A CULTURAL INTELLIGENCE TECHNOLOGY MODEL

Abstract

The cross-cultural management literature has yet to closely couple cultural intelligence (CQ) and digital/AI technology. This pioneering study fills this void with a proposed CQ technology model that parallels the seminal CQ four factors model. Currently, digital/AI can simulate human multicultural traditions and tendencies. Already, global enterprises have proven the strategic and societal advantages of cultural intelligence (CQ) for optimizing ethnically diverse staff and stakeholders. Unfortunately, these leading multicultural technology and cross-cultural management capabilities lack the comparable scholarly research to encode CQ principles into Digital/AI media platforms. Thus, this study imparts conceptual guidance for programming ethnic cultural identity into global enterprise technology using a CQ four factors model algorithm. A cross-discipline critical literature survey synthesizes research on digital/AI media and ethnicity/race designs to inform CQ technology dimensions, which parallel the seminal CQ four factors. Likewise, the emergence of versatile artificial intelligence (AI) and posthuman technology is addressed by an artificial ethnicity (AE) architecture hub for the proposed model. Concluding comments offer a synopsis of this study's contributions to cross-cultural management, two instructive case scenarios, and critical scholarly inquiry considerations.

Full text

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A CULTURAL INTELLIGENCE TECHNOLOGY MODEL
E. Vince Carter, Ph.D.*
1 School of Business & Public Administration, California State University, Bakersfield (CSUB),
Bakersfield, CA, USA
ABSTRACT
The cross-cultural management literature has yet to closely couple cultural intelligence (CQ) and
digital/AI technology. This pioneering study fills this void with a proposed CQ technology
model that parallels the seminal CQ four factors model. Currently, digital/AI can simulate human
multicultural traditions and tendencies. Already, global enterprises have proven the strategic and
societal advantages of cultural intelligence (CQ) for optimizing ethnically diverse staff and
stakeholders. Unfortunately, these leading multicultural technology and cross-cultural
management capabilities lack the comparable scholarly research to encode CQ principles into
Digital/AI media platforms. Thus, this study imparts conceptual guidance for programming
ethnic cultural identity into global enterprise technology using a CQ four factors model
algorithm. A cross-discipline critical literature survey synthesizes research on digital/AI media
and ethnicity/race designs to inform CQ technology dimensions, which parallel the seminal CQ
four factors. Likewise, the emergence of versatile artificial intelligence (AI) and posthuman
technology is addressed by an artificial ethnicity (AE) architecture hub for the proposed model.
Concluding comments offer a synopsis of this study’s contributions to cross-cultural
management, two instructive case scenarios, and critical scholarly inquiry considerations.
Key Words: artificial intelligence/life, cross-cultural management, culture, cultural intelligence,
digital/AI media, diversity, ethnicity, multicultural, posthuman, strategy, technology design

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INTRODUCTION: CULTURAL INTELLIGENCE MATTERS
“I want us to understand that, on the other hand, there’s a new culture taking
place about which we have to be very careful; I call it the Internet culture. This is
a marvelous technology; this is a liberating technology. … It’s creating new
generations of the illiterate, … to despise what I call the real meaningful culture
that improves the mind of humanity, expands our horizons, offers numerous
alternatives or interpretations of phenomena, ...” (Wole Soyinka, 2023)
Essential is the word used to describe cultural intelligence (CQ) by modern global
enterprises. For modern cross cultural managers CQ fills a pervasive void of employee distrust,
market disruptions, and leadership disconnection (Livermore, et al., 2022). Cross-cultural
management scholars have researched CQ aptitude and attributes, while global enterprise
strategists recognize CQ optimizing human capital and market growth. This study builds on these
antecedents to inform cross-cultural management why CQ matters.
Most global professionals know why CQ matters and strive to master the CQ four factors
model (Ang & Van Dyne, 2005; Earley & Ang, 2003; Livermore, 2009; Livermore, et al., 2020;
Van Dyne, et al., 2012; Yari, et al., 2020). Everyone knows that Digital/AI media are essential
to global enterprise planning, yet cross-cultural management scholars have not fully fashioned
the CQ model to digital technology. Thus, this study’s CQ technology model improves the ethnic
cultural compatibility of digital/AI media in global cross-cultural enterprises.
The argument for CQ technology proceeds by first reviewing cross-cultural management
technology studies to highlight this study’s contribution to fill a literature void. Second, the core
ethnic cultural properties are defined as different from race classifications. Third, an examination
of CQ technology model dimensions weaves an integrating thread through the literature on
digital/AI media innovations, ethnic cultural representation, and future posthuman expectations.
This account culminates by prefiguring the evolution of an Artificial Ethnicity posthuman
architecture. Finally, concluding thoughts summarize the conceptual study’s main themes and
pose critical inquiry topics to discern future scholarly and strategic implications.
MODELING CQ TECHNOLOGY SYSTEMS
Typically, the cultural orientation of technology is taken for granted because it functions as a
bodily/sensory extension, like eyeglasses or earbuds or clothes. We encounter ubiquitous global
cross-cultural technology platforms but rarely comprehend the cultural experience of technology
engagement. Thus, cross-cultural management scholars rarely explore the convergence of
cultural intelligence (CQ) and digital/AI media technology. This study fills that void by tracing
the expanding capability of digital, AI, and posthuman technology to simulate human
multicultural ethnic identities. However, in other fields, several computer interface models have

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applied Hofstede’s (1980, 2001) seminal cross-cultural values framework as well as other ethnic
and indigenous culture constructs. This CQ technology modeling discussion incorporates those
findings along with visionary techno-culture and cyberculture insights to curate concepts for
guiding global cross-cultural management scholars and strategists. This focal research problem is
further specified by scrutinizing the sparse literature on CQ management and technology. In turn,
the proposed CQ technology model is advanced as a hypothetical research problem resolution,
with a brief description and diagram of its four dimensions based on Ang and Van Dyne’s (2005)
seminal four factors model.
CQ Four Factors Model & Digital Technology Systems
Within the cross-cultural management literature, Ang and Van Dyne’s (2005) four factors
model unified multiple research streams (Ang, et al., 2004; Ang & Van Dyne, 2005; Ang, et al.,
2006; Ang, et al., 2007; Bucher, 2008; Earley & Ang, 2003; Thomas & Inkson, 2003). Shortly
thereafter, the CQ model was analyzed for individual characteristic relationships using the Big 5
personality traits (Ang, et al., 2006), which opened the door for other personal traits such as
race/ethnicity and technology user interface preferences. In turn, Koh, et al. (2009, 2010) applied
Ang and Van Dyne’s (2005) four-factor model to the burgeoning global IT workforce challenges
of culture and computer compatibility. A comprehensive study was conducted for 29 countries
consisting of most major race/ethnic groups, across all global regions outside of the United
States, and involving 9 of the leading global technology enterprises. The aim was to assess the
role of CQ in technology cross-cultural IT worker performance, collaboration, and conflict
resolution. Stressing technology gaps in cross-cultural and CQ research, as well as the eminent
role of CQ in optimizing IT worker capabilities, the following views are asserted:
“The current state of cross-cultural research in management and IT leaves an
important gap in our understanding of what IT professionals need to function
effectively in this global economy. To address this gap, we introduce and propose
that cultural intelligence (CQ) is an important individual capability that IT
professionals need to effectively overcome these cross-cultural challenges. …
Additionally, the nature of the IT task involved, and the nature of the IT system
being designed may moderate the effects of CQ on global team effectiveness.
(Koh, et al., 2010, pp. 828, 840, 841)
Those insights crystalize the agenda for bridging the CQ and technology research gap.
1. Cultivate human CQ to improve IT tasks and cross-cultural coordination among
workers/managers, as well as to optimize compatibility between digital/AI media and
global enterprise users/leaders

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2. Customize CQ designs for IT, as well as digital/AI media CQ, to improve IT tasks and
cross-cultural coordination among workers/managers, as well as to optimize
compatibility between digital/AI media and global enterprise users/leaders.
The first approach has been emphasized in the few CQ technology studies done to date.
Unfortunately, that large scale aggregate IT worker research prevents personalized CQ for ethnic
identity. It also neglects the potential for CQ technology designs that fit human/user ethnic
cultural identity. This study’s CQ technology model pursues the second approach of
customizing CQt digital/AI media to fill the literature void and address individual personal
ethnic identity aspects of human/technology compatibility into the posthuman future.
Setiawan, et al.’s (2018) survey research performed a path analysis of Ang and Van Dyne’s
(2005) four-factors – metacognition (strategy), cognition (knowledge), motivation, and behavior
(interpersonal/interface). All four CQ factors directly impact IT worker performance. But, the
highest R-square correlation coefficients are associated with metacognition of cultural
congruence with technology designs competency and behavior for improved technology affinity
through compatible interpersonal dialogues and computer interface designs.
Other recent management literature studies examine the impact of CQ on aggregate
organizational capabilities and global workforce coordination, utilizing virtual teams and social
media (Davidaviciene & Al Majzoub, 2022; Hazzam, et al., 2023; Richter, et al., 2021). Despite
updating the types of technology, cross-cultural management scholars remain stuck in an
aggregate organizational mindset for researching human/technology cultural compatibility.
Probing deeper into the human ethnic identities, this study develops conceptual models for
tailoring technology designs to those personal CQ orientations that shape individual and
collective experiences. Relying directly on Ang and Van Dyne’s (2005) foundational four
factors, a parallel CQ technology model is devised to customize the cultural orientation of future
digital/AI design dimensions. The proposed generic model designates the four factors of
technology design according to the dimensions of:
1. Meta-theory CQ milieu context
2. Cognitive CQ design coding
3. Behavioral CQ interface connections
4. Motivational CQ multisensory content.
These generic CQ technology model dimensions can obviously be elaborated using a variety
of available technology typologies in the multidisciplinary scholarly literature, as well as using
conventions adopted by professional communities of practice. The CQ technology model
introduced here is merely a design template for envisioning global cross-cultural enterprise
systems in the future era of digital transformation. (See Figure 1)

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Figure 1: CQ Technology Model.
(Source: Original Digital/AI Media Diagram from 4-Factor CQ Model; Ang & Van Dyne, 2005)
DEFINING ETHNICITY DISTINCT FROM RACE
Tailoring technology designs to personal user profiles and preferences is commonplace.
However, cultural design orientations present authenticity nuances, with ethnic cultural identity
raising concerns regarding stereotypes, correctness, and appropriation. The perplexities of race
and ethnicity have persisted “across time, space, and discipline” (Coates, 2004). Glazer’s classic
“ethnic dilemmas” addresses these cultural quandaries along with Zelinsky’s (2001) later
“enigma of ethnicity.” Therefore, precision and credibility are essential for designing technology
to encode cultural tendencies (Norman, 2013).
In lieu of this cultural design conundrum for domestic multicultural and global cross-cultural
situations, a precise ethnic identity definition assures a correct grasp with clear guidance. A
correct grasp of ethnic cultural identity is best achieved by contrasting ethnicity with race. For
clear guidance, this definition is conveyed in logical layers. First, the terminology origin roots
establish the meaning of ethnicity versus race. Second, epistemology knowledge branches are
examined to deduce the methods by which definitions are derived. Third, the objective factors

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that distinguish ethnicity from race are specified. These combined insights from antecedent
knowledge branches and objective factors are inferred for the ethnic identity orientations
encoded into CQ technology model dimensions.
The term ‘race’ and racial “identity’ are primarily based on visible physical features and
observable social behaviors in particular contemporary societies, without a standard basis of
categorization across societies/nations or over the span of historical time.
“Race, the idea that the human species is divided into distinct groups on the basis
of inherited physical and behavioral differences. … What most definitions have in
common is an attempt to categorize peoples primarily by their physical
differences.” (Smedley, et al., 2025)
The term ‘ethnic’ is traced to the Greek word ‘ethnos’ for nation or tribe, derived from the
Proto-Indo-European (PIE) word “swedh-no” meaning “people of one’s own kind” – ancestrally
and culturally. Both the Greek “ethnos” for nation and the PIE “swedh-no” for peoples’ kind
converge in the Latin root “natio” as birth origin, breed, species, tribe. Also, explained by the
older Latin root “gnasci” from the PIE root “gene,” and extended to generations of ethnic tribes.
Thus, the bio-genetic, ancestral, historical, and cultural intent of ethnicity is established in the
earliest records of human civilization.
Contemporary scholars further the etymological evidence of ‘ethnic identity’ pertaining to an
individual’s ancestral heritage and place of origin (Baumann, 2004). Linton (1936, 1955)
articulated this distinctive ethnic identity as a cultural “modal personality” observable in
consistent biological, social, and behavioral tendencies – unlike cultureless racial classifications.
Nash (1989) regards the "building blocks of ethnicity" (p. 5) as the body (genetic biology), a
language (shared symbolic meanings), and a cultural history (tenets and traditions).
Even in instances of mass societal assimilation (e.g., melting pot) or attempted elimination of
ethnic memory (e.g., slavery), self-ascribed ethnic cultural identity is retained in a manner that
can be validated by objective genetic and historical accounts. This ethnic stickiness is owed to its
primal origin, genetic psych-cultural markers, as well as the relational properties. In this sense,
ethnicity has an inherent auto-affinity and allo-aversion which instill a preservation agency.
Thus, ethnicity is anthropology’s main cultural unit of analysis (e.g., primitive, tribe, clan,
ethnicity) and supersedes variations in gender and age roles, as well the so-called ‘10 human
universals,’ as well as most of Brown’s (2004, 2000, 1991) meticulous index.
Cultural anthropology is an epistemological source for understanding ethnicity. Ethnicity can
be regarded as a bridge or channel between nature and culture. So, environmental ecology
generates ethnic phenotypes in combination with genetics and evolution, while also generating
ethnic cultures in combination with genetics and history. This primordial anthropological
synergy between nature and culture is addressed by the seminal perceptual philosophy of
“ecophenomenology” of Merleau Ponty (1962). Plainly stated, the ethnic cultural context of
anthropology represents and reflects the ethnic homeland natural ecological context. Humans

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emerged from nature and are intrinsically the “embodiment” of nature in themselves. Nature’s
ecosystem imbues cognitive knowing and primal memory through body sensing, through what
Merleau Ponty (1962) as well as Lakoff and Johnson (1999) call “philosophy of the flesh” –
cultural mind embodying corporeal body formed from earth soil.
“The nature in us must have some relation to Nature outside of us; moreover,
Nature outside of us must be unveiled to us by the Nature that we are.”
(Merleau-Ponty, 2003, p. 206)
By translating life beings into culture meanings, ethnicity mediates earth ecology and human
anthropology. Herskovits (1928, 1941) iconic “Africanisms” research demonstrates ethnicity’s
holistic distinctiveness, historical homeland continuity, allowing for free and fluid change within
an ancestral family tree evolution. His methods are equally applicable to Arab, Asian, European,
Latin, Pacific Islanders, and other ethnic cultural traditions. Therefore, coding CQ technology
with ethnic identity orientation harnesses ecophenomenology awareness, ancestral
biopsychology, historical traditions, and contemporary cultural preferences.
As Hall (1966, 1959) teaches, “culture is humankind’s primary extension.” Like ethnicity,
ecophenomenology embeds cultural teleology purpose/reasons and imbues cultural ontology
being/doing (Brown & Toadvine, 2003). Ethnic ‘homelands’ chart geo-coordinates of planet
location, climate, flora, fauna, and geology. Even human symbol systems and ethnic language
meanings, tacit knowledge, and intergenerational semantic metaphors are rooted in homeland
ecosphere (Abram, 1996; Lakoff & Johnson, 1980; Polanyi, 1966).
These particular ‘homeland’ ecology traits directly inform anthropology’s cultural context of
value/time orientations, symbolism, rituals, aesthetics, and crafts. In fact, besides a near complete
mapping of bio-physical DNA coding (Choudhuri, 2003), scientists have discovered human
genetic markers called “geolocation genotypes” that are globally homed on specific territories
(Arora and Dash, 2003) – like GPS signals. Likewise, advances with Genome Wide Association
(GWA) scans have discovered that ethnic traits/tags are genetically encoded as complex bio-
physical predictors (Yuan, et al., 2019; Jorgenson, et al. 2005), as well as accurate maps of
psycho-behavioral states within cognition (Munafò, 2011; Gilger, 2000). The folk idiom of
‘ethnicity’ abridges ‘earth necessity,’ inferring nature’s embodiment in human ethnic culture.
Now, the definition layers are completed by specifying objective factors that distinguish
ethnicity from race with observable findings. Furthermore, this study adopts an anthropological
“universal ethnicity” approach (Carter, 2009a, 2009b), which includes all global human ancestral
lineages and cultural traditions. By contrast, a common sociological approach for studying
ethnicity only includes non-European so-called people of color (POC). Importantly, these
ancestral lineages, cultural traditions, and homeland origins are particular to diverse human tribal
identities within larger continental territories (e.g., Africa, Americas, Arabia/Middle East, Asia,
Australia, Europe, New Zealand, Pacific Islands, etc.). As a convenience for contemporary
identification, ethnicity is often traced to larger continental lands like Africa and Americas (e.g.,

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North, Central, South). Like European and Asian tribal nationalities, the authentic ethnic
identities are traced to the heterogeneous tribal origins within shared homogeneous territories –
despite cultural pattern similarities.
Barth (1969) ethnicity criteria distill Kluckhohn’s (1949) earlier twelve cultural attributes.
1. is largely biologically self-perpetuating
2. shares fundamental cultural values, realized in overt unity in cultural forms
3. makes up a field of communication and interaction
4. has a membership which identifies itself, and is identified by others, as constituting a
category distinguishable from other categories of the same order.”
(Barth, 1969, p. 10-11)
Similarly, Hutchinson and Smith’s (1996, pp. 6-7) distill “six main features” of ethnicity.
1. Common proper name to identify and express the ‘essence’ of the ethnic community
2. Myth of common ancestry origin in time/place to form ‘super-family’ kinship
3. Shared historical memories of common past, heroes, events, for commemoration
4. Elements of common culture practices (e.g., worship, customs, language, etc.)
5. Homeland links as symbolic attachment to ancestral land with diaspora peoples
6. Sense of solidarity on the part of ethnic community contingent (e.g., ties, bonds)
This study’s ten overlapping ethnicity criteria are listed below in order of causal priority and
shown in Table 1 contrasted with race for a more acute interpretation.
1. Shared name for ethnic community affirmation (signify self – ‘identity claiming’)
2. Geo-Ecology (ethnic homeland locations and diasporas – ‘place/space’)
3. Primordial (pre-historical roots of human ecological habitats – ‘origin story’)
4. Genetics (bio-physical & psycho-behavioral ethnicity markers – ‘being/knowing’)
5. Historical Ancestry (chronology lineage, heritage, progression – ‘time/trajectory’)
6. Cultural Tradition (beliefs, values, symbols, customs – ‘context/purpose’)
7. Language (oral, written, nonverbal symbol systems – ‘semantics/meanings’)
8. Subjective/Emic (within, bottom-up, internalized, bonds – ‘kinship relevance’)
9. Individual identity continuum of importance and intensity (‘lifecycle development’)
10. Intermixing combinations retain multiple equally prized identities (‘ethnic equity’)

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Table 1: Factors Contrasting Ethnicity with Race
Ethnicity
Race
1. Shared name for ethnic community affirmation
(signify self – identity claim)
1. Assigned name for societal assimilation (lacks self-
significance – identity cue)
2. Geo-Ecology (ethnic homeland locations/diasporas
– place/space)
2. Non-Geo-Ecology (lacks location/history source –
situational)
3. Primordial (pre-historical roots of human ecological
habitats – origin story)
3. Non-Primordial (lacks pre-historical roots of human
ecological habitats – organized story)
4. Genetics (complex bio-physical & psycho-
behavioral ethnic markers – being/knowing/doing)
4. Genet (superficial bio-physical visible race indicators –
appearing/associating/acting)
5. Historical Ancestry (full chronological lineage of
heritage and progression – time/trajectory)
5. Generational Ancestry (limited parent/grandparent
chronology – interval/immediacy)
6. Cultural Tradition (beliefs, values, symbols,
customs, aesthetics, propensity – context/purpose)
6. Non-Cultural Tradition (contemporary values, modern
aesthetics, social propensities – content/presence)
7. Language (symbol systems for oral, written,
nonverbal communication – semantics/meanings)
7. Idioms (unstructured colloquy, e.g., Ebonics, Spanglish,
Anime, Hindi Slang, etc. – synthetic/messaging)
8. Subjective/Emic (within, bottom-up, internalized, tacit
affinity bonds/meanings – kinship relevance)
8. Objective/Etic (outside, top-down, imposed, explicit
affiliate bonds/meanings – kith/ relatives)
9. Individual identity continuum of importance and
intensity (lifecycle development)
9. Collective identity category without individual
agency (life determinism)
10. Intermixing combinations retain multiple equally
prized identities (ethnic equity)
10. Intermixing combinations lose category belonging
as pariah identities (racial inequity)
Ethnic Cultural Orientation Design Primer
A synopsis of ethnic consumer strategies in marketing, and cultural models in
communication/media studies, as well as personalized digital design perspectives, provide a
constructive primer for ethnically oriented CQ/CI technology.
Ethnicity is a pivotal variable used by marketing scholars to tailor cross-cultural and
multicultural strategies for all offerings including technology (Burton, 2000). As a determinant
of consumer values, customs, and motivations ethnicity is a specific and sustained attribute that
is indispensable to marketing practice (Webster, 1991). Venkatesh (1995) advanced
“ethnoconsumerism” to bring the cultural context to consumer market preferences. Importantly,
Venkatesh validates the distinctiveness and stickiness of ethnic identity (Barth 1969; Kluckhohn,
1949; Linton, 1945, 1955), which becomes more vivid and visceral in comparison to other ethnic
groups (Roosens, 1989; Barth, 1969). Ethnoconsumerism research has been done for African,
Asian, European, and Latin American markets. Venkatesh, et al. (2013) posit an

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ethnoconsumerism “cultural design model” using Geertz’s (1973) “thick descriptions,”
suggesting the viability of an ethnic CQ/CI model for digital/AI technology.
“Ethnomarketing” extends this viable cultural coding to facilitate the “social construction” of
ethnic community stakeholders, institutions, networks, and symbolic cultural capital (Penaloza
and Venkatesh, 2006). Due to the cultural “thickness” of socially constructed bonds, as a “web of
symbols” (Geertz, 1973), agency is accrued by ethnic (not racial) identity and meanings.
Certainly, the micro CQ/CI technology model’s four-factor dimensions, user relationships, and
societal environments are all socially constructed ethnic cultural constellations. Within the social
woven webs like market interactions and digital/virtual engagement, ethnic identity becomes
more dynamic and diverse. Ancestral kindred roots remain anchored, while simultaneously
ethnic culture becomes accessible adaptable as commercial commodities and postmodern media
content (Deshpande, et al., 1986; Firat, 1995). These blurred and bridged ethnic boundaries avail
“culture swapping” and “border crossers” capable of both retaining ethnic identity origins and
redefining ethnic identification opportunities (Askegaard, et al., 2005; Bouchet, 1995; Oswald,
1999; Penaloza, 1994). Future digital transformation advances and post-human acclimation will
benefit from ethnically oriented designs of CQ/CI technology, which will enable the familiarity
of enduring ethnic identity roots as well as the freedom for ethnic cultural exploration.
Marketing’s consumer culture research literature has prefigured the “posthuman”
convergence of human culture and future digital/AI beings (Giesler, 2004), including consuming
cyborgs, virtual commodities, humanness epistemologies, and consumer/technology bonds.
Importantly, the human cultural sphere is foreseen as central to digital transformation, AI, and
posthuman reality. Asserting that along with market forces and technology functions, “cultural
frameworks that orient how consumers interact with commodities … to alter our notions of what
means to be human” (Giesler, 2004, p. 400). The human cultural imperative is epitomized by
Simon’s (1982/1996) prescient Sciences of the Artificial.
“Simon did envision the possibility of the merging of the human and the machine.
He called this artificial intelligence. However, he was careful to note that what he
is after is not to turn the human into machine but make machines more human.”
(Giesler, 2004, p. 400)
This study follows Simon’s footsteps to conceptualize digital/AI media that “make machines
more human” by designing CQ/CI technology oriented towards ethnic cultural identity.
More recently, ethnic marketing linkages with social development has been mapped
(Peñaloza, 2018), which demonstrates collective societal advancement from availing ethnic
community choices. Separately, Pires and Stanton (2019) found that marketing to ethnic values
builds within group “homophily” resonance, as well as “panethnicity” across national borders to
infuse deep “cultural affinity” throughout the global diasporas of African, Asian, European, Indo,
Latin American, and Native American consumers. This cultural coding is especially enduring for

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value expressive offerings/messages with symbolic ethnic identity orientations, traditions, and
designs (Ogden, 2004; Rexha and Kingshott, 2001).
Research in the field of communication and media studies complements marketing’s ethnic
identity cultural coding with seminal concepts and generalizable exemplars. Specifically, the
“Circuit of Culture” (Du Gay, et al., 1997; Hall, 1997) has become a fundamental paradigm for
communication/media and cultural studies scholars, as well as an interdisciplinary framework. It
identifies five cardinal sites as sources of “culture production” and distribution, which form a
“circuit” of interconnected processes for “culture dissemination.”
1. Identity Codes (sites/sources)
2. Representation Codes (sites/sources)
3. Consumption Codes (sites/sources)
4. Production Codes (sites/sources)
5. Regulation Codes (sites/sources).
The circuit dimensions enable communication/media scholars to introduce a postmodernism
subjectively constructed approach towards defining the complexities of culture, in contrast to the
modernism objectively deterministic definition of culture that has persisted in Western
civilization. In particular, for matters of cultural identity like ethnicity, the traditional definition
is a fixed hierarchical view based on European superiority. Thus, non-Europeans are classified
using an etic outsider deficiencies versus emic insider appreciation.
By focusing the subjective circuit of culture on identity representation and meaning
interpretation systems, Hall (1997) brings the “social constructionist” method and “signifying
systems” to understand how ethnic identity codes are derived from shared ethnic cultural
meanings. Likewise, ethnic cultural representation codes are delivered by CQ/CI technology
media and multisensory language symbols. Further, practical everyday production and
consumption codes for ethnic cultural merchandise and messages are channeled through
digital/AI media as market exchanges. Then, regulation codes authenticate and adapt virtual
ethnic cultural traditions, norms, and values for digital/AI media design contexts, similar to
conventional social institutions and policies.
Elsewhere, Hall (1990, 1992) applies these social constructionist signifying systems to
analyze ethnic identity under older modernism criteria (e.g., Cartesian order) and newer
postmodernism conditions (e.g., constructivism). Ultimately, the circuit of culture and Hall’s
(1980) other contributions, enable greater discourse regarding ethnic identity coding of micro
CQ/CI technology from the perspective of participants’ cultural orientations, historical traditions,
shared meanings, and collective belonging.
Relevant digital/AI media parallels can be drawn between the Circuit of Culture five
processes and the CQ/CI four-factor model. Signifying systems for representation through
symbols as language, as well as media networks/content, are described from a cultural identity
perspective as meaning making and giving. This resembles the role of enterprise sensemaking

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and sense-giving described for external environment mapping and navigation. Similarly, the
meaning making/giving process of signifying system representation codes in the Circuit of
Culture, can be viewed as mapping and navigating the socio-virtual environment to attune ethnic
cultural identity meanings. Recognizing these corresponding routes and roles, the micro CQ/CI
technology model four dimensions are approximately aligned with the Circuit of Culture five
processes as shown below in Table 2.
Table 2: Aligning CQ Tech Model with Circuit of Culture
CQ Tech Model Dimensions
(Ethnic Cultural Identity Orientation)
Circuit of Culture Processes five
(Ethnic Cultural Identity Orientation)
Meta-theory CQ milieu context
Regulation Codes (sites/sources).
Cognitive CQ design coding
Identity Codes (sites/sources)
Behavioral CQ interface connections
Consumption Codes (sites/sources)
Production Codes (sites/sources)
Motivational CQ multisensory content
Representation Codes (sites/sources)
DECIPHERING CQ TECH MODEL DIMENSIONS
Ultimately, this study’s proposed CQ technology model coincides with ethnicity’s distinction
as a cultural meta-context, cognitive identity code, behaviorally shared connection, and
motivational aesthetic content.
Meta-Theory CQ Technology Milieu Context
Setiawan, et al.’s (2018) empirical survey path analysis findings cited earlier confirm the
importance of the CQ meta-theory factor -- referred to as a mental “strategy.” It recognizes that
all technology fits into the cognitive schema framing of physical space milieu within which it is
used. Even within an immersive 3D digital media mode, the physical surroundings remains the
paramount milieu for the technology interaction experience. Explicit cultural perception and
implicit cultural consciousness is shaped by the situational presence. Using a laptop at work will
have a different cultural ambiance that doing the same task at home or at a coffee shop.
Dissecting CQ technology meta-theory contexts reveals conducive human conditions
between digital/AI media spaces and physical real world places. While meta-theory context
shapes technology’s cultural affinity, it also shares real world ambiance. These meta-theory
conditions calibrate both human/technology harmony and human/terrain halo effects. Similarly,
De Vrie’s (2010) “ambient intelligence” attunes technology use to natural IOT context. These
contextual conditions make meta-theory a milieu wherein individual, relational, social, and

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material being is formed, akin to Nagy and Koles’ (2014) “conceptual model of virtual identity
in virtual worlds.” As a contextual milieu that combines real world and digital/virtual
experiences of self, situation, and surroundings, meta-theory can be construed with Harraway’s
(1989) “semiotic square” (e.g., Greimas square, 1983). Like Harraway’s cyberspace/cyborg
milieu, the meta-theory dimension orchestrates a hybrid cultural domain comprised of semiotic
signs and transformed self-identities. So, meta-theory synthesizes dual narratives to navigate the
ethnic cultural meanings signified in parallel real and digital experiences. Although meta-theory
forms a macro real/artificial perspective milieu, it also functions as a micro human/technology
process organism – coordinating the other three CQ tech model dimensions. This cultural
symbiosis between macro context milieu and micro calibrated organisms can be expressed as a
“meta-narrative” (Margolin, 1995). It balances digital/AI media experiences with socio-
ecological surroundings to find a baseline homeostasis for familiar pairings of “natural” places
with “artificial” platforms.
"’A meta-narrative of spirituality can help designers resist techno rhetoric that
sanctions the continuous colonization of the natural.’ He sees his work as an
attempt to manage the borders between the artificial and the natural.”
(Margolin, 1995, pp. 354-355, described by Sack, 1997, pp. 55-56.)
Bourdieu (1977, 1990, 2002) is particularly instructive for configuring the meta-theory
dimension’s dialectic between macro real/artificial milieu and micro human/technology
organism dialectic. As a milieu, meat-theory operates as a “habitus” generating cultural, social,
and symbolic capital fields. Like the habitus, a meta-theory milieu conditions familiar human
experience by imparting subjective cultural traditions and heuristic tendencies into objective
environmental settings and technology situations. Different ethnic cultures in the same spaces
interacting with the same symbolic technology artifacts, will experience different internal ethnic
cultural meanings with external terrain place and technology platform encounters. This habitus
transference “practice” explains how the meta-theory context imparts unique internal subjective
meanings and methods into the external objective properties of the other three CQ technology
dimensions, to culturally tailor human intelligence experiences.
“One of the fundamental effects of the orchestration of habitus is the production
of a commonsense world endowed with the objectivity secured by consensus on
the meaning (sens) of practices and the world, in other words, the harmonization
of agents’ experiences and the continuous reinforcement that each of them
receives from the expression, individual or collective …” (Bourdieu, 1977, p. 80)
Just as the “habitus” (Bourdieu, 2002) construes macro context milieu, Bourdieu’s (1977)
corollary concept of “doxa” corresponds to the meta-theory dimension’s micro calibration
organism, as a cultural narrative of cognitive beliefs, behavioral interactions, and motivational

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affect. For the CQ technology model, “doxa” is a meta-theory compass for coordinating design
coding, interface congruity, and content curating.
“Doxa refers to the learned, fundamental, deep-founded, unconscious beliefs, and
values, taken as self-evident universals, that inform an agent's actions and
thoughts within a particular field. … A doxic situation may be thought of as a
situation characterized by a harmony between the objective, external structures
and the 'subjective', internal structures of the habitus. In the doxic state, the social
world is perceived as natural, taken-for-granted and even commonsensical.”
(Wikipedia.org, 2024, “Pierre Bourdieu; Habitus and Doxa”)
Selected digital technology research highlights the cultural meta-theory context.
Penley and Ross’ (1991) seminal “technoculture” highlights the transition from
conventional static reality and identity to relative, dynamic, and malleable electronic
media experiences. Similarly, the CQ technology meta-theory dimension embeds
malleable ethnic cultural experiences for merging physical and digital milieus. In
Cyberspace, Benedikt (1991) elaborates an emerging computer-mediated realm with
reimagined spatial geography, situated place culture, temporal episode experience, and
human body/self-identity. Importantly for this study’s research objective, Escobar (1994)
introduces “cyberculture” meta-theory context as an anthropological reality with
structural implications for human experience – not just semantic intellectual surmising.
Bell’s (2001) later take on “cyberculture” and Srinivasan’s (2004) “knowledge architecture”
techniques emphasize “cultural narratives” as essential for representing the contextual nuance
and novel character of human ethnic identity through symbolic digital/AI media artifacts and
applications. Buccitelli (2017) has more recently addressed the cohering role of cultural
narratives, akin to meta-theory congruence among all four CQ technology model dimensions.
Most recently, “digital anthropology” (Geismor & Knox, 2021; Horst & Miller, 2012) posits a
cultural normative that weds anthropology with technology, similar to this study’s meta-theory
coupling of human real world culture with digital/AI media contexts.
“We shall argue that it is this drive to the [cultural] normative that makes attempts
to understand the impact of the digital in the absence of anthropology unviable.
As many of the chapters in this volume will demonstrate, the digital, as all
material culture, is more than a substrate; it is becoming a constitutive part of
what makes us human. … Not only are we just as human within the digital world,
the digital also provides new [design] opportunities for anthropology to help us
understand what it means to be human.” (Horst & Miller, 2021, p. 4)

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Cognitive CQ Technology Design Coding
The cognitive CQ technology design coding addresses behind the scenes predetermined
hardware form properties, software function programming, vendor platform protocols, and
network connection parameters. In essence the technology logic and DNA. Just as human
cultural identity perceptions are produced by hidden brain wiring and ancestral genetics,
digital/AI media have design logics with cultural orientations. In essence, these technology DNA
patterns can generate ethnic self-identity. Extending Michel Foucault’s (1988) classic
philosophy, widely cited by technology researchers, the cognitive CQ design dimension codes
ethnic “technologies of the self.” Digital/AI-media have advanced to encompass posthuman
artificial life, thereby encompassing each of Foucault’s technology types.
“As a context, we must understand that there are four major types of these
‘technologies,’ each a matrix of practical reason: (I) technologies of production,
which permit us to produce, transform, or manipulate things; (2) technologies of
sign systems, which permit us to use signs, meanings, symbols, or signification;
(3) technologies of power, which determine the conduct of individuals and submit
them to certain ends or domination, an objectivizing of the subject; (4)
technologies of the self, which permit individuals to effect by their own means or
with the help of others a certain number of operations on their own bodies and
souls, thoughts, conduct, and way of being, so as to transform themselves in order
to attain a certain state of happiness, purity, wisdom, perfection, or immortality.
…The Delphic principle [“know yourself” gnothi sauton] was not an abstract one
concerning life; it was technical advice, a rule to be observed for the consultation
of the oracle.” (Foucault, 1988, p. 18)
Entering an era where the Delphi oracle is a digital/AI agent, knowing thyself to optimize
identity and interaction is logically furthered by coding ethnic CQ technology designs. Several
contemporary digital identity scholars posit congruent cultural perspectives. Luppicini’s (2013)
Technoself repurposes Foucault’s “technologies of the self” to research the oracular power of
digital/AI media to amplify cognitive human minds. Collectively, the digital/AI media types
canvassed by Technoself Studies (TSS) span Foucault’s (1988) four technology types –
production, sign systems, power/culture control, and self/identity agency. More recently,
Shibuya’s (2020) “digital transformation” research on “identity in the age of artificial
intelligence” also accounts for digital/AI media spanning Foucault’s (1988) four technology
types. Particular attention is paid to identity for discerning digital transformation’s advancement
from human to artificial and hybrid. The multiple identity modes addressed help to portray
ethnicity as cultural values, individual self, social connections, and quantified self.
Besides aligning human identity with digital transformations, the cognitive CQ technology
dimension intentionally designs ethnic cultural codes. An established research stream validates

16
the relevance of cultural and identity “human factors” in digital technology designs, using a
compatibility scale of positively beneficial versus negatively biased. In addition to ethnicity/race
characteristics (Buolamwini, 2023; Harrell, 2007, 2008, 2010), digital cultural and identity
designs have been coded for gender (Haraway, 1991; Turkle, 1984, 1995), global cross-cultural
values (Marcus, 2002; Marcus & Gould, 2012), and age/generation (Donohue, 2016, 2024;
Taipale, et al., 2017). Presently, the cultural orientation of digital design is largely pre-coded.
Yet, customized ethnic cultural identity orientations will be quite capable in the future.
More than any other information technology scholar to date, Harrell has raised awareness of
the ethnic cultural coding of digital technology designs. In particular, he justifies the dialectic of
a hybrid hyper-reality helix for formulating digital ethnicity beings, virtual ethnicity scenes, and
postmodern consciousness meanings. Harrell’s (2007) “GRIOT system digitally codified African
“Cultural Roots for Computing,” and his “Algebra of Identity” (2008) highlighted the ethnic
cultural morphology capable in intelligent technology. Harrell (2010) later posits a “Theory of
Critical Computing” for designing diverse ethnic and social identity presences. He envisions
immersive 3D VR “phantasmal media” with ethnic cultural environments and “avatar dreams”
(Harrell, 2013; Harrell & Lim, 2017). These digital identity blueprints in theory and practice
yield viable cognitive CQ technology design coding. At the command and control level of
microchip programming, network protocol, memory power, and device performance, Harrell’s
design coding transforms technology appliances into ethnic artifacts.
Behavioral CQ Technology Interface Connections
The behavioral CQ technology interface dimension is a human cultural compatibility
lynchpin. Traditionally, this vital nexus of human usability and technology capability has been
chartered to the practice of human-user interface (HUI) within the fields of computer science and
information systems. Behavioral CQ technology interfaces customize ethnic identity
engagement in congruence with Barber and Badre’s (1998) HUI concept of “Culturability.”
“The basic premise behind the research outlined here is simple: No longer can
issues of culture and usability remain separate in design for the World Wide Web.
Cultural preferences and biases (colors, text/graphics, spatial orientation, etc.)
impact what is deemed ‘user friendly;’ thus, usability issues must take on a
cultural context.” (Barber and Badre, 1998, p.1)
Ironically, defying the “culturability” calling, ethnicity remains among the last included and
least well studied human aspects in the HUI literature. Ethnic culture is specifically overlooked
as a primary HUI factor, given its global prominence and pertinence for behavioral engagement.
Thus, this study’s CQ technology model emphasizes ethnic cultural interface connections. Still,
the majority of interface specifications are determined by manufacturers in preproduction stages
without including options for ethnic cultural identity. Given this asymmetric control and the

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profit motive of standardization, research on ethnic interface invention is fairly nascent.
However, Buccitelli’s (2017) addresses multiple technology interfaces that mediate human
interaction using metaphors, memes, and symbols from ethnic cultural values and vernacular.
“To boil this idea down into a short form, we can think of traditional culture, or
folklore, as vernacular patterns, practices, and performances that display multiple
existence and variation. … it stresses that folklore is “vernacular,” … about what
it means to talk of ‘tradition’ in the context of race, ethnicity, and digital
technologies.” (Buccitelli, 2017, pp. 5, 6)
Certain researchers cited in the prior section for advancing culturally tailored digital/AI
media design coding concepts are also extending those ideas to appliances for physical and
sensory interface behaviors as well. For instance, interfaces attuned to diverse ethnic
representation and recognition of facial features, gestures, and vocal tendencies (Buolamwini,
2023). Likewise, Harrell’s (2007, 2008, 2010; 2013; Harrell & Lim, 2017) extensive
development of ethnically coded cultural realms described above include behavioral interfaces.
Importantly, building on Hofstede’s cross-cultural values framework, pioneering HUI models
oriented towards ethnic/national culture have been introduced by Marcus (2015, 2007, 2002a,
2002b, 2001, 2000, 1999), as well as Marcus and Gould (2012, 2007, 2002a, 2002b). Marcus
adapts HUI features to fit the ethnic profiles of users in diverse countries to improve worker
comfortability and work competence. Prior to the cultural adaptations, users report performance
ambiguity, unusable technology functions, as well as omitted ethnic content. The “Marcus Model
of User Interface” (Marcus, 1993) designates six evaluative criteria, shown in Figure 2.
Implementation of the model was later furthered by what Marcus and Gould (2007, p.348)
described as “critical aspects for globalization specific guidelines.”
1. Hofstede (1980, 2001) cultural dimension users profile
2. Set of digital technology/media appliances, applications, access networks.
The resulting digital interface matrix of HUI components and cultural dimensions (Marcus,
et al., 2003; Marcus and Baumgartner, 2004) is shown in Figure 5.
“In addition, with greater understanding of the relationships, it becomes feasible
to develop databases and tools that might enable designers to ‘semi-automatically’
adjust user-interface designs per culture dimension and, ultimately, per culture. …
A future scenario might find designers (from one or more cultures) designing a
user interface for a specific culture, then asking the computer to adjust the entire
user- interface design for another culture.” (Marcus, 2000b, p.24)

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Figure 2 Marcus Model Digital Interface Matrix with Hofstede Value Dimensions
Source: Author’s original design adapted from Marcus & Baumgartner (2004).
Similar approaches for using cultural dimensions with technology have been advanced by
cross-cultural scholars. In the information sciences literature, Zakour (2004) distills the cross-
cultural studies of digital technology acceptance and puts forth his own model combining
Hofstede (1980, 2001) dimensions with TAM factors. Cyr (2008) furthers Marcus’ HUI digital
design matrix (Marcus and Baumgartner, 2004) with a cross-cultural website study.
The media ecology perspective (Slate, 2004) provides a holistic view of CQ interfaces.
McLuhan’s (1964, 1975) media ecology classifies sociocultural engagement effects from cool
(implicit high-context) to hot (explicit low-context). These simple building blocks are assembled
to assess complex message patterns in media interfaces that combine cool/hot tendencies, for
diverse high/low context users. Newer “communicative ecologies” methods directly study global
ethnic cultures to broaden the media mix, deepen the cultural context, and heighten the role of
social networks (Tacchi, 2004; Lennie & Tacchi, 2013).
Papacharissi (2018) explores this media interface ecology from the locus of a “networked
self” mediating digital bio-neural augmentation, immersive virtual/social media, AI, and sentient
technology including humanoid robots. In prior social-media identity research, Papacharissi
(2010) found that behavioral interface affordances bond communities and imbue cultural values.
These clues help to formulate an ethnic networked self for behavioral CQ interfaces.
Of course, social media design networks, platforms, and multisensory content are constantly
changing. Hardware network intelligence codes become interface platform features (accessing
commands and communities) and even knit into aesthetic content fabric (hyperlinks). Greater
function bundling and blending in social media makes “networks” a primary platform interface.

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Contemporary media platforms, however, represent a nebulous constellation of
features that evoke older channels as well as newer interfaces, … social media
platforms iterate incessantly. … The network provides the interface for engaging
with the catalog of accounts (people) that users are connected to on social media.”
(Bayer, et al., 2020, pp. 474, 479)
Digital/AI media interfaces are installed across device types and network platforms including
immersive AR/VR/XR settings and multimodal cross-platform systems adapted to users with
data analytics, biometric profiles, and experiential preferences (Rasalingam & Rasli, 2024).
These AI multisensory mixed reality advances make ethnic CQ technology interfaces viable.
Mobile interfaces allow for freer and adaptable mediation of human and digital realms as
personalized “embodied space” through “sensory-inscribed” human body (Farman, 2020). In
addition, mobile interfaces are also more readily molded to individual and collective ethnic
identity. American ethnic minorities own/use more mobile devices than majority consumers
(Pew Research Center, 2024). Globally, cell phone usage/growth is highest among non-European
countries as well, with only the United States and Russia in the top 10 (Howarth, 2025).
The flexibility and familiarity of coupling mobile with social-media interfaces is evident in
the growth of digital/AI bots, software agents, device dialogue methods, as well as IoT
interactive modes. Happ (2020) defines these “smart bots” as “autonomously operating systems
designed for the purpose of quasi-communication with human beings,” (e.g., Apple Siri, Amazon
Alexa, Google Gemini, etc.). Collectively, these smart interfaces that automate or simulate any
natural human proprties are defined as “virtual humans” (Burden & Savin-Baden, 2019;
Magnenat-Thalmann & Thalmann, 2005; Rickel, et al., 2002). While customizing these
emerging bots/agents, simple race color features are more readily used than sophisticated ethnic
cultural figures. However, in these instances, racial appearance serve to inform ethnic
authenticity as cultural representation in digital/AI media evolves. This study regards ethnic
cultural identity in advanced bot/agent interfaces as the most relevant application of Nass and
Moon’s (2000) principles of “computers are social actors” (CASA).
Constructive chatbot, social robot, and artificial humans findings emerge from academic
research in different societal sectors. A recent computer science study (Go & Sundar, 2019)
concluded that “chatbot visual, identity, and conversational cues” directly impact the
“humanness perceptions” of users. Assessing public sector responsiveness for the Florida
hurricane season, an extensive experiment proved that disaster preparedness and relief is
improved by tailoring generative AI chatbots to multiethnic communities (Zhao, et al., 2025).
Tailoring AI chatbot visual and verbal traits to particular ethnic/racial populations has been
shown to improve perceptions of healthcare service, “mirror’ comfortable communication cues,
and spur greater patient response to information requests (Liao & He, 2020; Nadarzynski, et al.,
2025). Li, et al. (2016) found that animated virtual agents achieved higher student lecture recall
than human lecturers, with lessons for ethnic identity animation. AI chatbots also promise to

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benefit educational learning with realistic human portrayals of race and gender (Miranda, et al.,
2024; Zhao, et al., 2024). The agency provided by compatible ethnic properties extends beyond
chatbots to all social robots (Barfield, 2023). Vernon’s (2023, 2024) innovative projects design
“culturally competent social robotics for Africa” based on “culture specific knowledge derived
from diverse social and cultural norms in African countries.” Similarly, Finch, et al.’s (2025)
natural language AI chatbot that accurately emulates the African American vernacular affirms
ethnic CQ technology interfaces
The foregoing discussion depicts an array of behavioral CQ technology interfaces spanning a
vast digital/AI-media ecology constellation across many strata of the “networked self”
(Papacharissi, 2018). Huber’s (1987, 1992) “Geodesic Network” adopted a similar circular
model for charting telecommunications technology innovations. This study’s constellation of
potential CQ technology interfaces is illustrated in Figure 3 with seven sociocultural domain
strata orbiting a networked self-identity sphere. The diagram informs the capabilities of adapting
the constellation of CQ technology.

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Figure 3: Behavioral CQ Technology Interface Constellation around Networked Self-Identity Sphere
(Source: Author’s original diagram adapted from Carter, 1997 Agents of Exchange)

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Motivational CQ Technology Multisensory Content
More than any other dimension, CQ technology content has been customized for diverse
ethnic cultural orientations. Its accessible messages and available means of production allow
motivated consumers to become masterful creators with inexpensive digital tools. Consequently,
most current digital/social-media scholarship addressing ethnicity and race is based on
multisensory content aesthetics. Ethnic traditions are imparted using what Bell (2001) calls
“storying cyberspace,” to conjure “cyberspace as culture and as cultural artefact” with “material,
symbolic and experiential dimensions.”
“What I want to stress is that any attempt to understand cyberspace and
cyberculture must look at the stories we tell about these phenomena. … at the
intersection of different knowledges and metaphors … as (1) material stories, (2)
symbolic stories and (3) experiential stories.” (Bell, 2001, p. 5)
Ethnic identity content in digital media can be traced back to Penley and Ross’ (1991)
premier volume Technoculture, which canvassed popular and counter culture online content,
including Houston Baker’s “hybridity rap race” novel racial narrative and Donna Haraway’s
prescient cyborg gender identity deconstruction. Digital media content became more familiar
following Benedikt’s (1991) Cyberspace acquaintance with interactive computer experiences
through the lens of science fiction introductions to William Gibson’s (1984) “cyberspace”
revelations and Rosanne Stone’s (1991, 1996) musings on virtual identity presence. Both Porter
(1996) and Shields (1996) continued to reveal the incipient nuances of Internet culture content,
such as virtual body/behavior identities and convivial virtual space/place communities.
The development of distinctly virtual self and social experiences was elaborated by Jones’
(1998) Cybersociety 2.0, with the emergence of Web 2.0 collaborative motivation. Within this
volume, Poster’s (1998) “virtual ethnicity” highlights the propensity towards global “tribal
identity” with newly found connectivity freedom. The same year, Bosah’s (1998) seminal
“cyberghetto or cybertopia” reminded the technology vanguard about the severe race/ethnicity,
gender, and social class disparities in the real and digital world. Congruent with the premise for
ethnic CQ technology content, truer “virtual ethnicity” (Carter, 2015) imbues culturally rooted
sensemaking for navigating social networks, whereas “cyberghetto” segregates digital domains.
Mindful of the void in diverse digital representation, Diamandaki (2003) expands Poster’s earlier
notion to encompass globally shared “virtual ethnicity and digital diasporas.” Thereafter, Foster
(2005) chronicled fictional depictions to ponder “vernacular theory” for future digital
multisensory content aesthetics that represent ethnic/racial “souls of cyberfolk.”
This evolving digital content literature stream reached critical mass with Nakamura’s (1995,
2002, 2006, 2007) seminal recognition of a compelling motivation to concentrate on digital
racial/ethnic identity. Importantly, Srinivasan (2006) has kept the digital content focus on deeper
richer “thick descriptions” (Geertz, 1973) of cultural ethnic properties – including global

23
indigenous people and homeland places. In this regard, the prevalent thin depictions of
race/ethnic profiles should transition towards meaningful authentic ethnic cultural identities.
These deeper ethnic cultural representations enable “technologies of the self” (Abbas & Dervin,
2009) for digital content identity construction, enactment, and community.
Interdisciplinary research shows that ethnically oriented content on websites and social-
media platforms has high interest for ethnically diverse users (Appiah, 2004; Appiah & Elias,
2014; Brock, 2009, 2012; Kolko, et al, 2013). These growing technology adoption of ethnic
content is evidenced by Yu & Matsaganis’ (2018) sourcebook, Ethnic Media in the Digital Age.
The motivational potential of multisensory content aesthetics is best experienced as an
“immersive VR self,” personalized “avatar dream,” and parallel “digital twins” (Harrell, 2013;
Harrell & Lim, 2017; Polito & Hitchens, 2021; Ruiu, et al., 2024; Schwartz & Steptoe, 2018).
Clearly, ethnic culture is compelling and catalytic for motivational CQ technology content.
With accelerating AI innovation, the impending virtual human digital transformation into
posthuman singularity becomes a pragmatic CQ technology planning scenario. In the past,
ethnicity factored prominently in the emergence of CQ for global cross-cultural enterprise human
capital strategies. Presently, the evolution of digital/AI media makes it feasible to digitize
ethnicity following the proposed CQ technology dimensions, which directly extend the seminal
CQ four factors model (Ang & Van Dyne, 2005). Therefore, this study incorporates AI advances
to introduce “artificial ethnicity” (AE) as a central axis and animated hub for CQ technology
dimensions. As a directional planning paradigm, artificial ethnicity (AE) provides strategic
vision, technological viability, cultural values, and innovation vitality.
ARTIFICIAL ETHNICITY ARCHITECTURE
Artificial ethnicity (AE) is this study’s original concept for culminating and cohering the four
CQ technology dimensions. As a central axis and animating hub, AE architecture anchors CQ
technology to an anthropological life history while simultaneously aspiring towards an artificial
life horizon. These dual AE autopoieses aims germinate the human past and generate the
posthuman future. This concise sketch of AE architecture prefigures the posthuman future with
core anthropological theories, ethnic identity logic, and digital/AI media evidence.
An acute framing of the artificial future is articulated by Leaver’s (2011) identity vectors.
Fittingly, they directly correspond to CQ technology dimensions, with “artificial culture” as
meta-theory context, “artificial life” as cognitive design coding, “artificial space” as behavioral
interface, and “artificial people” as embodied motivational content. In this manner, Lever’s
artificial identity pattern informs the AE anatomy.
“Artificial Culture is an examination of the articulation, construction, and
representation of ‘the artificial’ ... we live in an artificial culture due to the deep
and inextricable relationship between people, our bodies, and technology at large.
While the artificial is often imagined as outside of the natural order and thus also

24
beyond the realm of humanity, paradoxically, artificial concepts are
simultaneously produced and constructed by human ideas and labor. The artificial
can thus act as a boundary point against which we as a culture can measure what
it means to be human.” (Leaver, 2011, Artificial Culture, publisher description)
Although numerous anthropology theories are relevant to the artificial existence of human
ethnicity, three core determinants shape the artificial ethnicity (AE) identity logic architecture.
1. Cultural Agency (ethos) – the focal premise for artificial anthropological ethnicity.
2. Cultural Embodiment and Embeddedness Duality (axios) – the formative values
endowed in artificial ethnicity technologies as anthropological ethnicity teleology.
3. Cultural Identity Narrative (logos) – the functional human expression of artificial
ethnicity becoming, as well as the fabricated technology expression of artificial
ethnicity being.
As a focal premise, cultural agency clarifies AE’s intention as human affirmation, not
technology advancement. In Ristic’s (2001, p. 72) words, “Technology is, thus, defined as
culturally determined ways of satisfying human needs/functions.” Digital/AI media and
posthuman technology is the ontological means for achieving a human teleological end, with
ethnic culture as a tenable anthropological property for insuring that purposeful outcome.
Agency epitomizes ethnic culture’s individual and collective power, self-determination, and
liberty with a distinct historical and heuristic character -- what cybernetics calls autopoiesis
(Maturana & Verela, 1980, 1992).
As an inherent cultural duality, embodiment shapes the individual ethnic qualities which are
collectively shared through embeddedness. In this sense, embodiment explicitly actualizes ethnic
aspirational volition from implicitly acquired ethnic ancestral values. In turn, embeddedness
incorporates a social ethnic quest with individually embodied ethnic qualities. As a theoretical
parameters, cultural embodiment solidifies AE structure with internally verified anthropological
reasoning, while cultural embeddedness syncretizes AE structure with externally validated
sociological findings.
Embodiment philosophy is Merleau-Ponty’s (1962/1981) attribution of human intelligence
origins to the body (“flesh”) as a primal phenomenological vessel of nature and channeled into
meanings by mental circuits. Anthropology also introduced embodiment (Csordas, 1994) to
bridge mind/body and body/nature dualism with a unified cultural intelligence pattern that is
produced in multiple forms. Bourdieu (1977, 1990) extends the concept of embodied cultural
intelligence patterns to situated social places through the “habitus.”
Polanyi (1968) and later Granovetter (1985) and others (Dequech, 2003; Scott, 2013)
advanced “embeddedness” as a social economics theory for explaining societal exchange
networks in non-market economic contexts, wherein “substantive” cultural beliefs, kinships (i.e.,
ethnicity), and institutions perform the functions of rationalized “disembedded” economic
systems. The validity and reliability of cultural embeddedness for explaining the tacit, implicit,
informal sources and influences of human interactions has been extended to culturally distinct

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patterns in countries and global regions/diasporas (Schwartz, 2009), industry evolution (Li, et.
al., 2021), organizational culture (Choi, et al., 2023; Goldberg, et al., 2016), global
product/market adaptation (Jakubanecs & Supphellen, 2016; Sasaki, et al., 2021), global supply-
chain networks (Wu & Pullman, 2015), architecture (Cromley, 2008), virtual knowledge
management (Chumg & Huang, 2021), ethical reasoning (Thorne & Saunders, 2002), learning
(Mentz & De Beer, 2021) , and individual/social psychology as Haynes’ (2010, p. 491) Cultural
History Activity Theory (CHAT) – “cultural embeddedness implies that human action and
interaction cannot be understood without including the social and cultural context.”
A cultural identity narrative operationalizes anthropological ethnicity as signified meanings
and resonating sensibilities, commonly experienced as kindred soul. It evokes an anthropological
story of ancestral lineage, ecophenomenology homeland memories, and historically woven
traditions. It is a humanly felt culture that engenders a fully known identity expressed through a
family bonding narrative. This historically grounded and humanly guiding cultural identity
narrative cannot be replicated by technology, only represented by CQ technology with
authenticity and agency.
Having distilled anthropological theory AE determinants, the fundamentals of AE identity
logic architecture are explained in the sequence below. First, a “technology singularity” purpose
is articulately set to for AE identity meaning script. Second, an AE identity mapping screen is
analytically framed with “four threads” of “cyberspace.” Third, “integral research paradigm”
actionable propositions chart an AE identity method scope. Fourth, AE identity measurement
scales are primed with accountable education psychology instruments.
1. Seminal AI/Singularity quotes as AE identity logic purpose meaning script
2. Cyberspace “4 threads” as AE identity logic plan mapping screen (Benedikt,1991)
3. Integral Research Paradigm as AE identity logic propositions method scope
(Lessem, 2017; Lessem & Schieffer, 2016)
4. Digital Ethnicity Scale (DES) as AE identity logic procedure measurement scales
(Adams, 2018; Adams & DeVaney, 2019; Adams, et al., 2010; Longstreet, 1978).
Seminal AI/Singularity scholars set the AE identity meaning script by articulating new ideas:
• AI “Turing Test” programming comparable to human reasoning (Turing, 1950)
• AI computers with human cognition (Simon, 1965, 1969; Minsky, 1967)
• AI virtual person human identity (Cole, 1991)
• AI technological singularity surpassing humans (Vinge, 1993; Kurzweil, 2005)
• AI “Godfather” neural networks design and deployment (Hinton; in Town, 2024)
Early AI meanings impart purposeful AE identity logic for future human and technology
scripts. These logical points of reference shape AE architecture directions and domains.
“We can introduce the concept of a Virtual Person (or Virtual Mind), an entity
that may be realized by the activity of something that is not a person (or mind).”
(Cole, 1991, p. 399)

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Consequently, the seminal AI/Singularity purpose of “virtual person/mind” propels an arc
towards the artificial. It traces an ambit of human identity told as a science fiction narrative of
meaningful characters and posthuman technology scripts. Seeing this meaning script as the
bedrock purpose of an artificial identity narrative allows ethnic cultural narratives to be instilled
like deep foundations in architecture construction. Importantly, the future arc projected by
seminal AI/Singularity scholars keeps pace with the meaning scripts of current “artificial
identity” research. Devedzic’s (2022) analysis of AI identity meanings as word clusters and AI
message scripts in intellectual discourse is anchored by the profiles of AI/Singularity pioneers.
The “Turing Test” (Turing, 1950) is implicit in the human benchmarking and mind mimesis that
defines AI identity. DiGiovanna (2017) examines the equivalence of artificial identity and
human virtue qualities, to remain cognizant of remaking better selves as “responsibility-bearing
parts of selves and the persistence conditions of these parts,” not just plaint synthetic beings.
Cautioning that AI’s continuous capacity to change morphs human identity beyond the essence
of the self, he advocates for ethical AI models bound by human virtue traditions. The AE
meaning script prescribes an ethnic identity purpose with shared cultural vernacular for “Turing
Test” validity, as well as historical preservation properties that are planted in virtue ethics.
Benedikt (1991) analytically maps a plan for AE identity logic architecture with a “four
threads” quadrant screen of cyberspace agency and ambiance. The “four threads” stitch the
futuristic fabric of cyberspace based on the historical continuity of human culture – including
ethnicity. The first thread is “language” for conceiving, conveying, and collecting meaning –
which corresponds to seminal AI logic purpose as meaning scripts. The second thread is “media”
for transmitting, transcribing, translating, transporting, and transacting intelligence. The third
thread is “architecture” (art) to create, curate, and construct human AI experiences. The fourth
thread is “mathematics” for formulating scientific, systematic, and statistical AI design codes.
Aptly, Benedikt’s (1991) four threads correlate with CQ technology model dimensions:
1. Meta-theory milieu context … first thread language and meaning
2. Cognitive design coding … fourth thread mathematics algorithms
3. Behavioral interface connections … second thread media transacting
4. Motivational multisensory content …third thread architecture/art creation
The “four threads” quadrants hone AE identity logic planning for human self, social, and
societal AE multicultural orientations. While detailing the different types digital/AI media within
each quadrant is beyond this study’s boundaries, extracts from selected scholars afford a
glimpse. Luppicini’s (2013) Technoself critically analyzes the entire scope of digital/AI media as
sapient human archetypes and technology systems. From “cyber identity” to “digital soul,”
human nature is profiled as technoself native. Technological innovations are presented as digital
platforms, online websites, social-media networks, virtual worlds, cyber navigation, brain
interfaces, humanoid robotics, as well as human cognitive, affective, physical enhancements. As
a result, “Technoself Studies (TSS)” overlays Benedikt’s (1991) four cyberspace threads and

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organizes digital/AI media advances across the four CQ technology dimensions of identity meta-
theory parameters, intelligent design programs, interface platforms, and content portrayals.
“Technoself Studies (TSS) contributes important and indispensable insights to
help situate and explain technology’s intertwinement with human identity and
constructions of the self. (i.e., cyborg, saturated self, transhuman, posthuman,
android, humanoid, techno-human, digital/online identity, avatar, virtual life)”
(Luppicini, 2013, p.xxv)
The “artificial life” literature parallels Benedikt’s (1991) “four threads” mapping screen. As
the biological corollary of digital concepts (Langton, 1989; Steels & Brooks, 1995), artificial life
literally grows AI biology species matching AI technology systems (Akama, 2024; Aguilar, et
al., 2014; Johnston, 2008; Kim & Cho, 2006; Steels & Brooks, 1995). This artificial life pairing
with the AE identity logic planning “four threads” is outlined below:
1. Language Meanings -- Artificial life has a natural language (Ackley & Ackley, 2016;
Kirby, 2002; Parisi, 1997; Steels & Kaplan, 1998).
2. Media Transacting -- Artificial life has interactional media as virtual organisms,
synthetic wetware, and lenia-biology and avida cellular automaton simulation
platforms, evolutionary computing applications, as well as Cortical Labs first
biological computer generating synthetic biological intelligence (Adami, 1998;
Akama, 2024; Aguilar, et al., 2014; Chan, 2018; Kim & Cho, 2006; Lee, 2023; Ward,
2000; Yazgin, 2025).
3. Architecture/Art Creation – Artificial life has bio-technology art as metacreation,
vitalism and emergence, immersive virtual ecologies of virtual creatures, garden in
the machine (Bartlem, 2005; Emmeche, 1994; Penny, 2009, 2010; Whitelaw, 2004).
4. Mathematics Algorithms – Artificial life has neural network algorithms, genetic
algorithms, complexity science, evolutionary data mining, quantitative theory of the
origin of life, biological geometries, statistical mechanics and thermodynamics
(Adami, 1998; Akama, 2024; Langton, 1989; Rabunal & Dorado, 2006)
Papacharissi’s (2018) assesses numerous sapient/sentient technologies spread across the AE
identity logic “four threads” quadrants planning screen, which are nested in the “networked self.”
Referenced earlier to expand the range of CQ technology interfaces, the “networked self”
positions an AE identity epicenter for configuring the vast array of advanced digital/AI media.
Going further, Cyber Behavior’s (IRMA, 2014) encyclopedic taxonomy of future AE designs the
IRMA account is representative and replete.
Now that the AE identity logic purpose is established with an “AI/singularity” meaning
script, and a “four threads” quadrant mapping screen is elaborated for AE identity logic planning,
the method scope can be charted with AE identity logic propositions. This AE architecture
methodology is customized to the “four threads” quadrants for reliable AE identity logic
planning. The “Integral Research Paradigm” (IRP) asserts valid propositions with

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methodological veracity (Lessem, 2017; Lessem & Schieffer, 2016). Ingeniously, IRP
propositions chart ethnic global history and culture orientations as a cardinal axis, used here for
the AE identity logic method scope. As the authors address, IRP orientations are not
stereotypical predeterminants or preconditions, only studied propensities which can be freely
adopted and applied singularly or in combination. Similar to global development of geo-climate
weather patterns, the evolution of global ethnic cultural traditions chart tendencies (not exact
predictions) to improve planning geo-destinations and AE designs respectively.
Whereas this study models for digital/AI media innovation using ethnic cultural orientations
for CQ technology, IRP models social enterprise innovation using geo-ethnic cultural
orientations of philosophy/intelligence. Just as this study’s CQ technology propositions are
prompted by the dearth of ethnic culturally oriented digital/AI media models, the IRP
propositions are put forth to globally unify Southern and Eastern cultural knowledge with the
predominant Northern and Western cultural standard. Thus, the IRP global ethnic cultural
cardinal axis and underlying propositions construct an ideal AE identity logic method scope.
In relation to AE identity logic, the cardinal axis method scope charts consistent ethnic
cultural orientations for calibrating CQ technology dimensions. Yet, the North/South and
West/East axes do not limit or constrain the nature or number of global ethnic cultural identities
plotted by the method scope. Rather, it merely charts primary vectors of “archetypal features”
supported by IRP analysis, which can be augmented with other ethnic cultural angles.
“Social innovation has a fourfold heritage: building equally on the four human
modes we shall outline – being, becoming, knowing, doing – as well as on the
Four Worlds of the South, East, North, and West. … This archetypal social and
psychological design is a distillation of our research and observations in different
cultures and personalities. It is also a design that resonates with core cultural and
artistic artefacts (visual designs) that occur in most societies.”
(Lessem, 2017, pp. 32, 38)
The four fundamentals of IRP and actualized as Transformation Management are:
1. East (holism) -- nature and community becoming/emerging through renewal
2. South (humanism) -- culture and spirituality being/grounding through relations
3. North (rationalism) -- science and technology knowing/navigation through reason
4. West (pragmatism) -- management and economics doing/effecting through realization
(Lessem, 2017; Lessem & Schieffer, 2016)
Specific IRP propositions forming the AE identity logic method scope, are as follows:
1. True social/technology innovation is organically rooted in the specific ethnic cultures
wherein it is applied.

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2. While all global ethnic cultures share similar abilities and attributes, each ethnic
culture over time evolved particular properties, propensities, and preferences that
distinguish them from other ethnic cultures.
3. The global ethnic culture distinctions can be validly charted using a cardinal axis of
North, South, West and East archetypes. Ethnic culture variants from the four
cardinal vector archetypes can be plotted as corresponding angles.
4. Social/technology ethnic culture cardinal axis archetypes are applicable to all
methods of innovation for macro societal, micro strategic, memetic personal, and
matrix technological outcomes.
5. Social/technology innovation requires “action learning” methodologies with
analytical scope (scholarly world) that directly result in “action research” methods to
actualize scope (real world).
The IRP propositions are particularly adept at molding methodological approaches to fit a
wide variety of cardinal and combined ethnic cultural archetype orientations. Intended as “action
learning” mirroring “action research,” IRP propositions advance both intellectual learning and
real-world enterprise activity. So, IRP propositions must specify methods for generating
knowledge that directly galvanizes actual social innovation. Similarly, this study embraces
“action learning” and “action research,” because the proposed CQ technology model can directly
propel actual ethnic cultural identity versions of digital/AI media.
“These involved, firstly, an integral perspective on research (leading to social
innovation), out of which research method and methodology were each parts of a
more integral whole. … There was a vast gap between methodology
(incorporating philosophy) and method (incorporating technique) of which only
the enlightened few had become aware. … Research methodologies were often
profound in scope, but they were all too often very difficult to follow or indeed
apply. … In summary, there were virtually no attempts to bridge the gap between
methodology as research philosophy, and method as research activity.”
(Lessem, 2017, pp. 12, 13)
Ongoing studies hone the IRP cardinal axis of geo-cultural ethnicity with indigenous
knowledge systems (Ayittey, 1991; Dyson, et al., 2006; Emeagwali & Shizha,, 2016; Goulding,
et al., 2016; Islam & Banda, 2011; Kanu, 2020; Roya & Ngcobo, 2023; UN, 2021).
Lastly, this sketch of AE architecture describes procedures for AE identity logic
measurement scales. These education psychology “learning scales” serve as survey instruments
and set data collection procedures as part of the IRP method scope. Education psychology is the
ideal literature to probe for AE identity logic learning scales, because the testing volume and
technique validity far surpasses any other publicly available database. Generative AI learning
models acquire reckoning and rapport from repetitive training using ample information sources
(Foster, 2022; Zewe, 2023). An equally vital consideration is the intimate and instrumental roles

30
of public education systems and the academic education psychology community in building an
objective knowledge archive devoted to race and ethnicity across age groups. This widely
known about U. S. education psychology studies, but a similar role exists globally, due to the
societal duty of education institutions to acculturate diverse ethnic and racial backgrounds.
The learning symbiosis between education and ethnicity/race studies extends beyond school
metrics and societal mediation. Although most of the present media focus is on how Generative
AI learns, a potentially more consequential query is how does AI help humans learn? (Johnson,
2024). That profound fathoming yields findings directly related to both human anthropological
ethnicity and posthuman artificial ethnicity. Along with shared primordial ancestry and geo-
ecological homelands, ethnic cultural identity is acquired through instrumentalism instruction
and knowledge constructivism. Both are human learning processes that AI can accelerate and
amplify. In other words, as digital/AI media learn ethnic cultural orientations to create AE,
engaging with AE improves human ethnicity learning and empowers human ethnicity beings.
Further, as an inherently intelligence-based process, educational instruction can be plausibly
replaced by AI. This adds yet another layer of ethnicity relevance for developing multicultural
AI learning systems in schools (Baidoo-Anu & Ansah, 2023; Wu, 2023).
Given this organic synergy between ethnic/racial identity and education psychology research,
it is not surprising that the earliest racial/ethnic identity scales surfaced as psychological
instruments administered to students. Most notably Williams’ (1972) Black Intelligence Test of
Cultural Homogeneity (BITCH-100), and the Cross “Nigrescenece” model and Cross-Racial
Identity Scale (CRIS) instrument (Cross, 1978; Cross & Vandiver, 2001; Vandiver, et al., 2001).
Sandwiched between those seminal African American racial identity research pillars is
Longstreet’s (1978) pioneering ethnic identity research in education psychology. Importantly, it
was the first “universal ethnicity” approach (Carter, 2009a, 2009b), because all global ethnic
diasporas are represented (e.g., African, Arab, Asian, European, Hispanic, Pacific Island, etc.). I
also traced specific ancestral heritages and homelands. This “aspects of ethnicity” scale provides
empirical factor reliability analysis to assure consistency in ethnic group designation. As a result,
the scale categories largely evaluate what Gardner (1983) later called “multiple intelligences.”
1. Verbal communication
2. Nonverbal communication
3. Body/spatial orientation modes
4. Social value patterns
5. Intellectual modes
a) verbal fluency
b) numerical ability
c) spatial reasoning
d) mechanical reasoning
e) abstract reasoning
f) memory

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The “aspects of ethnicity” scale sufficiently validates the main quantitative criteria (listed
above) with cultural anthropology research and performs the necessary ethnic group
differentiation using qualitative considerations of cultural influences, tendencies, and contexts.
This level of ethnic cultural authentication, ethnic identity instrumentation, and ethnic group
inclusion remains rare to this date across all fields of cross-cultural and multicultural
intelligence. Direct inquiry regarding ethnic ancestry and cultural acclimation captures precise
“thick descriptions” (Geertz, 1973) for particular ethnic traditions not general racial categories or
only ethnic minority groups (e.g., German, Swedish, Jewish, Irish, Italian, Korean, Japanese,
Chinese, Mexican, Puerto Rican, African American, Jamaican, Nigerian, etc.).
Comparable research in that era focused on a particular ethnic/racial group identity. These
include the African American BITCH-100 test and “Nigrescenece” model (Cross, 1978; Cross &
Vandiver, 2001; Williams, 1972), as well as a Hispanic identity scale, “Multidimensional
measure of cultural identity for Latino and Latina adolescents” (Félix-Ortiz, et al., 1994; Dillon,
et al., 2009), and an Asian identity instrument, “Suinn-Lew Asian self-identity acculturation
scale” (Ownbey & Horridge, 1998; Suinn, et al., 1992). Thereafter, the “Ethnic Identity Scale”
(EIS) was designed as an inclusive universal ethnicity instrument like Longstreet (1978), based
on deeper cultural experiences of exploration, resolution, and affirmation (Douglass & Umaña-
Taylor, 2015; Umaña-Taylor, 2005, 2024; Umaña-Taylor, et al., 2004).
As with digital content, many educational psychology ethnic/racial identity scales lack
historical authenticity depth and cultural dimension breadth. Hence, they are racial identity
models serving as a bridge towards richer and more rigorous ethnic identity meanings. Certain
prominent studies substantiate the advancement of ethnicity/race scales in the education
psychology and counseling literature. Phinney (1992) advanced the “multigroup ethnic identity
measure” with culturally anchored criteria and inclusive universal ethnicity credibility to
“compare ethnic identity and its correlates across groups.” Meca, et al.’s (2023) recent critical
analysis and extension of Umaña-Taylor’s (2005) “ethnic identity scale” (EIS) improves its
application and affirmation. Likewise, Juang, et al. (2023) ethnic-racial identity research in
European countries proves the global and universal relevance of ethnic cultural orientations.
As noted earlier, data collection limitations prevent tracing authentic ethnic identities to the
heterogeneous tribal origins within larger continental territories typically used in ethnic scales
(e.g., African, Hispanic/Latin American, Arab, Asian, European, Pacific Islander, etc.).
Eventually, DNA data will afford more accurate ethnic identification. Still, these categories of
universal ethnicity are more valid than either partial ‘people of color’ ethnic scales or culture
negated race classifications.
Notwithstanding recent enhancements in ethnic identity measures, such as the EIS (Umaña-
Taylor, 2005, 2024), this initial configuration of AE identity logic uses Longstreet’s (1978)
“Aspects of Ethnicity” scale. It confers universal ethnicity inclusion, conceptual validity,
measurement reliability, ancestral specificity, and practical adaptability to all instrument

32
circumstances (e.g., various digital/AI media). Moreover, this rationale is reinforced by the
tailoring of Longstreet’s (1978) instrument to create a groundbreaking “Digital Ethnicity Scale”
(Adams, 2018; Adams & DeVaney, 2019; Adams, et al., 2010).
“Longstreet, unlike other scholars, provides the only constructed model for
describing identified aspects of ethnicity. This model provides the socio-
biological definition of ethnicity … an appropriate and useful framework for
investigating the impact digital communication tools are having on cultures and
societies. … The ultimate goal for the development of the Digital Ethnicity Scale
is to describe those aspects of ethnicity using a digital lens and collect these
descriptions along with demographic data to develop profiles of various digital
ethnicities.” (Adams, et al., 2010, p. 1823)
Like AE identity logic, the “digital ethnicity scale” (DES) combines cultural human
meanings and contemporary digital/AI media. The DES is composed of the five aspects of
ethnicity scale modes, with Verbal and Nonverbal communication as a single Communication
mode. The four resulting DES modes are fully validated and tested for reliability. Whereas DES
research improves future digital education experience, this study uses DES to precisely measure
the cardinal axis cultural orientations charted by IRP method scope.
“The structure of the 16-item set was examined using a factor analysis with an
eigenvalue greater than 1 extraction criterion, Varimax rotation, and 0.50 display
criterion for factor loadings. … consistent with the common structure proposed by
Osgood et al 1957.” (Adams, 2018, pp.165-166)
Developing diverse “digital ethnicity” profiles is pivotal for DES research, as well as for this
study’s proposed CQ technology model culminating in AE architecture. Longstreet’s (1978)
authentic ethnicity measurement scales impart ancestral specificity and experiential resonance
into the IRP method scope cardinal axis of ethnic cultures. The feasibility of ethnically oriented
digital/AI media is furthered by the practical fit of DES modes with tech designs and their
alignment with the four CQ technology dimensions:
1. Meta-Theory Milieu Context … Orientation (self/body & space/boundary condition)
2. Cognitive Design Coding … Intellectual (self/mind construction)
3. Behavioral Interface Connection … Social (self/society mediation)
4. Motivational Multisensory Content … Communication (self/experience curation).
In a recurrent renewing cycle, the AE architecture identity logic stages construct ethnic
profiles to customize each CQ technology model dimension. In turn, each of the dimensional
properties refines and reinforces the AE architecture. Thus, global cross-cultural managers can
strategically sharpen their technology stacks with ethnically diverse digital/AI media tools. A
diagram of the combined AE architecture of identity logic stages is shown in Figure 4.

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Figure 4: Artificial Ethnicity Identity Logic Stages Architecture
(Source: Author’s original diagram)

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CONCLUSION – SYNOPSIS, SCENARIOS, SAPIENCE
Ethnic culture is universal, timeless, ubiquitous, and ever-present in people, places,
processes, products, and platforms. Human ethnic identity and relationships are catalysts for
global cross-cultural enterprise success. Harnessing this reservoir of human capital diversity,
Ang & Van Dyne (2005) formalized earlier research by Earley and Ang, (2003) to advance the
seminal genius of cultural intelligence (CQ/CI) as a scholarly model and cross-cultural
management strategy. The widely cited seminal work coalesces a literature stream of cultural
intelligence researchers (Ang, et al., 2004; Ang & Van Dyne, 2005; Ang, et al., 2006; Ang, et al.,
2007; Bucher, 2008; Earley & Ang, 2003; Earley, et al., 2007; Livermore, 2009; Ng, et al., 2012;
Thomas, 2006; Thomas & Inkson, 2003; Van Dyne, et al., 2012; Van Dyne, et al., 2015; Yari, et
al., 2020). As a testament to the global success of CQ/CI, it is unthinkable to manage cross-
cultural and multicultural workforces without deploying CQ/CI techniques. In an era of global
enterprise digital transformation, it is equally rational to develop CQ/CI technology models that
represent and resonate with cross-cultural and multicultural ethnic identity. This study provides
the conceptual assemblage and critical literature analysis to further an ethnically diverse
digital/AI media reality.
Synopsis of CQ/CI Technology Premise and Progression
Focusing on fundamentals, the proposed CQ/CI technology is directly modeled based on the
seminal four dimension CQ/CI model (Ang & Van Dyne, 2005; Earley & Ang, 2003), by
deductively discerning the appropriate technology properties that correspond with attributed
human propensities. The resulting CQ/CI technology model dimensions were extensively
examined as:
1. Meta-theory CQ/CI milieu context
2. Cognitive CQ/CI design coding
3. Behavioral CQ/CI interface connections
4. Motivational CQ/CI multisensory content.
The pervasive integration of digital/AI media in global enterprise operations as well as the
paucity of research applying CQ/CI dimensions to technology was regarded as justifying the
proposed model and conceptual study. Moreover, ethnicity was asserted as a focal CQ/CI
technology lens and human user identity filter. Ethnicity is uniquely tenable because it is a
universal bio-psychological and behavioral user attribute, a viable technology architecture
orientation criterion, as well as a vital strategic market and human capital factor for cross-
cultural global enterprise leadership.
Subsequent analysis using a critical literature review substantiated the alignment of CQ/CI
technology determinants with seminal CQ/CI model dimensions. Meta-Theory was depicted as a
milieu context that meshes technology users’ awareness of real world and digital realm cultural

35
contours. This parallel reality context and purposeful resonance with ethnic cultural identity was
conceptually grounded in Bourdieu’s (1977, 1990) “habitus” and “doxa.” Likewise, cultural
contexts advanced in the digital/AI media literature were chronicled to shape the meta-theory’s
human ethnicity meanings for future technology modes. Contextually, Harraway’s (1985, 1989,
1991, 1992) “semiotic square,” Nagy & Koles’ (2014) “virtual identity in virtual worlds” model,
and Margolin’s (1995) “meta-narrative” established the foundation for constructing meta-theory
“cyberculture” (Bell, 2001, Escobar, 1994; Lévy, 1999) as “digital anthropology” (Geismor &
Knox, 2021; Horst & Miller, 2012)
Cognitive design was examined as a technology mirror for human cognition as the
brain/mind of digital intelligence and generative DNA logic of algorithm coding. Foucault’s
(1988) classic “technologies of the self” philosophically undergirds this design blueprinting
dimension across the four enduring human technology types. In addition, the practical utility of
designing a digital/virtual ethnic self-identity was demonstrated by Harrell’s innovative ideas
(2007, 2008, 2010, 2013; Harrell & Lim, 2017). These viable ethnic cultural orientations for the
CQ/CI technology design dimension were reinforced by other digital/AI media scholars.
Behavior interfaces were shown to encompass wide ranging digital/AI media artefacts that
overlap and blend CQ/CI technology dimensions. The aim of ethnically compatible interfaces
was avowed by Barber and Badre’s (1998) seminal “culturability” approach. Buccitelli’s (2017)
cultural anthropology perspective reveals how technology interfaces serve human mediation
purposes through a variety vernacular patterns, practices, and performances. This myriad of
mediating technologies is tailored to an ethnic cultural human-user interface (HUI) model
(Marcus, 2000a, 2000b; Marcus & Baumgartner, 2004), based on Hofstede’s (1980, 2001) global
cross-cultural values framework. Afterwards, the digital/AI media literature was canvassed to
present examples of the vast technology interfaces constellation. These examples span a
continuum from enhanced interactivity in contemporary systems, to AI software agents/bots, to
personified avatars in immersive virtual experiences, to embedded sapient IoT networks and
humanoid robots. The array of sapient and sentient interface capabilities was conveyed as
“virtual humans” features (Burden & Savin-Baden, 2019; Magnenat-Thalmann & Thalmann,
2005; Rickel, et al., 2002) Collectively, Papacharissi’s (2010, 2018) “networked self” paradigm
provided a nexus of coherence for this vast digital/AI media interface constellation.
Motivational multisensory content was conveyed as the most ethnically diverse CQ/CI
technology dimension. Given the global and national growth in ethnic diversity demographics,
the demand for multicultural digital/AI media content is immense. In addition, advances in
digital/AI content creation tools and expanding media channel access makes it easier to produce
and distribute ethnically oriented ideas. Bell’s (2001) “cyberspace and cyberculture” perspective
highlighted the common thread of story compositions and components that characterizes
digital/AI media content. The replete repertoire of digital cultural content was chronicled for
historical perspective. Early contributions such as Penley and Ross (1991) Technoculture,

36
Benedikt (1991) Cyberspace, and Jones (1998) Cybersociety 2.0 are included, as well as the
instrumental and ongoing contributions of Nakamura (1995, 2002, 2006, 2007, 2013) to
transition “digitizing race” towards more culturally based profiles of “virtual ethnicity” (Carter,
2015; Diamandaki, 2003). As noted, most digital/AI media content is formulated for racial
groups based on surface demographics and physical traits, even when the term ethnicity is used
(e.g., Appiah, 2004; Appiah & Elias, 2009/2014; Poster, 1998). Gradually, richer authentic
cultural identities are being developed such as Harrell’s (2010, 2017) “empowering and critical
identities” and “avatar dream,” to render better ethnic representation (Buccitelli, 2017) across
various technology platforms (Yu & Matsaganis, 2018). The progression of CQ/CI technology
content aesthetics encompasses immersive 3D multisensory virtual environments (Polito &
Hitchens, 2021; Ruiu, et al, 2024). This historical spectrum of multicultural content typifies the
motivational CQ/CI technology dimension as a source of compelling aesthetic experiences.
The architecture of “artificial ethnicity” (AE) was explained as the axis of CQ/CI technology
dimensions. AE’s role as an animating hub generates posthuman potential by synthesizing the
digital/AI media properties of each dimension and simulating diverse ethnic cultural identity
orientations. This original AE architecture idea brings the CQ/CI technology model to life with
viable digital/AI media prospects for the future, while preserving valuable human ethnic cultural
mooring in the past. Lever’s (2011) dissection of the technology pieces of the Artificial Culture
puzzle provided a detailed AE anatomy inventory. Following this preface, AE identity logic was
first addressed by anthropology determinants (e.g., cultural agency ethos, cultural
embeddedness/embodiment duality axios, cultural identity narrative logos). After establishing
those core cultural tenets, the innerworkings of AE was delineated with literature analysis as:
1. Seminal AI/Singularity quotes as AE identity logic purpose meaning script
2. Cyberspace “4 threads” as AE identity logic plan mapping screen (Benedikt,1991)
-- digital/AI media portrayed by “technoself studies,” “artificial life,” “networked self”
3. Integral Research Paradigm as AE identity logic propositions method scope
(Lessem, 2017; Lessem & Schieffer, 2016 … cardinal axis of ethnic cultural 4 worlds)
4. Digital Ethnicity Scale (DES) as AE identity logic procedure measurement scales
(Adams, 2018; Adams & DeVaney, 2019; Adams, et al., 2010; Longstreet, 1978).
Scenarios for CQ/CI Technology and AE Architecture Case Study
Many of the CQ/CI technology model and AE architecture advantages for global cross-
cultural enterprise management are observable in case study scenarios. In this way, strategic
aims and digital/AI applications can be optimized using CQ/CI technology and AE architecture.
Case 1: Innovative CQ/CI Technology for Global Video Streaming Network Growth
The potential of CQ/CI technology to improve global multicultural video entertainment in cross-
cultural markets is illustrated by a Netflix fictional projection. Presently, Netflix is a dominant
leader in the exploding market for direct-to-consumer video streaming services accessible on any

37
device and media platform. Yet, despite 2024 revenue of $39 billion, representing an annual
increase of nearly 16%, cultural barriers in addition to economic factors hinder expansion in the
Global South and East, compared to the Global North and West.
Netflix global regional shares in order of revenue share:
• North American … $17.35B @ 45%
• Europe … $12B @ 30%
• Latin America … $4.84B @ 13.5%
• Asia-Pacific … $4.4B @ 11%
• Africa … $200M @ 0.5%
(Source: Iqbal, M., 2025, Business of Apps)
Netflix plans to navigate growth in the Global South and West by relying on the CQ/CI
technology model dimensions. Recognizing that Netflix employees are the most loyal user
category, a triangular strategy is devised for using CQ/CI technology to grow customer assets
(users), employee assets (jobs), and intelligence assets (data). Preparations are made to develop
highspeed network facilities and training programs in South Africa, Mexico, and India, as global
region digital streaming hubs.
Well versed in cross-cultural management and cultural intelligence (CQ/CI) techniques,
Netflix executives grasp the role of meta-theory as a CQ/CI technology dimension. Both staff
training aptitude and user streaming access are improved by configuring a compatible ethnic
cultural context in real and digital/AI media realms. Field observations and depth interviews
capture the natural cultural work/home milieus for the multiple ethnic groups in South Africa,
Mexico, and India. Those elements including detailed studies of ethnic cultural history, traditions
and symbolism comprise the meta-theory compass for coordinating cognitive device/software
design, behavioral interface engagement, and motivational content experiences.
Among the many ethnic motifs gleaned, the philosophies of Ubuntu in South Africa, Mayan
Polpol Vuh in Mexico, and Hindu Vedic Vedanta for India, are isolated for molding cognitive
design prototypes. These conducive historical ethnic value systems for each market are attuned
with contemporary observations of work/home ethnic norms. Ethnic cultural identity narratives,
geometric symbolism, space/time orientation, guide cognitive device/software design protypes in
each of the three markets. Likewise, traditional ethnic relational patterns, language idioms, and
bodily/sensory preferences (visual, oral, gesture, etc.) are depicted as best practices for
behavioral interface forms, functions, and features. Motivational multisensory content was
curated by sampling ethnic cultural experiences in each location for traditional, contemporary,
and various original expressions.
In this way, Netflix pursues growth in the Global South and East with CQ/CI technology
investments in South Africa, Mexico, and India. Ethnic CQ/CI technology dimensions applied to
customize digital/AI media in the workplace and personal space. The Roblox and Sandbox
gaming companies have successfully developed metaverse avatars with ethnic, gender, and

38
lifestyle diversity in a campaign to make the metaverse “meta-diverse,” in partnership with
cosmetics brands and other clients (Murphy, 2022). Market growth results from an improved
ability to attract/retain employees, appeal/resonate with users, and harness ethnic “virtual
human” symbiosis to generate database intelligence of ethnic cultural profiles for future strategy,
sponsorship, and systems innovation.
Case 2: AE Architecture Deployment for Growing Digital Agriculture Artificial Life
Another case scenario for future digital agriculture capability helps to recognize the ingenuity
of AE architecture. Digital agriculture is poised for an artificial life renewal. Quietly, global
agriculture firms have been planting silicon seeds and in the technology grid, along side produce
crops. As a result, data science has taken root in agricultural portals like the John Deer
Operations Center and digital agriculture practices like Corteva Digital Solutions. Yet, despite
ample farming data analytics and field robotics, food will always be eaten by people. So,
technology could connect the plants cultivation with the people’s culture. That means attuning
sowing and yielding cycles to ethnic cultural customs and biorhythms for using time (planting
labor) and food (produce demand), as well as global commerce import/export curves. In addition,
AI technology with ethnic cultural orientations raises understanding and trust of communication
with the people planting, picking, packing, storing, moving, selling, servicing, buying, and
eating. Like engaging kin in a natural manner with shared ethnic cultural vernacular and motifs.
Given the AI’s potential for feeding the planet’s people, both firms are partnering in a cutting
edge digital agriculture lab for growing ethnic artificial life species using AI simulation. A two-
pronged plan strives to regulate quantity levels for profit with field rhythms datamining systems
and raise quality of work/life for people through kindred messaging systems with diverse ethnic
cultural presence, personas, and parlance.
Recent posthuman research reinforces the field rhythms datamining system, using an AI
artificial life “ecosystematic” network of technology, human culture, and ecological life patterns.
“With this ecosystemic approach we consider the possibility of an artificial
intelligence (AI) that supports well-being in a broad sense, accommodating
relationships across different layers of living worlds and involving local and
global communities of all kinds. … We envision the [artificial life] creation of
sociotechnical systems that could be modeled on networked lifeforms that have
optimized themselves across millions of years, like the organism Physarum
polycephalum, which occurs globally in moist environments, or like those
microbial populations within and outside of human bodies, whose percussive
biological processing interacts with and alters many layers of lifeforms.
(Solomon & Baio, 2020, p. 559, 560)
Ongoing artificial life experimentation also supports the actual implementation of a kindred
messaging system using “artificial neural networks” (ANN) with bio-intelligence autonomous

39
learning, adaptability, and forecast prediction (Rabunal & Dorado, 2006). Among the various
ANN domains, customized multimedia content messaging and multisensory signal coordination
can be designed to connect historical archive datacenters, digitally recorded human interaction,
and ecological sensor circuits. These tailored designs import existing ANN intelligence and
spatial mapping properties for eco-climate monitoring, weather forecasting, bio-medical
diagnosis, facial/voice identity recognition, social media analysis, and stock market predictions
(Goel, et al., 2023). The flexibility of ANN is demonstrated in vehicle messaging networks for
secure routing of logistics and private information to selected nodes (Sekhar, et al., 2023).
Similarly, the contemporary use of ANN to create collaborative AI chatbots with combined
IQ/EQ competencies (Lee, et al., 2022) shows its effectiveness as a kindred messaging system.
Besides neural networks, artificial life techniques like cellular automata, evolutionary
computing, and wetware, can be deployed with kindred messaging system functionality.
Recalling that AE identity logic mapping screens help to plan a portfolio of AI appliances,
the Director of Rhythm-Kin Labs knows that she has chosen the artificial life approach towards
AI (Parisi, 1997). Still, strategic portfolio planning analyzes the full mapping screen of AI
innovations, spanning the ambit of Benedikt’s (1991) “4 threads” and surveying the angles of
Luppicini’s (2013) Technoself Studies (TSS). Early on, a common vision was embraced for
Rhythm-Kin Labs (RKL) to follow the artificial life path towards deploying AI (Steels &
Brooks, 1995), similar to Cortical Labs. Their recent successful test and deployment of the first
biological computer demonstrates the viability of growing human neurons into intelligence
circuitry, alongside conventional electric circuitry intelligence (Lee, 2023; Yazgin, 2025).
Life science and anthropology experts have found the prevalence of distinguishable ethnic
neurological tendencies, which fill ethnic medicine and pharmacology databases. RKL is
hypothesizing that human neuron artificial life intelligence can be customized based on the
ethnic cultural identity of the human neuron source. The immense agricultural and bio-
agriculture acumen accumulated over several decades, directly contributes to rapid incubation of
artificial life neurons for networking. In addition, as leaders in digital agriculture John Deer and
Corteva have accessible AI coding and connection assets.
Returning to the AE identity logic mapping screen quadrants, other types of AI were selected
to supporting the central artificial life ethnic neuron incubator. Smaller narrowly targeted AE
culturally conducive appliances are designed to feed the central kindred messaging system
neuron computer. Multisensory ethnic identity “virtual humans” (Burden & Savin-Baden, 2019;
Magnenat-Thalmann & Thalmann, 2005; Rickel, et al., 2002). are deployed throughout
residences surrounding the fields using preferred modes/media.
Similarly, field rhythm datamining systems are fed by narrowly tasked advanced AI sapient
circuits connected to government, university, and private archives. They are trained to distill the
cultural history traditions of each ethnic culture’s time/usage cycles for earth, soil, plant, food,
farmwork, and leisure renewal. Indigenous knowledge systems (IKS) are respectfully retrieved

40
for deeper richer perspectives (Ayittey, 1991, 2016; Dyson, et al., 2006; Emeagwali & Shizha,
2016; Goulding, et al., 2016; Islam & Banda, 2011; UN 2021). Thus, by pairing field rhythm
datamining and kindred messaging systems, mapping screens assist in strategically planning an
AE identity logic portfolio with artificial life and other AI innovations.
Synthesizing CQ/CI Technology Contributions with Iconic AI Forerunners
Transitioning from case scenario lessons to closing intellectual contributions, iconic AI
foreseers continue to bring future clarity from their clairvoyant past. Sherry Turkle (1984, 1997)
was among the first the artificial posthuman as a “second self” human personality and
sociocultural “life on the screen,” not merely an intelligent digital identity. Turkle turns people
towards learning themselves, not technology, to artificially replicate and culturally render human
identity. Contrary to other tech futurists, she carves a personal zone of familiarity to embrace
posthuman AI. For Turkle, digital/AI media should be designed with realistic self-reflections in
shared relationships, not unrealistic abstractions of shady aliens. These personalized AI have
“thick descriptions” (Geertz, 1973) and “technoself” designs (Luppicini, 2013) to create
ethnically diverse artificial selves within global multicultural immersive-hybrid societies.
Certainly, the CQ/CI technology model makes a relevant contribution to cross-cultural
management of posthuman artificial selves.
“The Second Self remains a primer in the psychology of people’s relationships
with computers. Computational objects, poised between the world of the animate
and inanimate, are experienced as both part of the self and the external world. …
To be provocative, one is tempted to speak not merely of a second self but of a
new generation of self, itself.” (Turkle, 1984, p. 5)
Comparably, Brenda Laurel (1991) furthers the architecture for artificial human nature using
a culturally rich dramaturgical context. Like Goffman (1959, 1974), she draws upon the ancient
history of theatrical performance for portraying human identity through roles and relational plots
that frame experiences as scenes. Just as Turkle embodies the artificial with personal human
“second self” characteristics, Laurel embeds the artificial with cultural human “computer as
theater” context. Keenly, Laurel recognizes the meta-theory pattern which cultivates a cultural
milieu habitus and identity meaning doxa (Bourdieu, 1977, 1990, 2002). Unlike any other
scholarly paradigm, dramaturgy authentically represents the entirety of human history and
contemporary societal dynamics as experiential scenes, collective casts, and individual
characters. Proof of dramaturgy’s panoramic life depiction and potential for human duplicity is
exhibited by realistic theater and motion picture performances.
“So now we have at least two reasons to consider theater as a promising
foundation for thinking about and designing human-computer experiences. First,
there is significant overlap in the fundamental objective of the two domains – that

41
is representing action with multiple agents. Second, theater suggests the basis for
a model of human-computer activity that is familiar, comprehensible, and
evocative.” (Laurel, 1991, p. 30)
The “computers as theater” metaphor furthers human agency based on inherently familiar
narrative traditions. It’s brilliance is the capacity to transform technological encounters into
commonly shared cultural experiences for human/digital-AI collaboration. Likewise, CQ/CI
technology harnesses ethnic cultural identity to orient all dimensions of digital/AI-media design
towards familiar human traditions, tendencies, and tastes. Similarly, AE architecture configures
emic ethnic role selves with coded cultural narrative scenes.
Pattie Maes (1991, 1995) individualized artificial agents as sapient sentient human “artificial
life.” Whereas Turkle (1984, 1997) designs digital human selves within collective virtual lives,
and Laurel (1991) creates digital dramas with familiar contexts, Maes tailors AI into unique
personal companions with autonomous artificial life capabilities (Maes, 1991). New intelligent
agent architectures enable them to think, reason, learn, and dynamically function in
environments – much like biological species (Maes, 1995), In addition, AI agents are interwoven
in human market, commerce, and work environment interactions (Maes, et al., 1998).
“Artificial life shares with artificial intelligence (AI) its interest in synthesizing
adaptive autonomous agents. … The artificial life community has initiated a
radically different approach to this goal, ... inspired by biology, and more
specifically the field of ethology, which attempts to understand the mechanisms
animals use to demonstrate adaptive and successful behavior. Autonomous agents
can take many different forms, depending on the nature of the environment they
inhabit.” (Maes, 1995, p. 108)
Naturally, just as people prefer plant and animal pet species that are suited to personal and
cultural penchants, the CQ/CI technology model and AE architecture prefigure Maes’ (1991,
1995) “artificial life” species customized to ethnic identities and shared cultural inclinations.
Coinciding with timing of Turkle, Laurel, and Maes is Negroponte’s (1995) iconic Being
Digital trajectory direction for the design of posthuman artificial beings. Building upon the prior
trio, he formulates future digital beings by transforming biological human atoms into artificial
algorithmic bytes. As such, Negroponte accounts for the true digital DNA markers that enable
human identity to be encoded in artificial entities.
“Possibly The idea that twenty years from now you will be talking to a group of
eight inch-high holographic assistants walking across your desk is not farfetched.
… It has to be able to expand and contract signals as a function of knowing me
and my environment so intimately …” (Negroponte, 1995, pp. 148, 151)

42
Although the “being digital” formulation produces vivid human avatars and valid cultural
aesthetics, it processes entirely numerical ‘big data analytics’ using binary codes. To that extent,
“being digital” parallels posthuman digital/AI beings as “quantified self” compiled as the
“statistical individual” (Bjerring & Busch, 2024; Abend & Fuchs, 2016). As De Vries (2010)
substantiates, abundant data and powerful AI models can generate ethnic identity profiling
algorithms and IoT ambient intelligence cultural aesthetics. Projected into the posthuman AI
future, Galván (2003, 2015; Galván & Luppicini, 2012) posits new species of “cyber-sapiens”
and “homo-technicus.” Whereas cyber-sapiens embed quantified human intelligence into AI
appliances, homo-technicus embodies quantified self through AI enhancements of human
agency. Therefore, “being digital” conceivably encodes the quantified self in diverse ethnic
versions of CQ/CI technology and AE architecture.
Amazingly, the posthuman AI future that synthesize this study’s contribution is paved by the
four iconic oracles highlighted above -- Turkle, Laurel, Maes, and Negroponte. Although a wide
array of AI innovations can be projected by probing these prescient scholars, the posthuman era
is unique and ubiquitous with unforeseen possibilities. Carrigan and Porpora (2021) concur that
posthuman reality and identity are unfathomable. They assess different scholarly camps
championing idyllic “transhuman” tech singularity endowed with human consciousness,
artificially amplified “hybrid-human” cyborgs and virtual humans, as well as autonomous
“posthuman” AI beings and artificial life species. Of course, this study has emphasized the
inherent ethnic cultural identity of these posthuman research streams, using the CQ/CI
technology model and AE architecture. Selected posthuman possibilities include:
1. Holistic “digital technological matrix” governing human enhancement and
replacement technology (Donati, 2021, p. 27).
2. Heuristic models for quantified self data analytics produce “more than human
assemblages” able to “generate agential capacities that shape people’s sense of
selfhood.” (Lupton, 2019, pp. 98-99).
3. Humanities “transmutation of values” of modernist self-identity (ethnicity/race,
gender, etc.) to embed AI with postmodern self-constructivism, enabling “generative
encounters with others” of diverse human/artificial types (Braidotti, 2019, p.175).
Scholarly CQ/CI Technology Considerations for Critical Inquiry
CQ/CI technology model and AE architecture research contributions and considerations can
be crystalized for critical scholarly inquiry. This dialogue only scratches the surface of the
multilayered CQ/CI technology reasoning which can benefit global modern cross-cultural
scholars and strategists. Since so much of the intellectual discourse about the future of digital/AI
media converges on the human/technology polemic, the critical inquiry is framed by that topic.
1. Is ethnicity a tenable global human universal capable of supporting CQ/CI technology
research agenda that echoes and extends classic and contemporary CQ/CI literature?
2. Can CQ/CI technology and AE contribute a redeeming human balance between:

43
a) Analytic “quantified self” (yang-data dada-masculine) use of AI algorithmic
coding as virtueless hypertext data
b) Anthropological ‘qualified self’ (yin-mata mama-feminine) ethnic ancestral
culture as virtuous “habitus doxa” (Bourdieu, 1977, 1990, 2002).
c) How should the most conducive CQ/CI technology sphere be primarily mapped:
a) work space for cross-cultural management
b) personal space for multicultural networks
c) community space for cultural, faith, education, arts, science access
d) Which digital/AI media will best embody diverse ethnic cultures, and how?
* Clue: Are entertainment technologies serious digital/AI media platforms for
supporting or merging with dominant work and professional tech systems?
e) Can human Artificial Ethnicity be embedded into digital/AI media, and if so, what are
the most promising cross-cultural management benefits and costs? If not, why? And
could potential cross-cultural management advances be missed?
f) Is CQ/CI technology the logical cross-cultural management evolution of the classic
CQ/CI model for learning and managing competencies in people?
g) Which of the CQ/CI technology dimensions are best supported by parallel logic with
classic CQ/CI dimensions and are most viable for ethnically diverse future digital/AI?
h) Which technology types are made most human by Artificial Ethnicity architecture?
a) humanoid robots
b) software agents/bots
c) immersive 3D avatars
d) interactive vehicle dashboards
e) home management systems
f) other types
A multitude of cross cultural management ideas should emerge from these critical inquiry
topics. Incubating this intellectual curiosity and technological speculation should spawn shared
cross-cultural themes for optimizing future global multicultural human capital with original
CQ/CI technology generating Artificial Ethnicity cultural capital. It is a collaborative duty to
keep the cross-cultural mental juices cooking. If CQ/CI technology creates an appetizing
futuristic multiethnic gumbo, then all are welcome at the thinking table.

44
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