Construal Level Theory and Maslow's Hierarchy with Machine

Learning for Enhanced Consumer Demand Analysis

Xin Chen

School of Business, University of Sydney, 361 Sussex Street, Sydney, Australia

Keywords: Construal Level Theory (CLT), Maslow's Hierarchy of Needs, Brand Differentiation, Supervised

Classification Learning, Psychological Labeling.

Abstract: This study delves into the intersection of Construal Level Theory (CLT) and Maslow's Hierarchy of Needs

through the lens of advanced machine learning. By adopting psychological labeling and supervised

classification learning, it engages with Maslow's model to scrutinize the market's terrain—differentiating

emergent brands from established counterparts and examining the fulfillment of consumer needs. This inquiry

provides a granular view of how brands cater to the various psychological and spatial dimensions outlined by

Maslow and CLT. The fusion of these psychological frameworks with computational analytics serves to shed

light on the subtleties of brand performance and consumer preferences. The methodology bridges the gap

between abstract psychological theories and their tangible implications in machine learning. The resultant

insights afford a richer comprehension of consumer behavior, equipping businesses with the means to fine-

tune their marketing endeavors. The enhanced understanding gained through this interdisciplinary approach

paves the way for more targeted marketing interventions, thereby improving business decision-making

processes and fostering more effective consumer engagement.

1 INTRODUCTION

As psychological research has deepened our

understanding of how psychological processes affect

behaviors in various fields such as communication,

memory, decision-making, and emotion (British

Psychological Society, 2021), people are increasingly

interested in applying these insights to consumer

research. This method helps to better understand the

intricacies of consumers' personal thoughts, desires,

and experiences (Malter et al., 2020).

In addition, psychology has recognized the great

potential of machine learning to explain theoretical

constructs and build more dynamic and predictive

models (Yarkoni & Westfall, 2017). This paper

proposes to combine CLT and Maslow demand

hierarchy theory with machine learning technology.

These methods can handle complex nonlinear

relationships and huge data sets to better understand

consumer needs.

Our goal is to explore how to apply these

advanced technologies to enhance the brand's product

https://orcid.org/0009-0007-0483-4239

strategy, improving efficiency as well as addressing

the psychological and emotional contours of

consumer decisions. As a result of such integration,

the interaction between the brand and the consumer

should be closer to their psychology, as well as

increase artificial intelligence's ability to understand

and respond to the subtle needs of digital users.

2 BACKGROUND AND RELATED

WORK

This chapter explains three theories, starting with

objects as part of the extension of the self, as a means

of rationalizing the movement toward ownership,

control, and proximity in the theory of mental

framing, and proposing Maslow's needs as a model

for a staged interpretation of consumer needs. This is

used as a theoretical basis for the following chapter

on mental labeling.

188

Chen, X.

Construal Level Theory and Maslow’s Hierarchy with Machine Learning for Enhanced Consumer Demand Analysis.

DOI: 10.5220/0012922800004508

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 188-193

ISBN: 978-989-758-713-9

2.1 Theories

2.1.1 Sartre and Belk

In his 1988 study, Property and the Extended Self,

Belk explores how objects play an integral role in

who we are and introduces the concept of the

extended self as an extension of us. In addition to

examining individual ownership of objects, the study

examines the way we relate to them from a broader

perspective. Objects define our sense of self, as the

writer Yi-Fu Tuan (1980) points out, because we are,

in a large part, what we own and possess. He states,

"Our fragile sense of self needs support, and we get

that support by owning and possessing objects."

It is advisable to keep all the given values. Belk's

view further clarifies how objects are seen as an

extension of our self-concept, from a part of our body

to the physical environment around us. According to

Jean-Paul Sartre's (1943) theory, the ways in which

objects become "extensions of the self" primarily

involve controlling, creating, and making sense of

them. These processes extend beyond the individual

to include collective identities such as families,

groups, subcultures and nations. David (1951)

suggests that by creating or deeply understanding an

object, an individual can internalize it as part of their

own identity. This involves both the creation of

material objects and abstract concepts and the process

of becoming intimate with the object and seeing it as

part of the self.

Belk's (1988) research suggests that by owning

and controlling objects, consumers effectively reduce

the psychological distance between themselves and

those objects. For example, by knowing and

customizing a smartphone to reflect personal tastes

and preferences, an individual may feel that the phone

is an integral part of him or her, thereby significantly

reducing the psychological distance from the object.

2.1.2 Construal Level Theory

The core motivation for viewing objects as extensions

of the self is partly motivated by the quest for

completeness, which evolves into control and

possession of objects. This dovetails with Construal

Level Theory (CLT), which focuses on narrowing

psychological distances and emphasizes four types of

distances - temporal, social, spatial, and hypothetical

- that converge psychologically to form psychological

distances that influence decision-making in different

contexts.

Jean-Paul Sartre (1943) theorized that controlling

and owning objects effectively reduces the

psychological distance between us and those objects.

Ownership extends beyond the physical to include the

perception of consumer goods, their personalization,

and their reflection of one's identity and social status.

For example, personalizing smartphone settings to

reflect personal preferences makes the phone an

integral part of the individual, thus significantly

reducing psychological distance. Psychological

distance is positively related to the level of mental

representation an individual establishes of an object,

event, or person (Sordi et al., 2022).

Influenced by emotional or cognitive processes,

consumer decision-making varies depending on the

level of abstraction of their thinking, with higher

levels of abstraction preferring modern products that

are feature-rich (Ding et al., 2017). Direct experience

with a product shortens the psychological distance

and makes the consumer's interaction with the

product more concrete (Trope & Liberman, 2010).

Combined with the insights of Sam Maglio

(2019), psychological distance also influences how

consumers envision and act on possibilities. Even at

the initial decision-making stage, this distance can

cause systemic shifts, as what is psychologically

distant must be mentally visualized, while what is

close can be directly observed. For organizations,

managing social distance is critical to managing

relationships with customers, and it influences how

consumers perceive brands that are psychologically

close or distant. Therefore, understanding and

applying cultural communication techniques can

have a significant impact on marketing strategy and

consumer engagement by addressing the

psychological distance that affects consumer

perception and behavior.

2.1.3 Maslow Demand

When discussing consumer goods as extensions of the

self, it is clear that the symbolism of consumer goods

varies over time, cultures, and individual and

collective contexts. Such variations reflect the

diversity exhibited by the different characteristics and

attributes of consumer goods. Maslow's (1943)

Hierarchy of Needs describes human needs as ranging

from basic physiological needs like food and air to

higher level needs like self-esteem and self-

actualization.

Jordan (1999) extended this framework to the

hierarchy of needs of product users, defining levels of

functionality, usability, and pleasure, highlighting

how products evolve to satisfy these multiple levels

of human needs. Oghenemaro (2023) updated

Maslow's hierarchy of needs by emphasizing that

Construal Level Theory and Maslow’s Hierarchy with Machine Learning for Enhanced Consumer Demand Analysis

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once the individual has met the basic needs, he

progressively seeks to satisfy more complex needs

that motivate behavior until self-actualization. This

refined understanding allows us to better recognize

and satisfy the different needs of consumers and to

understand the tendency of products to migrate to

higher Maslowian needs during their gradual

formation and maturation.

By further exploring Maslow's Hierarchy Theory,

we can categorize needs into multiple dimensions,

such as product value, social value and personal

value, thus laying a solid foundation for

understanding consumer behavior and designing

products and services that satisfy these needs.

2.2 Gaps

2.2.1 Theoretical Gaps

The theoretical gaps identified through the review and

analysis of the existing literature suggest that while

Construal Level Theory (CLT) has been widely used

in various fields, especially in marketing and online

retailing through high-frequency word analysis, there

is a relative paucity of research on the intersection of

CLT and consumer behavior. Furthermore, an

important theoretical gap in the consumer decision-

making process relates to the mental representation of

unchosen alternatives. Existing literature emphasizes

how mental representations of situations influence

product choice, but there is still little discussion on

how non-selective decisions are constructed. For

example, Sordi et al. (2022) argue that consumers

may also employ rejection strategies that

systematically eliminate options until a final choice is

made. As noted by Mourali & Nagpal (2013), such

strategies can produce different outcomes even when

the same set of options is considered.

Cultural linguistics has been used in many

different domains, but few studies combine its

analysis with other theories or models (such as

Maslow's Hierarchy of Needs) or with artificial

intelligence to model and annotation consumer

demand. There is an urgent need for research that

explores the intricate relationship between culture

and consumer behavior, with a particular focus on

how to incorporate machine learning to analyze

consumer demand and the corresponding product

offering strategies of brands.

2.2.2 Methodology Gaps

In relation to Maslow's classification of needs,

Jastrzebska and Homenda (2012) provide a detailed

categorization of needs, including applying Maslow's

Hierarchy Theory to their model to quantify the

impact of needs on an individual's behavior on a scale

of zero to one. They categorized needs into

physiological, safety, love and belonging, esteem and

self-actualization needs and defined these needs

throughout the needs space. This helps to differentiate

consumer need vectors based on their similarities and

dissimilarities and provides a methodological basis

for further exploration of consumer similarity

measurement.

However, there are obvious limitations to these

models, as well as the fact that the research relies

primarily on qualitative rather than quantitative

analysis, which results in a lack of relevant word

frequencies to label needs and limits the effective

integration of consumer cues into the algorithm. In

addition, while cluster analysis has been used to

identify CLT or Maslow's high-frequency words,

these words have not been used in conjunction with

machine learning methods to model consumer needs.

This identifies a theoretical gap where the application

of CLT in consumer behavior research, combined

with advanced computational techniques, can

increase the depth of consumer demand models.

3 METHOD

Using online retail data and psychographic labels, I

will categorize products and brands in this section.

My approach is to use Maslow's Hierarchy of Needs

Theory in conjunction with Constructive Level

Theory (CLT) to develop a comprehensive demand

model for each brand that primarily addresses

Maslow's hierarchy of needs.

3.1 Data Preparation

To analyze the relationship between product sales and

consumer needs, sales data from 16 e-commerce

brands were collected, covering the top 30-50 best-

selling products for each, resulting in 600 data points.

This data was processed and labeled according to

Maslow's hierarchy of needs—basic, safety, social,

esteem, and self-actualization—and four CLT

distance metrics: temporal, spatial, social, and

hypothetical.

With the use of automatic keyword matching

technology (see table 1), products or brands were

categorized based on Maslow's needs and CLT

distances, allowing branch and node modeling to take

place.

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Table 1: Synonyms and keyword matching.

CLT Distance Synonyms

Physiological

Needs

• Time Distance: Instant Delivery, 24/7

Service

• Space Distance: Locally Produced,

Local Supply Chain

• Social Distance: Group Buying Offers,

Family Packages

• Hypothetical Distance: Product Quality

Guarantees, Satisfaction Refunds

Safety Needs

• Time Distance: Extended Warranties,

Ongoing Maintenance Services

• Space Distance: Safety-Certified

Products, Traceability Systems

• Social Distance: User Safety Ratings,

Trusted Seller Badges

• Hypothetical Distance: Insurance

Services, Risk Alerts

Social Needs

• Time Distance: Seasonal Social Events,

Holiday Sales

• Space Distance: Community Stores,

Social Media Interactions

• Social Distance: User Groups, Social

Sharing Rewards

• Hypothetical Distance: Influencer

Endorsements, Customer Testimonials

Esteem

Needs

• Time Distance: Limited Edition

Products, Loyalty Reward Programs

• Space Distance: Exclusive

Merchandise, Customization Services

• Social Distance: Brand Membership

Privileges, User Rating Systems

• Hypothetical Distance: Excellence

Service Certifications, Elite User Groups

Self-

Actualization

Needs

• Time Distance: Personal Development

Courses, Creative Workshops

• Space Distance: Global Creative

Marketplaces, International Cultural

Products

• Social Distance: Creator Communities,

Artist Live Sessions

• Hypothetical Distance: Self-Expression

Tools, Inspiration-Triggering Products

3.2 Demand Model

Using Maslow's five hierarchies of needs as nodes,

the model is represented as a node-and-branch

diagram (see Figure 2). These need nodes are

interconnected through specific links, known as

"branches", and each branch describes the

physiological distance associated with a product

through the four characteristics of CLT.

With CLT features, this model maps the dynamic

relationship between needs and assesses the degree of

connection between products and needs. For

example, how a product meets a user's security needs

through its functionality and features, while at the

same time adapting to the consumer's social needs in

time and space. By analyzing these connections and

distances in detail, we can gain insights into how a

product "moves" or evolves between different levels

of needs.

3.3 Result

The categorization of brands into clusters according

to how they satisfy these needs and their respective

CLT distances provides an insightful approach to

analyze brand market. Particularly, Cluster 1 (see

Figure 1), featuring brands like Xiaomi and

Lululemon, showcases an extensive coverage of

Maslow’s demands, each paired with a unique CLT

distance feature. It's notable how these brands span all

five of Maslow's demands (see Figure 2), suggesting

a comprehensive strategy to meet diverse consumer

needs.

The additional observations from Cluster 1 align

with the principles of CLT. By minimizing

hypothetical and temporal distances, these brands

help consumers more vividly imagine using their

products in the near future. Detailed presentations

enable a clearer and more immediate vision of

product use, fostering a sense of proximity and

tangibility.

In addition, the study emphasizes the value of

reducing spatial and hypothetical distances. Many of

these brands categorized as Cluster1 have their own

offline physical stores. By providing a direct product

experience, these brands are able to reduce spatial

distance, thereby enhancing the tangibility and

credibility of their products. These factors greatly

influence consumer trust and confidence, which is

crucial for an in-person shopping experience.

Through the presence of a physical store, brands are

able to not only provide an opportunity to physically

touch and feel the product, but also enhance

consumers' willingness to buy through direct human

interaction and immediate service feedback.

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Figure 1: Brand Clustering Visulization.

Figure 2: Demand Model.

4 CONCLUSION

4.1 Summary

In this study, we constructed a graph that reflects the

aggregation of similar brands. This analysis initially

involved labeling the data according to Maslow's

hierarchy of needs, which are satisfied by the

products, and proceeded with additional labeling and

clustering in relation to the psychological distances—

such as temporal, spatial, and social distances—

outlined in Construal Level Theory (CLT). This

methodology allows us to uncover how various

brands meet distinct consumer needs and deepen our

understanding of brand positioning in terms of

consumers' perception of psychological distance.

Drawing on insights from existing literature and

leveraging recent advancements in Clustering, this

research demonstrates the effective combination of

CLT with Maslow's Hierarchy of Needs theory. As a

result of aligning products and services with

Maslow's hierarchy and psychological labels

provided by CLT, we enable an accurate assessment

of consumer needs, which benefits consumers as well

as third parties in achieving their strategic goals.

4.2 Recommandations and Future

Agenda

Based on the insights derived from combining

Construal Level Theory (CLT) and Maslow's

Hierarchy of Needs Theory with machine learning

models, future research could utilize more

sophisticated techniques such as Graphical Neural

Networks (GNNs) to enhance the personalization and

effectiveness of customer experience strategies. As

described by Posner and Rothbart (2007), neural

networks mimic the functioning of brain regions that

work together to perform complex psychological and

physiological functions. GNNs are very similar to the

model presented in this article, and this analogy

extends to how neural networks interact in a dynamic,

nonlinear manner that may not be captured by

traditional models to synthesize and process

consumer needs Modeling.

As highlighted by Perez-Vega et al. (2021), there

are high expectations for AI systems to personalize

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the customer experience and improve marketing

outcomes through active and inactive forms of

customer engagement. Combining GNN with this

model can enhance the ability of these systems to

provide real-time, context-aware personalization that

psychologically satisfies consumers' individual

needs.

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