Smart Cities and Marketing: The Female-Relational-Orientation

Abstract

This chapter introduces the female-relational-perspective as the missing link in the Internet of the Things in smart cities because humans are organized when they use the Internet of the Things. Humans are privately organized in single, nuclear, and extended families, at work in teams and in public transport as streams of people going in and out of the neighborhood. Secondly, this chapter introduces the concept of superdiversity and its consequences for smart cities. In particular attention is paid to cultural bias and all-inclusive multiculturalism in smart cities. This chapter also proposes new vignettes for marketing in smart cities in which the female-relational-perspective, superdiversity, noncultural bias, all-inclusive multiculturalism and Internet of the Things are combined.

1 Introduction

Most definitions of smart cities are no more than striving for an advanced extension of the Internet of the Things (Albino et al. 2015). These extensions are dominated by males and their concerns about innovation and technology. This might be called the male-competition-orientation (Fiske 2011). The focus on alone “male-narrow-mindedness” has consequences for marketing communications about smart cities. The missing link in this communication is the female-relational-perspective (Fiske 2011). This chapter uses as the central question: “should marketing communication about smart cities cover both the male-competition-orientational perspective as well as the female-relational-perspective because these perspectives in daily contacts between people go hand in hand?” (Fig. 13.1).

Fig. 13.1

Classical Male-competition-technology-oriented definition (Charith et al. 2014: 3)

Therefore, the focus in this chapter will be alone on the female-relational-orientation. This perspective concerns the fluid or fixed/structural social organization (Blokland 2017) of human relationships apart from the male-competition-perspective because it broadens the look at marketing communication in smart cities. Our statement is that marketing activities in smart cities should also be designed for a variety of fluid or structural groups of people: single (with or without children), nuclear family, (parts of) extended families, tribes, and fluid communities (including social media communities) partly depending on the neighborhood and the number of incoming, permanent, and departing people in this neighborhood (these patterns can be studied with urbane rhythms) and similar human organization at (volunteer) work and in the leisure industry. This chapter about smart cities and the female-relational-orientations will also discuss Superdiversity, Cultural Bias, and All-Inclusive Multiculturalism and the consequences of these concepts for marketing in smart cities. This contribution ends with possible models/vignettes for education, health care, labor market and neighborhoods.

2 Definition of Marketing

The definition of marketing and the marketing process according to the business-dictionary is: “the management process¹ through which goods and services move from concept to the customer. It includes the coordination of four elements called the 4 P’s of marketing:

• 1. Identification, selection and development of a product;

• 2. Determination of its price;

• 3. Selection of a distribution channel to reach the customer’s place;

• 4. Development and implementation of a promotional strategy.”

Marketing is based on thinking about a business in terms of customer needs and their satisfaction. Marketing differs from selling because (in the words of Harvard Business School’s retired professor of marketing Theodore C. Levitt) “selling concerns itself with the tricks and techniques of getting people to exchange their cash for your product. It is not concerned with the values that the exchange is all about. And it does not, as marketing invariable does, view the entire business process as consisting of a tightly integrated effort to discover, create, arouse and satisfy customer needs. In other words, marketing has less to do with getting customers to pay for your product as it does developing a demand for that product and fulfilling the customer’s needs.”

This business-dictionary definition is hardly different from definitions of smart cities. Why? Because this definition of marketing does not distinguish like smart city definitions different types of human relationships (fluid and structural) at home, work, leisure, and transport. This marketing definition pretends that human relationships can be translated into elements of economic models like price and distribution channel. The following marketing definition however acknowledges indistinctly the female-relational-orientation: “a social and managerial process by which individuals and groups obtain what they need and want through creating and exchanging products and values with others” (Kotler et al. 2008: 6). Another definition that might be seen as a proxy of the female-relational-orientation is: “self-marketing can be defined as varied activities undertaken by individuals to make themselves known in the marketplace (Shepherd 2005: 590) which is also closely linked to the concept of self-promotion or self-branding” (Ward and Yates 2013; Resnick et al. 2016: 5). This definition acknowledges humans, unfortunately only as individual “male” entrepreneurs of small- and medium-sized enterprises. The American Marketing Association defined in 2007 marketing as “a set of processes for managing customer relationships to benefit the organisation and its stakeholders” (Resnick et al. 2016: 7).

In other words, the definitions of smart cities and marketing do have a striking similarity, with a focus on male-competition-orientation (such as Internet of the Things, price determination, and distribution channels) and the absence of the social organization of humans, both fluid and structural. Therefore, this chapter sets females and the female-relational-orientation down as equivalent to males and the male-competition-oriented-perspective. Within this context we will discuss superdiversity, cultural bias, and all-inclusive multiculturalism.

3 Smart Cities and Superdiversity

The concept superdiversity is coined by Vertovec (2007). Superdiversity is presented here in the context of smart cities since superdiversity might be understood as an organization principle (female-relational-orientation) of humans in smart cities. Superdiversity is a characteristic of large and smart cities in the small (western countries like the European countries and the US and the continent Australia) and big world (the continents Africa, Asia, and South-America). Another word for superdiversity that can be applied to large- and smart cities is majority–minority. In majority–minority cities the majority of the population comes from countries other than where we are (Crul 2015). It also means that the population of origin is mostly the largest minority. Recent Dutch research showed that the Netherlands contains 233 nationalities (Jennissen et al. 2018). Crul (2015) mentioned that “super diverse alerts us to other axes of difference between humans like gender, education, age cohorts and generations. The so-called new perspective is that between and within ethnic groups there is a growing difference between generations, between men and women and between more and less educated people. This calls for a shift of focus from fixed entities like ‘the ethnic group’ to a dynamic interplay between different characteristics of individual members of ethnic groups and the fluid relationships between them; in other words: a shift from an ‘ethnic lens’ to a multidimensional lens.” Furthermore, research of Crul (2015) showed that “children of immigrants nowadays no longer integrate into the majority group, but into a large amalgam of ethnic groups. Next to the diversification of ethnic groups, we see diversification within ethnic groups in the second and third generations.” Thirdly Crul (2015) finds “another cardinal new trend in European cities, namely that classical immigrant groups that came in the 1960s and 1970s as labour immigrants now extend into three generations living in the city. Children and grandchildren of the first generation are mostly born in these very same cities and grew up there. They now form one of the established groups in the city. They are strongly rooted in the city and, also, because most of their close family lives there. Often people of native descent come to the large and smart cities to study and to find work and often leave when they marry and start a family. This trend overthrows the picture of who are newcomers and who belong to the established group. To say that the grandchildren of immigrants have become part of the mainstream or the majority population seems more problematic in Europe than in the USA.” A nice example of the new world is portrayed for a neighborhood in the smart and superdiverse city Anvers Belgium (van der Vijver et al. 2015).

What are the lessons of superdiversity in smart cities for the female-relational-orientation in these cities and therefore marketing? The first lesson is that superdiversity outlines the social and organizational structure of inhabitants of smart cities. Immigrants, other newcomers and their children are the permanent inhabitants for three or four generations of smart cities and people of native descent are the temporarily inhabitants mainly trying to fulfill in smart cities the need to work and/or study. The second lesson is that newcomers learn from immigrants in their neighborhood about the characteristics of the culture of this particular homeland where they just settled. The consequences are that the original culture of the inhabitants of native descent disappears from the picture. These shifts in smart city cultures could lead to an increase in innovative forces but could also lead to far-reaching polarization. In Figs. 13.2 and 13.3 these trends in smart city cultures are shown.

Fig. 13.2

Schematic representation of interactions in the neighborhood Berchem, Antwerp, Belgium (van der Vijver et al. 2015: 39)

Fig. 13.3

Dimensions of superdiversity based on Fenley and Daele (2014)

In Fig. 13.3 attentions is paid to the two dimensions of superdiversity, a visible and invisible one. Important characteristics of the invisible dimension are stress language (differs for most mother- and fatherland cultures), the difference between collectivism and individualism, social organization of extended families or tribes, etc. The elements of these two dimensions are part of the structural and fluid social organization of large and smart city inhabitants. An example of a structural organisation is an extended family home in a neighborhood of a smart city and an example of a more fluid structure are the visitors of the local Mosque in the neighborhood. Inhabitants are sharing the Mosque and worship Allah there but need not to know each other.

Superdiversity for marketing of goods and services and the relation with the Internet of the Things in a smart city creates an opportunity to illuminate different groups of permanent or temporarily inhabitants of a neighborhood, taking into account that the neighborhood is at the same time the globalized world. Temporarily inhabitants are visitors of single person households with or without children, nuclear families with and/or without children and parts of extended families (other parts are living in a motherland and other fatherlands). Temporarily are also hotels or Airbnb tourists. Illuminating these groups seen from a marketing perspective means realizing that services and goods are one part of the medal and the inhabitants and how they organize themselves the other part. Behind illuminating services and goods we find mechanisms that direct society processes like adaption, exclusion, integration and inclusion. A favorite imbedding in society is inclusion since it acknowledges that we as inhabitants of smart cities differ from each other and are learning to accept these differences. Those differences and acceptation of it should be part of the marketing communication (Fig. 13.4).

Fig. 13.4

Two scenarios of superdiversity (Cantle 2015)

Cantle (2015) suggests that superdiversity might have two possible (temporarily) outcomes, in particular “racial and cultural conflicts” and “innovation and creativity.” In order to prevent “cultural and racial conflicts” Resnick et al. (2016) raises the question “how can social cohesion be fostered in this framework?” “Fostering social cohesion via for instance social services² should be contingent on adequate resources and efficient public spending, must contrast with governments who often provide subsidies/payments that benefit the non-poor, prevent or mitigate duality and segmentation, not solely be efficient in monetary terms, utilize targeted cash transfers; programmes in Brazil, Indonesia and Mexico have attained coverage of up to one-third of the population through targeted cash transfers whilst costing them less than 1% of their GDP, devolve institutions to better reflect labour markets’ realities so to produce fair outcomes with minimal strife and decouple social protection from job status through universal entitlements in order to offer best coverage at all levels.” According to Cantle (2015) “a cohesive community is one where there is a common vision and a sense of belonging for all communities, the diversity of people’s different backgrounds and circumstances are appreciated and positively valued, those from different backgrounds have similar life opportunities and strong and positive relationships are being developed between people from different backgrounds in the workplace, in schools and within neighbourhoods.” These Cantle (Cantle 2015) rules can be applied to marketing in smart cities.

4 Smart Cities and Cultural Bias

Cultural bias is a mechanism that prohibits inhabitants to share a smart city life, enjoy and appreciate all possible differences between inhabitants of smart cities. Cultural bias is according to Wikipedia³ “the phenomenon of interpreting and judging phenomena by standards inherent to one’s own culture. Cultural bias is sometimes considered a problem central to social and human sciences, such as economics, psychology, anthropology, and sociology.

Cultural bias occurs when people of a culture make assumptions about conventions, including conventions of language, notation, proof and evidence. They are then accused of mistaking these assumptions for laws of logic or nature. Numerous such biases exist, concerning cultural norms for colour, mate selection, concepts of justice, linguistic and logical validity, the acceptability of evidence, and taboos.” Cultural bias is not random, but structural and an anomaly. It is said that it is difficult to remove cultural bias than removing personal bias (Borgohain 2017). Why it is so difficult is been shown in Fig. 13.5.

Fig. 13.5

Implicit cultural bias and its consequences. (Source https://openi.nlm.nih.gov/detailedresult.php?img=PMC4872612_arcr-38-1-47f1&req=4)

In Fig. 13.5 implicit cultural biases is characterized by two mechanisms, stereotypes and differential treatment. “In social psychology, a stereotype is an over-generalized belief about a particular category of people. Stereotypes are generalized because one assumes that the stereotype is true for each individual person in the category. Such generalizations are useful when making quick decisions, however they may be erroneous when applied to particular individuals. Stereotypes create a barrier that leads to prejudice, making one assume they know a person just based on a stereotype. Stereotypes, prejudice, and discrimination are understood as related but different concepts. Stereotypes are regarded as the most cognitive component and often occur without conscious awareness, whereas prejudice is the affective component of stereotyping and discrimination is one of the behavioural components of prejudicial reactions. In this tripartite view of intergroup attitudes, stereotypes reflect expectations and beliefs about the characteristics of members of groups perceived as different from one’s own, prejudice represents the emotional response, and discrimination refers to actions (Wikipedia⁴).” Examples of differential treatment⁵ are “opening up certain position to a certain gender or race only, not promoting older employees because they may not stay around as long, having all members of a certain race or religion sit in the same section, only inviting couples that are man and woman to the holiday and company parties, demoting a pregnant woman from her current role upon her return to work and making certain tasks impossible for disabled employees. Different is an adjective most often used to show how certain employees are being treated differently because of something he or she cannot change about themselves.” Humans in large and smart cities and at any place do experience (implicit) cultural bias as micro aggression and stereotype treat. “Being exposed to the daily assault of racial micro aggressions has major psychological implications and consequences. In a study examining workplace harassment at five organizations, minority women were significantly more harassed in the workplace than minority men and majority men and women. This study identified ten categories representing the most common symptoms likely to manifest in employees who experience chronic micro aggressions, anxiety, paranoia, depression, sleep difficulties, lack of confidence, worthlessness, intrusive cognitions, helplessness, loss of drive and false positives (person overgeneralizes negative experiences with others due to persistent feelings of harassment).The range of these categories illustrates the serious implications racial micro aggressions have on mental health (Holder et al. 2015).” In addition, exclusion is dangerous since the results might be short-term psychoses (van der Ven 2016).

An example of (implicit) cultural biases in the education system (in smart cities) is pointed out by the OECD (2018): “students with an immigrant background tend to underperform in school. This is particularly true of first-generation immigrant students (foreign-born students of foreign-born parents). On average across OECD countries, as much as 51% of first-generation immigrant students failed to reach baseline academic proficiency in reading, mathematics and science, compared to 28% of students without an immigrant background who failed to reach that level. Similar differences are observed in most other wellbeing outcomes as well: 41% of first-generation immigrant students reported a weak sense of belonging, compared to 33% of students without an immigrant background who so reported; 31% of first-generation immigrant students reported low life satisfaction, compared to 28% of students without an immigrant background; and 67% of first-generation immigrant students reported high schoolwork-related anxiety, compared to 61% of students without an immigrant background.”

Next constructed figure portrays characteristics of cultural bias and what might happen if cultural bias is eliminated (Fig. 13.6).

Fig. 13.6

Characteristics of cultural bias and what if cultural bias is eliminated

This section about (implicit) cultural bias indicates that marketing must prevent that the majority of inhabitants (“non-whites”) of large and smart cities is ignored. They are ignored by the powerful minority of “white” native descents. An example of the contrary is the marketing ad of Coca Cola⁶ (see Dutch Ethno-Marketing.nl platform, René Romer/TransCity). Obvious topics for marketing communication and messages are the holy days of immigrants, expats and refugees, and inhabitants of local origin (Ramadan, Christmas, Holi, Manasa Puja, and Yom Kippur), a variety of services and goods like scarves, cupping, Turkish and Moroccan bread, etc., (Kirgiz et al. 2018) and a variety of cultural norms and values. Less obvious are subjects like politics, collectivism, and individualism and ways of living together, in single, nuclear, or extended families. A visible consequence of the latter subject can be seen in smart cities during the summer period. Large and smart cities in Western counties are often empty in the summer and Ramadan since immigrants are going to their motherland to visit family and share rituals with them. If marketing communication and messages dedicate itself to acknowledgement of this large variety of human beings and their behavior in large and smart cities, inhabitants might feel that they belong to the large and smart city. If used in this way, marketing is an instrument that ensures that polarization and exclusion of inhabitants of large and smart cities does not take place. Unfortunately, marketing is misused in the political debate. In the political debate in the United States and Europe large groups of people are alienated from each other and are set against each other (Buruma 2017). Marketing should not lend itself to that because the consequences are less resilience, creativity and innovation, less belonging, and not fit for the labor market.

5 Smart Cities and All-Inclusive Multiculturalism

Last but not least all-inclusive multiculturalism at the workplace in smart cities and the consequences for marketing need to be discussed as well. Companies and institutions have their own concepts of equipment to function in superdiverse smart cities. We will elaborate on these concepts and suggest how these concepts might be realized in practice. As a second step, the consequences of all-inclusive multiculturalism for marketing in smart cities are highlighted.

In short, how is superdiversity in smart cities organized in companies and institutions? What are the operational search directions and what are the conditions? Here we will elaborate on these themes. Three clarifying concepts are covered: (a) color-blind, (b) colorful, and (c) all-inclusive multiculturalism.

The struggle between color-blind and colorful (multiculturalism in the United States of America (Stevens et al. 2008)) takes place on the playing field of the second dimension of superdiversity, the less visible part ((Fenley and Daele 2014), see Fig. 13.3). Color-blind is a synonym for assimilation (adapt to the dominant culture). Color-blind is rooted in individual merits (meritocracy) and equality. The color-blind approach is based on the assumption that all people are treated equally. Feeling excluded is the price that immigrants, expats, and refugees are paying for a color-blind or color-neutral approach (Stevens et al. 2008). That certainly happens if exclusion and discrimination on top of the aforementioned approach are denied or denatured. There are even indications that racism becomes easier to commit if there is color-blindness. However, people of native descent experience color-blind as more inclusive for their own group (Fig. 13.7).

Fig. 13.7

“Wrong” exclusive concepts and characteristics based on Stevens et al. (2008)

The colorful or multicultural approach to a superdiverse society assumes that “all cultures are equal and must be treated equally and that the cultural norms of one culture are not (morally) higher than those of the other.” The implication of this view is in practice extra attention for immigrants, expats and refugees. Examples are diversity programs of state and municipal governments, institutions, and companies, such as addressing cultural disadvantages, intercultural care for the elderly, promoting inter- and cross-cultural awareness, and recruiting students from the expat population (University of Amsterdam). Diversity programs are said to be exclusive to people of native descent (Stevens et al. 2008: 121) because they are a break on the unity of the native population. This opinion is illustrated in the following statement: “multiculturalism originated in response to the central position that the United States and Europe occupy in our social system (ethnocentrism). At what point does centrism turn into ethnocentrism? The word multiculturalism mainly refers to non-western, non-white cultures instead of all cultures. When does this obsession with differences begin to threaten the bridging American nationality?” “People of native descent” job applicants find companies that are positive about immigrants, expats and refugees less attractive than companies that do not make that distinction. This result may be generally applicable, including to neighborhoods and municipalities.

5.1 Solution: All-Inclusive Multiculturalism

Color-blind and multiculturalism each exclude a party, namely immigrants, expats and refugees on the one hand and the people of native descent on the other hand. Exclusion here only relates to the first dimension of superdiversity (see Fig. 13.3), namely that of ethnic origin. Since superdiversity increases in large and smart cities one might expect a decrease of color-blindness. Due to the increase of the number of immigrants, expats, and refugees in large smart cities, being equal is not only determined by the formerly dominant group of native descent but also by immigrants, expats, and refugees. In short, on the basis of numbers of people alone, a legitimate basis of color-blind and multiculturalism has disappeared. The adage: Si vis pacem para castrum (If you want peace, prepare yourself for war) has been taken down (Fig. 13.8).

Fig. 13.8

All-inclusive multiculturalism and consequences based on concepts of van der Zee (2015)

These references have laid the foundation for a new adage “all-inclusive multiculturalism” (van der Zee 2015) in businesses and institutions. The following two frames indicate what this concept means in practice. First of all, organization and team indicate that each member of the team belongs to it and furthermore that the organization sees her/him as authentic. Secondly the concept of Social Safe is redefined. The new concept is Our Kind of People (OKP) is available and visible in the organization. Customers/patients/students are now able to identify with the organisation and its services. The additional connotation is that the organization cares about each of us. This leads to trust, motivation, and better performance.

5.2 All-Inclusive Multiculturalism and Marketing

What is the implication of all-inclusive multiculturalism for marketing is the remaining question in this section about smart cities? At least we can distil two messages from the above. Firstly, that marketing, if they are engaged in businesses and institutions in large and smart cities, their subcontractors, clients, patients, and students, should communicate messages from which these parties can conclude that they belong to the organization, regardless of their background. Secondly, that people who are involved in any role in these companies and institutions in these large and smart cities are told that they are authentic. These marketing messages will create a feeling of trust, motivation, and better performances. Thirdly, marketing should try not to fall into the trap of color-blind and multicultural/multicolor. Through these pitfalls different groups of people in these large and smart cities are excluded. Fourth, marketing can provide information that shows that Our Kind of People are workers, subcontractors, clients, patients, and students of businesses and institutions. Social safety increases if marketing could portray in organizations and businesses little groups of 7–12 persons with a similar background. This is called the checkerboard model (Van Soomeren and Steinmetz 2015). Social Safety increases as a result of the fact that our kind of people are represented in the organization and that there is a little group apart from all the other little groups with whom everyone can work together.

6 Discussion

This contribution pays attention to the twin-orientation in smart and large cities, namely the female-relational-orientation and the male-competition-orientation. What is our motive to call this the twin-orientation? Our motive is in the first place, that the internet of things which we call the male-competition-orientation exists by grace of human activities and contacts, the so-called female-relational-orientation. At this stage we should keep in mind that humans have fluid and structural activities and contacts in smart and large cities (Blokland 2017). Blokland (2017) calls this “cement” between contacts, activities and humans in a smart and large city “culture.” Blokland (2017) definition of culture is: “I drew on a concept of culture as shared symbolic practices, although the meanings we attribute to symbols don’t have to be the same for all of us with four types of social ties which varies on an axis of high versus low rationality (Weberian) and an axis of instrumentality versus sociability.” Examples of fluid contacts can be found in public transport, a park, a Mosque and as a tourist travelling in a smart city. Structural contacts can be found between children, singles, and single household with or without children, nuclear or extended family, elderly in elderly homes or members of a community, like a community center. Those fluid and structural contacts steer the Internet of the Things. We assume that social contacts are a “veil” of desired and unwanted activities between individuals and groups of people in a smart city. Possible activities are social media contacts, being at home or on the street, shopping, eating, studying, working, activities with other youngsters, singles, nuclear or extended family, friends, going out, etc.

In this contribution about smart cities and marketing we elaborate further on the consequences and meaning of superdiversity, preventing cultural bias and promoting all-inclusive multiculturalism in institution and businesses. Our motive to present this approach is that it is our opinion that these concepts are a second “veil” over human fluid and structural activities and contacts in smart and large cities. Superdiversity is at odds with the “white” power structure in large and smart cities. Fortunately there are now exceptions to this “white” dominance. London has an Indian Mayor, Sadiq Khan and Rotterdam has a Moroccan Mayor, Ahmed Aboutaleb. France is in Europe a modest exception. Of the more than 36,000 municipalities, a dozen have a mayor with an immigration background. Most of them are villages of a few hundred souls. The most important of this group is the left-wing politician Eddy Aït, with Berbers as parents, openly homosexual, and since May mayor of the Paris suburb Yvelines-sous-Poissy, with 14,000 souls (NRC, 18 December 2008⁷). According to Crul (2015) immigrants are the actual residents of large and smart cities and residents of native descent are in large and smart cities for work and study. As soon as they start a family they leave the city. Cultural bias might be explicit or implicit and is often imposed by the “white” power structure in large and smart (western) cities. Cultural bias is imposing others’ own culture and norms and values, as we often see in the political discourse of different EU countries (Holder et al. 2015). This political discourse is experienced by immigrants, refugees and expats as micro-aggression, exclusion and bullying. A result might be a phenomenon that is described as short-term psychoses. The consequence is that innovative power evaporates in large and smart cities. Finally, we also pay attention to the last element of the “veil” over institutions and businesses all-inclusive multiculturalism. It is of utmost importance that companies and institutions in smart cities prevent color-blindness and multiculturalism/colorful. Both urban practices exclude a group. Color-blindness excludes immigrants, refugees and expats and multiculturalism excludes people of native descent. The golden key is the concept “all-inclusive multiculturalism.” The urban practice of this concept is showing in word and deed that workers, clients, patients, students and contractors belong to the organization and be considered as authentic. This leads to trust, motivation and better performance of all parties.

Working with these two veils (Veil 1: activities, fluid and structural contacts between individuals and groups of people present in a smart city; Veil 2: superdiversity, prevention of cultural bias and all-inclusive multiculturalism as steering mechanism behind social contacts of humans in a smart city, private, public and work but also social media) provided the opportunity to elaborate on rules and vignettes of marketing. We introduce these rules in order to prevent risks to happen, like a (local) mismatch between services and products, isolation and polarization between groups of residents in a large and smart city, and designing and planning only for the privileged.

In order to design marketing messages, research is needed. We advocate in this chapter to use algorithms through machine-learning, like logistic regression with in- and output simulation. Below is a diagram that shows what we mean.

Figure 13.9 shows the structure of a possible algorithm that might help to construct the marketing messages based on the earlier-mentioned vignettes. In this scheme we distinguish between independent, dependent variables, and covariates. With the independent variables and covariates we will estimate superdiversity, prevention of cultural bias, and all-inclusive multiculturalism. The focus is a neighborhood in a large and smart city. We would like to work with an algorithm that is called logistic regression in combination with a simulation of input-output data.

Fig. 13.9

Algorithm and input-output reinforcement (based on literature)

7 Conclusions and Vignettes

In this section all conclusions about the female-relational-orientation and the consequences for marketing in large and smart cities are bundled. Secondly we propose marketing vignettes that fit large and smart cities, the female-relational-orientation and the male-competition-orientation in these smart cities. According to Wikipedia⁸ a vignette can refer to (the image of) a logo, a small image, a toll sticker and a very short story of several hundred words. For our vignettes we will combine these technics.

The conclusions that relate to superdiversity and marketing are:

• 1. Differentiate between permanent inhabitants (immigrants, and their (grand) children), temporary inhabitants of native descent, expats, refugees and tourists (single, couples, groups of youngsters and families).

• 2. Differentiate between face-to-face contacts (from rare too often), members of a community who share the same religion or passion and solely social media contacts.

• 3. Distinguish layers in large and smart cities like neighborhood, city, small villages and town around large and smart cities and globalization.

• 4. Distinguish classical social systems like single household (with or without children), nuclear or extended family, and tribe.

• 5. Distinguish females-males, old and young people, and people with or without a disability.

The conclusions that relate to cultural bias and marketing are:

• 1. Don’t interpret and judge phenomena related to marketing communication and messages by standards inherent to one’s own culture (white or non-white).

• 2. Prevent stereotypes and differential treatment.

• 3. Prevent microaggression, discrimination, and exclusion in marketing communication and messages.

• 4. Prevent cultural bias in marketing communication about education, labor market, leisure time, and living in large and smart cities.

The conclusions that relate to all-inclusive multiculturalism in businesses and institutions and marketing are:

• 1. Prevent that marketing communication is color-blind and/or colorful.

• 2. Promote all-inclusive multiculturalism in marketing.

• 3. Apply in marketing for businesses and institutions the concept of “small” available groups of Our Kind of People in the organization. The availability of Our Kind of People generates the feeling of being at home.

• 4. Apply in marketing for businesses and institutions the concept that worker, clients, patients, students, and subcontractors belong to the organization and are authentic. This increases the feeling of social safety.

• 5. Develop a toolkit for marketing in businesses and institution that covers marketing of all activities of the organization.

In this section we propose several vignettes that meet the requirements of superdiverse, anti-cultural bias, and all-inclusive multiculturalism for institutions and businesses in large and smart cities.

In the first vignette we like to pay attention to smart cities and marketing, in particular the female-relational and male-competition-orientation. Apart from these orientations we will use in all vignettes as an example services and products of a supermarket.

7.1 Super (Our Example for Producing Vignettes)

The smart city “New World” has a supermarket chain “Super” all over the city. “New World” has almost two million residents (immigrants, refugees, expats, and residents of native descent) and 100,000 illegals, all from 250 different countries. “Super” has in every neighborhood of the smart city “New World” an establishment, almost 1474 establishments (a neighborhood has 1400–1450 inhabitants). The supermarket has an inclusive marketing team of females, males (50–50%), people of the relevant four largest and four smallest communities in the smart city SMART, and people of three age groups (40% young, 20% middle, and 40% old). Ten to 15% of the members of this marketing team have a disability (one of them is an Olympic athlete). For “New World” and “Super” we will develop several marketing vignettes. The first vignette is aimed at the female-relational-orientation and the male-competition-orientation (Figs. 13.10 and 13.11).

Fig. 13.10

Vignette smart cities and marketing (Source of pictures Wikipedia)

Fig. 13.11

Vignette superdiverse, sustainable food and marketing (Source of pictures Wikipedia)

The second vignette aims marketing for sustainable food in a superdiverse environment.

“Super” directs its marketing to the hyperdiverse neighborhood “KitchenGarden” in the smart city “New World” with marketing messages for sustainable food. This neighborhood is relatively poor. Residents did eat mainly fried meat and potatoes and virtually no vegetables and fruit. “Super” knows this since it collects data through buying and selling the data of its clients, telephone data (to determine who the clients are), and interviews. For this purpose they developed a machine-learning-algorithm (logistic regression) that simulates output sustainable food data which then feeds the algorithm with the true data.

The residents, mainly young and old, are living in extended families and ethnic communities. They support each other. One of the initiatives of the elderly is the development of a vegetable and fruit garden street where vegetables and fruit are grown. “Super” therefore stimulates her marketing of sustainable food, seeds, and soil for vegetables, fruit, potatoes, and rice and also techniques to be able to build up a winter supply. “Super” did engage for her marketing older and younger female and male residents from a number of extended families. They evaluate together the eating pattern of the neighborhood residents, partly based on visualization of the algorithm data (Fig. 13.12).

Fig. 13.12

Smart city and prevention of cultural bias. (Source of pictures Wikipedia)

“Super” wants to transport the concept of ​​the vegetable garden street to other neighborhoods in the smart city “New World.” “Super” would like to try this idea in the neighborhood “Mix-To-The-Max.” Like in the neighborhood “KitchenGarden” “Super” rolled out the first steps. First an analysis was done of the neighborhood characteristics in combination with client data (mobile and buying services and products) in combination with an algorithm. In this neighborhood “Mix-To-The-Max” 40% is poor (new immigrants and refugees) and 60% had a more than average good income (3–4 generation immigrants and people of native descent). The latter outcome (a good income) was caused by gentrification. Through interviews “Super” found that in this neighborhood animosity has arisen between rich and poor. The rich blamed the poor for the presence of rats and the poor blamed the rich for taking over all facilities. They said that the “Super” only cares about the rich and that “Super” is only out to make money. “Super” installed a neighborhood committee to find out how the animosity between both groups can be addressed. The committee came with three solutions. Solution one was “Super” was divided in two parts, one for the rich and one for the poor. In the part for the poor “Super” copied the concept of the “vegetable garden street” with the intention to increase awareness about consuming sustainable food. Solution 2 was the rich are providing coaching services for educating children of the poor and adult who like to find a job. Solution 3 was that every rich person/family adopted one or two poor individuals or families, and also eats regularly with each other. Solution 4 was making use of the local ethnic communities to realize the goals of pleasant and happily living together, by means of the solutions that have been devised by the committee. The committee was successful since their work was not culturally biased, since they cared about the poor and rich and the newcomers and the established groups in the neighborhood (Fig. 13.13).

Fig. 13.13

Smart city and all-inclusive multiculturalism. (Source of pictures Wikipedia)

The supermarket “Super” with her inclusive marketing team of females, males (50–50%), people of the relevant four largest and four smallest communities in the smart city SMART, people of three age groups (40% young, 20% middle and 40% old), and people with a disability (10–15%; one of them is an Olympic athlete) discovered that they were in practice either working color-blind (behaving as if everyone has the right to equal treatment. Immigrants, refugees, and expats experience this approach as discriminative) or multicultural/colorful (behaving as if immigrants have more rights than resident of native descent. Residents of native descent experience this approach as against them). Examples of these defects are a victim-oriented approach (only taking care of the poor and the newcomers = colorful) and ignoring the effects of gentrification (many of poor extended families could no longer pay the rent after their house was renovated and had to move to a cheaper neighborhood). “Super” and her marketing team were thinking about these results again. They found that they violated several all-inclusive multiculturalism rules. The most important one was that the staff of the “Super” branch did not mirror local residents. The second violation was that this marketing team did not develop rules of thumb to keep pace with all-inclusive multiculturalism. The outcome was changing HR department and policy and also developing an all-inclusive multiculturalism toolkit for “Super.”