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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.
24
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
55
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.
56
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.
57
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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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 -----