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Sylvain Rochon, April 28 2022

Samuel Dada - Harnessing Individual Differences to Achieve Organizational Performance

A CykoMetrix Spotlight Production

Every week, the Spotlight shines on an amazing professional with a story to tell and lessons to teach.  Welcome to the CykoMetrix Spotlight.

The following is an adapted transcript of the exchange between Sylvain Rochon, CMO at CykoMetrix as host, and Samuel Dada (MSc, MMP, M.inst.AM). www.linkedin.com/in/samuel-dada-b59a1b141 )

Sylvain Rochon: Welcome to CykoMetrix Spotlight. My name is Sylvain Rochon. I am the Chief Marketing Officer at CykoMetrix, a company that is SaaS-based that measures people's cognitive abilities, emotional intelligence in a combinatory fashion to determine their effectiveness as a team member as well as teams over time. Today, I'm with a person that lives currently in Nigeria, about 100 kilometers from Lagos. His name is Samuel Dada, having an Academic Masters degree and a Professional Masters degree. He is a consummate professional with over 10 years of experience in various organizations as an Administrator, and he does have a Master of Science in Psychology and a Master of Managerial Psychology, working on his Ph.D. in the area of Social Psychology. This guy knows a lot about psychology, I gather. Nice to see you, Samuel. 

Samuel Dada: Thank you. 

Sylvain: Now Samuel, today you're here because you have an amazing subject that is very important to a lot of nations right now. It's all about differences. Like how do you harness individual differences to achieve organizational performance. How do you do that? 

Samuel: Alright. Thank you very much, Sylvain. Harnessing individual differences in order to achieve organizational performance has to do with understanding the fact that human beings are the most important factors in carrying out anything you want to do in an organization.  In the basics of Economics, factors of production: capital, human resource, and all that, most important is the manpower utilized in achieving your organizational effectiveness; so we cannot underestimate the importance of human factor. Now, we understand that people come to work; we find ourselves in organizations from various backgrounds, from various settings, from various horizons, as you would want to see it. That is a fact, we are different. The fact is that we come in with a lot of individual differences. That is the reason managers and academics believe it is important to understand human nature in order to move forward, in order to achieve anything in an organization. Without understanding human nature, we cannot achieve what we set out our organization to achieve. 

Now, understanding human nature primarily falls within the discipline of psychology. That's why I'm excited actually to talk about this today. Now, when we look at human individual differences, we're talking about the self-concept, we're talking about personal values, we're talking about personality. These 3 elements constitute human individual differences. Now, integrating these: because we come from various groups, various collections out there, we come from such collections into an organization. That is the reason we begin to look at human individual differences in terms of social differences; understanding human individual differences in terms of social differences. Now, when we look at that in that sense, then we'd begin to understand and appreciate the word 'diversity.’ We are from diverse places, diverse backgrounds, diverse social groups, diverse entities. 

Now, social differences of people within a unit is all about diversity. How can you put people from such various backgrounds together to achieve something without confusion, without conflict? Well, you may not be able to do that. That's the reason psychology comes in to tell us what data are important to collect in order to be able to manage all these differences.

Sylvain: Perhaps another question to ask as well is, why would we put such diverse and potentially problematic combinations of people together inside a workplace? Like, what does the research show as a benefit to do that? Because maybe we want to have all the individuals that are similar together that can communicate the same way. Is there a reason from the research point of view that we want to put all these diverse individuals together in the same organization? 

Samuel: Yes. Yes. It's important to understand the fact that we need a diverse organization. Now, we need talents. We need skills. We need knowledge of various types. We need abilities of various types. We'll get to that in the course of the discussion that is the essence of having a heterogeneous workforce. It's about what we call ‘value-in-diversity; value-in-diversity hypothesis’. There is value, there is benefit, in diversity. Now, the bottom line of that is that we are trying to harness knowledge of various kinds with skill, with abilities of various types and experiences, we'll try to put all that together to generate  the potential we want to maximize to achieve whatever we want to achieve in an organization. So basically, that is the reason for diversity in an organization. We'll get to that. It's about how diversity operates, and within how diversity operates is the benefit. We'll begin to see the benefit and some kind of data that diversity directs us to collect. 

So now, there are data we cannot underestimate. In human resource management and any human resource department, a manager must collect certain data to be able to manage diversity correctly, effectively. So, harness social differences, individual differences accurately, unleashing potentials of people at work, maximizing the potentials that different people bring into the workplace. We want to take advantage, maximum advantage, of a human resource pool. Now, there are data to be collected. We don't just gather people and put them to use just because ‘this is skilled, this is unskilled.’ That isn't enough. We should actually gather data and work with the data scientifically. Now, there are data various researches have given insight into, which are necessary. Now, there are 2 approaches in gathering data in managing social differences at work. 

The first approach is known as the categorical approach and the second approach is known as the proportional approach. Now, the categorical approach has to do with generating natural characteristics data, as well as acquired characteristics data. Such data are characteristics of people that are at the surface level, that you can easily see; easily observable characteristics of people, namely: gender, race, okay? Gender, race, age. Also, these are easily observable, easily detected differences. That's the reason we refer to them as surface-level differences. We collect data of people in this regard. Before I go on, let me advise that we will be collecting a lot of data from a lot of people. And we should know that we may not be able to manually manipulate or manage the data. It's important to have another side (platform) to be able to manage such data. From my experience and training exposures, I want to recommend the Database Management System -  Structured Query Language (DBMS - SQL), in dealing with social differences data. 

Now, you create a database of people and call it social differences, tell the DBMS to create a database, and social differences database is created. Then you begin to form various tables. Like the categorical approach I'm talking about now, we have natural characteristics data as a table. So, we have race, we have age, we have gender, we have ethnicity, things that you could easily detect, you collect such data. Then another category of data could be a table of acquired characteristics of people. In that, we have the functional background of people, we have the organizational tenure of people, we have the educational level of people. You'll see that these examples I've given are not easily observable things. You cannot just look at somebody and say, "oh this is your educational level" or say, "oh, I believe this is your functional background. You've been in accounting; you've been in customer service for 5 years. I can see that in you!" You can hardly do that. 

Sylvain: That's where the resume comes in or other instruments like that. 

Samuel: Okay, yeah, you must. You rely on self-report. The person has to say to you, "this is what I have." That's the reason we call those ‘deep level’ characteristics of persons. They are not at the surface, they are deep-level data you must collect from people. So, you name them deep level data or name them acquired characteristics of persons that you're working with. Now, the reason for this is that it's been discovered by research. I keep saying research, research, research, but let me mention one: Roberson 2019. Robertson has been doing a lot of work in this area. He's a diversity expert, individual differences expert, social influences expert. So we have Roberson 2019, the research report's a bit recent. We have Roberson 2017, various releases within the years - those years we have them. So those are references you could consult. Now, the reason we collect acquired and natural data of persons working in the organization is that we discovered that the natural characteristics work well with relationships, how people interact with one another. Interpersonal relationship as well as intergroup relationships. 

Sylvain: So, you mean, for example, if I understand correctly, if a person of a certain ethnicity and gender, for example, according to the research, may have a better time socializing or interacting with the person with the same ethnicity and gender? Is that what you mean? 

Samuel: That is it. So natural characteristics benefit relationships in an organization. While acquired ones, task-oriented, make people work well. Task: what you do, your tasks, your job, your duty; all these things are intertwined, one incorporating the other. So, let's just use the word task; the task people engage with at work. People who have acquired characteristics similar to one another perform very well when it comes to task initiatives. 

Sylvain: So just do it together on for the viewers. Like, an example would be 2 engineers?  They have similar tasks and they would work well together because they understand the same path and same work? 

Samuel: Very well. That's it. So, collecting that data would inform us of, (okay?), who to put together. We're gonna expect or have less conflict in terms of relationship where we're going to have high performance, good quality of work, and why that would not happen. If that is not happening, let's look at the data we have collected of people that are working. Let's see what could be the issue. It gives guidance as to assigning people to work, to tasks, to teams. What kind of team do you want to put together and all that. We are guided by data, by such data. Now, that is for the first approach of collecting data, that's categorical approach. Call that categorical approach, or factor approach as well. 

Sylvain: Based on what you were you've been telling me so far because I know you have more to say. Then you want to have your team that have similar skills, similar backgrounds, because that seems to be a good idea.  Then they understand each other and they can better communicate and collaborate together through understanding of their background. That seems to be, so far, the ideal situation, which so far says well, we don't want to have a diverse team. We want to have something that's very similar in the team. 

Samuel: Okay, okay. 

Sylvain: So, is that what the research shows though? That a non-diverse team is better than a diverse team? 

Samuel: We will get that, too. Not necessarily, not necessarily. A non-diverse team might not be better than a diverse team. The reason is that what informs of such composition of teams in such regards is the nature of the task. We'll get there. A heterogeneous team is good for certain purposes. Homogeneous team, as we have discussed so far, is good for variety of many other purposes in the organization. That is the traditional arrangement. That should be what, that ought to be what we look out for first in putting people together. That would be the first approach. Now, with the first approach, we could begin to shuffle, reshuffle, as data suggests, but then we gather data in that regard first. Now, then the other approach, which is the proportional approach.  Let me address the question of heterogeneous team better than homogenous team fully before we get to the point of theory and all that. Now, when you have a task that has to do with information processing, a heterogeneous team is better than homogeneous team; that is, diverse people coming together is better than same persons (similar persons coming together). 

When the task involved has to do with information processing, when the task has to do with problem-solving, when the task has to do with decision making. So, for example, those 3 nature of work: information processing, decision making, problem-solving require no alternative to heterogeneous team. Diverse persons, diverse talents coming together. I think that addresses the question you raised. Okay, so, now, the second approach, which is the proportional approach is also known as the numerical approach because it has to do with numbers  - gathering data. Have them in a database. That is, the arrangement of our workforce in terms of minority, in terms of majority. Okay? Now, we could say that what's the nature of this data? Of these people  - this collection of persons we have? Okay?  Majority, we have female majority, we have male minority or vice versa. So, it's important also to have in our database the composition of our folks in that regard, in terms of which ones are more, which ones are less. We could estimate in percentages, ratios and all that. Just to have an idea of minority-majority composition of our workforce. So, we collect data in that regard as well. 

Sylvain: Now just to clarify, I think we have something like this in Canada. I think the federal government insists that there are, they call them quotas, I think, right? 

Samuel: There you go. 

Sylvain: Where inside the workplace, you need to have- I don't know percentages like at least 30%, aboriginal, at least 50% female. And they hire to make sure that those proportions are respected in the general workforce. 

Samuel: Okay. Thank you so much for that insight. Now, the essence of that is political. It comes from the political viewpoint, (okay?), where the minority persons are cheated, the minority persons cannot access advantages. The minority person is undermined.  The Western democracy is an ideology of inclusivity.  Now, the politics behind minority-majority composition is to safeguard the minority. We are running democracy in a number of parts of the world. The Western part of the world runs democracy. Africa, most countries of Africa run democracy. So, in democracy it is a game of numbers. Majority rules. Minority could have their say but majority rules. Now, when minority has a say, but what minority says does not come to force. Whereas what majority says obtains. What happens to the interests of the minority? These are human beings we are talking about, and we are talking about human nature. So, are we going to undermine the human nature of minority? No. Are we going to undermine the interest of our minority? No. So there must be some form of affirmative action to protect minority, not to neglect minority, although majority rules. But there has to be something to be done to protect the interest of minority. That is the reason, for example, the reason for the affirmative action by the court that you suggested the United Nations comes up with. Okay, 35% affirmative action for women on boards and so on and so forth, so it's... such affirmative action policies, like I said, are political. 

Now, the reason we have to go beyond that, we cannot work with that a lot in the workplace because we are dealing with human nature. If you say that, okay, aborigines must be 40%, the majority must be 60% and all that, what does that portend for human nature? We can't deal with that beyond politics. How about human nature? We can't really do much with that data  - many people, less people - We can't really do much with that data. How about when the increased quota you have given us is of less quality? You get what I'm trying to say? If it must be 40%, but if 40% does not have the experience, does not have the knowledge, they do not have the abilities or skills, but like 25 or 20% of them do have it, are we going to be running at deficit?  Are we going to be running at a loss with the remaining balance of 15% on the job? Being there and doing nothing? So that is why, that is a reason that majority-minority has limitation of its utility. 

Sylvain: Well, we see the cases here, these complaints about it. But people are smart. Say okay, especially in HR, it's like I need this quota, right? So, they the offer the job and they, many people apply but they're looking for a specific subset like a minority. But then they need to fill, let's say 5 positions, right? Let's say black people, or colored. Because of that percentage that needs to be filled, right? Because that's a quota. 

Samuel: Yes. 

Sylvain: And they only have, let's say 7 candidates show up. Well, choosing 5 out of the 7, it's not a lot of choice, right? So, you're not really getting the best out of the crowd because it's not a lot of applicants. Sometimes that happens because you're forced to get your quota, whereas in in business, what we want to have the highest quality person. Notwithstanding whatever race or subculture, whatever they come from. So that's where sometimes it becomes a problem, when the quota gets in the way of the results and the actual quality of candidates you're looking for. 

Samuel: That is it. Okay? We're talking about human nature here. Human nature, the plasticity of human nature, like you're just giving the example, not really like that. Now, it could work well for other things, other political engagements of the society. For example, in the UK, we had the Brexit-Bremain dichotomy in recent past. That was not human nature. That was geopolitical restructuring of Europe; okay? It was not human nature. So, it was not difficult to go with that, too, and to put things in place, and all that, because it was not human nature. It was due political restructuring. So, minority-majority was good in that regard. But when it comes to human nature, it has a limitation. That is a reason psychologists do not really work so much with the minority-majority thing. Yes, we could have the quota, try to satisfy it, but then we have devised other things rather than look at it in terms of hiring, in terms of majority-minority proportional approach. We look at 3 other classes of data. We divide workforce or the labor market into 3 classes; that is, the majority-minority dovetails, as a result of the limitations that approach has, into 3 classes of data, collecting what you call separation data, variety data, as well as disparity data. 

What do I mean by separation data? Separation data refers to characteristics of people as of their values, their beliefs, and specifically their attitude. We want to collect data about these because of minority-majority limitation; we can't work with that as such. Performance, productivity are key things. Now, then, we look at the variety data. The variety data comprise the knowledge, type of knowledge, the network the person belongs to, and the networking skill of the person. Then, the experienced: what kind of experience? What experience are you bringing in? What's the extent of experience? So, we gather data about the experience, about knowledge, and about network that individuals have rather than just a majority-minority. Then, lastly, Disparity data! What is your Pay? What privileges do you have access to? We want to know that. Then, lastly, the position status of a person. We collect data of positions, data of Pay, data of privilege. Now, we are able to work with these better in terms of relationships at work, in terms of tasks to be achieved, to be accomplished at work. So, the majority-minority data dovetails into these  as a result of the limitation that proportional approach has. So, we classify the workforce into these 3 categories. So, we generate data in this regard of people. 

Now, having done this, we want to look at a very important thing, too, culture. Alright, then we collect data of culture. Now, culture as we know it, is global, it is local, as well, and it's dynamic. It keeps changing from time to time; civilization period, modernization era and all that. So, we're interested in the culture of people. Cultures are diverse, the aboriginals, as I say, the Spanish, the whites, the blacks, they have different cultures. Now, when it comes to that, the approach we use in psychology is an approach of a school of thought  - Gestalt. The conceptualization of cultural classification that instructs us of what data to collect. At least there is- the global influence of culture, as well as local influence of culture on people. Global influence of culture in terms of globalization, cultural synthesis (cultures are coming together) within the same geographic location. When cultures come together, we call that cultural synthesis. We could be having a melting pot. It would be having various ideologies emerge at a global level. 

At the local level, you have enculturation. That's a case where the culture of a person evolves within itself and... so, we try to juxtapose all that in cultural relativism and cross-cultural studies. We  (psychology) have come up with 3 specific types of data to collect to be able to harness the cultural differences of people at work. So, we collect what is known as the geographic data. That is the region from which you come. We collect data about associations, that is, the affiliations you have -  tell us what affiliations you have. We discovered that the affiliation data are similar to network data. That is, one kind of data featuring in another similar field now, but with the different caption, they (caption) could be different. That is when you discover that people are supplying different information. You ask for someone's network that gives you a particular item. As for the person's affiliation, it gives you a different item that you expected. In some other person's case, they could be the same. So, we want to have or collect all the data. Now, the other one is -  that I've talked about the geographic data  - the demographic data, that is the physical composition of people. The age comes in, marital status, family background, and all that. Demographic status of people. 

So, cultural studies inform us that in workforce management we would collect geographic data, demographic data, and associative data. Now, you remember that I told you that in managing this data, it's important that we choose database management system. It's, the beauty of database management system, as I am exposed to, that you have different attributes. Or let me say we have the same attribute featuring in different fields. Now, age features in categorical field of nature, natural data. Age features again in demographic data of cultural attributes. So, you see that same attributes featuring in different fields occurs. Now, DBMS has the potential of managing such data. We use aside the primary key, the composite key to be able to write languages, query languages, control languages, and all these things. We generate for you an output relevant to what command you have given it. Showing all these things, showing that - okay - this belongs to demographic, this belongs to natural data. So that's the beauty of using DBMS to manage these similar data. 

Now, having collected cultural data, too, it gives us more information about the people we will be working with and working for us. We want to see the possibility of juxtaposition. You've seen that we have collected different data from different fields, different categories; we want to juxtapose everything. We want to see how we can easily utilize everything and for that, some psychologists come up with what is known as the fault line. The fault line has to do with alignment of attributes. Basically, in layman's terms, it has to do with creating homogeneous teams. Not heterogeneous teams! Let similar persons (attributes) flock together. 

Sylvain: So, you're using the database of all the information to find these correlations or these associations, the similarities essentially between people and have them work together assuming that they'll be able to communicate better, they'll be able to work better because of similarities, essentially. 

Samuel: Absolutely, that is it. Now, so we work with the fault line foremost, like I told you, or like your initial question about which is better: homogeneous team or heterogeneous team. So far, with a fault line, we have created homogeneous teams. We'll now begin a look at (okay?) these teams that are homogeneous that we have now. Let's look at their tasks; the tasks they are to perform -  which task is information processing related? Which one is problem-solving related? Which one is decision-making related? So, from that we begin to extract and to generate heterogeneous groups. 

Sylvain: Because heterogeneous groups are better at those types of tasks like you explained earlier? 

Samuel: Absolutely. Absolutely. So that's how we work with such data. Now, for example, the fault line arithmetic works well with what is known in leadership as -  what is known as leader-member exchange. You have a leader, you have a team leader, for example, or a supervisor, or a manager of a department. Now, the people who are working directly with such leaders, what characteristics do they have? We first analyze the leader with whom they must be working. So, it's important to first create leadership position before creating other things that would work with the leader. Now, leadership style is divided into relationship or task orientation. In most cases there are different styles, but for example, task-oriented leader or relationship-oriented leader. A leader that achieves purpose through relationships. Or a leader that achieves purpose through focus on tasks. Work, work, work, work, it's all work. You want to play, "oh, no, no, no, no, less play, please." Well, what was the feedback on that chore? That's a task-oriented leader! Well, relationship-oriented leader would put task aside; seemingly put task aside: "Hey, how are you doing today? How are your children? Hey, I like your bowtie! What's happening?" They generate some smiles, and all that. 

So, after exchanges of pleasantries and all that, he asks, ‘how about the feedback, by the way? That's a relationship-oriented leader. Now, remember that we have analyzed workforce in terms of task-oriented characteristics, and relationship-oriented characteristics. Now, when we match the workforce with such a leader, you want to determine is this leader task-oriented? If the leader is task-oriented, then you have to prioritize task orientation in those that are working with him. You have to prioritize their deep-level characteristics, less obvious characteristics. That is, the acquired characteristics of functional background, of educational level. Because the person is like, all the time, their job... 

Sylvain: They are not focused on the personal. 

Samuel: Good. So, that is how awareness of the fault line alignment works very well for us. It helps us to match leadership with followership, to aligned leadership and followership in the workplace. That's an advantage of the fault line in collecting such data. We are reading, matching, and all that. So that's an example of the benefit of such data. Now, leaving the fault line now, let's see how these social differences and individual differences operate.

It's not enough for somebody to just understudy human resources, nor for  manager who is able to create all these database and fix in characteristics of people. It's important to understand the rationale behind connecting the data. It makes one to be able to judge well, it makes you be able to do better with the data you've collected. So, it's important to understand what is known as Social Identity Theory, Social Categorization Theory of Turner, Turner 1987, Social Identity Theory of Tajfel 1978, Turner 1987. Then Similarity Attraction Paradigm, Similarity Attraction Paradigm, then they evaluate value-in-diversity hypothesis that I mentioned earlier for heterogeneous teams. Okay? Then, it's important to understand categorization-elaboration model. So, understanding these theoretical frameworks in psychology would also inform us further of what data to still collect aside what we have collected.

So now, these theories actually enable us to get an individual to understand the social network he has come into within the workplace. Making sense of the workplace. It helps us to place an individual in better vantage point of being able to locate himself or herself in the workplace. After making sense of the workplace -  where am I? I mean, where am I? Where am I? He understands where he is, or where she is, he understands the environment. Then he's able to locate himself or herself in the environment, situate himself or herself in the environment; as of, oh yeah, from the sense made here, “I belong to this place. This is my position here. Oh, I shouldn't go beyond this. But this is where I ought to stay. These are the people I want to align with.” So, it makes an individual to be able to situate himself or herself in the organization in order to forestall conflicts. To forestall low productivity or less performance. 

Sylvain: So, Samuel, if I can summarize many things that you've said because there's a lot of information. You said, what seems to be central is, of course, the data collection, having a lot of information available in a database so that you can use queries and whatnot. You can create and you can isolate correlations, similarities, dissimilarities, and so on. And do matchmaking in some way, to create change, right? And by really truly understand the dynamics and the tasks, what type of teams, what type of leader that you have and doing proper matchmaking using the data, and you're creating proper, like a higher performance team. 

Samuel: Very good. 

Sylvain: Because you're creating teams that are of a certain type trying to do a certain test that would be either homogeneous or heterogeneous. Depending on what's favorable for that specific type of tasks, for example. You can go through the whole organization using this technique, using the data, and create all these teams that would be essentially higher performance. And, I imagine higher accordingly if you're missing pieces and you can look for specific individuals that would fit something that may be missing in a team, for example. 

Samuel: Very good. Yeah. 

Sylvain: Well, that's very much what, like in CykoMetrix, as you know, we are interested in teams. So, that is really, really interesting, really interesting because we're looking for how team compositions become effective and become performers. So, it's all about using the data and measuring it. So, very interesting. I'd like to thank you for this very big package of information because there's a lot to unpack. So, I didn't ask you a lot of questions. Just kind of lets you going through the categories and really defining everything and somebody can go through this interview and go back to really understand the different parts. And I think it could be extremely helpful for an organization to perhaps go through this and I would suspect would need some expertise to actually help them because it is complex. It's a matrix of complexity, correct? 

Samuel: Yes, it is. Yes, highly technical, highly technical. 

Sylvain: Well, I think so. But that's why you have tool builders, you have consultants that are specializing, and psychologists. Including Masters and those that are working on their PhDs in psychology that are like specialists in that to kind of help companies figure that out. That's amazing, Samuel. Thank you so much for participating in this and unpacking all the details of this for us. This has been Samuel Dad.  He has a master's in Science in Psychology, a master of Managerial Psychology. And again, I want to mention again the work in this PhD in the area of Social Psychology, which is why he knows all this stuff. We can come back to them. So, one final question before we wrap, Samuel. Because I'm here in Canada and you are over there across the Atlantic in Nigeria. I don't know if you 1could just, to close, if you could tell us, if there is evidence of differences in what you were talking about and how to organize teams like homogeneous, heterogeneous, like the theories you were mentioning. Is there a difference in different parts of the world of this applicability? Or is it fairly universal no matter where you are? 

Samuel: Thank you very much for that question. Now, let me just approach that from the cultural diversity perspective. Like I said, it is a Gestalt conceptualization of global influence of culture on behavior as well as local influence of culture on behavior. That is, the work behavior, work attitudes, of people are influenced as a result of global cultures and local cultures. With that, the data necessary to be collected are our geographic data, demographic data, and associative data. Now, regardless of where you are, you have global cultures affecting behavior, as well as local culture - your local culture - affecting the workplace setting. Now, that is the reason we need to collect the geographic data, associative data - to whom are you affiliated, or associations you belong to - and all that, as well as demographic data, to be able to manage people well in the face of cultural differences. 

Now, the essence of social diversity is to include people regardless of your cultural background. Let them have a sense of belonging in the organization. So, we do not work specifically with specific cultures, but cross-culturally in terms of region, in terms of affiliations, and in terms of your demography. We don't want to specify (doting around any specific culture) that yes, this is this. Yes, we have another category that specifies that; the natural (ethnicity) data, for example, racial data. But when it comes to cultural relativism and the utility of culture in workplace, we don't want to emphasize one culture over or against another culture. Like I said, minority-majority cultures are there, and the limitations of such, but because of human nature, such breed issues of not feeling well at work. Psychologically not being (feeling) well, then loss of self-esteem. 

Sylvain: So, in other words, Samuel, if you apply the technique, let's call it like a technique, the data is going to be different depending on where you are, right? Because of different languages, different cultures, and different places. But the technique in itself carries dividends no matter what. The data's going to be a bit different, but you're going to get the same outcomes because you're matchmaking anyway, right, in a similar way, with the data that you have. So, yes, there are differences in different parts of the world. I think everybody knows this. It's not a very big mystery. But the approach is ubiquitous, it's valid anywhere. The method of data collection and use. Excellent. 

Samuel: Yeah. Well, there is a disconnect between research and practice in terms of diversity training at work. Now, like all this data, what do we use that for? Managing Diversity! In fact, in managing diversity, we have about 5 or 6 approaches to manage diversity, social differences at work. The first is the staffing, that is recruitment, selection, placement. Then, the training. We have formal mentoring. Okay? We have sociological studies that dovetail into managerial responsibilities of overseeing diversity and being accountable for diversity at work. Then lastly, we have building inclusion. Now, where we have a disconnect is in training. In some organizations, we still have diversity policy. Because in recruitment exercise, we have discovered that organizations that publish their diversity management policy attracts applicants more than organizations that do not have that. Having a good diversity philosophy in your recruitment advertisement, you are likely to get more applicants than if you do not incorporate diversity in your recruitment.

Now, when it comes to managing diversity by training, we have discovered that many organizations do not actually identify the training needs, specifically for diversity management. Training needs in terms of attitudes, in terms of behavior, in terms of let's know what's your need as a staff member. What's the gap in your life in terms of diversity? We want to know. They don't do that. We discover that what most organizations do as diversity training is just Diversity Awareness Training. 

Sylvain: Yeah. It's more about the awareness, yeah. 

Samuel: Mind-body awareness, not finding out in the first place what are the issues. Training has a cycle. There's what we call the training cycle. Training cycle begins with the corporate policy. Then, the objectives. Importantly, regardless of the style of approaching issues in your training cycle, the first step is identification of training needs in people. Many organizations do not carry this step out. They do not identify the training needs specific to diversity management. They could do that for other things, but when it comes to diversity management, they don't do that. They just go in and say, “Okay, Diversity Awareness Training for everybody.” That's what they do. They may call it something else. They could brand it as of performance-related, but they don't treat it. What they treat is just the awareness. So, that is the disconnect we have observed, through research, in terms of managing diversity through training. 

So, it's important for organizations to do identification of needs when it comes to diversity training because what obtains in one organization does not in another. Like, communication could be an issue. Diversity training targeted at communication. Diversity training targeted at conflict resolution. In diversity management of social differences at work, we have identified 3 types of conflicts that come up. Task conflict, relationship conflict, and process conflict. So, these are various conflicts that are found in teams. We have 4 types of team. Action team, Production team, Advice team, and Project team. So regardless of the kind of team you have, what is the nature of the conflicts they're having? Is it relationship-related? Is it process-related? If it is task- related, is it of diversity? So, all these things are what they should actually find out before launching its diversity training. 

Sylvain: That's right. Now, I'd like to close this discussion, Samuel, because we're getting in into completely different area about the training and all that stuff. So, we can carry that into another conversation at another time. I'd like to thank you again for your time and participating in this interview. Now, people know a lot about your expertise, and your knowledge and encouraged that our viewers to contact Samuel, if you need some further insights and advice on diversity training, perhaps? But, you know, given the context of this talk about how to use diversity data to improve their organizational performance of the company. That's really the first thing we talked about. So, thanks a lot for being here, Samuel. 

Samuel: It is a pleasure.

About Samuel Dada - www.linkedin.com/in/samuel-dada-b59a1b141

Samuel Dada is a consummate professional with over 10 years experience in various organizations as an administrator with a Master of Science (MSc) in Personality & Social Psychology as well as Master of Managerial Psychology (MMP), prospecting his PhD in Personality & Social Psychology. He is a Member of Institute of Registered Administrative Managers of Nigeria (M.inst.AM).

About CykoMetrix - www.CykoMetrix.com

CykoMetrix is a leading edge combinatorial psychometric and human data analytics company that brings the employee assessment industry to the cloud, with instant assessments, in-depth analysis, trait measurements, and team-based reporting features that simplify informed decision-making around recruiting, training, and managing today’s modern workplace.

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Sylvain Rochon

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