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Mapping Cultural Differences

AN EMPIRICAL STUDY TO UNDERSTAND CULTURAL DIFFERENCES IN

AN ORGANIZATIONAL SETTING

11/12/2017

Thesis Circle – XM 4

Details of student:

Name: Pooja Ravi Shankar

ANR: 440057

Name of the Supervisor(s):

Supervisor: dr. J. van Dijk

Second Reader: dr. S.W.M.G. Cloudt

Professional Supervisor: Elis Yamaguchi

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Acknowledgement

This thesis is the final work of my Extended Masters in Organization Studies at Tilburg University.

This was inspired from the findings of the book “The Culture Map: Breaking Through the Invisible

Boundaries of Global Business” by Erin Meyer.

As part of my master’s program I had the opportunity to do a traineeship at global health

technology organization for the Magnetic Resonance Business. During the internship year, I was

involved in a couple of projects and it has brought me immense learnings, experience and insights

both at a personal and professional front.

I am very grateful to my academic supervisor Hans van Dijk for his critical viewpoint and for

challenging me to improve the quality of my work. I would like to thank Stefan Cloudt for his

constructive feedback during the IRP defense and the final defense sessions. I am thankful to my

circle mates for all their support and feedback.

Elis Yamaguchi, my professional supervisor has inspired me with her passion for work and has

always been open for discussion and provided me with valuable feedback at every step. I am very

grateful to her for all the support and for being a wonderful mentor. I would also like to thank my

colleagues for assisting me through my traineeship and my thesis.

I hope you enjoy reading my thesis and gain interesting insights from it.

Pooja Ravi Shankar

Tilburg, December 2017

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Abstract

Business success in a globalized and virtual world requires individuals to navigate cultural

differences and to understand cultures that are not similar to theirs (Meyer, 2014a). Cultural

diversity is a well-researched field; however only recently Meyer conceptualized it into eight

independent dimensions through the lens of interactions that take place between individuals.

In this research I argue the relevance of Meyer’s taxonomy in an organizational setting as it focuses

on the cultural differences that manifest through conversations and in working together, and that

may lead to misunderstanding and conflict. The goal of this research is to firstly, develop a

questionnaire to capture cultural differences as conceptualized by Meyer. Secondly, it aims to

understand the relationship between cultural differences and individual performance by

investigating the effect of relationship conflict, cultural intelligence and degree of virtuality on this

relationship.

This research uses a quantitative approach wherein data from 122 respondents who work in

multicultural teams that are geographically dispersed, was collected through a survey. The main

findings of this study is, the development of a 25-item reliable questionnaire to assess an

individual’s cultural differences as conceptualized by Meyer (2014a) and it exhibited different

operationalization as compared to Hofstede’s dimensions. However, further research is necessary

to test the questionnaire and the research question in various other contexts.

Key Words: Cultural Difference, Individual Performance, Relationship Conflict, Cultural

Intelligence, Degree of Virtuality, Meyer’s Taxonomy.

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Table of Contents

Acknowledgement ...................................................................................................................... 2

Abstract...................................................................................................................................... 3

Table of Contents ....................................................................................................................... 4

1. Introduction ......................................................................................................................... 7

1.1 Research Question ........................................................................................................... 8

1.2 Conceptual Model ............................................................................................................. 9

1.3 Relevance ........................................................................................................................ 9

1.3.1 Scientific Relevance ................................................................................................... 9

1.3.2 Practical Relevance ................................................................................................... 9

2. Theoretical Framework ......................................................................................................10

2.1 Individual Job Performance .............................................................................................10

2.2 Cultural Differences .........................................................................................................11

2.2.1 Dimensions of Cultural Differences ...........................................................................12

2.3 Relationship Conflict as a Mediator ..................................................................................16

2.4 Cultural Intelligence as a Moderator ................................................................................17

2.5 Degree of Virtuality as a Moderator .................................................................................19

2.6 Entire Model ....................................................................................................................20

3. Methods .............................................................................................................................21

3.1 Research Context ............................................................................................................21

3.2 Research Design and Sampling strategy .........................................................................22

3.3 Data Collection ................................................................................................................23

3.4 Data Handling ..................................................................................................................24

3.5 Measurements .................................................................................................................24

3.5.1 Measurement of Variables: .......................................................................................24

3.6 Data Analysis - Testing for Assumptions .........................................................................32

4. Results ...............................................................................................................................35

4.1 Descriptive Statistics .......................................................................................................35

4.2 Culture Map .....................................................................................................................37

4.3 Hypothesis Testing ..........................................................................................................37

4.3.1 Mediation Analysis ....................................................................................................38

4.3.2 Moderation Analysis ..................................................................................................39

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4.3.3 Moderated Mediation Analysis ..................................................................................42

4.4 Summary of Results ........................................................................................................44

4.5 Additional Analysis ..........................................................................................................45

4.5.1 Descriptive statistics ..................................................................................................45

4.5.2 Hypothesis Testing ....................................................................................................46

4.5.3 Summary Additional Results .....................................................................................53

5. Discussion .........................................................................................................................53

5.1 Main findings ...................................................................................................................54

5.1.1 Cultural Difference Questionnaire .............................................................................54

5.1.2 Non-Significant Effect of Relationship Conflict and Degree of Virtuality .....................55

5.1.3 Moderating Role of Cultural Intelligence ....................................................................56

5.1.4 Low Variance ............................................................................................................57

5.2 Practical Implication .........................................................................................................58

5.3 Limitation .........................................................................................................................58

5.4 Future Research ..............................................................................................................60

6. Conclusion .........................................................................................................................61

7. References ........................................................................................................................63

8. Appendix ............................................................................................................................74

Appendix A - Invitation Email to Participants..........................................................................74

Appendix B - Questionnaire ...................................................................................................75

Appendix C – Factor Analysis ................................................................................................82

Factor Analysis – Cultural Difference .................................................................................82

Factor Analysis – Relationship Conflict ..............................................................................83

Factor Analysis – Cultural Intelligence ...............................................................................83

Factor Analysis – Individual Performance ..........................................................................84

Factor Analysis – Hofstede’s Cultural Dimensions .............................................................84

Appendix D – Test for Multicollinearity ...................................................................................85

Appendix E – Test for Normal Distribution .............................................................................86

Appendix F – Test for Outliers ...............................................................................................88

Appendix G – Output for Main Hypothesis .............................................................................91

Mediating effect of Relationship Conflict ............................................................................91

Moderating Role of Cultural Intelligence .............................................................................93

Moderating Role of Degree of Virtuality ..............................................................................95

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Entire Model .......................................................................................................................97

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1. Introduction

Globalization and technological revolution have become an irreversible trend and an objective

reality not only in the societies we live in but also in the organizations we work for (Herbsleb &

Moitra, 2001). As a result, organizations increasingly feature culturally diverse teams (Haas &

Cummings, 2015; Maznevski & Chui, 2013). Hence, research to understand cultural differences

is becoming more widespread (Mooij & Hofstede, 2010), and several taxonomies have been

developed which arise from different theoretical propositions (Hinds, Liu & Lyon, 2011). In this

study, I focus on the newly developed taxonomy of Meyer (2014a), which illustrates how cultural

differences manifest themselves in interactions and collaborations among individuals.

Meyer (2014a) argues that cultural differences often determine what one views as acceptable

workplace behavior, and knowing these differences is crucial to minimize conflict and enhance

performance in today’s global environment. Specifically, Meyer’s taxonomy suggests that people

from one culture have different patterns of communication when compared to other cultures.

Hence, I argue that this taxonomy is more relevant in comparison to other existing taxonomies in

assessing cultural differences between individuals as it is more proximal to an organizational

setting and it operationalizes culture at an individual level. Thus, in this study, I aim at examining

the extent to which Meyer’s taxonomy predicts differences in interaction patterns. Further, I am

studying the degree to which these differences result in misunderstandings between individuals

within a team. I do so by looking at the extent to which these differences lead to conflict and

consequently its effect on an individual’s performance.

Conflicts are of four types – task, process, relationship (Jehn, 1995) and status (Bendersky &

Hays, 2012). In this study, I focus on relationship conflict because cultural differences trigger

conflict primarily at a relationship level (Pelled, Eisenhardt, & Xin, 1999). To elaborate further,

different cultures have different patterns of both interactions and interpretations. These differences

lead to miscommunication between individuals, which is the basis of relationship conflict (Pelled

et al., 1999). These conflicts between individuals lead to reluctance of information sharing among

them, which affects critical task-related matters and is likely to impair the individual’s

performance (Moye & Langfred, 2004). Hence, I expect cultural difference to routinely trigger

relationship conflict, which in turn reduces an individual’s performance. (Meyer, 2014a).

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Cultural differences are reduced with increase in cultural intelligence, which are a set of cross-

cultural competencies that enable individuals to interact optimally in culturally diverse settings

(Adair, Hideg, & Spence, 2013; Ang & Van Dyne, 2008). I anticipate cultural intelligence to

moderate the relationship between cultural differences and relationship conflict. I argue so

because, individuals with high cultural intelligence can successfully cope with cultural differences

(Ang & Van Dyne, 2015). They have a more accurate understanding of verbal cues they receive

from other individuals and they also are better aware of their own assumptions in decoding these

cues (Groves & Feyerherm, 2011).

At the same time, I argue that virtuality increases conflict that results from cultural differences.

Over the past several decades, there has been a monumental growth in organizations' use of virtual

environment to organize work. Teams having such work arrangements have team members who

are dispersed (Joy-Matthews & Gladstone, 2000). I expect that an increase in the degree of

virtuality between members of a team will reduce visual cues and increase miscommunication,

and hence amplify the negative effects of cultural differences such as misunderstandings and

conflict between these individuals (Staples & Zhao, 2006). Thus, I investigate the moderating

effect of degree of an individual’s virtuality on the relationship between cultural differences and

relationship conflict.

In conclusion, I test whether Meyer’s Taxonomy of cultural differences is useful in understanding

how cultural differences affect individual’s performance. Specifically, based on the reasons

mentioned above I expect that cultural differences amongst individuals in a team setting at a

workplace positively influences conflict, which in turn influences the individuals performance

negatively. Furthermore, I expect the former relationship to be moderated by cultural intelligence

and degree of virtuality. This leads to the following research question:

1.1 Research Question

To what extent does relationship conflict mediate the relation between cultural differences

between individuals and individual job performance and to what extent does cultural intelligence

and degree of virtuality moderate the relation between cultural difference and relationship

conflict?

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1.2 Conceptual Model

Figure 1: Conceptual Model

1.3 Relevance

1.3.1 Scientific Relevance

Meyer performed a qualitative study by interviewing individuals to develop this taxonomy. While

the theoretical proposition behind Meyer’s taxonomy is becoming increasingly relevant in today’s

multi-cultural organizational setting, no research has taken a quantitative approach to measure

Meyer’s taxonomy.

This study develops a questionnaire that quantitatively measures Meyer’s taxonomy for use under

an organizational setting. To ensure the questionnaire is robust, this study (a) measures the

questionnaire’s reliability, (b) empirically tests Mayer’s newly developed taxonomy and (c)

verifies whether Meyer’s and Hofstede’s dimensions have unrelated measurements.

This study contributes to literature by providing a questionnaire that can measure Meyer’s

dimensions. This questionnaire can be used to develop a culture map of an organization and

quantitatively measure cultural difference at individual / organization levels. Along with

Hofstede’s questionnaire, researchers are now able to measure different aspects of culture.

1.3.2 Practical Relevance

From an organization’s perspective, this study will help individuals / teams / organizations to

understand the relative position of their culture with respect to others. It will enable them to

comprehend the similarities and differences in communication patterns. This will also allow them

to develop communication strategies, which will improve individual and team performance by

Cultural Differences

Relationship Conflict

Individual Job Performance

Cultural Intelligence

Degree of Virtuality

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reducing cross-cultural misunderstandings and conflicts. In addition, organizations can emphasize

on training aimed to increase the cultural intelligence of employees. Moreover, organizations can

focus on structuring their teams better by bringing in the right variation of cultural patterns or by

minimizing the impact of virtuality and hence reducing conflict.

2. Theoretical Framework

2.1 Individual Job Performance

In this study, I focus on understanding how an individual’s performance is affected and shaped in

a multicultural team. Individual job performance can be defined as “scalable actions, behavior, and

outcomes that employees engage in or bring about that are linked with and contribute to

organizational goals” (Viswesvaran & Ones, 2000, p.216).

According to Koopmans et al., (2012), individual job performance consists of task performance,

contextual performance and counterproductive work behavior. However, in this study I only take

task and contextual performance into account. This is because, the organization under study was

undergoing major changes and measuring counterproductive work behavior would create more

unrest in the organization.

Task performance refers to the technological aspects of a job besides the actual establishment of

products. These technological aspects enfold the distribution of finished products, planning,

administration, coordination, and supervision” (According to Motowidlo & Scotter, 1994, p.476).

Contextual performance does not so much concern the establishment and technical process of

products but is more concerned around the organizational support behind the core of the

organization (Mototwidlo & Scotter, 1994).

Prior research has identified a number of factors that shape Individual job performance, including

the nature and requirements of the job, income and rewards, co-workers, the managerial system of

the organization, personality characteristics and workplace environment (Attia, 2013). However,

the effects of cultural differences on an individual’s job performance has not received enough

attention (Randel & Jaussi, 2003). Hence, I aim to understand the potential effect of cultural

difference on an individuals’ performance in the context of a multicultural team and I expect

(cultural) differences to lower individuals’ performance.

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2.2 Cultural Differences

In a multicultural team, individuals bring in distinct values, beliefs, attitudes, and expectations that

are shaped by their culture and experiences (Loden & Rosener, 1991) which is the root cause of

cultural differences. Cultural differences between individuals can be defined as the degree to which

an individual’s cultural characteristics are different from other members of his/her social unit (Tsui

& O’Reilly, 1989) or teams in the case of this study.

In the field of cross-cultural interactions, there have been various attempts to understand cultural

differences. The famous taxonomies of cultural differences are by Hofstede, Fons Trompenaars,

Edward T.Hall, and House & Colleagues’ GLOBE Cultural Framework. Among these attempts,

a national culture perspective as modelled by Hofstede (1984, 2001) has been regarded as a

paradigm in the field of cross-cultural studies. Specifically, his five cultural values (power

distance, uncertainty avoidance, masculinity-femininity, individualism-collectivism, and long

term orientation) have been frequently cited by researchers in the past few decades.

All the above mentioned taxonomies focus on national level. However, in organizations, the

reflection of culture at individual level is more relevant (Kamakura & Novak, 1992) and business

efforts would be effective when an individual-level measure is developed (Farley & Lehmann

1994). This is of importance because sometimes variations among individuals from a single

country could be as big as those among individuals from different countries. (Offermann &

Hellmann, 1997). Meyer’s taxonomy provides such an individual-level conceptualization and

thus, I develop a questionnaire and study it by adjusting and validating this taxonomy.

In this paper, I argue that even though both Meyer and Hofstede try to understand cross-cultural

communications, they use different dimensions to understand this concept. Hofstede tries to

understand cultural differences between modern nations along dimensions that represented

different answers to universal problems of human societies. Thus, he derived the following five

dimensions: Power Distance (related to the problem of inequality), Uncertainty Avoidance

(related to the problem of dealing with the unknown and unfamiliar), Individualism–Collectivism

(related to the problem of interpersonal ties), Masculinity–Femininity (related to emotional gender

roles) and Long- versus Short-Term Orientation (related to deferment of gratification) (Hofstede,

2006). Whereas, Meyer focuses more on interactions between individuals, which is a key

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determinant of effective team functioning. It would help understand the most common business

communication challenges that arise between individuals due to cultural differences (Meyer,

2014a). Therefore, I argue that Meyer’s taxonomy is more relevant for analyzing an individual’s

performance in an organizational setting.

2.2.1 Dimensions of Cultural Differences

Meyer’s (2014a) cultural differences are conceptualized into eight independent dimensions. These

are as follows: 1) Communicating 2) Evaluating 3) Persuading 4) Leading 5) Deciding 6) Trusting

7) Disagreeing and 8) Scheduling. Each of these vary along a spectrum from one extreme to its

opposite. In the following paragraphs, I provide a brief description of each dimension and include

a figure where countries’ are positioned based on Meyer’s interviews and experiences.

1) Communicating: It ranges between low-context culture and high-context culture. In low-

context cultures, good communication is precise, simple and clear. Messages are expressed and

understood at face value. Repetition is appreciated if it helps clarify the communication. Whereas,

in high-context cultures good communication is sophisticated, nuanced and layered. Messages are

both spoken and read between the lines. Messages are often implied and not plainly expressed.

Figure 2: Dimension 1 - Communicating

2) Evaluating: It ranges between giving direct negative feedback and giving indirect negative

feedback. In cultures that accept direct negative feedback, negative feedback is provided frankly,

bluntly and honestly. The negative feedback stands alone not softened by positive ones and

criticism may be given to an individual in front of a group. However, cultures characterized as

giving indirect negative feedback, negative feedback is provided softly, subtly and diplomatically.

Positive messages are used to wrap negative ones and criticism is given only in private.

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Figure 3: Dimension 2 - Evaluating

3) Persuading: The two extremes of this dimension are principles-first and applications-first. In

cultures that prefer principles-first reasoning, individuals first develop theory and then present

supportive facts and conclusions. While, in cultures that prefer applications-first reasoning,

individuals first begin with real-world patterns or facts, and then derive conclusions.

Figure 4: Dimension 3 - Persuading

4) Leading: The two extremes of this variable are cultures that are egalitarian or that are

hierarchical. In an egalitarian culture, the ideal distance between a boss and subordinate is low.

Workers can disagree with their superiors without fear of reprisals. Organization structures are

generally flat and communication often skips hierarchical levels. In a hierarchical culture, the

ideal distance between a boss and subordinate is high. Workers consider it impertinent to

contradict the boss and wait for approval before acting and communicating through the

appropriate channels. Organization structures are multilayered and fixed and communication

follows set hierarchical levels.

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Figure 5: Dimension 4 - Leading

5) Deciding: This variable ranges between consensual decision making and top-down decision

making process. In consensual decision making process, decisions are made in groups through

unanimous agreement. In a top-down decision making process, decisions are made by individuals

(usually the boss).

Figure 6: Dimension 5 - Deciding

6) Trusting: This dimension ranges from task-based cultures to relationship-based cultures. In

task-based cultures, trust is built through business-related activities. Work relationships form and

grow around functionality and mutual usefulness, and often end when the business concludes.

However, in relationship-based cultures, trust is built slowly as people get to know each other.

Work relationships build up slowly and over time.

Figure 7: Dimension 6 - Trusting

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7) Disagreeing: It ranges between disagreeing using confrontation and avoiding confrontation. In

cultures that use confrontational technique, disagreement and debates are considered to yield

positive results. Open confrontation is appropriate and will not negatively affect the relationship.

Whereas, in cultures that avoids confrontation, disagreement and debates are considered to yield

negative results. Open confrontation is inappropriate and will break relationships.

Figure 8: Dimension 7 - Disagreeing

8) Scheduling: The two extremes of this variable are linear-time culture and flexible-time culture.

Cultures where time is considered linear, the focus is on adhering to schedules, respecting

deadlines, and completing one task at a time. Cultures where time is considered flexible, the focus

is on flexibility, schedules are adaptable and many activities occur simultaneously.

Figure 9: Dimension 8 – Scheduling

Plotting out preferences on the eight scales and drawing a line connecting the eight points creates

a culture map. This map represents the overall pattern of that individual/culture. It is important to

note that the relative gap between two maps also known as cultural relativity determines how

people view one another. Moreover, this cultural relativity is the key to understanding interactions

(Meyer, 2014a).

To understand relative cultural differences, I use the theory of relational demographics as it

provides the basis for predicting how individual demographic characteristics and social context

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interact (Mowday & Sutton, 1993). To elaborate, the absolute (cultural) differences, by itself, does

not adequately reflect the full meaning and impact of diversity within a work setting, rather it is

the relative (cultural) differences that are predictive of individuals' performance (Tsui, Egan, &

O'Reilly, 1992; Tsui & O'Reilly, 1989). Hence, I aim to understand the potential effect of relative

cultural differences in a team, on an individual's performance and I expect cultural differences to

lower individuals’ performance.

2.3 Relationship Conflict as a Mediator

Literature on cultural differences and performance have focused their attention around theories,

which have quite contrasting views (Mannix & Neale, 2005). Researchers argue that cultural

difference is a double-edged sword because it has the potential to both benefit and disrupt

performance (van Knippenberg & Schippers, 2007; Williams & O’Reilly, 1998). These studies

can be categorized into two theoretical traditions: information processing and social categorization

(van Knippenberg & Schippers, 2007; Williams & O’Reilly, 1998). According to information

processing approach, the cognitive benefits of cultural differences, which are increase in creativity,

innovation and flexibility, lead to increase in an individual’s performance (Jehn, Northcraft &

Neale 1999; Lau & Murninghan 1998; McLeod, Lobel, & Cox Jr, 1996). In contrast, Social

Categorization (Mannix & Neale, 2005), argues that cultural differences will inhibit individual

performance. This is because cultural differences leads to increase in communication difficulties

and misunderstandings which then reduces social cohesion and hence are more likely to lead to

relationship conflict.

According to Jehn and Mannix, (2001) relationship conflict is defined as an awareness of

interpersonal incompatibilities which includes affective components such as feeling tension and

friction. However, conflict between members of a work group, in literature has been categorized

into four types: relationship conflict (interpersonal frictions and disagreements concerning

personal issues), task conflict (disagreements concerning the task), process conflict

(disagreements concerning the way in which the task is to be achieved) (Jehn, 1995, 1997) and

status conflict (disputes or disagreements over an individual’s relative status position in the social

hierarchy of the team) (Bendersky & Hays, 2012). These four types of conflict are expected to

have a differential impact on performance. This study focuses only on relationship conflict

because cultural differences trigger conflict at a relationship level (Pelled et al., 1999).

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I argue that relationship conflict mediates the relationship between cultural differences and

individual performance. To elaborate when individuals are positioned differently on the culture

map, they have a different styles of interacting and interpreting (Meyer, 2014a) and relational

demographics helps capture these relative (cultural) differences. As these relative difference in

interaction patterns between individuals’ increases, misunderstandings between them also

increases. This leads to interpersonal disagreements which is a consequence of miscommunication

and misinterpretation, and according to Jehn and Bendersky (2003) is characteristic of relationship

conflict. Consequently, when individuals experience relationship conflict they work less

effectively and produce suboptimal products (Argyris, 1962) as they simply lose perspective about

the task being performed and thus inhibiting individual performance (Evan, 1965). Thus, I expect

a mediating role of relationship conflict.

Hypothesis 1: The relationship between cultural differences and individual performance is

mediated by relationship conflict, in such a way, that cultural difference leads to an increase in

relationship conflict, which in turn leads to a decrease in individual performance.

2.4 Cultural Intelligence as a Moderator

Cultural intelligence (CQ) is defined as an individual’s capabilities to function and manage

effectively in culturally diverse settings (Earley & Ang, 2003). CQ allows individuals to

understand and act appropriately across a wide range of cultures (Thomas, 2006). This individual

characteristic reduces cultural differences by enabling individuals to “adapt to, select, and shape

the cultural aspects of their environment” (Thomas et al., 2008, p.126).

CQ is composed of four dimensions: meta-cognition, cognition, motivation, and behavior.

Individuals with a high CQ make use all the four dimensions (Ang et al., 2004; Ang et al.,

2006; Earley & Peterson, 2004; Ng & Earley, 2006). The four dimensions are as follows:

Meta-cognition CQ is defined as an individual's knowledge or control over cognition that leads to

deep information processing (Ang et al., 2004). It tries to understand the mental processes that

individuals use to acquire and comprehend cultural knowledge, including knowledge of individual

thought processes (Flavell, 1979) relating to culture. Relevant capabilities include planning,

monitoring and revising mental models of cultural norms for countries or groups of people. In

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lines with Triandis (2006) those with high metacognitive CQ are consciously aware of others’

cultural preferences before and during interactions. Consequently they are aware of potential

differences in interaction processes, they tend to interpret behavior from the other person’s

perspective and give it the same meaning as that intended by the other person. Accordingly, they

have more accurate understanding of expected interactions during cultural diverse situations.

Hence I believe that it would reduce misunderstandings and therefore reduce relationship conflict.

While metacognitive CQ focuses on higher-order cognitive processes, cognitive CQ reflects

knowledge of the norms, practices and conventions in different cultures acquired from education

and personal experiences (Ang et al., 2004). It includes the general knowledge about the structures

(economic, legal and social systems) of a culture (Ang et al., 2006; Ng & Earley, 2006). In lines

with Cushner and Brislin (1996) those with high cognitive CQ understand similarities and

differences across cultures. They have elaborate mental representations of interactions in

particular cultural groups. This should allow individuals to identify and understand key areas of

miscommunications and are thus likely to reciprocate appropriately.

Motivation CQ understands a person's interest in learning and functioning in situations

characterized by cultural differences (Ang et al., 2004; Ang et al., 2006). This dimension includes

three primary motivators: enhancement (wanting to feel good about oneself), growth (wanting to

challenge and improve oneself) and continuality (the desire for continuity and predictability in

one's life) (Earley et al., 2006). Consequently, those with higher motivational CQ have intrinsic

interest in other cultures and expect to be successful in culturally diverse situations (Bandura,

2002). This engagement and persistence leads to an individual practicing new interacting patterns

and thereby adapting to the new cultural setting.

The final dimension of CQ is behavior - the action aspect of the variable (Earley et al., 2006). It

includes a person's ability to exhibit the appropriate verbal and non-verbal behaviors when

interacting with others from a different cultural background (Ang et al., 2004; Ang et al., 2006; Ng

& Earley, 2006), and to generally interact competently with individuals from diverse backgrounds

(Thomas, 2006). Consequently, in lines with Gudykunst, Ting-Toomey, and Chua (1988) those

with high behavioral CQ exhibit correct behavior based on their broad range of verbal and

nonverbal capabilities, such as exhibiting culturally appropriate words, tone, gestures and facial

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expressions, based on cultural settings. When individuals are flexible, they are less offensive to

others and misunderstandings as well as conflict are lowered.

Individuals with high CQ have the ability to recognize cultural differences and adapt to them

accordingly and individuals with low CQ are unaware of the cultural cues being conveyed to

them. Thus, I expect a moderating role of CQ.

Hypothesis 2: The relationship between cultural difference and relationship conflict is moderated

by cultural intelligence, in such a way, that cultural intelligence of an individual will buffer the

effect of cultural difference on relationship conflict

2.5 Degree of Virtuality as a Moderator

Global expansion and mobility, accompanied with technological developments, have led to teams

moving from traditional face to face arrangements, to ubiquitous global virtual teams (Davison,

Panteli, Hardin, Fuller, 2017). According to Curseu and Wessel (2005, p. 271), a virtual team is a

“collection of individuals who are geographically and/or organizationally or otherwise dispersed

and who collaborate, using varying degrees of communication and information technologies in

order to accomplish a specific goal”. However, because of the increasing use of virtual

communication tools in teams, “all teams can be described in terms of their level of virtuality”

(Kirkman and Mathieu, 2005, p. 701).

Gibson and Cohen (2003, p.5) define Degree of Virtuality as, “where a team exists on this

continuum is a function of the amount of dependence on electronically mediated communication

and the degree of geographic dispersion”. The two ends of this continuum are face-to-face teams

and virtual teams. Thus, co-located teams that mainly relies on technology in order to

communicate are also virtual teams (Curşeu & Wessel, 2005: 270).

I argue that degree of virtuality moderates the relation between cultural difference and relationship

conflict. That is miscommunications that arise due to cultural differences potentially exacerbated

as the degree of Virtuality between individual’s increases. To elaborate, virtual environment

presents considerable challenges to effective communication including time delays in sending

feedback, lack of a common frame of reference between individuals and differences in salience

and interpretation of written text (Mark, 2001). Given that individuals have disparate expectations

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for how and when to communicate various information, the lack of a common understanding about

communication norms, miscommunications or misunderstandings are further created. These

issues are amplified when cultural differences exist (Carte & Chidambaram 2004). Moreover,

virtual communication tools have a low capacity to transfer non-verbal cues (Sproull & Kiesler,

1991), which are important for building trust and reducing conflict between individuals (Curseu,

2006a). And it eliminates visual cues which reduces the visibility of different communication and

interaction styles, and hence amplify the negative effects of cultural differences.

Therefore, I expect the positive effects of cultural difference on relationship conflict to be stronger

in virtual teams than in face-to-face teams (Staples & Zhao, 2006).

Hypothesis 3: The relationship between cultural difference and relationship conflict is moderated

by degree of virtuality, in such a way, that degree of virtuality will strengthen the effect of cultural

difference on relationship conflict.

2.6 Entire Model

Finally, I test the entire model. Assuming CQ and degree of virtuality moderates the association

between cultural difference and relationship conflict, it is also likely that CQ and degree of

virtuality will influence the strength of the indirect relationship between relationship conflict and

individual performance—thereby demonstrating a pattern of moderated mediation between the

study variables, as depicted in Figure 1. Hence, I hypothesize as follows:

Hypothesis 4: Relationship conflict mediates the relation between cultural difference and

individual performance in such a way, that increase in cultural difference leads to an increase in

relationship conflict, which in turn leads to a decrease in individual performance. The relation

between cultural difference and relationship conflict is moderated by cultural intelligence and

degree of virtuality in such a way that, cultural intelligence of an individual will buffer the effect

of cultural difference on relationship conflict and that degree of virtuality will strengthen the effect

of cultural difference on relationship conflict.

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3. Methods

3.1 Research Context

This research was performed in a Netherlands based organization, which operates in the health

technology sector. The organization has over 70,000 employees globally however, the study was

conducted in the global R&D department of one of the business units. The reason for choosing

this business unit is that I am doing my internship here and hence it is convenient in terms of

getting access to employee details and approvals. Further, the rational to choose the global R&D

department is that they have employees from diverse nationalities as they operate from seven

different countries across the world. Thus, it is most suitable for my study as it comprises of

culturally diverse individuals.

To elaborate on the structure of the R&D department, employees are part of both location based

team and program team. Location based teams are structured in such a way that employees in each

location report into managers from the same location and are rather culturally homogeneous.

However, employees are also assigned to one or more program team. The program teams are

responsible for the development / improvement of the product being designed. These teams

constitute of members who belong to seven locations across five countries and are culturally

heterogeneous and have higher degree of virtuality (Figure 10). Thus, this study focuses on these

program teams. Gathering data from employees located at five countries, improved the external

validity.

At the time the study was conducted, there were 27 ongoing programs. However only 10 of these

program teams were chosen because they were the largest programs and hence most of the

employees were part one of these programs. In total, these program teams consisted of 558

employees.

22

Figure 10: Organization Structure – To help understand where data was collected from

3.2 Research Design and Sampling strategy

All concepts used and hypotheses proposed in this study are derived from earlier studies and

theories, therefore this study can be characterized as deductive. A cross sectional research design

has been followed as data is collected at one particular point in time. Given the nature of the study

and the time frame of this study, this design best suits the purpose. In addition, since the aim of

this research is to determine the relation between variables, it is a descriptive study.

A quantitative analysis with the help of an online questionnaire was conducted as questionnaires

allow collection of a large amount of data and the data is standardized and therefore

comparable (Saunders, Lewis, & Thornhill, 1997). Also, this method of data collection is

important for this research because there is lack of any quantitative testing of the questionnaire

that is developed for the variable cultural diversity. Further, the survey tool used to send out the

questionnaires was frequently used within the firm and hence the respondents were familiar with

it. This ensured less error and more flexibility to collect data from the respondents.

Given the aim of this study, respondents had to belong to different cultural background. Since, the

whole organization had employees from diverse nationalities, convenience sampling technique

was used to select the department where the research was conducted (Ritchie, Lewis, Nicholls, &

Ormston, 2014). This sampling technique was used as I had easy access to the employees’ email

Id and had approval to collect data (Ritchie et al., 2014).

23

Data was collected among individual team members and conclusions were drawn on the individual

level as well. Thus, the unit of observation and the unit of analysis are both individual (employee).

3.3 Data Collection

To begin with, following the privacy guidelines of the organization, approval from the company’s

workers council and the privacy committee was obtained. Further, approval of the R&D

department head and support from the program managers was gathered.

Data was then collected using an online survey tool – EFM, which is officially used in the

organization. The name list and email ID of all employees was obtained from the HR department

and a list of employees belonging to each program team was obtained from the chief of staff.

As part of the communication strategy, an email with a brief description of the study and details

of data privacy was sent to the respondents along with the survey link (Appendix A). Furthermore,

a mandatory informed consent form was filled in by the respondent before filling in the survey.

In accordance with Bryman and Bell (2007), the following ethical considerations were taken into

account. Research participants were informed that participation in the research was voluntary and

that he/she may refuse participation at any time. Adequate level of confidentiality of the research

data and the anonymity of individuals and organization participating in the research was ensured.

The data collection process included sending out the initial invitation followed by three reminders

to the respondents. After four weeks, 254 responses were obtained of which 175 were complete.

Only 121 of these responses were used for further analysis as at least two team members from a

program team were required to responded to the survey. This is because, relational demographics

is used to capture cultural differences and it requires two individual responses to compare and

compute the differences. Also, to understand diversity, conflict and performance better, data from

respondents was captured in the context of the program team. Hence, a response rate of 21.68%

was achieved. It is important to mention that, for the purpose of testing the reliability and validity

of the cultural difference questionnaire that I developed, I used all 184 responses who completed

the questions on cultural difference.

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3.4 Data Handling

The survey tool - EFM tracks individuals and provides their email ID along with their responses.

To ensure data anonymity, the first step of the data handling process was to delete the email ID’s

and other details that enable tracing back to the respondent.

After the computation of cultural difference, the raw quantitative data was transferred into SPSS.

The data was first cleaned by checking for missing value. Two SPSS data sheets were made, one

to run Confirmatory factor analysis for the variable cultural difference and another to perform

multiple linear regression. In the first SPSS file, respondent who completed all questions related

to cultural difference alone (irrespective of whether they completed the questionnaire or not) were

taken into account. In the second SPSS file, only respondent who completed the whole

questionnaire were considered. Next, the data with the option – 8 (not applicable / do not wish to

answer) was replaced by the mean of other items measuring the variable. This was done in order

to maintain the final mean value. For example, if for a variable the respondent filled in 1, 2 and 8

respectively for each item, 8 was replaced with 1.5. Finally, both team tenure and location tenure

data was standardized by converting all the responses into number of months.

3.5 Measurements

The final version of the questionnaire is shown in Appendix B. Each of the scale captured data on

a 7 point Likert scale. An additional option 8 (N/A) was provided wherein the respondent could

choose to not answer the question either because he/she was not comfortable providing the answer

or because he/she did not know the answer because the item was not applicable to them.

In addition, results pertaining to the factor analysis are reported in Appendix C. The items of each

questionnaire of the below mentioned variables were modified to meet the context of the

organization. It is important to note that the quality of this research was maintained by using

existing scales and by doing a factor analysis for the scale being developed (cultural difference).

3.5.1 Measurement of Variables:

Individual Job Performance: Individual performance was measured to capture the performance

of a team member with the IWPQ scale developed by Koopmans et al., (2012). The dimensions

25

captured in this study are task performance and contextual performance. Respondents were asked

to rate their individual performance in a particular program team that they are a part of.

Only 5 items out of the 29 items were included in the questionnaire because 29 items were too

many and these 5 items most suited the organization. The 5 items were modified in order to capture

an individual’s performance in a team setting. An example is, “How do you rate the quality of

your own work in the past 3 months?” was modified to “How would you rate the quality of your

own work in this team?” Moreover, to capture the element of interaction between individuals that

would affect their performance, 2 questions were added to this scale. An example of such item is

“How would you rate your interpersonal skills during your interaction with this team?” These

items were measured using a 7-point Likert scale. For 4 items, 1 indicated strongly agree and 7

was strongly Disagree. For the other 3 items, they were asked to rate themselves on a scale from

1 (Very Poor) to 7 (Excellent). These three items were later reverse coded to compute the score

for Individual performance in the team. The reliability of the scale used was acceptable as

Cronbach’s α = .76 which is greater than .7 (Warner, 2013). According to Warner (2013), a

Cronbach’s α score greater than .7 is ‘acceptable’, a score greater than .8 is ‘good’ and a score

greater than .9 is ’excellent’. A factor analysis using principal component analysis (PCA)

technique was performed and a Kaiser-Meyer-Olkin’s (KMO) Measure of Sampling Adequacy of

.77 was attained which it is good as the value should be > .5 (Warner, 2013). Further, Bartlett’s

Test of Sphericity with p = .00 was obtained. This indicates that the variables in the dataset were

sufficiently correlated to apply factor analysis. Factor analysis showed that the items loaded on 2

factors as predicted and hence none of the items were removed from the scale. A sample item for

task performance includes “How would you rate the quantity of your own work during your

interaction with this team?” and a sample item for contextual performance includes

“Communication with others in this team led to the desired results.”

Cultural Difference: A questionnaire to capture cultural diversity was developed as part of this

research. A deductive method was used to develop the items for the questionnaire on cultural

differences by Meyer (2014a). This approach was used because Meyer’s research was conducted

using a qualitative method and in order to perform a quantitative analysis, a questionnaire has to

be designed to measure these eight dimensions.

26

A preliminary analysis was conducted and data was analyzed using the method presented by

Churchill (1979) and Hinkin (1995). Firstly, 29 items were developed based on the existing

untested questionnaire developed by Erin Meyer for the HBR article (Meyer, 2014b). These

questions were based on the interview data that she gathered to develop the culture map. Since the

items were a result of interview data, it made sure that it reflects the facet they were intended to

measure. Further, each of these items were of the semantic differential measurement scale type

and hence it is of importance to test the bipolarity of each item (Dickson & Albaum, 1977). To do

so, 58 items were administered during the preliminary analysis, where each item represented one

end of the semantic differential item. The questions were modified to meet the basic guidelines of

developing items. For example, the items were reviewed to find contraindicative items.

Secondly, a 7 point Likert scale was used to capture data from respondents using Snowball

sampling. To further justify the use of this strategy, the scale is measuring aspects of culture and

for this reason it is important that data is gathered from respondents who belong to various cultural

backgrounds and in particular that a certain number of respondents from each cultural background

fill out this questionnaire. Hence, I reached out to friends from various nationalities namely –

Bangladesh, China, Columbia, Greece, India, Indonesia, Netherlands, Romania and USA. Each

individual was then requested to send out my questionnaire to other individuals, thereby obtaining

a robust sample for further analysis. A total of 51 respondents filled out the questionnaire

Thirdly, an exploratory factor analysis was conducted. However, prior to the factor analysis, an

inter-item correlation among the variables was conducted so that variables that correlate at less

than .4 with all other variables could be deleted (Kim and Mueller, 1978). However no such item

was found after the analysis. Next, exploratory factor analysis (EFA) using orthogonal rotation

was performed, which tests the hypothesis that every item in the scale is associated with a specific

factor. This resulted in eight factors as hypothesized, however not all item loaded as expected.

The reason behind a few items not loading correctly was that the questionnaire was designed for

individuals in an organization, but for the preliminary analysis data was collected primarily from

students. This target group was chosen because of time constraints and because it was convenient

to do so.

27

Fourthly, to evaluate the unidimensionality of the questionnaire developed, a preliminary analysis

was conducted wherein the internal consistency coefficient (Cronbach’s alpha) for the items are

computed. The items had a reliability of Cronbach’s α = .74 which is greater than .70 (Warner,

2013). None of the items were deleted after this analysis because the Cronbach’s α if item deleted

was checked and the highest Cronbach’s α = .75, which is not a very big increase. Further, as

mentioned above, double the number of items were administered, where each item represented

one end of the semantic differential item. The results indicated that for each dimension, exactly

one factor was obtained and the items loaded in such a way that, one half of the items loaded

positively on the factor and the other half loaded negatively on the same factor. This implies that

these sets of items correlate with the factor negatively hence providing evidence for being bipolar

items. Thus, the 58 items were combined to form 29 semantic differential items. In addition, four

items were deleted and a few items were modified to better suit the context of the organization.

Finally, the questionnaire was administered for this research.

Cultural difference (in the actual survey administered at the organization) was measured using

eight dimensions of cultural differences by Meyer (2014a). This scale incorporates 25 items using

a 7-point Likert scale and captures an individual’s preference across the items. Respondents were

asked to rate their Cultural preference on a scale from 1 to 7 where each item was captured using

a bipolar scale. Since the questionnaire was subjected to a preliminary analysis in a multicultural

environment, it helped in the development of a reliable measurement instrument. The reliability

of the scale used was acceptable as Cronbach’s α = .75 (Warner, 2013). An exploratory factor

analysis using principal component analysis (PCA) technique was performed and a Kaiser-Meyer-

Olkin’s (KMO) Measure of Sampling Adequacy of .65 was attained which it is good (Warner,

2013). Further, Bartlett’s Test of Sphericity with p = .00 was obtained. This indicates that the

variables in the dataset were sufficiently correlated to apply factor analysis. Factor analysis

showed that the items loaded on 8 factors. Six of the 26 items did not load as per the initial

expectation. However, none of the items were removed from the scale because they fit into the

new factor. An example of such an item is “If I do not agree with my manager, I express my

opinion even in front of others” was supposed to load on the Leading scale. However, in the factor

analysis, it loaded with the items of Evaluating. This makes sense, as this item can also be

perceived as giving feedback to the manager which is what the Evaluating dimension tries to

capture.

28

However, in this research I decided to carry out all analysis based on how Meyer defined the

dimensions, even though it did not reflect in my factor analysis. Firstly, I did so because the aim

of my study is to understand better the variable culture and its dimensions as defined by Meyer.

Secondly, this research is in a preliminary stage that is not many quantitative researches have been

performed to prove the dimensions; hence, I continue with the dimensions defined by Meyer as it

has a stronger theoretical support.

The questionnaire was subjected to a Discriminant validity analysis, to check whether the

operationalization of Meyer’s taxonomy and Hofstede’s dimensions are unrelated. According to

Fornell and Larcker (1981), discriminant validity is established if a latent variable accounts for

more variance in its associated indicator variables than it shares with other constructs in the same

model. Hence, each construct’s average variance extracted (AVE) must be compared with its

squared correlations with other constructs in the model. This analysis showed that the correlation

between Meyer’s taxonomy and Hofstede’s dimensions (r = .02) which is less than the average

variance extracted between the two scales (AVE = .19) hence confirming that the two scales were

indeed different.

Relational demographics is used to convert individual preferences into individual cultural

differences with respect to their team. This method was used to understand diversity better, it is

important to address individuals within the context of their teams (Mowday & Sutton, 1993).

Further, it is in line with Meyer’s theory as she emphasizes that what matters is the relative cultural

difference, not the absolute cultural scores.

Researchers have used three approaches for measuring demographic similarity: a difference score

(D-score; e.g., Tsui et al., 1992), an interaction term (e.g., Riordan & Shore, 1997), and a

perceptual measure (e.g., Kirchmeyer, 1995). In this study, I use the D-score method. This is

because the structure of the other two methods will have much more severe implication on my

study given the responses received. To elaborate further:

Interaction term: Even in the best circumstances, tests for interaction effects have extremely low

power (e.g., Aguinis, 2004; Aguinis, Beaty, Boik & Pierce, 2005; McClelland & Judd, 1993) so

that the researcher risks committing a Type II statistical error in the search for interaction effects

(Stone-Romero, Alliger, & Aguinis, 1994). This limitation is thought to be quite severe,

29

particularly in situations where the subgroup proportions are skewed rather than balanced and

where there is range restriction in the predictor variable. Given that I do not have balanced sub

groups in terms of employees belonging to the same nationality, this method is not used in my

study.

Perceptual measure: The perceptual approach for operationalizing demographic similarity directly

asks respondents how similar they think they are on some demographic characteristic or

characteristics to the rest of their work group (Kirchmeyer, 1995). Since my questionnaire is not

designed to ask questions regarding an individual’s own psychological meaning to differences in

demographic characteristics, this method is not used in my study.

The D-score formula used to calculate cultural difference using the method of relational

demographics (Tsui et al., 1992) is

−√1

𝑛 ∑(𝑆𝑖 − 𝑆𝑗)

2

Where;

Si = A focal individual’s score on cultural difference

Sj = Each other focal individual’s team member’s score on cultural difference

n = The number of members who answered the questionnaire from the focal individual’s

team

When a score gets closer to zero it would imply that the individual is increasingly similar to others

in the work group.

Relationship Conflict: Relationship conflict was assessed with a scale developed by Jehn and

Mannix (2001). These items capture relationship conflict in the group for example, an item in the

scale is “How much emotional conflict is there in the group”. However, these items were designed

to assess team level conflict. Since my study is trying to understand the amount of relationship

conflict an individual, in particular a team member experiences while working in their program

team, these items were modified and as an example, the above item was modified to “I experience

30

emotional conflict in this team.” This technique has been previously used by other researchers,

see Anderson and West (1998).

In total, relationship conflict was measured using a 3-item scale and was measured using a 7 point

Likert scale where 1 is Strongly Agree and 7 is Strongly Disagree. The scale had a high level of

internal consistency, as determined by Cronbach’s α = .86 which is good as the value is greater

than .8 (Warner, 2013). A Factor analysis using principal component analysis (PCA) technique

was conducted and a Kaiser-Meyer-Olkin’s (KMO) Measure of Sampling Adequacy of .71 was

attained which is good (Warner, 2013). Further, Bartlett’s Test of Sphericity with p = .0 was

obtained and hence factor analysis was performed. Factor analysis showed that the items loaded

on 1 factor as expected and hence none of the items were removed from the scale. A sample item

is “I experienced relationship tension in this team?”

Cultural Intelligence: CQ was measured using a 20-item cultural intelligence scale (CQS)

developed by of Ang and Van Dyne (2008). However only 7 out of the 20 items were used in the

questionnaire. The items were deleted in order to reduce the length of the questionnaire and the 7

items were chosen as the better fit the organization context. The items were measured using a 7-

point Likert scale where 1 was Strongly Agree and 7 was Strongly Disagree. The reliability of this

scale was computed to be Cronbach’s α = .85 which is good as the value is greater than .8 (Warner,

2013).

A Factor analysis using principal component analysis (PCA) technique was performed and a

Kaiser-Meyer-Olkin’s (KMO) Measure of Sampling Adequacy of .77 was attained which it is

good (Warner, 2013). Further, Bartlett’s Test of Sphericity with p = .0 was obtained and thus

factor analysis was conducted. Factor analysis showed that the items loaded on 2 factors.

However, according to the original questionnaire 4 factors should be obtained. Going through the

factor and the factor loadings, it made sense to get 2 factors. To further elaborate, the 4 factors

were Motivation, Knowledge, Behavior and Strategy. However in my results, Motivation and

knowledge loaded together as one factor and Behavior and Strategy loaded as the second factor.

This can be justified because factor 1 internal facets of CQ and they have more to do with

knowledge content and innate cognitive abilities. The internal facets of CQ are less clearly related

to how one might adjust behaviorally, and they do not predict adaptation and adjustment in cross-

31

cultural settings (Ang et al., 2007). Factor 2 on the other hand is the external facets of CQ, which

are directly related to how people adapt to their environment (Ang et al., 2007). Thus, none of the

items were removed from the scale.

A sample item of CQ is “I change my verbal behavior (e.g., accent, tone, pauses, rate of speech)

when a cross-cultural interaction requires it.”

Degree of Virtuality: Degree of virtuality is the proportion of teamwork time spent working

virtually. Data was collected by asking participants about the number of hours they spent on tasks

related to the teams and the number of hours they spent virtually on tasks related to that team. It

was calculated using the following formulae (Schweitzer & Duxbury, 2010)

𝐷𝑜𝑉 = ∑ ℎ𝑜𝑢𝑟𝑠 𝑚𝑒𝑚𝑏𝑒𝑟𝑠 𝑠𝑝𝑒𝑛𝑡 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 𝑣𝑖𝑟𝑡𝑢𝑎𝑙𝑙𝑦

∑ ℎ𝑜𝑢𝑟𝑠 𝑚𝑒𝑚𝑏𝑒𝑟𝑠 𝑠𝑝𝑒𝑛𝑡 𝑜𝑛 𝑡𝑒𝑎𝑚 𝑡𝑎𝑠𝑘𝑠∗ 100%

An individual who performs the entire team task without ever meeting would score 100% and one

who performs all of the team’s tasks face-to-face would score zero on this dimension.

Hofstede’s cultural dimensions: In order to assess the extent to which the measures of Meyer’s

taxonomy and Hofstede’s dimensions are unrelated, Hofstede’s cultural dimensions was captured

in the questionnaire administered.

Hofstede’s cultural dimensions was measured using a 26-item scale as developed by of Yoo,

Donthu and Lenartowicz (2011). However only 11 out of the 26 items were used in the

questionnaire. This was because, the resulting questionnaire was too long and hence items that

better fit the organization context were chosen. The items were measured using a 7-point Likert

scale where 1 was Strongly Agree and 7 was Strongly Disagree. The reliability of the scale used

was acceptable as Cronbach’s α = .78 (Warner, 2013). A Factor analysis using principal

component analysis (PCA) technique was performed and a Kaiser-Meyer-Olkin’s (KMO)

Measure of Sampling Adequacy of .77 was attained which it is good (Warner, 2013). Further,

Bartlett’s Test of Sphericity with p = .0 was obtained. This indicates that the variables in the

dataset were sufficiently correlated to apply factor analysis. Factor analysis showed that all the

items except for one item loaded on 5 factors as predicted. Since the variable was considered as a

whole and I did not use each dimension separately in this study, I decided to follow the theory and

32

hence none of the items were removed from the scale. A sample item of Hofstede’s scale is

“Individuals should sacrifice self-interest for the group”

Control variables:

In this research, I included two control variables: Tenure in team and Tenure in location. I use

these variables to control for the spurious relationships and to improve internal validity (Warner,

2013).

Tenure in team: Tenure is the amount of time a team has spent together and it plays an important

role in group development process (Weick, 1969). Thus, to prevent tenure from affecting the

relationships in this study, I control for it. To elaborate, the longer a team works together, the less

the amount of conflict in the team (Jehn & Mannix, 2001). Watson et al. (1993) and Harrison et

al. (1998) found that the negative effects of cultural diversity decreased over time. In addition,

according to Earley and Mosakowski, (2000) time allows culturally different individuals to create

a common identity, which then contributes positively to their performance. Hence, data was

collected by asking participants their total tenure in that particular team.

Tenure in location: I believe that apart from working for a team, working in a particular location

will also have an impact on the relationships in this study particularly because one of the variables

is cultural differences. Thus I control for the number of years a participant has spent in that

particular location. To further emphasize, during long-term foreign stays, generally longer than a

year (McNulty & Tharenou, 2004; Puccino, 2007) individuals gain a fairly complex cultural

understanding, via multiple cues provided by observing others and their reactions (Earley &

Peterson, 2004). Further, Crowne (2008) in their study prove that the number of countries an

individual visits for employment has a significant influence on a person’s level of CQ. Hence,

data was collected by asking participants their total tenure in the location.

3.6 Data Analysis - Testing for Assumptions

Before conducting the analysis to test the above-proposed hypothesis, several key assumptions

were tested for (Pallant, 2013; Statistics Solutions, n.d. a).

33

Firstly, the sample size that is the number of respondents who filled in the questionnaire should

satisfy a minimum criterion. To do so, the G*power calculator was used to compute the sample

size. According to the calculation as shown in Figure 11, the minimum sample size required for

this study is 89 as I have two predictor variables. The sample size of this research was 121 which

is greater than 89 and hence this implies that the results from this research can be generalized to

the overall population. Thus, criteria one is met.

Figure 11: Sample size calculated using G*Power

Secondly, there should be no or very little multicollinearity (Statistics Solutions, n.d. a).

Multicollinearity can be tested using the criterion of Variance Inflation Factor (VIF). A VIF > 10

suggests that there is an indication that multicollinearity may be present. The VIF scores for the

four variables namely the independent variable, the mediator and the moderator variable was

calculated. None of the scores was greater than 1.21 and hence meets the criteria of VIF < 10

which indicates no multicollinearity between the variables (Appendix D). Thus, criteria two was

met.

Thirdly, the variables were tested for normal distribution by plotting the histogram for each

variable (Statistics Solutions, n.d. a). The histograms showed that all variables were normally

distributed, so no logarithmic transformations were necessary (Appendix E).

Fourthly, there should be not be any autocorrelation, so that the standardized residuals are

independent of each other (Statistics Solutions, n.d. a). The Durbin-Watson test was conducted to

34

check for autocorrelation in the data. The score for the model was 2.08 which satisfies the criteria

that the score of the Durbin-Watson test should be between 1.5 and 2.5 to ensure that there is no

autocorrelation.

Finally, the presence for outliers was tested as regression analysis is very sensitive to outliers

(Pallant, 2013). Thus the assumption of homoscedasticity (Statistics Solutions, n.d. a) was tested

for. To do so, first the box-and-whisker plot was plotted. Then the 'outlier labeling rule', which is

based on multiplying the Interquartile Range (IQR) by a factor of 2.2 (Tukey, 1977) was used to

detect the outliers. It showed that in total 6 individuals were outliers based on CQ, 3 individuals

on Relational Demographics calculated and 1 individual were outliers based on Cultural difference

(Appendix F). However, these outliers were not removed from further analysis. To emphasize, I

closely inspected each individual’s responses to check for the presence of any response biases,

especially for extreme response bias and for central tendency bias. After the inspection, I believe

that these individuals did not randomly fill out the questionnaire; rather they tried to give a realistic

opinion on where they were on the CQ scale / cultural difference scale. In this study, these

individuals were those who scored low (a mean of 3) in both scales. Thus, I decided to keep them

as they do represent the population.

Following the testing of the assumptions, all the hypotheses were tested for. To begin with,

descriptive statistics and Pearson’s correlations were obtained. After this Regression analysis was

performed. To do so, throughout the analysis, a confidence interval of 90% and the significance

level of p < .05 was used.

Hypothesis 1 and Hypothesis 4 were tested with the help of regression analysis and in particular

the PROCESS macro for SPSS (Hayes, 2013) because, the bootstrap method of the PROCESS

macro has more power than the Sobel-test of a mediation analysis following Baron and Kenny

(1986) (Zhao, Lynch, & Chen, 2010). Further, the mediation effect (H1) was tested using model

4, and the overall model (H4) was tested using model 9. In addition, covariates were used to

control for possible effects that the control variable may have on the dependent variable (Pallant,

2013). Hypothesis 2 and Hypothesis 3 were tested with the help of a hierarchical linear regression

which included 4 models per hypothesis. While testing for the moderation, the independent

35

variable along with the moderator was mean centered before computing for the interaction term

in order to remove multicollinearity.

4. Results

4.1 Descriptive Statistics

Table 1 shows the mean, standard deviation, minimum and maximum of all variables. Moreover,

it shows the correlations between the variables. From the table below, it is important to note that

the average tenure at the location of the respondent is 91.35 months, which is approximately 7.5

years and the maximum tenure at a location in which the respondent works is 34 years. Further,

the average tenure of the respondent in his/her program team is 1 year and the maximum tenure in

the team is 8 years. It is noteworthy that, only 12 % of the sample (15 employees) had a tenure of

less than 6 months. In addition, on an average an individual spends 47.18% of his/her time working

virtually.

It is interesting that the average scores of the variables CQ and Individual performance are 5.72

and 5 respectively. It is important to note that on a scale of seven the minimum score respondents

obtained for both these variables was three. Also, for cultural difference as computed using

relational demographics, the minimum value is .11 which implies similarity in cultural preferences

and the maximum value is 2.63 which implies a larger difference in cultural preferences.

Table 1 also demonstrates Pearson’s correlation coefficients, which measures the association

between two variables (Field, 2013). The values of correlation indicate that none of the variables

strongly correlates one another. The descriptive statistics also shows the distribution of the

respondents across various nationalities and the spread of location. Elaborating on the cultural

differences in the sample, respondents from 23 nationalities filled in the questionnaire. However,

for 19 of these nationalities the response rate was really low (less than 5 employees per country).

Consequently, 146 respondents from 177, which implies 82% of the respondents, belonged to one

of the four nationalities - China, India, The Netherlands and The United States of America (USA)

(Figure 12). This was expected as the organization had its R&D presence in these four locations

(Figure 13).

36

N Minimum Maximum Mean

Std. Deviation

Tenure in Work

Location

Tenure in Team

Cultural differences

Relationship Conflict

Cultural Intelligence

Degree of Virtuality

Tenure in Work Location

121 0 408 91.35 103.68

Tenure in Team 121 0 96 12.27 13.94 .21*

Cultural differences (Relational

demographics) 121 0.11 2.63 0.89 0.42 -0.08 0.03

Relationship Conflict 121 1 7 3.02 1.52 0.05 0.1 0.03

Cultural Intelligence 121 3 7 5.72 0.82 -.23* -0.04 0.07 0.13

Degree of Virtuality 119 0 100 47.18 35.26 -0.04 0.05 -0.01 0 -0.04

Individual Performance

121 3.14 6.71 5 0.76 -0.17 0.1 -0.07 -.30** .38** 0.02

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level 2-tailed).

Table 1: Descriptive Statistics

Figure 12: Distribution of respondents by nationality

1 1 3 1 1

26

1 1 1 2 1 4

42

1 3

51

1 1 1 1 2 1 3

27

0

10

20

30

40

50

60

Nu

mb

er o

f R

esp

on

den

ts

Nationality

37

Figure 13: Distribution of respondents by work location

4.2 Culture Map

Figure 14 illustrates the culture map plotted with the responses gathered in this study. It denotes

how majority of respondents in a particular culture interact and it also helps compare the relative

position of the cultures. For the purpose of interpreting the results, only the four cultures with high

responses was taken into account.

Figure 14: Culture Map representing the four cultures whose respondents filled in the questionnaire.

4.3 Hypothesis Testing

To begin with, the relation between the independent variable and the dependent variable was tested

for curvilinear relationship. By including the independent variable and its squared term in a new

model of the hierarchical multiple regression, the unstandardized regression coefficients (b) was

studied. When the signs of the independent variable and its squared term are opposite and

significant, it indicates support for a curvilinear relationship. None of the relationship in this study

showed a curvilinear effect.

38

4.3.1 Mediation Analysis

Hypothesis 1 argues that the relationship between cultural differences and individual performance

is mediated by relationship conflict, in such a way, that cultural difference leads to an increase in

relationship conflict, which in turn leads to a decrease in individual performance.

Model 4 of PROCESS macro for SPSS (Hayes, 2013) was used to test the mediation effect. This

model tests for a simple mediation effect wherein an independent variable (Cultural Difference),

a dependent variable (Individual job performance), and a mediator variable (Relationship Conflict)

are involved. To prove a mediation effect, four relationships are tested (Figure 15):

1) The effect of cultural difference on relationship conflict – denoted as path a,

2) The effect of relationship conflict on individual job performance – denoted as path b,

3) The effect of cultural difference on individual job performance – denoted as path c,

4) The effect of cultural difference on individual job performance when controlling for the

relationship conflict – denoted as path c’.

Each of these relationships were controlled for tenure in the team and tenure in the location. A

mediation effect exists if zero is not contained within the confidence intervals (CI) and one can

conclude that the effect is indeed significantly different from zero at p < .05. The results of the

mediation analysis are shown in Table 2. As per the results both control variables, Tenure in team

(b = .01, p = .05) and Tenure in location (b = -.00, p = .02) showed significant results in the overall

model. Cultural difference is not a significant predictor of relationship conflict (b = .12, p = .72),

cultural difference is not a significant predictor of individual job performance (b = -.18, p = .29)

and cultural difference is not a significant predictor of individual job performance (b = -.16, p =

.32) after controlling for relationship conflict. However, relationship conflict was a significant

predictor of individual job performance (b = -.15, p = .00). Consequently, the indirect effect (b =

-.01, p = .74) is also non-significant. Consequently, as relation a, c, c’ and indirect effect are not

significant, Hypothesis 1 can be rejected.

39

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 15: Visualization of Output of Mediation (Model 4 in Hayes)

Independent Variable (IV)

Mediator Variable (MV)

Total effect (path c)

IV MV

(path a)

MV DV

(path b)

Direct effect (path c’)

Cultural Differences

Relationship Conflict

b = -.18 b = .12 b = -.15** b = -.16

Dependent variable: Individual Performance

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Table 2: Results of Relationship Conflict as a Mediator (Model 4 in Hayes)

4.3.2 Moderation Analysis

Hypothesis 2 argues that the relationship between cultural difference and relationship conflict is

moderated by CQ, in such a way, that CQ of an individual will buffer the effect of cultural

difference on relationship conflict.

To test this hypothesis, a four step hierarchical linear regression was conducted. In the first three

steps, the control variables (tenure in the team and tenure in the location), the independent variable

(Cultural difference) and the moderator variable (CQ) were introduced sequentially in the same

order. To understand a moderation effect, three relationships are studied (Figure 16):

1) The effect of cultural difference on relationship conflict

2) The effect of CQ on relationship conflict

3) The effect of interaction term (cultural difference * CQ) on relationship conflict

The results of the moderation analysis are shown in Table 3. As per the results, cultural difference

was not a significant predictor of relationship conflict (b = -.08, p = .81). However, CQ was a

40

marginally significant predictor of relationship conflict (b = .27, p = .12) and the interaction term

was also a marginally significant predictor of relationship conflict (b = .94, p = .07). To elaborate

on the results, the relationship between cultural difference and relationship conflict is non-

significant which means that cultural difference does not have an effect on relationship conflict

when CQ is equal to the mean value. Although this may be true, cultural difference does have an

effect on relationship conflict for other values of CQ, which is why the interaction is significant.

Consequently, a crossover interaction exists as the interaction is significant, but the main effect

does not (Figure 17). Examination of this interaction plot showed a marginally significant effect

of high CQ (one standard deviation below the mean), that is as Cultural differences increased,

relationship conflict increased (b = .69, p = .14). However, at low CQ (one standard deviation

above the mean) as Cultural differences increased, relationship conflict decreased. However this

slope was not significant (b = -.85, p = .16). Consequently, it is not possible to determine if there

was a buffering effect or a strengthening effect at high CQ as I could not compare it with the low

levels of CQ. As a result, Hypothesis 2 was rejected.

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 16: Visualization of Output of Moderation (Cultural Intelligence)

Model 1 Model 2 Model 3 Model 4

Tenure in Work Location b = .00 b = .00 b = .01 b = .01

Tenure in Team b = .01 b = .01 b = .01 b = .00

Cultural Difference b = .12 b = .09 b = -.08

Cultural Intelligence b = .27† b = .27†

Cultural Difference X Cultural Intelligence

b = .94†

Dependent variable: Relationship Conflict

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Table 3: Regression results of Cultural Intelligence as a Moderator

41

Figure 17: Graph representing the Interaction effect at low (-1 SD below the mean) and high (+1 SD above the

mean) levels of cultural intelligence.

Hypothesis 3 discusses that the relationship between cultural difference and relationship conflict

is moderated by degree of virtuality, in such a way, that degree of virtuality will strengthen the

effect of cultural difference on relationship conflict.

An analysis similar to Hypothesis 2 was performed to investigate the moderating effect of Degree

of virtuality. A four step hierarchical linear regression was conducted. In the first three steps, the

control variables (tenure in the team and tenure in the location), the independent variable (Cultural

difference) and the moderator variable (Degree of Virtuality) were introduced sequentially in the

same order

Further, to prove this effect, the following three relationships are tested (Figure 18):

1) The effect of cultural difference on relationship conflict

2) The effect of degree of virtuality on relationship conflict

3) The effect of the interaction term (degree of virtuality * CQ) on relationship conflict

The results of the moderation analysis are shown in Table 4. As per the results, cultural difference

was not a significant predictor of relationship conflict (b = .13, p = .71). Degree of virtuality was

also not a significant predictor of relationship conflict (b = -.00, p = .99) and the interaction term

was not a significant predictor of relationship conflict either (b = -.00, p = .89). Consequently, as

the three relations are not significant the hypotheses can be rejected. This implies that Hypothesis

3 was not supported.

42

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 18: Visualization of Output of Moderation (Degree of Virtuality)

Model 1 Model 2 Model 3 Model 4

Tenure in Work Location b = .00 b = .00 b = .00 b = .00

Tenure in Team b = .00 b = .01 b = .01 b = .01

Cultural Difference b = .12 b = .12 b = .13

Degree of Virtuality b = -.00 b = -.00

Cultural Difference X Degree of Virtuality

b = -.00

Dependent variable: Relationship Conflict

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Table 4: Regression results of Degree of Virtuality as a Moderator

4.3.3 Moderated Mediation Analysis

Hypothesis 4 states that relationship conflict will mediate the relation between cultural difference

and individual performance, and that the relation between cultural difference and relationship

conflict is moderated by CQ and degree of virtuality.

Model 9 of PROCESS macro for SPSS (Hayes, 2013) was used to test the moderated mediation

effect. This model tests whether the prediction of a mediating variable (Relationship Conflict),

from an independent variable (Cultural Difference), differs across levels of two moderating

variables (CQ and Degree of Virtuality) which then influences the dependent variable (Individual

Performance). In short, it tests whether CQ and Degree of Virtuality function as moderators of

path a as shown in Figure 19. To prove a moderated mediation effect, seven relationships are

tested:

1) The effect of cultural difference on relationship conflict

2) The effect of CQ on relationship conflict

43

3) The effect of the interaction term (cultural difference * CQ) on relationship conflict

4) The effect of degree of virtuality on relationship conflict

5) The effect of the interaction term (degree of virtuality * CQ) on relationship conflict

6) The effect of relationship conflict on individual job performance

7) The effect of cultural difference on individual job performance when controlling for the

relationship conflict, CQ, degree of virtuality and the indirect mediating effect.

Further, each of these relationships were controlled for tenure in the team and tenure in the

location. The results of the moderated mediation analysis are shown in Table 5. As per the results,

cultural difference was not a significant predictor of relationship conflict (b = -.09, p = .81), degree

of virtuality was not a significant predictor of relationship conflict (b = -.00, p = .84) and the

interaction terms were not a significant predictor of relationship conflict (b = -.00, p = .80). In

addition, the main effect i.e. cultural difference was not a significant predictor of individual job

performance (b = -.15, p = .34) after controlling for relationship conflict.

However, CQ was a marginally significant predictor of relationship conflict (b = .31, p = .10) and

the interaction term was a marginally significant predictor of relationship conflict (b = 1.00, p =

.12). Furthermore, relationship conflict was a significant predictor of individual job performance

(b = -.16, p = .00). Consequently, as the mediation is not significant, Hypothesis 4 is not supported.

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 19: Visualization of Output of the entire model (Model 9 in Hayes)

44

Independent Variable (IV) Mediator Variable (MV) Total effect

Cultural Differences

Relationship Conflict

b = -.09

Cultural Intelligence b = .30†

Cultural Difference X Cultural Intelligence b = 1.00†

Degree of Virtuality b = -.00

Cultural Difference X Degree of Virtuality b = -.00

Independent Variable (IV) Dependent

Variable (DV) Total effect

Cultural Differences

Individual Performance

b = -.16**

Relationship Conflict b = -.15

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Table 5: Results of the entire model (Model 9 in Hayes)

The results of the main hypothesis tested above are in Appendix G.

4.4 Summary of Results

Table 6 shows a summary of the results per hypothesis.

Hypotheses Result

Hypothesis 1: The relationship between cultural differences and individual

performance is mediated by relationship conflict, in such a way, that cultural

difference leads to an increase in relationship conflict, which in turn leads to a

decrease in individual performance.

Rejected

Hypothesis 2: The relationship between cultural difference and relationship

conflict is moderated by cultural intelligence, in such a way, that cultural

intelligence of an individual will buffer the effect of cultural difference on

relationship conflict

Rejected

Hypothesis 3: The relationship between cultural difference and relationship

conflict is moderated by degree of virtuality, in such a way, that degree of

virtuality will strengthen the effect of cultural difference on relationship conflict.

Rejected

Hypothesis 4: Relationship conflict will mediate the relation between cultural

difference and individual performance, further the relation between cultural

difference and relationship conflict is moderated by cultural intelligence and

degree of virtuality

Rejected

Table 6: Summary of the hypothesis

45

4.5 Additional Analysis

4.5.1 Descriptive statistics

Table 7: Descriptive Statistics for Additional Analysis with dimension of Cultural Difference after computing for Relational Demographics

N Minimum Maximum Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Tenure in Work Location

121 0 408 91.35 103.68

2. Tenure in Team 121 0 96 12.27 13.94 .21*

3. Communicating 121 0.14 3.92 1.46 0.57 -0.07 -0.12

4. Evaluating 121 0 3.25 1.34 0.65 -0.09 0.02 .35**

5. Persuading 121 0 4.81 1.81 0.86 -0.05 -0.09 .30** .29**

6. Leading 121 0.35 2.89 1.36 0.46 -0.09 0.03 .22* .29** 0.1

7. Deciding 121 0.17 3.37 1.5 0.58 -0.07 0.04 0.18 .33** .30** .30**

8. Trusting 121 0 3.7 1.59 0.67 -0.12 0.07 .39** .38** .40** .43** .28**

9. Disagreeing 121 0 4.3 1.12 0.58 -.29** -0.12 .31** 0.07 .31** 0.13 .20* 0.15

10. Scheduling 121 0 3.14 1.57 0.56 0.01 -0.04 .31** 0.18 0.14 .34** .19* .35** 0.12

11. Relationship Conflict

121 1 7 3.02 1.52 0.05 0.1 0.08 0.02 -0.01 0.04 0.06 0.12 -0.1 0.03

12. Cultural Intelligence

121 2.75 7 5.86 0.79 -.20* -0.04 0.03 0.14 -0.14 0.04 0.07 0.07 0.07 0.07 0.07

13. Cultural Intelligence

121 1 7 5.52 1.09 -.21* -0.04 0.02 0.1 -0.06 0.08 0.07 0.08 0.04 0.06 0.15 .591**

14. Degree of Virtuality

119 0 100 47.18 35.26 -0.04 0.05 0.1 0.03 0.11 -0.04 -0.15 0.08 0.13 -0.05 0 -0.07 0

15. Individual Performance

121 3.14 6.71 5 0.76 -0.17 0.1 -0.18 -0.14 -0.16 -0.03 0.02 -0.02 0.01 -0.11 -.29** .37** .31** 0.01

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

46

N Minimum Maximum Mean Std. Deviation

Communicating 121 1.00 6.20 3.12 1.16

Evaluating 121 1.00 6.50 4.02 1.15

Persuading 121 1.00 7.00 2.53 1.52

Leading 121 1.00 5.50 3.26 1.04

Deciding 121 1.00 6.33 3.39 1.20

Trusting 121 1.00 6.33 3.60 1.23

Disagreeing 121 1.00 6.00 1.89 0.91

Scheduling 121 1.00 6.00 3.20 1.28

Table 8: Descriptive Statistics for each dimension of Cultural Difference (before computing for Relational

Demographics)

I begin my additional analysis by examining the descriptive statistics and correlations results that

are shown in Table 8.

As expected there is significant correlation between the dimensions of cultural difference and types

of diversity. Table 8 indicates the minimum and maximum value per dimension which helps

understand individual preferences versus Table 7 where the value indicates the relative (cultural)

difference that exists between individuals.

4.5.2 Hypothesis Testing

In order to further deep dive into the above four hypothesis proposed, additional analyses have

been carried out. To further examine the mediating role of relationship conflict (Hypothesis 1), I

tried to understand the impact of each dimension of cultural difference on the relationship. This

approach was taken as each dimension captures a different aspect of the culture. For example,

evaluating captures how feedback is given and deciding captures how decisions are made. Hence,

I believe that it would help investigate which dimension in reality is important towards

understanding an individual’s performance. Thus, instead of using cultural difference as a whole

variable, analyses were performed using each of the eight dimensions of cultural difference

separately as conceptualized by Meyer. As per the results for all the eight dimensions, despite

having a significant path b, c and c’, there was no mediation effect as path a*b (indirect effect)

was not significant. According to Baron and Kenny it is referred to as a direct-only non-mediation

effect, or a no mediation effect.

47

To further examine the moderating role of CQ (Hypothesis 2), once again I replaced cultural

difference with each of the 8 dimensions and performed a four step hierarchical linear regression.

Only Evaluating and Leading showed marginally significant moderating effect. However, when I

plotted the simple slopes to understand the association between cultural difference and relationship

conflict at low (-1 SD below the mean) and high (+1 SD above the mean) levels of CQ, each of

the slopes showed a non-significant association between cultural difference and relationship

conflict.

To understand the moderating effect of CQ better I decided to perform some more analysis. To do

so, I decided to see how each factor of CQ had an impact on the relationship between the cultural

difference and relationship conflict. The two factors of CQ are the intention to behave or the

internal facets (factor 1) and the behavior itself or the external facets (factor 2). The results of this

analysis indicate that factor 1 (CQ) had no significant effect on this relationships. Factor 2 (CQ)

on the other hand had a significant effect.

To begin with I checked the moderating effect of factor 2 on the relation between Cultural

difference (as a whole variable) and relationship conflict. Cultural difference was not a significant

predictor of relationship conflict (b = .12, p = .71). However, Factor 2 (CQ) was a marginally

significant predictor of relationship conflict (b = .24, p = .06) and the interaction term was a

significant predictor of relationship conflict (b = .70, p = .03). The results are depicted in (Figure

20 and Table 9). In addition, these variables accounted for a marginally significant amount of

variance in Relationship conflict (R sq = .20, p = .07) and the interaction term accounted for a

significant 4% of proportion of the variance in relationship conflict (ΔR sq = .04, p = .03). Hence

it can be concluded that behaviour or external facet (factor 2 of CQ) has a marginally significant

moderating effect on the relationship between Cultural difference and relationship conflict. To

understand the moderating effect of external facets, simple slopes were plotted. Only the slope for

high value of CQ (factor 2) revealed a marginally significant association between cultural

difference and relationship conflict. Thus, higher values of CQ (b = .89, p = .07) of an individual

has an effect on the relationship between cultural difference and relationship conflict. However,

the slope for lower values (b = -.64, p = .17) of an individual did not show any significant results

(Figure 21).

48

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 20: Visualization of the relationship between Cultural Difference and relationship conflict with Cultural

Intelligence_Behaviour as a moderator

Model 1 Model 2 Model 3 Model 4

Tenure in Work Location b = .00 b = .00 b = .00 b = .00

Tenure in Team b = .01 b = .01 b = .01 b = .00

Cultural Difference b = .12 b = .10 b = .12

Cultural Intelligence _Behaviour

b = .24† b = .24†

Cultural Difference X Cultural Intelligence_Behaviour

b = .70*

Dependent variable: Relationship Conflict

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Table 9: Regression results of the relationship between Cultural Difference and relationship conflict with Cultural

Intelligence_Behaviour as a moderator

Figure 21: Graph representing the Interaction effect at low (-1 SD below the mean) and high (+1 SD above the

mean) levels of cultural intelligence_Behaviour.

Since factor 2 of CQ had a marginally significant moderating effect on the relation between

cultural difference and relationship conflict, I also decided to see how each factor of CQ had an

impact on the relationship between the eight dimension of cultural difference and relationship

conflict. Again, the results of this analysis indicate that factor 1 (CQ) had no significant effect on

49

each of these relationships. Factor 2 (CQ) on the other hand had a significant effect. Furthermore,

the results were significant / marginally significant only for dimensions, evaluating and leading.

Firstly, dimension 1: evaluating was not a significant predictor of relationship conflict (b = -.01, p

= .95). However, Factor 2 (CQ) was a marginally significant predictor of relationship conflict (b

= .22, p = .10) and the interaction term was a significant predictor of relationship conflict (b = .47,

p = .03). The results are depicted in (Figure 22 and Table 10). Hence it can be concluded that

external facet (factor 2 of CQ) has a marginally significant moderating effect on the relationship

between evaluating and relationship conflict. In addition, these variables accounted for a

marginally significant amount of variance in Relationship conflict (R sq = .20, p = .07) and the

interaction term accounted for a significant 4% of proportion of the variance in relationship conflict

(ΔR sq = .04, p = .03). Plotting the simple slopes showed that both the slope for low value and high

value of factor 2 (CQ) had a marginally significant association between evaluating and relationship

conflict. Thus, at lower values of factor 2 (CQ) (b = -.53, p = .11) an individual has an antagonistic

effect, as there is a reverse effect of evaluating on relationship conflict. However, it shows that

higher values of factor 2 (CQ) (b = .50, p = .11) of an individual has a marginal effect on the

relationship between evaluating and relationship conflict (Figure 23).

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 22: Visualization of the relationship between Evaluating and relationship conflict with Cultural

Intelligence_Behaviour as a moderator

Model 1 Model 2 Model 3 Model 4

Tenure in Work Location b = .00 b = .00 b = .00 b = .00

Tenure in Team b = .01 b = .01 b = .01 b = .00

Evaluating b = .05 b = .02 b = -.01

Cultural Intelligence _Behaviour

b = .24† b = .22†

Evaluating X Cultural Intelligence_Behaviour

b = .47*

Dependent variable: Relationship Conflict

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

50

Table 10: Regression results of the relationship between Evaluating and relationship conflict with Cultural

Intelligence_Behaviour as a moderator

Figure 23: Graph representing the Interaction effect at low (-1 SD below the mean) and high (+1 SD above the

mean) levels of cultural intelligence_Behaviour.

Secondly, dimension 2: leading was not a significant predictor of relationship conflict (b = .10, p

= .75). However, Factor 2 (CQ) was a marginally significant predictor of relationship conflict (b

= .25, p = .05) and the interaction term was also a marginally significant predictor of relationship

conflict (b = .49, p = .07). The results are depicted in (Figure 24 and Table 11). Hence it can be

conclude that external facet (factor 2 of CQ) has a marginally significant moderating effect on the

relationship between leading and relationship conflict. In addition, these variables accounted for a

marginally significant amount of variance in Relationship conflict (R sq = .20, p = .08) and the

interaction term accounted for a marginally significant proportion of the variance in relationship

conflict (ΔR sq = .03, p = .07). Again, to understand the moderating effect of external facets, a

simple slopes was drawn. Only the slope for high value of factor 2 (CQ) revealed a marginally

significant association between evaluating and relationship conflict. Thus, it shows that higher

values of factor 2 (CQ) (b =.63, p = .14) of an individual has a marginal effect on the relationship

between leading and relationship conflict. However, the slope for lower values of factor 2 (CQ) (b

= -.44, p = .30) of an individual did not show any significant results (Figure 25).

51

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 24: Visualization of the relationship between Leading and relationship conflict with Cultural

Intelligence_Behaviour as a moderator

Model 1 Model 2 Model 3 Model 4

Tenure in Work Location b = .00 b = .00 b = .00 b = .00

Tenure in Team b = .01 b = .01 b = .01 b = .01

Leading b = .13 b = .09 b = .10

Cultural Intelligence _Behaviour

b = .23† b = .25†

Evaluating X Cultural Intelligence_Behaviour

b = .49†

Dependent variable: Relationship Conflict

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Table 11: Regression results of the relationship between Leading and relationship conflict with Cultural

Intelligence_Behaviour as a moderator

Figure 25: Graph representing the Interaction effect at low (-1 SD below the mean) and high (+1 SD above the

mean) levels of cultural intelligence_Behaviour.

52

To examine the moderating role of degree of virtuality (Hypothesis 3), once again I replaced

cultural difference with each of the 8 dimensions and performed a four step hierarchical linear

regression. Only Trusting had marginally significant moderating effect.

Trusting was a marginally significant predictor of relationship conflict (b = .32, p = .14). However,

degree of virtuality was not a significant predictor of relationship conflict (b = -.00, p = .85) and

the interaction term was also a marginally significant predictor of relationship conflict (b = -.01, p

= .15). The results are depicted in (Figure 26 and Table 13). Hence it can be concluded that degree

of virtuality had a marginally significant moderating effect on the relationship between trusting

and relationship conflict. In addition, the interaction term accounted for a marginally significant

proportion of the variance in relationship conflict (ΔR sq = .02, p = .15).

Contrary to what I expected, the results of this study (figure 27) shows that the slope for lower

levels of degree of virtuality (b = .59, p = .06) is marginally significant and hence the relation

between trusting and relationship conflict strengthens in such a way that with increase in trusting,

relationship conflict increases. On the other hand, the slope was not significant for higher values

of degree of virtuality (b = .04, p = .88).

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

Figure 26: Visualization of the relationship between Trusting and relationship conflict with Degree of Virtuality as

a moderator

Model 1 Model 2 Model 3 Model 4

Tenure in Work Location b = .00 b = .00 b = .00 b = .00

Tenure in Team b = .01 b = .01 b = .01 b = .01

Trusting b = .25 b = .25 b = .32†

Degree of Virtuality b = .00 b = -.00

Trusting X Degree of Virtuality b = -.01†

Dependent variable: Relationship Conflict

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.15

53

Table 12: Regression results of the relationship between Trusting and relationship conflict with Degree of Virtuality

as a moderator

Figure 27: Graph representing the Interaction effect at low (-1 SD below the mean) and high (+1 SD above the

mean) levels of Degree of Virtuality.

4.5.3 Summary Additional Results

Table 13 shows a summary of the results per additional results.

Independent

Variable

Moderated By Dependent Variable Moderator

(High level)

Moderator

(low level)

Evaluating Cultural Intelligence

Relationship Conflict

non-significant non-significant

Leading Cultural Intelligence non-significant non-significant

Cultural Difference External Facet of CQ Positive effect non-significant

Evaluating External Facet of CQ Positive effect Negative Effect

Leading External Facet of CQ Positive effect non-significant

Cultural Difference Degree of Virtuality Positive effect

(stronger)

Positive effect

(weaker)

Table 13: Summary of additional results

5. Discussion

The aim of this study can be divided into two elements. Firstly, I created a questionnaire to capture

cultural differences as conceptualized by Meyer and I evaluated its discriminant validity against

Hofstede’s cultural dimensions. Secondly, I investigated the effect of cultural differences on

individual’s performance, with a focus on individuals working in a multicultural team. I studied

this by assessing the mediating role of relationship conflict and the moderating role of CQ and

54

degree of virtuality. The results are based on the data collected from individuals who mainly belong

to either China, India, Netherlands or USA and who work in multicultural program teams.

5.1 Main findings

5.1.1 Cultural Difference Questionnaire

I developed a 25-item reliable questionnaire to assess an individual’s cultural differences as

conceptualized by Meyer (2014a) and it exhibited different operationalization as compared to

Hofstede’s dimensions. I also found that the measure was valid in student and employee samples,

which indicates cross-sample generalizability. This is of academic relevance because it provides a

questionnaire that can quantitatively measure Meyer’s dimensions. Along with Hofstede’s

questionnaire, researchers can now measure different aspects of culture. Hofstede’s dimensions

captures answers to universal problems of human societies and Meyer’s dimensions captures

interactions (manifestation of culture) between individuals.

Figure 28 illustrates a culture map which is based on Meyer’s findings (interviews and

experiences). India and China occupies the right end of the spectrum across the eight dimensions

and Netherlands and USA occupies the left end of the spectrum across most of the eight

dimensions. This indicates that the respondents of this study are culturally diverse and have

different interaction patterns.

Figure 28: Culture Map representing the four cultures as plotted by Erin Meyer (2014a)

Comparing Figure 28 and Figure 14, it is evident that a shared interaction pattern (not typical to

one culture) has evolved amongst individuals, despite coming from different cultural backgrounds

that has distinct interaction patterns. As an illustration, referring to Figure 28 both USA and

Netherlands use low context communication pattern in combination with a direct negative

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feedback. Whereas, India and china use high context communication pattern in combination with

an indirect negative feedback. Examining Figure 14, it can be seen that across the four cultures a

combination of low context communication pattern and giving indirect negative feedback has

evolved. This is in line with Earley and Gibson (2002) who suggest that shared culture, which is

developed over a period, allows individuals to understand and interpret each other better. We see

that individuals learn and modify their communication pattern to fit their environment.

Consequently, the map (Figure 14) denotes the most often-used interaction pattern in the

organization and it is possible that employees’ exhibit patters that are a lot different.

To appropriately plot the map at a culture level (not individual), the mode (value that appears the

most) of the sample per culture instead of the average should be used. This is because averages are

sensitive to outliers.

To aptly interpret the map, it is important to note that each end of the scale has its own unique

value. An individual with a score of 7 on communicating for example cannot be characterized as

a great communicator. It only implies that the individual prefers to communicate on the right tail

of the scale.

Looking at it from a Meta perspective, in particular the social identity theory, it is important to

note that organizational members define the self in relation to the organization (Turner, 1987),

which then determines some critical behaviors. This is because employees most likely have an

increased tendency to identify with organization. As a result, the behavior of the employee and the

organization becomes increasingly integrated and congruent. This theory is a potential underlying

mechanism from which the emergence of a common interaction pattern can be explained.

5.1.2 Non-Significant Effect of Relationship Conflict and Degree of Virtuality

In this study, I did not find any support for the mediating effect of relationship conflict. Also,

nearly all analyses performed to understand the moderating effect of degree of Virtuality were

non-significant. I found marginally significant interaction effect on trusting however, as the chance

rate of this effect being random is 1 in 20, I am hesitant in attaching any value to this marginally

significant interaction. In the following paragraphs, I argue why these findings were non-

significant.

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It is not surprising that the findings of this research have been inconsistent, perpetuating the lack

of consensus on how cultural differences can influence conflict. One of the reasons is that the

discussed theory does not hold good for this particular research context. This is because, difference

in interaction patterns lead to relationship conflict when people are getting to know each other/ in

a newly formed culturally diverse team. In my study, the tenure of individuals is relatively high.

This implies that individuals become familiar with others interaction patters and they begin to

share a common team culture especially over a period of time. This may then decrease variability

in interaction patterns and may diminish any tendency for diversity to trigger conflict (Katz, 1982)

hence explaining the non-significant relationship between cultural differences and relationship

conflict (Jehn & Mannix, 2001). In addition, Watson et al. (1993) and Harrison et al. (1998) found

that the negative effects of cultural diversity decreases over time.

Furthermore, it is irrespective of whether the interaction occurs at low degree of virtuality (face to

face interactions) or at high degree of virtuality (virtual interactions), thereby explaining the non-

significant results.

Yet another reason is that the culture of the organization is very inclusive. People interact with

different cultures, if not a lot on a regular basis. This exposure makes them sensitive to other

cultures and it helps them to cope with other cultures in a better manner. This is supported by the

fact that the CQ score in general was high. Thus, the effect of relationship conflict and degree of

virtuality is non-significant.

Last but not the least it is also possible that cultural differences as captured by Meyer’s Taxonomy

does not lead to conflict between individuals, however future research should be done in order to

accept or reject this claim.

5.1.3 Moderating Role of Cultural Intelligence

Nearly all analyses performed to understand the moderating effect of cultural intelligence were

non-significant. The findings indicate the presence of this effect on higher values of CQ and in

particular the behaviour. Since, there was no evidence of how this relationship would manifest at

a lower level of CQ, I cannot draw conclusions on the buffering effect of CQ on the relation

between cultural difference and relationship conflict.

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Nevertheless, CQ across the sample was relatively high. This is because, the organization is highly

diverse and the culture is such that employees generally communicate intensively with people from

diverse background. Further, employees are exposed to short visits to international divisions

(Yamazaki & Kayes, 2004) and organization-initiated expatriate work assignments (Inkson et al.,

1997), which helps them become culturally intelligent. Thus, it is likely that the environment of

the organization is such that employees find it easy to develop their CQ which helps understand

cultural differences.

Despite the fact that employees develop and improve their CQ, I believe that the score of this scale

are a bit too skewed toward being highly culturally intelligent. To further elaborate, the mean of

CQ (factor 2) was 5.52 and the standard deviation was 1.09. This implies that people with low CQ,

are actually individuals who have a score of 4.43 on an average and hence still have a fair amount

of CQ. I believe that it is quite plausible due to extreme response bias (Taras, Rowney, & Steel,

2009) where the tendency of respondents is to overestimate their level of CQ. Consequently, in

lines with Livermore (2009), CQ is not innate, but a developmental skill that comes with coaching,

training, and dialogues. Thus, despite the high scores additional training in order to become more

culturally sensitive should be provided regularly.

5.1.4 Low Variance

The variance of cultural difference was rather low, despite an acceptable value of Cronbach’s

alpha. This might indicate that the construct being measured is either redundant or too specific

(Briggs & Cheek, 1986). This could have been the result of modifying the items too much that the

existing variation across individuals is not captured accurately. Hence, the questionnaire must be

reviewed for language and wordings and should take into consideration that items should be

simple, precise and unambiguous terms that all respondents should understand in the same way.

This will enable capturing accurate individual responses (Brancato et al, 2006). Thus, I believe

that the questionnaire needs to be tested by researchers from different cultural background/

language, in order to develop a robust scale.

The variance of cultural intelligence and individual performance was also rather low, despite an

acceptable value of Cronbach’s alpha. This could have been the result of creating a shorter version

of the questionnaire by deleting items from the existing questionnaire. Hence, it involves the risk

of changing the meaning of the dimension and diminish its sensitivity to detect changes / capture

58

variation. However, the final questionnaire was too long and in order to get a good response rate

items, were deleted (Cummings, Kohn & Hulley, 2013).

It is however, interesting to note that there was a strong negative relation between relationship

conflict and an individual’s performance. However, it is safe to conclude that cultural diversity

does not contribute towards this result. To support this argument, the effect size obtained was non-

significant which implies that cultural difference in reality does not matter when determining

relationship conflict in this organization.

5.2 Practical Implication

When organizations / teams / individuals want to collaborate in a culturally diverse setting, the

culture map can help them understand both the prevailing culture and the cultural differences. This

understanding enables them to develop communication strategies. Following paragraphs discuss a

few potential scenarios.

At an individual level, understanding cultural differences can be used to develop strategies to break

down cultural barriers and ensure their ideas are received well. For example, a person can choose

to bridge the culture gap on a dimension like 'evaluating' by fitting in with the prevailing culture

while simultaneously leveraging cultural difference on another dimension like 'leadership' by

leading from the front.

At a team level, each team can generate its own culture map and compare it with a new hire's

preferences. This will enable them plan a more effective on-boarding process.

At an organization level, during a merger and acquisition scenario for example, a culture map can

be used to understand the other party's working culture. This can be a precursor to interaction

patterns that may develop in the foreseeable future and give the leadership teams additional time

to strategize a seamless integration.

5.3 Limitation

There were several limitations which hindered the study. Firstly, the sample chosen exhibited

relatively high tenure in the team and in the location. This in general impedes the findings in a

culture study as individuals learn to adjust and the negative effects of culture, which is the focus

of the study, decreases over time.

59

Secondly, the questionnaire captured the perception of an individual, as it assumes that individuals

are capable of providing an accurate estimation for each question. However, based on the results I

believe that this assumption may not be correct as the results were subjected to response biases.

To elaborate further, the results could have been affected by extreme response bias and in this case,

the “Strongly Agree - 7” end of the Likert scale. In addition, a central tendency bias is also seen in

the responses where in respondents have a preference to choose the middle anchor, and in this

case, the “Neither agree nor disagree - 3” end of the Likert scale. This can be seen in the descriptive

statistics table where the min value is 3 and the max value is 7. Hence the questionnaire could have

been modified to reduce these errors. However, I could have validated the data obtained by

conducting a few interviews with individuals to understand if it was indeed an effect of bias or if

the questionnaire did capture the true value. Also, I could have interviewed non respondents to

validate the general level of individual’s performance and CQ in the organization. However, due

to lack of time, I did not conduct a qualitative study.

Thirdly, I could have captured the data on an individual’s performance better. Performance

questionnaires are of two types one that captures team performance and the other that captures and

individual’s performance. However, my research required understanding an individual’s

performance in the context of the team and especially how an individual’s performance was while

working in a particular team. Hence I had to combine two questionnaires and modify items to

capture this data. Thus, the final questionnaire might not have captured exactly what was required

to be captured.

Fourthly, I believe that in the context of cultural differences, the scale of CQ does not fully capture

the dimensions. In other words, the CQ scale used is too generic for this study as it tries to capture

an individual’s intention to behave/ behavior in a situation characterized as culturally diverse.

However, a scale that would measure how individuals would behave or would intend to behave in

an organizational setting would better suit the purpose of this study. For example, cultural

difference talks about an individual’s preference over hierarchy and egalitarian structure however,

the CQ scale does not capture the elements of how an individual would behave in a situation when

they have to deal with hierarchy/ egalitarian structure.

Fifthly, despite deciding to not remove outliers from the study for the reasons mentioned earlier,

this has affected the scores of cultural differences. This is because calculations of relational

60

demographics using D-score method is such that outliers have an effect on the final Euclidean D-

score (Riordan & Wayne, 2008).

Finally, the sample size of this study was 121 which is categorized as a medium sample size. Thus,

the study is prone to a type two error. A type two error implies that a hypothesis is supported, while

in reality an alternative explanation is correct. Thus, a false hypothesis is accepted. In the presence

of this error, the validity of the research is reduce (Statistics Solutions, n.d. b). However, it is

important to note that the sample size does satisfy the minimum requirement.

5.4 Future Research

Firstly, given that a new and reliable scale for cultural diversity was developed in this study, it is

important that the reliability of the questionnaire (same results within some acceptable margin of

error) is tested for. It is also important to test the generalizability of the scale by using the

questionnaire in a different research setting that is a different population, organization, industry or

environment. More empirical research is needed to understand the effects of cultural diversity, as

defined in this research on an individual’s performance and in particular in teams or organizations

that are newly formed in order to capture the effects of cultural differences on relationship conflict

better.

Secondly, in this study to compute the score of cultural difference, I averaged the scores across all

the items. However, similar to how Hofstede has a formula to calculate the score of cultural

diversity, I believe that there has to be a better way of compute score of cultural differences per

dimension as well. To elaborate, the score should accurately capture the preference of an individual

and should take the nationality of the individual into account. Further, this would help interpret

preference across each dimension better.

Thirdly, given that only a few of the eight factors of cultural difference showed significant results

in at least one of the hypothesis defined, it would be interesting to see if the same factors or other

factors or all of the factors would show significant results in a different research setting. Based on

these future researches, it would be interesting to analyze if any of the factors contribute more

towards conflict or if importance of factors towards creating conflict would change in a different

industry. Moreover, future research can also be conducted in a sample where individuals have less

tenure (before individuals start adjusting their behavior with regards to interaction, to suit the group

61

behavior) so as to understand the effect of tenure on the relationship between cultural difference

and individual performance.

Fourthly, this study shows that there is a tendency for increase in the negative effect of cultural

diversity on relationship conflict as CQ increases. However, most of the studies in the field of CQ

show only the positive effects of CQ. Hence, it is important that future research validate this result

as if proven right, it could add to the literature of CQ. In addition, referring to the limitation of the

CQ scale, future research could also focus on developing a more robust scale that would measure

CQ of an individual in depth, in an organization setting.

Finally, from the perspective of the organization, studies to identify the cause of relationship

conflict that impact an individual’s performance should be conducted.

6. Conclusion

Following the research of Meyer (2014a), this research was conducted to develop a questionnaire

for cultural difference and to understand the effect of cultural difference on an individual’s

performance. Thus the following research question was developed:

To what extent does relationship conflict mediate the relation between cultural differences

between individuals and individual job performance and to what extent does cultural intelligence

and degree of virtuality moderate the relation between cultural difference and relationship

conflict?

A reliable questionnaire was developed to measure cultural preferences of an individual that

influences their interaction with others. This tool allows individuals to develop a culture map to

help them understand and assess their cultural orientation across the eight dimensions. Further,

when individuals compare their culture map, it would help them identify similarities and

differences and thus help them create awareness regarding the same. Given the setting of this study,

there is no evidence of cultural difference contributing to relationship conflict. Cultural

intelligence and degree of virtuality do not have a significant impact on the relationship between

cultural difference and relationship conflict. However, analysis indicates that certain dimensions

of cultural difference could influence this relationship.

62

With literature suggesting both positive and negative effects of cultural difference on individual

performance, this taxonomy (cultural difference questionnaire) can be used in different

organizational settings and varying levels of tenure, future research is imperative.

63

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74

8. Appendix

Appendix A - Invitation Email to Participants

Dear participant,

I am working with XXX as a Human Resources intern. I will be studying the impact of working

in a multi-cultural environment on an individual’s performance. I am also pursuing my master’s

degree with Tilburg School of Social and Behavioral Sciences and I hope to leverage the study as

part of my thesis work.

Culture plays an important role in how we interact with people and interpret what people say. In a

multi-cultural organization like XXX, patterns of communication emerge (due to interaction

among individuals) that may affect an individual's work. The goal of this study is to identify

patterns that are detrimental to an individual's work and develop strategies to mitigate them.

Please click here to access the survey.

Your data will be handled with utmost care and confidentiality. No information on a single

employee or a single team will be published or shared. The information you provide cannot be

traced back to you. Encrypted data will be used to generate a report for XXX and for my thesis.

Please reach out to me in case of concerns about data handling.

If you have any questions about the questionnaire or if you are interested to know more about this

research, please contact me at [Email ID]. Also, for further reference, you may read the book “The

Culture Map: Breaking Through the Invisible Boundaries of Global Business” by Erin Meyer.

Your insights are extremely valuable. Thank you for your participation.

Kind Regards,

Pooja Ravi Shankar

75

Appendix B - Questionnaire

Please indicate where your preferences lie.

1 2 3 4 5 6 7

I strive to communicate in an explicit manner. I strive to communicate in an implicit manner.

I cannot read between the lines. I read between the lines.

I prefer a presenter to set a context, then discuss the

facts and figures and summarize at the end, to

ensure that the communication is clear.

I prefer a presenter to get to the point directly

When I present, I set the context, discuss the facts

and figures and then end my speech with a

summary.

When I present, I go straight to the point

After a meeting, I expect the minutes of the meeting. After a meeting, I do not expect the minutes of the

meeting.

If I have done a poor job, I prefer to get a frank,

blunt and honest feedback.

If I have done a poor job, I prefer to get a soft and

subtle feedback.

I prefer to give negative feedback immediately and

all at once.

I prefer to give negative feedback carefully or just

avoid giving it at all.

When I give negative feedback, I pay more attention

to how clearly I have expressed my criticism.

When I give negative feedback, I pay more

attention on the feeling of the person receiving the

message.

In my view, negative feedback can be given to an

individual in front of a group

In my view, negative feedback should be given to

an individual only in private

76

When I communicate, I try to explain the "why"

before sharing the "how"

When I communicate, I try to explain the “how”

before sharing the “why”

I prefer to understand all the details of a situation

and then draw a conclusion based on the big picture

I prefer to understand the big picture and then

see how all the pieces fit together

If I do not agree with my manager, I express my

opinion even in front of others

If I do not agree with my manager, I will not

express my opinion to him either individually or in

front of others.

I prefer to work in an egalitarian organization without

hierarchy

I prefer to work in an hierarchical organization

In meetings, I do not pay much attention to the

hierarchical positions of the attendees.

In meetings, I pay attention to the hierarchical

positions of the attendees.

If I have ideas to share with someone several levels

above or below me, I speak to that person directly.

If I have ideas to share with someone several

levels above or below me, I communicate it

through my immediate boss or immediate

subordinate.

I think decision-making process should involve

everyone.

I think decision-making process must not involve

everyone.

I believe that decisions should only be taken when

everyone agrees

I believe that decisions should be made by the

manager

If my manager takes a decision I disagree with, I

speak up.

If my manager takes a decision I disagree with, I

still comply with the decision.

In my opinion, it is better not to get too emotionally

close to colleagues.

In my opinion, getting emotionally close to

colleagues is needed to build trust and work

relationship.

I prefer to talk only about work with colleagues. I prefer to invest time just getting to know my

colleagues —without discussing work much.

77

I trust colleagues based on our interactions in the

work environment and their task competence.

I trust colleagues only after I spend time getting to

know them personally.

I believe that open debate, dialogue and discussion,

is an indicator of a healthy team

I believe that open debate, dialogue and

discussion, is likely to ruin relationships.

I openly express my point of view when I disagree

with my colleague.

I do not express my point of view when I disagree

with my colleague.

If I have a meeting at 9:00 a.m., then I will make sure

that I do not arrive any minute later.

If I have a meeting at 9:00 a.m., It is acceptable to

arrive 5, 10, or 15 minutes later.

In my opinion professionalism has more to do with

being organized and structured.

In my opinion, professionalism has more to do

with being flexible and adaptive.

I believe that a meeting agenda should be followed

closely.

I believe that a meeting agenda is a broad

guideline that can be changed based on the

group’s preference

Please indicate to what extent you agree with the following statements.

Strongly

Disagree Mostly

Disagree Somewhat

Disagree

Neither Agree nor Disagree

Somewhat

Agree Mostly

Agree Strongly

Agree

I enjoy interacting with people from different

cultures.

I know the cultural values and religious beliefs of

other cultures.

I am confident that I can socialize with locals in a

culture that is unfamiliar to me.

I am sure I can deal with the stresses of

adjusting to a culture that is new to me.

78

I change my verbal behavior (e.g., accent, tone,

pauses, rate of speech) when a cross-cultural

interaction requires it.

I change my non-verbal behavior when a cross-

cultural interaction requires it.

I am conscious of the cultural knowledge I use

when interacting with people with different

cultural backgrounds.

Please indicate to what extent you agree with the following statements.

Strongly

Disagree Mostly

Disagree Somewhat

Disagree

Neither Agree nor Disagree

Somewhat

Agree Mostly

Agree Strongly

Agree

People in managerial positions should avoid social interaction with people in different levels.

It is important to have instructions spelled out in detail so that I always know what I’m expected to do.

Roles and responsibilities are important because they inform me of what is expected of me.

Standardized work procedures are helpful.

Individuals should sacrifice self-interest for the group.

Group success is more important than individual success.

Group loyalty should be encouraged even if individual goals suffer.

Individual should aim to achieve Personal steadiness and stability in the long run.

79

Long term planning is important in life

It is important to work hard to achieve success in the future

It is important for both men and women to have a professional career.

Please indicate your Nationality ______________.

Please indicate your location of work

(If you cannot identify your location of work from the below mentioned locations, please select "others" and fill-in the name of your

location in the space below) _____________

Please indicate your overall tenure in this location (in years). _____________

Although you may be a member of several different program teams, please select only one program team from the list below.

Since this study aims at understanding the effects of working in a multicultural team, please select a team where your engagement

with people from other nationalities is high.

Please note that you will have to keep the below-mentioned program team in mind when you fill out the remaining questions.

(If you cannot identify your team from the below mentioned teams, please select "others" and fill-in the name of your team in the

space below) ____________________

Please fill in the name of your team. ____________________

Please indicate your total tenure (in months) in this program team. ____________________

What is the number of weekly hours spent on work activities related to this team? ____________________

What is the number of weekly hours spent virtually (i.e. not face-to-face) on work activities related to this team? ____________________

80

Please indicate how you would rate the following statements.

Very poor Poor Fair Good Very good Excellent Exceptional

How would you rate your interpersonal skills during

your interaction with this team?

How would you rate your commitment towards this

team?

How would you rate the quality of your own work

during your interaction with this team?

How would you rate the quantity of your own work

during your interaction with this team?

Please indicate to what extent you agree with the following statements.

Strongly

Disagree

Mostly

Disagree

Somewhat

Disagree

Neither Agree nor Disagree

Somewhat

Agree

Mostly

Agree

Strongly

Agree

It took me less time to complete my work tasks

than intended

Interacting with the team helped me keep my job

knowledge up to date.

Communication with others led to the desired

results

81

Please indicate to what extent you agree with the following statements.

Strongly

Disagree Mostly

Disagree Somewhat

Disagree

Neither Agree nor Disagree

Somewhat

Agree Mostly

Agree Strongly

Agree

I experienced relationship tension in this team.

I often get frustrated while working in this team

I experienced emotional conflict in this team.

82

Appendix C – Factor Analysis

Factor Analysis – Cultural Difference

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .653

Bartlett's Test of Sphericity Approx. Chi-Square 1121.540

df 300

Sig. .000

Rotated Component Matrixa

Component

1 2 3 4 5 6 7 8

C1 0.468

C2 0.686

C3 0.886

C4 0.879

C5 0.518

EV1 0.303

EV2 0.614

EV3 0.683

EV4 0.761

P1 0.535

L1 0.459

L2 0.673

L3 0.576

L4 -0.656

D1 0.703

D2 0.758

D3 0.768

T1 0.844

T2 0.747

T3 0.654 0.318

DA1 0.765

DA2 0.678

S1 0.486

S2 0.588

S3 0.605

83

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 10 iterations.

Factor Analysis – Relationship Conflict

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .714

Bartlett's Test of Sphericity Approx. Chi-Square 166.909

df 3

Sig. .000

Component Matrixa

Component

1 Conflict1 .863

Conflict2 .877 Conflict3 .917

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Factor Analysis – Cultural Intelligence

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .770

Bartlett's Test of Sphericity Approx. Chi-Square 396.579

df 21

Sig. .000

Rotated Component Matrixa

Component

1 2

CI1 .821

CI2 .581

CI3 .815

CI4 .701

CI5 .795

CI6 .920

CI7 .760

84

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 3 iterations.

Factor Analysis – Individual Performance

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .773

Bartlett's Test of Sphericity Approx. Chi-Square

343.794

df 21

Sig. .000

Rotated Component Matrixa

Component

1 2

IP1 .829

IP2 .880

IP3 .883

IP4 .862

IP5 .857

IP6 .778

IP7 .819

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 3 iterations.

Factor Analysis – Hofstede’s Cultural Dimensions

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .766

Bartlett's Test of Sphericity Approx. Chi-Square

345.417

df 55 Sig. .000

Rotated Component Matrixa

Component

1 2 3 4 5

H1 .795

85

H2 .482

H3 .775

H4 .886

H5 .841

H6 .865

H7 .695

H8 .577 .578

H9 .775

H10 .851

H11 .865

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6 iterations.

Appendix D – Test for Multicollinearity

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 RD_Team .996 1.004

CIMean .994 1.006

DoV .997 1.003

a. Dependent Variable: RCMean

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 RCMean .983 1.018

DoV .997 1.003

CIMean .980 1.021

a. Dependent Variable: RD_Team

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 RD_Team .994 1.006

CIMean .980 1.021

RCMean .981 1.020

a. Dependent Variable: DoV

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 RD_Team .997 1.003

RCMean .997 1.003

DoV 1.000 1.000

a. Dependent Variable: CIMean

86

Appendix E – Test for Normal Distribution

87

88

Appendix F – Test for Outliers

89

90

91

Appendix G – Output for Main Hypothesis

Mediating effect of Relationship Conflict

Matrix

Run MATRIX procedure:

************* PROCESS Procedure for SPSS Release 2.16.3 ******************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com

**************************************************************************

Model = 4

Y = IPmean

X = RD_Team

M = RCMean

Statistical Controls:

CONTROL= Ten_LocM Ten_T_M

Sample size

121

**************************************************************************

Outcome: RCMean

Model Summary

R R-sq MSE F df1 df2 p

.1113 .0124 2.3426 .4891 3.0000 117.0000 .6905

Model

coeff se t p LLCI ULCI

constant 2.7506 .3691 7.4528 .0000 2.1387 3.3626

RD_Team .1187 .3354 .3539 .7241 -.4374 .6749

Ten_LocM .0004 .0014 .3042 .7615 -.0019 .0027

Ten_T_M .0105 .0103 1.0231 .3084 -.0065 .0275

**************************************************************************

Outcome: IPmean

Model Summary

R R-sq MSE F df1 df2 p

.3857 .1487 .5141 5.0672 4.0000 116.0000 .0008

Model

coeff se t p LLCI ULCI

constant 5.6176 .2100 26.7552 .0000 5.2695 5.9658

RCMean -.1519 .0433 -3.5062 .0006 -.2237 -.0800

RD_Team -.1571 .1572 -.9994 .3197 -.4178 .1036

Ten_LocM -.0015 .0006 -2.2992 .0233 -.0026 -.0004

Ten_T_M .0097 .0048 2.0149 .0462 .0017 .0177

************************** TOTAL EFFECT MODEL ****************************

Outcome: IPmean

92

Model Summary

R R-sq MSE F df1 df2 p

.2419 .0585 .5637 2.4244 3.0000 117.0000 .0692

Model

coeff se t p LLCI ULCI

constant 5.2000 .1811 28.7211 .0000 4.8998 5.5001

RD_Team -.1751 .1645 -1.0644 .2893 -.4480 .0977

Ten_LocM -.0016 .0007 -2.2906 .0238 -.0027 -.0004

Ten_T_M .0081 .0050 1.6161 .1088 -.0002 .0165

***************** TOTAL, DIRECT, AND INDIRECT EFFECTS ********************

Total effect of X on Y

Effect SE t p LLCI ULCI

-.1751 .1645 -1.0644 .2893 -.4480 .0977

Direct effect of X on Y

Effect SE t p LLCI ULCI

-.1571 .1572 -.9994 .3197 -.4178 .1036

Indirect effect of X on Y

Effect Boot SE BootLLCI BootULCI

RCMean -.0180 .0638 -.1395 .0684

Partially standardized indirect effect of X on Y

Effect Boot SE BootLLCI BootULCI

RCMean -.0240 .0852 -.1858 .0931

Completely standardized indirect effect of X on Y

Effect Boot SE BootLLCI BootULCI

RCMean -.0101 .0333 -.0753 .0361

Ratio of indirect to total effect of X on Y

Effect Boot SE BootLLCI BootULCI

RCMean .1029 24.9884 -.6297 3.4670

Ratio of indirect to direct effect of X on Y

Effect Boot SE BootLLCI BootULCI

RCMean .1147 8.9769 -.3651 8.8473

Normal theory tests for indirect effect

Effect se Z p

-.0180 .0532 -.3387 .7348

******************** ANALYSIS NOTES AND WARNINGS *************************

Number of bootstrap samples for bias corrected bootstrap confidence

intervals:

5000

Level of confidence for all confidence intervals in output:

95.00

------ END MATRIX -----

93

Moderating Role of Cultural Intelligence

Regression

Variables Entered/Removeda

Model Variables Entered

Variables

Removed Method

1 Ten_T_M,

Ten_LocMb . Enter

2 MC_RD, MC_CIb . Enter

3 RD_CIb . Enter

a. Dependent Variable: RCMean

b. All requested variables entered.

Model Summary

Model R

R

Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .106a .011 -.005 1.52488 .011 .676 2 118 .511

2 .179b .032 -.001 1.52184 .021 1.236 2 116 .294

3 .245c .060 .019 1.50602 .028 3.450 1 115 .066

a. Predictors: (Constant), Ten_T_M, Ten_LocM

b. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_RD, MC_CI

c. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_RD, MC_CI, RD_CI

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 3.144 2 1.572 .676 .511b

Residual 274.380 118 2.325

Total 277.524 120

2 Regression 8.867 4 2.217 .957 .434c

Residual 268.657 116 2.316

Total 277.524 120

3 Regression 16.692 5 3.338 1.472 .204d

Residual 260.832 115 2.268

94

Total 277.524 120

a. Dependent Variable: RCMean

b. Predictors: (Constant), Ten_T_M, Ten_LocM

c. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_RD, MC_CI

d. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_RD, MC_CI, RD_CI

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 2.858 .210 13.619 .000

Ten_LocM .000 .001 .026 .276 .783

Ten_T_M .011 .010 .098 1.046 .298

2 (Constant) 2.814 .211 13.315 .000

Ten_LocM .001 .001 .061 .631 .529

Ten_T_M .010 .010 .096 1.027 .306

MC_CI .267 .175 .144 1.531 .128

MC_RD .089 .334 .024 .266 .791

3 (Constant) 2.883 .212 13.572 .000

Ten_LocM .001 .001 .066 .690 .491

Ten_T_M .002 .011 .022 .217 .829

MC_CI .274 .173 .147 1.583 .116

MC_RD -.082 .343 -.023 -.240 .811

RD_CI .941 .506 .190 1.857 .066

a. Dependent Variable: RCMean

Excluded Variablesa

Model Beta In t Sig. Partial Correlation

Collinearity

Statistics

Tolerance

1 MC_CI .145b 1.556 .123 .142 .949

MC_RD .033b .354 .724 .033 .992

RD_CI .183b 1.852 .066 .169 .843

2 RD_CI .190c 1.857 .066 .171 .782

95

a. Dependent Variable: RCMean

b. Predictors in the Model: (Constant), Ten_T_M, Ten_LocM

c. Predictors in the Model: (Constant), Ten_T_M, Ten_LocM, MC_RD, MC_CI

Moderating Role of Degree of Virtuality

Regression

Variables Entered/Removeda

Model Variables Entered

Variables

Removed Method

1 Ten_T_M,

Ten_LocMb . Enter

2 MC_DoV,

MC_RDb . Enter

3 RD_DoVb . Enter

a. Dependent Variable: RCMean

b. All requested variables entered.

Model Summary

Model R

R

Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .093a .009 -.009 1.52557 .009 .501 2 116 .607

2 .098b .010 -.025 1.53804 .001 .063 2 114 .939

3 .099c .010 -.034 1.54470 .000 .018 1 113 .892

a. Predictors: (Constant), Ten_T_M, Ten_LocM

b. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_DoV, MC_RD

c. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_DoV, MC_RD, RD_DoV

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 2.330 2 1.165 .501 .607b

Residual 269.973 116 2.327

Total 272.303 118

2 Regression 2.629 4 .657 .278 .892c

96

Residual 269.674 114 2.366

Total 272.303 118

3 Regression 2.673 5 .535 .224 .951d

Residual 269.630 113 2.386

Total 272.303 118

a. Dependent Variable: RCMean

b. Predictors: (Constant), Ten_T_M, Ten_LocM

c. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_DoV, MC_RD

d. Predictors: (Constant), Ten_T_M, Ten_LocM, MC_DoV, MC_RD, RD_DoV

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 2.903 .213 13.617 .000

Ten_LocM .000 .001 .022 .237 .813

Ten_T_M .009 .010 .085 .903 .368

2 (Constant) 2.901 .215 13.494 .000

Ten_LocM .000 .001 .025 .265 .792

Ten_T_M .009 .010 .084 .876 .383

MC_RD .120 .338 .033 .355 .723

MC_DoV -1.682E-5 .004 .000 -.004 .997

3 (Constant) 2.902 .216 13.432 .000

Ten_LocM .000 .001 .025 .254 .800

Ten_T_M .009 .010 .084 .871 .385

MC_RD .126 .342 .035 .369 .713

MC_DoV -5.146E-5 .004 -.001 -.013 .990

RD_DoV -.001 .009 -.013 -.136 .892

a. Dependent Variable: RCMean

Excluded Variablesa

Model Beta In t Sig. Partial Correlation

Collinearity

Statistics

Tolerance

97

1 MC_RD .033b .357 .722 .033 .991

MC_DoV -.001b -.011 .991 -.001 .995

RD_DoV -.008b -.085 .932 -.008 .994

2 RD_DoV -.013c -.136 .892 -.013 .971

a. Dependent Variable: RCMean

b. Predictors in the Model: (Constant), Ten_T_M, Ten_LocM

c. Predictors in the Model: (Constant), Ten_T_M, Ten_LocM, MC_DoV, MC_RD

Entire Model

Matrix

Run MATRIX procedure:

************* PROCESS Procedure for SPSS Release 2.16.3 ******************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com

**************************************************************************

Model = 9

Y = IPmean

X = RD_Team

M = RCMean

W = CI_Mean

Z = DoV

Statistical Controls:

CONTROL= Ten_T_M Ten_LocM

Sample size

119

**************************************************************************

Outcome: RCMean

Model Summary

R R-sq MSE F df1 df2 p

.2566 .0658 2.2917 .9891 7.0000 111.0000 .4430

Model

coeff se t p LLCI ULCI

constant 2.9344 .2390 12.2803 .0000 2.4609 3.4079

RD_Team -.0918 .3858 -.2378 .8125 -.8563 .6728

CI_Mean .3047 .1851 1.6464 .1025 -.0620 .6714

int_1 .9997 .6299 1.5870 .1154 -.2485 2.2479

DoV -.0008 .0039 -.2065 .8368 -.0085 .0069

int_2 -.0025 .0101 -.2501 .8030 -.0225 .0175

98

Ten_T_M .0003 .0157 .0189 .9850 -.0308 .0314

Ten_LocM .0009 .0013 .7292 .4674 -.0016 .0034

Product terms key:

int_1 RD_Team X CI_Mean

int_2 RD_Team X DoV

**************************************************************************

Outcome: IPmean

Model Summary

R R-sq MSE F df1 df2 p

.3898 .1519 .5174 4.1742 4.0000 114.0000 .0034

Model

coeff se t p LLCI ULCI

constant 5.4990 .1622 33.8933 .0000 5.1776 5.8204

RCMean -.1567 .0513 -3.0564 .0028 -.2582 -.0551

RD_Team -.1498 .1559 -.9607 .3387 -.4586 .1591

Ten_T_M .0095 .0049 1.9474 .0539 -.0002 .0191

Ten_LocM -.0015 .0006 -2.5472 .0122 -.0026 -.0003

******************** DIRECT AND INDIRECT EFFECTS *************************

Direct effect of X on Y

Effect SE t p LLCI ULCI

-.1498 .1559 -.9607 .3387 -.4586 .1591

Conditional indirect effect(s) of X on Y at values of the moderator(s):

Mediator

CI_Mean DoV Effect Boot SE BootLLCI BootULCI

RCMean -.8207 -35.2558 .1290 .1142 -.0489 .4159

RCMean -.8207 .0000 .1429 .0986 -.0007 .4235

RCMean -.8207 35.2558 .1569 .1128 -.0081 .4653

RCMean .0000 -35.2558 .0004 .1047 -.2267 .1937

RCMean .0000 .0000 .0144 .0648 -.1081 .1512

RCMean .0000 35.2558 .0283 .0613 -.0826 .1684

RCMean .8207 -35.2558 -.1281 .1545 -.4864 .1326

RCMean .8207 .0000 -.1142 .1169 -.3808 .0791

RCMean .8207 35.2558 -.1002 .0988 -.3635 .0482

Values for quantitative moderators are the mean and plus/minus one SD from

mean.

Values for dichotomous moderators are the two values of the moderator.

***************** INDEX OF PARTIAL MODERATED MEDIATION *******************

Moderator:

CI_Mean

Mediator

Index SE(Boot) BootLLCI BootULCI

RCMean -.1566 .1054 -.4240 -.0008

Moderator:

99

DoV

Mediator

Index SE(Boot) BootLLCI BootULCI

RCMean .0004 .0016 -.0022 .0043

******************** ANALYSIS NOTES AND WARNINGS *************************

Number of bootstrap samples for bias corrected bootstrap confidence

intervals:

5000

Level of confidence for all confidence intervals in output:

95.00

NOTE: The following variables were mean centered prior to analysis:

RD_Team CI_Mean DoV

NOTE: Some cases were deleted due to missing data. The number of such cases

was:

2

NOTE: All standard errors for continuous outcome models are based on the HC3

estimator

------ END MATRIX -----