4. Current Inconsistencies
• Ethno-cultural diversity and innovation
– Mixed findings!
• 2 theoretical mechanisms
– Social categorization (Tajfel & Turner, 1986):
• Ingroup vs. outgroup, conflict
• Negative impact on creativity
– Information processing (Cox & Blake, 1991; Dahlin, Weingart, &
Hinds, 2005):
• Cognitive diversity (Horwitz & Horwitz, 2007)
• Positive effect on creativity
5. Conceptual Mix-up
Figure 2. Two teams with identical degree of ethnic diversity but differing degrees of
cultural diversity. The distance in between the circles representing ethnic groups
denotes their cultural distance or dissimilarity.
Korean-
American
Vietnamese-
American
Team A
Chinese-
American
Korean-
American
Mexican-
American
Team B
German-
American
6. Research Objectives
• Objective of the present study:
– Conceptually distinguish between diversity of
ethnic grouping categories and diversity of
culture.
– Theoretically map these two aspects of diversity
onto two distinct mechanisms via which diversity
influences creativity and innovation.
– Empirically tease apart these two aspects of
diversity and test their respective effects on the
innovation process at the national level.
7. Constructs
Innovation Input Innovation Output
Ethnic Diversity
Cultural Diversity
Ethnic Polarization
Multiplicity arising from
ethnic categorical variety.
Richness arising from
cultural distance or
dissimilarity among all
pairs of ethnic groups.
Extent to which the population is
divided into sizable sub-groups.
Aspects of the environment
conducive to innovation
within an economy.
The results of innovative
activities within the
economy.
9. Method
• Empirically tease apart ethnic and cultural
diversity: Fearon’s (2003) dataset of ethnic and
cultural fractionalization
– Ethnic fractionalization: blau’s index
– Cultural fractionalization: captures linguistic distance
• We removed its shared variance with ethnic fractionalization
• Polarization index: Montalvo & Reynal-Querol (2005)
• Global Innovation Index
• Control: total value add accounted for by
industries typically generating large numbers of
innovations, democracy, geographical conditions
11. Figure 3. Simple slope plot with ethnic polarization as the moderator variable on
the cultural diversity-innovation output relationship for year 2014. High
polarization and low polarization groups were created using median split.
12. Theoretical Contribution
• Conceptually distinguished two aspects of
diversity: ethnic categorical variety and
cultural distance or dissimilarity
• Empirically demonstrated the contrasting
effects of ethnic diversity and cultural diversity
• At the macro level, using a process model to
understand when and how diversity affects
innovation
13. Managerial Implications
• Successful diversity management requires
minimizing the negative effects of ethnic diversity
while maximizing the positive effects of cultural
diversity.
• How to achieve that?
– Override sub group identification and interests with
shared identity and interests
– Form teams with members from distant cultures
– Recruit and nurture multicultural employees
– Avoid polarized group structure, e.g., in merger and
acquisition
Whether people like it or not, two irreversible trends have been on the rise, both in societies at large, as well as in the business world. The first trend is an increase in ethno-cultural diversity. Many factors have contributed to it. For example, globalization and worldwide mobility have brought together people from previous separated places. Moreover, legislative requirements for a more inclusive workforce are also pushing companies toward greater diversity. Finally, an increasing number of multi-national corporations are also actively embracing diversity to tap into a wider talent pool and access unique knowledge and ideas.
At the same time, another rising trend is the growing importance of creativity and innovation. As the market becomes increasingly competitive, creativity and innovation are more important than ever as societies and businesses must come up with unique goods and services that set them apart from competitors.
Despite these two important trends taking place simultaneously, their relationship with each other is not well understood.
Specifically, the effect of ethno-cultural diversity on innovation remains controversial, with many conflicting findings.
Behind these conflicting findings, 2 theoretical mechanisms have dominated the debates. On the one hand, social categorization theory has been adopted widely to predict a negative effect of diversity as it divides people into conflicting ingroup and outgroups which do not work well together. One the other hand, advocates of the information processing perspective view diversity as a source of rich ingredients for creative recombination therefore predict that it should benefit innovation.
While we concur with these theoretical perspectives, we contend that previously unrecognized conceptual confusion could have also contributed to these inconsistencies.
Let me illustrate this conceptual mix-up using these two hypothetical teams here.
As shown in this figure, as far as diversity of ethnic categories are concerns, Team A and Team B have identical degree of ethnic diversity because they each consists of identical number of unique ethnic groups, with one member in each group. Let’s call this ethnic diversity; it is measured as categorical variety using the Blau’s or Herfindahl index.
However, let’s take a step further to consider the cultural content represented in these two groups. Team A consists of three ethnic groups that are culturally similar while Team B consists of three ethnic groups that are culturally dissimilar. Therefore, taking culture into consideration, we contend that Team B is more diverse than Team A.
Unfortunately, in the current literature, this aspect of diversity arising from cultural distance or dissimilarity has not been taken into consideration conceptually or operationally. Instead, when researchers make theoretical arguments about cultural diversity, they are really referring to and measuring ethnic categorical diversity.
We believe this conceptual and operational mix-up has partly contributed to the inconsistent findings. Therefore we address this issue in this present research.
In this study, we will first conceptually distinguish between diversity of ethnic grouping categories and diversity of culture, we will then map these two aspects of diversity onto the two contrasting theoretical mechanism, specifically, social categorization and information processing, via which diversity influences creativity and innovation. Finally, we will empirically tease apart these two aspects of diversity and test their respective effects using national level data.
Next, I’ll define our key constructs before presenting our theoretical model. We refer to ethnic diversity as the Diversity or multiplicity arising from ethnic categorical variety. We refer to cultural diversity as Diversity or richness arising from cultural distance or dissimilarity among ethnic groups. The more distant groups are in terms of cultural contents such as cultural knowledge, cognitive representations, and mental models, the more culturally diverse they are.
The other key constructs in our model are innovation input, innovation output, and ethnic polarization. Innovation input is defined as Aspects of the environment conducive to innovation within an economy. Some examples include political stability, quality education, and R&D investment. We define innovation output as The results of innovative activities within the economy, in the form of patents, trade marks, and so on. And we define ethnic polarization as the extent to which the population is divided into sizable sub-groups. This index has been commonly used as an indicator of potential for intergroup tension and conflict.
Now I will walk you through our theoretical model. Based on the national system of innovation perspective, we know that innovation input will positively contribute to innovation output.
According to the social categorization theory, sub groups organized around ethnicity can have many incompatible goals and interests. Therefore we predict that the more ethnicallty diverse societies will have more difficulty in reaching consensus in their decision making, which then leads to retardation, if not deadlock, in their development of innovation input and output. In other words, we hypothesize that ethnic diversity will be negatively associated with innovation input and output, and innovation input partially mediates the negative relationship between ethnic diversity and innovation output.
On the other hand, cultural diversity should present a positive potential for innovation because the more unique cultural knowledge, cognitive representations, and mental models are available, the more likely they can be combined in novel ways to produce original solutions. The more dissimilar they are, the more likely their combination will result in greater originality. However, this potential benefit can only be realized if the sub groups are not divided and are willing to share, collaborate, and integrate unique cultural contents. Therefore, we hypothesize that cultural diversity will have a positive effect on innovation input and output only when ethnic polarization is low.
To empirical demonstrate these contrasting effects, we first took advantage of Fearon’s (2003) data of ethnic and cultural fractionalization to tease apart these two aspects of diversity. Fractionalization as economists call it, is equivalent to diversity in management literature, computed using Blau’s index. Fearon’s cultural fractionalization index captures rich information about cultural distance via linguistic similarity and dissimilarity between each pair of ethnic groups. It is a very innovative way to measure culture based on established theories in anthropology and linguistics. Due to time constraint, I will not elaborate on it however I’ll be happy to share the reference with you afterwards. As cultural fractionalization is computed based on pairs of all existing ethnic groups in a given society, these two measures are highly correlated. We removed their shared variance to create a unique cultural fractionalization index that is independent from the ethnic fractionalization index.
We adopted the polarization index created by Montalvo and Reynal-Querol (2005), as well as the innovation input and innovation output sub indices from the global innovation index. Innovation input and output measure from this framework have been normalized against GDP and country size. We controlled for some variables typically associated with innovation, such as the total value add accounted for by industries typically generating large numbers of innovations, democracy, and geographical conditions.
