different effect between high and low individualism team members. Consequently, process control decreases project efficiency for low individualist team members but there is no statistical effect for high individualists as shown in Figure 4-20 and in the third panel of Figure 4-21. These results suggest that process control promotes radical innovation for both high and low individualism team members but the effect is stronger for high individualism team members. In addition, process control also enhances incremental innovation and decreases project efficiency for low individualism team members. Therefore, hypothesis 12 is partially supported. Hypothesis 14 is stated that the effect of output control is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for high Page | 142 Hypothesis 13 is stated that process control is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for high power distance team rather than low power distance members. As shown in Table 4-18, the path coefficient from process control to radical innovation is insignificant for high power distance with 0.11 (t-value= 1.315, p>0.10), but it is significant for low power distance with 0.27 (t-value= 3.718, p <0.001). Furthermore, the chi square difference test shows that there is a difference between high and low power distance on this path coefficient due to a significant p-value (X 2 = 3.511, p<0.10) as shown in Figure 4-23 in the first panel. This test reveals that the effect of process control on radical innovation is stronger for low power distance team members than for high power distance team members. The results also show that process control increases incremental innovation for high power distance team members with 0.24 (t-value= 2.649, p<0.05), but it is insignificant for low power distance with 0.08 (t-value = 0.959, p >0.10). However, the chisquare test shows that there is no difference in the effect of process control on incremental innovation between high and low power distance members based on a non-significant p-value (X 2 = 1.506, p>0.10). In addition, process control does not significantly affect project efficiency for both high and low power distance team members with values of -0.06 (t-value= -1.001, p>0.10), and -0.02 (t-value= -0.345, p>0.10), respectively. In addition, chi square test confirms that there is no difference in the effect of process control on project efficiency (X 2 = 0.243, p>0.10) between these two groups. Therefore, process control has different effects on innovation performance for different groups of members. Process control increases the growth of radical innovation for low power distance members and increases the growth of incremental innovation for high power distance members. However, process control had no effect on project efficiency for either group. Hence, hypothesis 13 is partially supported. Hypothesis 15 is stated that output control is likely to increase innovation performance in radical innovation, incremental innovation, and project efficiency for low power distance members rather than high power distance members. In Table 4-18, the results show that output control has an insignificant effect on radical innovation for high and low power distance members based on values of 0.13 (t-value= 1.490, p>0.10) and 0.03 (tvalue= 0.472, P>0.10), respectively. Output control has a significant effect on incremental innovation for low power distance team members with 0.28 (t-value= 3.077, p<0.05) but an insignificant effect on incremental innovation for high power distance team members with 0.08 (t-value = 0.859, p>0.10). Additionally, the path coefficient from output control to project efficiency is significant for both high and low power distance Page | 143 team members with 0.18 (t-value= 2.424, p <0.05), and 0.21 (t-value = 3.126, p<0.05), respectively. As shown in Table 4-18, the chi square test of all paths from output control to radical innovation (X 2 = 0.666, p >0.10), to incremental innovation (X 2 = 2.127, p>0.10), and to project efficiency (X 2 = 0.003, p>0.10) between two groups are insignificant indicating no difference between high and low power distance team members. Therefore, the results suggest that output control increases project efficiency for low and high power distance members. It also promotes incremental innovation for high power distance members as shown in Figure 4-22 and Figure 4-23. Thus, hypothesis 15 is partially confirmed. . Page | 144 High Power distance team members (n=184) Low Power distance team members (n=245) Figure 4-22: Path Coefficients Comparing Path Coefficients (High and Low PD) Notes: Model Fit with X 2 /DF = 1.727, CFI = 0.900, RMSEA = 0.041. Solid lines are significant paths at ***p<.0001 **p<.001 *p<0.05 † p<0.10. Dotted lines/ (n.s.) are non-significant paths. n.s. - 0.15 † Autonomy (Freedom) Monitoring Progress Process Control Output Control Incremental Innovation 0.15 † n.s 0.32*** 0.24 * 0.1 8* n.s n.s Project Efficiency n.s. n.s Radical Innovation - 0.1 2 † n.s n.s 0.19 * Autonomy (Freedom) Monitoring Progress Process Control Output Control Incremental Innovation 0.37 *** n.s Project Efficiency 0.2 7** 0.12 † n.s n.s n.s 0.2 8* 0 .21* Radical Innovation Page | 145 Figure 4-23: Differences of PMMs on Innovation Performance (High and Low PD) Notes: The heavy solid lines are significant difference of path coefficient between two groups. Solid lines are significant paths at ***p<.0001 **p<.001 *p<0.05 † p<0.10. Dotted lines/ (n.s.) are non-significant paths. Radical Innovation +0.15 † +0.12 † High PD Low PD Process Control Output Control Autonomy (Freedom) Monitoring Progress Process Control Output Control Autonomy (Freedom) Monitoring Progress +0.13(n.s) +0.03(n.s) + 0. 27** +0.11(ns ) + 0. 19* - 0. 15 † Incremental Innovation Process Control Output Control Autonomy (Freedom) Monitoring Progress Process Control Output Control Autonomy (Freedom) Monitoring Progress +0.24* +0.28* High PD Low PD -0.03(n.s) +0.05(n.s) +0.08(n.s) -0.08(n.s) +0.06(n.s) +0.08(n.s) Process Control Output Control Autonomy (Freedom) Monitoring Progress Process Control Output Control Autonomy (Freedom) Monitoring Progress +0.12 † +0.18* +0.32*** +0.37*** High PD Low PD +0.21* -0.02(n.s) -0.06(n.s) +0.02(n.s) Project Efficiency Page | 146 4.6 Summary of this Chapter The results from the testing of all hypotheses with AMOS are summarized in Table 4-19. Most hypotheses are partially confirmed. The results will be discussed in Chapter 5. Table 4-19: Summary Direct and Indirect Effects in Hypotheses Testing Hypotheses Confirmation 1 Autonomy increases innovation performance (radical innovation, incremental innovation, and project efficiency). Rejected 2 Monitoring progress increases innovation performance (radical innovation, incremental innovation, and project efficiency). Partially Confirmed 3 Process control increases innovation performance (radical innovation, incremental innovation, and project efficiency). Partially Confirmed 4 Output control increases innovation performance (radical innovation, incremental innovation, and project efficiency). Partially Confirmed 5 Process control has a stronger effect on innovation performance (radical innovation, incremental innovation, and project efficiency) than the other control mechanisms (output control and monitoring progress) Partially Confirmed 6 a Communication mediates the relationship between autonomy and innovation performance (radical innovation, incremental innovation, and project efficiency). Confirmed 6 b Communication mediates the relationship between monitoring progress and innovation performance (radical innovation, incremental innovation, and project efficiency). Partially confirmed 6 c Communication mediates the relationship between process control and innovation performance (radical innovation, incremental innovation, and project efficiency). Partially confirmed 6 d Communication mediates the relationship between output control and innovation performance (radical innovation, incremental innovation, and project efficiency). Partially confirmed 7 a Coordination mediates the relationship between autonomy and innovation performance (radical innovation, incremental innovation, and project efficiency). Confirmed 7 b Coordination mediates the relationship between monitoring progress and innovation performance (radical innovation, incremental innovation, and project efficiency). Confirmed 7 c Coordination mediates the relationship between process control and innovation performance (radical innovation, incremental innovation, and project efficiency). Confirmed 7 d Coordination mediates the relationship between output control and innovation performance (radical innovation, incremental innovation, and project efficiency). Confirmed 8 Autonomy is likely to increase innovation performance (radical innovation, incremental innovation, and project efficiency) for high individualists rather than low individualists. Partially confirmed 9 Autonomy is likely to decrease innovation performance (radical innovation, incremental innovation and project efficiency) for low PD rather than high PD. Partially confirmed Page | 147 Hypotheses Confirmation 10 Monitoring progress is likely to increase innovation performance (radical innovation, incremental innovation and project efficiency) for high individualist team members rather than low individualist team members. Partially confirmed 11 Monitoring progress is likely to increase innovation performance (radical innovation, incremental innovation and project efficiency) for both low power distance and high power distance team members. Partially confirmed 12 Process control is likely to increase innovation performance (radical innovation, incremental innovation and project efficiency) for low individualist team members rather than high individualist team members. Partially confirmed 13 Process control is likely to increase innovation performance (radical innovation, incremental innovation and project efficiency) for high power distance rather than low power distance team members. Partially confirmed 1 4 Output control is likely to increase innovation performance (radical innovation, incremental innovation and project efficiency) for high individualist rather than low individualist team members. Confirmed 1 5 Output control is likely to increase innovation performance (radical innovation, incremental innovation and project efficiency) for low power distance rather than high power distance team members. Partially confirmed Page | 148 Chapter 5: Discussion This chapter presents the results and findings of this study in two parts. The first part discusses the direct effects of project management and control mechanisms on innovation performance and the indirect effects of project management mechanisms on innovation performance through teamwork processes of communication and coordination. The second part discusses the effect of project management mechanisms on innovation performance given different cultural values of team members (high/low individualism and high/low power distance). 5.1 Direct and Indirect Effects of Project Management Mechanisms 5.1.1 Autonomy on Innovation Performance Direct Effect of Autonomy on Innovation Performance. As shown in Table 4-6 and Figure 4-7, autonomy had no effect on radical innovation, incremental innovation, and project efficiency. Surprisingly, these findings differ from the other studies, where highly autonomous environments within a firm drive radical innovation (Chandy and Tellis, 1998), increase organizational innovation (Paolillo and Brown, 1978), speed up radical innovation projects (Kessler and Chakrabarti, 1999), support incremental innovation projects (Bart, 1993), increase effectiveness of innovation projects (Angle, 1989), and are associated with project execution success (Tatikonda and Rosenthal, 2000). Though there is no statistical support in this study, the results are consistent with the findings of Abbey and Dickson (1983), and Kang and Park (1992) in that autonomy had no-significant relationship with the number of technological innovations or commercialization of new products (as cited in Kim and Lee, 1995). Granting autonomy had no relationship with incremental innovation which is supported by other related research which indicates that incremental innovation is associated with centralized decision making and a formal structure within firms (Cohn and Turyn, 1984; Stamm, 2003) rather than a decentralized structure. In addition, granting autonomy did not have any effect on project efficiency. This is supported by the study of Thamhain (1990) where autonomy had no correlation with R&D team performance. It may be that the development of incremental innovation product or enhancement of project efficiency may not require autonomy as innovation tasks are less complex (e.g., small improvement of projects or redeveloping products). Increasing performance of incremental innovation or Page | 149 project efficiency may need a formal structure within a firm to facilitate cooperation of all members involved in the project. The lack of statistical support between autonomy and innovation performance in the current study does not mean that autonomy is not important to innovation performance. It could be that providing autonomy for individual team members in executing their tasks, exploring their own ideas, and making decisions on their own might enhance individual creativity, by instilling a sense of ownership and control over their own tasks, which is needed for developing radical innovation products (Amabile, Conti, Coon, Lazenby, and Herron, 1996). However, complex tasks such as developing radical innovation products need not only individuals’ creativity, but also the diverse knowledge and perspectives of team members in generating new ideas and linking creative ideas with the abilities of the firm and market needs to develop new products (Nonaka and Takeuchi, 1995). In addition, during radical innovation product development, project team members need to be tuned-in with the other team members through communication and coordination (Souder and Moenaert, 1992). As such, individual team members need not only autonomy for their tasks, but also communication and coordination with other team members to share diverse knowledge and experiences related to tasks and to ensure that developed subsystems are well integrated (Bacon, 1985). Indirect Effect of Autonomy on Innovation Performance through Communication and Coordination. In order to facilitate a clear picture of mediating effects, the indirect effect of autonomy on innovation performance via communication and coordination are depicted in Figure 5-1. As previously mentioned, autonomy had no effect on innovation performance in this current study. The results of indirect effect testing revealed that: (1) autonomy had both a direct negative effect on incremental innovation and an indirect positive effect on incremental innovation through communication and coordination; and (2) autonomy had an indirect effect on radical innovation and project efficiency through communication and coordination of team members. Page | 150 Figure 5-1: Autonomy and Teamwork Processes on Innovation Performance Notes: The solid lines are significant paths at ***p<0.0001 **p<0.001 *p<0.05 † p<0.10. Dotted lines (n.s.) are non-significant paths. These indirect effect results are partially supported by several scholars on the subject of autonomy and communication and coordination and the effects of communication and coordination on different types of innovation performance. For example, Bacharach and Aiken (1977), on the subject of decentralized structures and communication of subordinates in organizations, states that given a high level of autonomy, subordinates tend to engage in more communication. In relation to communication and performance, Brown and Eisenhardt (1995), Allen (1971; 1977), and Tushman and Scanlan (1981) found that communication among project team members enhanced the performance of development teams. The results in this study are partially related to Kivimäki et al., (2000) on the issue of communication and coordination and innovation performance. Their study found that a participative climate (i.e., frequent communication) and coordination were associated with perceived organizational innovation. Additionally, Zhang and Gao (2010) found from their simulation experiment that effective communication among the developers of intermodules developing incremental innovation would increase performance of the improvement. In particular, both direct negative and Coordination - 0.12 † Communicat ion 0.17* 0. 14 * 0.28*** 0.00 (n.s) Autonomy (Freedom) Incremental Innovation Radical Innovation Project Efficiency 0.36*** 0.26*** - 0.10 (n.s) Autonomy (Freedom) Radical Innovation Incremental Innovation Project Efficiency - 0.0 4 (n.s) 0.35*** 0.17* 0.21*** 0.04 (n.s) - 0.02 (n.s) Page | 151 indirect effects of communication on incremental innovation were found in this study, but the direct negative effect was weaker than the indirect effect. A possible explanation for the direct negative effect of autonomy on incremental innovation performance could be that providing high autonomy directly to team members without control of their tasks may lead to many created solutions which are unnecessary for the development of small technical improvement projects. While given autonomy encourages team’s communication which in turn drives incremental innovation. This study is partially supported by previous studies related to autonomy and communication and coordination. In terms of communication and coordination and innovation performance, it could be suggested that providing autonomy does not directly encourage innovation performance, but it enhances communication and coordination of team members. Communication within a project team facilitates the dispersion of ideas and exchange of information among team members, whereas coordination facilitates the correct integration and interaction of sub-systems, modules, and components. As such, given autonomy to the team facilitates communication and coordination, which in turn drives innovation performance as shown in Figure 5-1. 5.1.2 Monitoring Progress on Innovation Performance Direct effect of monitoring progress on innovation performance. As shown in Table 4-6 and Figure 4-7, the results from this study revealed that monitoring progress had a direct effect on both radical innovation and project efficiency. In addition, the effect of monitoring progress on project efficiency was stronger than the effect of monitoring progress on radical innovation. This finding seems to be consistent with the previous researches in that increasing monitoring progress kept the project on track (Salomo et al., 2007), encouraged creation of technical knowledge and project efficiency (Lewis et al., 2002), accelerated product development (Eisenhardt and Tabrizi, 1995), and sped up radical innovation (Kessler and Chakrabarti, 1999). The above studies further explained that assessment of a project according to milestones forces team members to focus on innovative tasks. Nevertheless, monitoring progress had no direct effect on incremental innovation in this study. It could be argued that overly detailed tasks, scheduling and frequent monitoring by project managers might support tasks related to the development of complex projects (e.g., radical innovation) rather than tasks related to projects developing minor changes (e.g., incremental innovation). A possible explanation for this argument is that project managers and team members are familiar with existing technologies, and improving only some sub-systems or small components of products