Examining the impact of price sensitivity on customer lifetime value: empirical analysis

Abstract

This study examines how price sensitivity parameters affect customer lifetime value in the luxury business. Quality, position, information, time, and customer lifetime were examined as price sensitivity factors followed by a conceptual model and research hypotheses were produced based on previous studies. Secondary analysis and in-depth interviews with industry specialists and customers. A representative sample of 232 A class consumers was included in a survey that was given to A-class customers in Egypt. A new level of price sensitivity known as ‘quality positioning value’ was found through the use of factor analysis. Multiple discriminant analysis was performed to validate the hypotheses and Cronbach’s alpha was utilised to assess the reliability of the data. This analysis sheds light on the effect of price sensitivity on customer lifetime value in the luxury industry.

Full text

MARKETING | RESEARCH ARTICLE
COGENT BUSINESS & MANAGEMENT
2024, VOL. 11, NO. 1, 2366441
Examining the impact of price sensitivity on customer lifetime
value: empirical analysis
Sarah Ahmed Awaada, Wael Kortamb and Nihal Ayada
aDepartment of Business Administration, Faculty of Business, Arab Academy for Science, Technology & Maritime Transport in
Cairo, Cairo, Egypt; bDepartment of Business Administration, Faculty of Business, British University, Al Shorouk City, Egypt
ABSTRACT
This study examines how price sensitivity parameters affect customer lifetime value in
the luxury business. Quality, position, information, time, and customer lifetime were
examined as price sensitivity factors followed by a conceptual model and research
hypotheses were produced based on previous studies. Secondary analysis and in-depth
interviews with industry specialists and customers. A representative sample of 232 A
class consumers was included in a survey that was given to A-class customers in Egypt.
A new level of price sensitivity known as ‘quality positioning value’ was found through
the use of factor analysis. Multiple discriminant analysis was performed to validate the
hypotheses and Cronbach’s alpha was utilised to assess the reliability of the data. This
analysis sheds light on the effect of price sensitivity on customer lifetime value in the
luxury industry.
1. Introduction
This study investigates how customers’ sensitivity to pricing impacts customer lifetime value (CLV) in the
luxury goods industry. Through an extensive literature review, four key dimensions of price sensitivity
were identified that will be examined: Quality Value, Time Value, Position Value, and Information Value.
Previous research shows that price plays a major role in how customers evaluate product options and make
purchasing choices (Hsu et al., 2017). Specifically, price heavily influences customers’ judgments about a brand’s
pricing relative to competitors and decisions between brands and product types (Niedrich et al., 2009).
Price is an integral part of a company’s marketing strategy, representing the ascribed value of a prod-
uct and amount charged. Academics define price as the exchange value of goods and services. Appropriate
pricing is crucial for companies to establish market share and profitability. While pricing decisions can be
made quickly, poor pricing can severely hinder a company’s success. Firms should recognise the oppor-
tunity to create value by aligning their revenue generation approaches (Sjödin et al., 2020).
Moreover, customers typically compare the objective price with their internal reference price, which is
the overall price range they associate with the product category. After purchasing a product or service,
customers may not remember the actual price but will encode it in a way that is meaningful to them,
such as ‘cheap’ or ‘expensive’. The objective price only becomes significant to customers when they inter-
pret it subjectively (Chua et al., 2015).
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
CONTACT Sarah Ahmed Awaad sarahahmed6294@gmail.com Department of Business Administration, Faculty of Business, Arab
Academy for science, Technology & Maritime Transport in Cairo, Cairo, Egypt.
https://doi.org/10.1080/23311975.2024.2366441
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been
published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
ARTICLE HISTORY
Received 26 October
2023
Revised 6 March 2024
Accepted 29 May 2024
KEYWORDS
Price sensitivity; quality
value; position value;
time value; information
value; customer lifetime
value (CLV)
REVIEWING EDITOR
Kaouther Kooli,
Bournemouth University,
United Kingdom of Great
Britain and Northern
Ireland
SUBJECTS
Marketing; Marketing
Management; Consumer
Behaviour

2 S. A. AWAAD E TAL.
Therefore, this research centres on the luxury sector, a notion characterised by subjectivity that has
evolved throughout history. In ancient Greece, it carried unfavourable implications, whereas in Rome, it
encompassed both positive and negative connotations. Its roots trace back to the Latin term ‘luxus’,
denoting ‘lavishness’ or ‘wealth’, and subsequently acquired associations with ‘illumination’ (Rezzano &
Fallini, 2021).
While, understanding price sensitivity is critical in marketing, but it is not the only element influencing
consumer behaviour. There are dimensions that influence how buyers perceive value, and these factors
can influence their price sensitivity, as developed from previous studies, such as quality value, time value,
position value, and information value. Higher costs may improve perceived quality as the perception of
quality can therefore influence how much a buyer is willing to pay for a product (Danes &
Lindsey-Mullikin, 2012).
In markets where timely access to products is critical, the speed at which items become available
often heavily influences consumer purchases. Customers in these time-sensitive situations may agree to
pay premium pricing to underscore their perceived value of obtaining products rapidly (Boyaci & Ray,
2003). Furthermore, when brands strategically highlight their uniqueness and differentiation, they can
partly mitigate price sensitivity that consumers may attach to that offering (Kaul & Wittink, 1995). In
determining optimal pricing for enhanced products, customer value perceptions represent vital data
points (Ingenbleek et al., 2010).
In this study, customer lifetime value (CLV) was examined from a non-financial standpoint. consumer
lifetime value (CLV) is related to consumer purchasing patterns, which may include repeat purchases or
increased purchases through methods like as up-selling and cross-selling (Kumar et al., 2010). Customer
lifetime value (CLV) is becoming more widely recognised as a key indicator in customer relationship
management (CRM) for acquiring, cultivating, and maintaining the most valuable customers (Venkatesan
& Kumar, 2004).
2. Aim of research
The aim of this study is to examine the impact of price sensitivity dimensions on customer lifetime value
in the Egyptian luxury sector. The goal is to delve into the numerous facets and insights that define this
interaction, providing a thorough grasp of the dynamics at work, thus filling potential knowledge gaps
in this area of study.
3. Literature review
3.1. Price sensitivity
Sensitivity to price, or elasticity, is a crucial concept in consumer behaviour. It describes how consumers
respond to price changes or product or service modifications (Yue et al., 2020). Customers are willing to
pay more when perceived value exceeds cost (Fang etal., 2016). This pricing response is extremely valu-
able for marketing strategies (Rundh, 2013). Nonetheless, it is essential to recognise that consumer price
sensitivity varies.
It changes based on a number of variables, such as personal traits, the product itself, needs,
brand familiarity, budget, and time (Natarajan etal., 2017). Price decisions made by a business are
heavily influenced by this price-sensitivity fluidity, which also directly affects overall profitability
(Gao et al., 2017). Moreover, Monroe (1990) and Zeithaml (1988) contend that consumers’ assess-
ments of a product or service’s worth are impacted by what they expected and what they actually
received.
3.1.1. Quality value
In consumer behaviour, the term ‘quality’ refers to a customer’s assessment of a product’s superiority or
perfection. This is distinct from the product’s actual quality and is a holistic assessment rather than a
specific trait (Zeithaml, 1988). Quality comprises all a product’s or service’s features and components that

COGENT BUSINESS & MANAGEMENT 3
contribute to its ability to meet consumer needs. When the quality of a product is exceptional, it is more
likely to influence purchasing decisions by increasing the perceived value (Waluya et al., 2019). As a
result, consumers seek long-lasting products, emphasising the importance of great quality for long-term
growth (Aliyev etal., 2019).
Furthermore, product quality mediates the link between price sensitivity and purchasing desire. When
the quality of a product is high, customers are less influenced by price changes and more likely to buy,
demonstrating a willingness to pay more for greater quality (Gomes et al., 2023). It is noteworthy in the
luxury business that brands prioritise craftsmanship and quality assurance, even allowing no faults. As a
result, while acquiring luxury items, shoppers should be wary of unjustifiable price increases (Simon &
Fassnacht, 2019).
3.1.2. Time value
Time-based value is the perceived value of a product by a customer depending on factors such as avail-
ability, promotions, and decision time. Customers prioritise time when they are time-pressed, a condition
known as ‘time famine’, which leads to greater price sensitivity and eventual brand switching if their time
is not appreciated (Granter etal., 2015).
Businesses can prioritise the customer experience by respecting time, responding quickly to com-
plaints, and maintaining clear communication to increase loyalty (Granter et al., 2015). Customers who
value their time, on the other hand, are more willing to invest in time-saving products and services.
Luxury consumers place a high value on customisation and personalisation and are willing to pay more
for products and services that may be tailored to their preferences while also saving them time (Choo
et al., 2012).
Excessive wait times can have a detrimental influence on consumer satisfaction, causing annoyance
and dissatisfaction. Customer happiness can be increased by accurately estimating wait times, providing
interesting activities during waits, and giving online or mobile alternatives to physical waiting. Therefore,
businesses should be cautious about saving time for customers (Palawatta, 2015).
3.1.3. Position value
Price sensitivity may be reduced by brand positioning, particularly when a brand is positioned to empha-
sise its distinctiveness (Kaul & Wittink, 1995). Positioning, a 1960s concept, is essential in strategic mar-
keting, along with segmentation and targeting. Its purpose is to distinguish a brand from competitors,
respond to customer expectations, and generate brand loyalty and equity, hence improving long-term
competitive advantage (Alpert & Gatty, 1969). Effective brand positioning can result in considerable com-
petitive advantages, while high brand presence can result in major market benefits (Cambra-Fierro
et al., 2021).
To properly position a brand, marketers must define and differentiate their brand from competitors.
This includes determining the target market and relevant competition, determining the best points of
parity and difference in brand associations, and developing a brand slogan that encompasses the brand’s
positioning and essence (Kotler & Keller, 2016). Another important part of brand positioning is a brand’s
competitive stance, which is generally represented in its aggressive market posture. This includes the
distinct brand image that buyers perceive, which is developed by both regular and emotional consumer
research (Mindrut et al., 2015).
3.1.4. Information value
The information value dimension is concerned with the importance of knowledge received through
co-creative behaviours, underscoring the importance of group interactions in the generation of percep-
tive knowledge (Zhu et al., 2022). The primary determinant of information’s value is its availability. This
is due to the importance of educating consumers and giving them the information, they need to make
the best decisions (Kim & Phua, 2020).
Building on this, consumers make quality decisions based on a variety of cues, such as information
contained inside the goods or services as well as data acquired from other sources. Customers rely less

4 S. A. AWAAD E TAL.
on these indicators when they learn more about a company’s product offerings. Interestingly, purchasers
who are better knowledgeable about a product or service rely less on external signs to assess its quality
(Alshibly, 2015). Customers often rely on reputable sources of information when making purchasing deci-
sions (An & Han, 2020).
In addition, businesses use customer information to enhance their services, goods, and overall cus-
tomer experience through the strategic process of customer knowledge management (Smith & McKeen,
2005). In-depth product details can influence consumers’ perceptions of luxury goods, increasing their
sense of reward and encouraging them to spend more money (Lim etal., 2014).
In general, when customers actively engage with a service, it enhances their understanding of the
service’s worth and its unique features. This, in turn, tends to decrease their responsiveness to price
fluctuations and has an impact on their choices when making purchases (Hsieh & Chang, 2004).
3.2. Customer lifetime value
Customer lifetime value (CLV) is related to consumer purchasing patterns, which may include repeat
purchases or increased purchases through methods like up-selling and cross-selling (Kumar etal., 2010).
Customer lifetime value (CLV) estimations include non-financial characteristics such as happiness and
loyalty in measuring a customer’s overall value over time, supporting firms in identifying and maintaining
important customers. To maintain these, exceptional customer service and experiences are essential,
which can be offered through swift issue responses across several channels. Customer satisfaction is
boosted by devoted, skilled customer service personnel who instil a sense of worth and loyalty in their
customers. Transparency regarding product features, pricing, and potential issues builds trust and reduces
negative comments (Ali etal., 2018).
Providing high-quality goods or services that continuously exceed client expectations boosts loyalty
even more. Listening to clients and addressing their concerns increases happiness and loyalty (Kumar,
2008). Long-term connections may not necessarily result in increased profitability due to significant
income and cost variances across consumers. As a result, recent research has emphasised the importance
of fine-tuning customer acquisition, retention, and development strategies to boost customer lifetime
value (CLV), hence increasing shareholder value (Lemon & Verhoef, 2016).
It is increasingly becoming identified as an important indication in customer relationship management
(CRM) for obtaining, growing, and retaining the most valued customers. However, many firms fail to use
CLV metrics effectively. It may start with less profitable customers, or they may lack the skills required to
customise the client experience to optimise value generation (Venkatesan & Kumar, 2004).
Given the lack of non-financial measures of customer lifetime value (CLV), this study used a multidi-
mensional approach based on prior academic research. The previous study provided four separate
dimensions, providing a more thorough and nuanced knowledge of CLV from non-financial perspectives.
For psychological value, customers strive to develop an effective and constant sense of self-worth; satis-
fying this psychological requirement leads to a connection between customers and brands (Segarra-Moliner
& Moliner-Tena, 2022).
For social value, Customer behaviour in the luxury business is frequently impacted by social norms
and social institutional standards, such as those established by reference groups. Understanding the
impact of social value judgements on luxury purchasing is crucial for luxury brands to properly target
and manage their brand image (Shukla, 2012). Financial value is the monetary value attributed to a
customer’s relationship with a corporation over the life of that relationship (Irvine et al., 2016).
For functional value, understanding the concept of functional value is critical in determining how well
products, services, or solutions fit the needs and expectations of customers or users (Kato, 2021).
4. Exploratory evidence
The purpose of this exploratory study is to delve deeper into the factors that influence price sensitivity
and customer lifetime value in the context of Egyptian luxury consumers. Given the scarcity of existing
studies in this field, an exploratory research methodology was selected as appropriate. This form of

COGENT BUSINESS & MANAGEMENT 5
research fills knowledge gaps and provides basic insights that can be used to lead further confirmatory
studies.
4.1. Secondary data analysis
This research investigates how luxury brands in the hospitality, real estate, and automobile industries
lower price sensitivity while increasing customer value. Hotels with international recognition, such as the
Burj Al Arab Jumeirah, The Ritz-Carlton, and Four Seasons Hotel George V, provide great services,
high-quality amenities, and personalised experiences. Emaar Misr and SODIC, for example, offer distinc-
tive designs, community interaction, and a varied range of amenities. Similarly, luxury automakers such
as Ferrari, Porsche, and Rolls-Royce emphasise their distinctive brand features such as heritage, crafts-
manship, performance, and commitment to quality.
These tactics justify premium pricing while also increasing client loyalty. According to the survey, cre-
ating outstanding, personalised experiences is critical to minimising price sensitivity and enhancing cus-
tomer lifetime value in the luxury industry. To gather diverse perspectives, in-depth interviews were
conducted with both A-class customers and industry professionals.
4.2. Qualitative analysis
In-depth interviews with industry experts and A-class consumers uncover additional values and tech-
niques that boost client lifetime value and alleviate price sensitivity. Hotel managers place a premium on
client happiness, service excellence, and feedback, whereas real estate professionals place a premium on
the unique value of their products, customer-centric strategies, and community involvement. Automobile
industry professionals use brand prestige to offset price sensitivity, focusing on creating outstanding
client experiences. Similarly, A-class clients consider service quality, timeliness and efficiency, brand rec-
ognition, and location value to be important factors in their purchasing decisions. These aspects impact
the overall customer experience, which leads to enhanced loyalty and customer lifetime value.
5. Conceptual model and hypothesis
5.1. Hypotheses development
These hypotheses development presents the associations proposed by the theoretical framework pro-
duced from the previous studies (Figure 1).
H1: There’s a signicant impact of quality value on customer lifetime value.
H2: There’s a signicant impact of time value on customer lifetime value.
H3: There’s a signicant impact of position value on customer lifetime value.
H4: There’s a signicant impact of information value on customer lifetime value.
Figure 1. Estimated research model. Source: Based on authors.

6 S. A. AWAAD E TAL.
6. Research methodology
6.1. Research design
Qualitative Approach: As previously stated, an exploratory study in the form of in-depth interviews with
industry professionals and customers will gain insights into the values of price sensitivity and customer
lifetime value after reviewing previous studies.
Quantitative approach: This study was conducted to gain a ‘single cross-sectional data collection
design’ to collect respondents’ comments at a single point in time. The cross-sectional design has been
a basic strategy in many disciplines of research employed a ‘snapshot’ of the population at a certain
time, allowing for the simultaneous examination of multiple attributes without the need for extensive
study. This characteristic not only saves time and resources but also allows for the rapid development of
meaningful information (Levin, 2006).
Primary data for this study will be gathered through two main methods: structured surveys to acquire
quantitative data, and in-depth interviews to gather qualitative data. Structured surveys consisting of
close-ended questions will be used to collect quantitative data. In-depth semi-structured interviews with
open-ended questions will be conducted to gather qualitative data. Qualitative interviews are particularly
useful during the exploratory phases of research to gain a thorough understanding of the phenomena
under investigation (Creswell, 2012). Using both quantitative surveys and qualitative interviews will allow
for collecting robust primary data to address the research questions. While quantitative data might pro-
vide numerical precision, it may lack the depth and context that qualitative data provide. As a result, it
is frequently necessary to describe the relationships between variables to demonstrate how they interact
with one another (Creswell, 2012).
6.2. Sampling design and plan
The study relies on a sample of Class A clients from Egypt’s luxury sector. Given the population’s size
and diversity, a careful selection guide is employed to identify and authenticate these clients. Because
the population is indeterminate, probability sampling approaches, in which each instance has a
known and non-zero probability of being chosen (Saunders et al., 2009), are substituted by
non-probability and judgement sampling methods. These methods entail selecting specific units from
the population that are assumed to reflect the larger universe (Kothari, 2017). Participants were
selected using a convenience selection strategy based on their accessibility, availability, and proximity
to the study. This method entails getting information from people who are eager, friendly, or easily
accessible to the researcher (Scholtz, 2021). In contrast, judgmental sampling is based on the research-
er’s opinion of who will provide the best information to meet the study’s objectives. The researcher
must seek out people who have the necessary expertise and are prepared to provide it (Etikan &
Bala, 2017).
6.3. Measurement development (Table 1)
7. Analysis of results
A total of 232 surveys were completed and acquired from respondents who participated in this study.
The descriptive results, as shown in Table 2, show that males made up most of the sample (58.1%). The
age group was dispersed in such a way that most of the sample (76.4%) was beyond the age of 40.
Because the study targeted people with salaries greater than $50,000, it is understandable that just 5
(2.6%) of those in the sample were under the age of 30. More than half of the sample (70.7%) had
postgraduate studies as an educational background. According to the sample’s occupations, administra-
tive work for either the public or private sector (51.3%) was the most often reported job, followed by
specialised profession (22.5%), freelance (13.6%), and academic (12.6%). 112 people reported incomes

COGENT BUSINESS & MANAGEMENT 7
ranging from $50,000 to $100,000 (58.6%). Only 18 people (9.4%) were reported to have an income
greater than $150,000.
7.1. Dimensional reliability and validity
Principal component analysis was utilised to extract the items from the exploratory factor analysis. Time
value, information value, and brand quality positioning value were the three criteria. The quality posi-
tioning value combines the quality and position of the brand. Following the exploratory factor analysis,
the new dimensions’ reliability and validity should be assessed. As a result, a confirmatory factor analysis
is carried out (Table 3).
Table 1. Variables scale items and measures.
Variable Sub variable Conceptual denition Operational denition
Price Sensitivity Quality Value Refers to a product’s or service’s perceived advantage
based on its fundamental attributes and performance
(Zeithaml, 1988).
4 statements A 5-point Likert scale.
(Kapferer & Valette-Florence, 2021;
Tak, 2020)
Position Value To achieve market leadership, a company or product
must be distinguished from competitors based on
actual dimensions, which may include product
features or corporate values that are signicant to
customers (DiMingo, 1988).
4 statements A 5-point Likert scale
(Özkan & Evrim, 2020; Kaleka &
Morgan, 2019; Researcher).
Time Value Time is commonly described by researchers as a
restricted and scarce resource. The word ‘saving time’
refers to redistribute time across various tasks to
boost overall eciency (Berry et al., 2002).
3 statements A 5-point Likert scale
(Hennigs et al., 2013; Researcher).
Information Value The amount of detailed product information provided
to customers by a corporation (Hou & Tang, 2008).
4 statements A 5-point Likert scale.
(Hennigs et al., 2013; Researcher).
Customer Lifetime
Value
Psychological Value Pleasure, enjoyment, and sensory satisfaction are
elicited as emotional or aective states (Kujala &
Väänänen-Vainio-Mattila, 2009).
4 statements A 5-point Likert scale.
(Hennigs et al., 2012; Kapferer &
Valette-Florence, 2021)
Social Value It is necessary to have social and external esteem,
position, power, control and dominance,
achievement, compliance, equality, helpfulness,
honesty, and loyalty (Sheth et al., 1991).
4 statements A 5-point Likert scale.
(Kapferer & Valette-Florence, 2021;
Tak, 2020)
Financial Value The outcome of balancing the benets obtained vs the
costs incurred during a transaction (Kataria and
Saini, 2020).
4 statements A 5-point Likert scale.
(Hennigs et al., 2012; Researcher).
Functional Value The perceived benet obtained from an option’s
potential for functional, utilitarian, or physical
performance is referred to as practical value. This is
the value a client obtains from the practical and
tangible functionality of a product or service (Sheth
et al., 1991).
5 statements A 5-point Likert scale.
(Hennigs et al., 2012)
Source: Tak (2020); Kapferer and Valette-Florence (2021), Özkan and Evrim (2020), Kaleka and Morgan (2019); Hennigs etal. (2013), Hennigs
et al. (2012), Kapferer and Valette-Florence (2021), Tak (2020), and Kapferer and Valette-Florence (2021).
Table 2. Frequency tables for demographic variables.
Variable Categories Frequency Percentage
Gender Female 80 41.9
Male 111 58.1
Age 21 less than 30 5 2.6
30 less than 40 40 20.9
40 less than 50 78 40.8
50 and above 68 35.6
Academic Experience
Occupation
Bachelor degree 56 29.3
Postgraduate Degree 135 70.7
Occupation Academic 24 12.6
Administrative 98 51.3
Freelance 26 13.6
Specialized Profession 43 22.5
Monthly income 100,000 − 150,000 61 32
150,000+ 18 9.4
50,000 − 100,000 112 58.6
Sample size (n) 191 100
Source: Field data (January–May, 2022), retrieved from Google form.

8 S. A. AWAAD E TAL.
To discover common technique bias, a complete collinearity methodology is applied. VIFs are deter-
mined to have a value of fewer than five. This is in line with Kock (2017). The common approach bias
does not need additional investigation. A confirmatory factor analysis was then used to test the reliability
and validity.
To test reliability, the Cronbach alpha was calculated. It was revealed that it exceeded 0.6. As a result,
there is enough evidence to conclude that the claims are reliable and internally consistent. The factors
are subsequently validated using the calculated composite reliability and average variance. Each dimen-
sion has an extracted average variance better than 0.5 and a composite reliability greater than 0.7. This
means that the measurements are accurate. The loadings were all more than 0.5, showing that the mod-
el’s claims were all significant and crucial to building structural equation modelling (Table 4).
The square root of the extracted average variance, according to Fornell-Larcker, should be bigger than
any other dimension-to-dimension connection. Because the square root of the average variance recov-
ered in the study was smaller than the rest of the calculations, the discriminant validity table shows that
there was validity.5.2.4 Correlation Analysis (Table 5).
At 99% confidence, the association between customer lifetime value and information value was shown
to be a significant moderate relationship. At 99% confidence levels, the quality positioning value
Table 3. Model measurements of the phenomenon.
Variable Components Loadings Outer VIF Cronbach’s alpha
Composite
reliability
Average variance
extracted (AVE)
Customer lifetime
value
CL1 0.707 2.228 0.891 0.910 0.505
CL2 0.715 2.552
CL3 0.684 1.998
CL7 0.706 1.743
CL9 0.755 1.992
CL10 0.780 2.352
CL12 0.780 2.070
CL13 0.660 1.891
CL16 0.672 1.968
CL17 0.628 1.940
Information value I1 0.745 1.343 0.685 0.749 0.536
I2 0.501 1.343
I3 0.559 1.349
I4 0.791 1.335
Quality positioning
value
QP1 0.588 1.426 0.691 0.812 0.52
QP2 0.644 1.301
QP3 0.562 1.513
QP4 0.684 1.554
QP5 0.547 1.386
QP6 0.561 1.521
QP7 0.618 1.297
QP8 0.731 1.68
Time value T1 0.753 1.401 0.717 0.839 0.635
T2 0.831 1.347
T3 0.804 1.509
Source: Calculations based on sample collected through surveys using SmartPLS.
Table 4. Fornell-Larcker criterion for measuring discriminant validity of the theoretical model.
Customer lifetime value Information value Quality positioning value Time value
Customer lifetime value 0.71
Customer lifetime value 0.483 0.66
Customer lifetime value 0.543 0.573 0.62
Customer lifetime value 0.564 0.486 0.631 0.797
Source: Calculations based on sample collected through surveys using SmartPLS.
Table 5. Spearman correlation coecients in phenomenon.
Customer lifetime value Information value Quality positioning value Time value
Customer lifetime value 1.00
Customer lifetime value .421*** 1.00
Customer lifetime value .289*** .003 1.00
Customer lifetime value .314*** .094 .174** 1.00
Sig values: ***<0.01, **<0.05, “”>0.05.
Source: Calculations based on sample collected through surveys using SPSS At 99%.

COGENT BUSINESS & MANAGEMENT 9
demonstrated a weakly significant association with the customer lifetime value. Furthermore, at the 0.01
significance level, the time value demonstrated a statistically weak connection with customer lifetime.
This provides a decent sense of the model that can be developed and its outcomes. However, correla-
tion analysis does not account for the effect of other variables; therefore, a model would be a better way
to support the hypotheses in the study.
7.2. Structural equation modelling
To According to the Path coefficients Table 6, at the 99% confidence level, both information value and
time value had a significant positive impact on customer lifetime value. It has been discovered that time
value (=0.331) has a greater and stronger impact on customer lifetime value than information value
(=0.209). At 99% confidence, the quality positioning value (=0.239) was determined to have a substantial
positive impact on the customer lifetime value (Figure 2). As a result, the higher the availability of infor-
mation, the company’s quality position, and the ease of access to the brand, the more the client would
value and prefer the luxury brand (Table 7).
The model’s R2 value was 0.401. It is possible to explain this by claiming that the model, which
includes quality positioning, information, and time values, accounts for 40% of the variation in customer
lifetime value. It is the predictive accuracy metric, with a Q2 value of 0.174. If it is greater than zero, it
Table 6. Path coecients of the model.
Original sample Standard deviation P values
Information value -> customer lifetime value 0.209 0.079 0.008
Quality positioning value -> customer lifetime value 0.239 0.089 0.008
Time value -> customer lifetime value 0.331 0.08 0
Source: Calculations based on sample collected through surveys using SmartPLS.
Figure 2. Structural equation model for phenomenon.
Note: The preceding Figure 2. Displays the phenomenon’s structural equation model. It illustrates all the variables’ relationships. It also displays
the nal assertions that were used in the analysis.

10 S. A. AWAAD E TAL.
indicates that the PLS model was good. Because the SRMR is close to zero or equal to 0.1, the model
was an excellent fit for the data.
8. Theoretical implications
This research greatly expands our understanding of price sensitivity in luxury markets by introducing and
validating new components—quality value, time value, and information value. Integrating these factors
provides a more nuanced perspective on how customers perceive and react to pricing. This theoretically
broadens and solidifies the foundations for further consumer behaviour investigation in premium segments.
Given minimal existing research on price sensitivity facets (like information, quality, time, position
value) and their interplay with customer lifetime value (CLV), this work carries major theoretical implica-
tions. By empirically examining the relationship between price sensitivity dimensions and CLV, it advances
current knowledge and charts direction for future exploration into this area. In turn, this can refine the-
oretical frameworks describing price sensitivity traits, dynamics, and links to CLV.
Additionally, the discovery of a novel connection between price sensitivity and customer lifetime
value demonstrates that orchestrating price sensitivity factors can boost CLV specifically in high-end set-
tings. Moreover, the research enhances reliability and validity of the scales used to measure price sensi-
tivity and CLV factors. This further cements methodological rigour in this research domain.
9. Managerial implications
Given the considerable impact of quality positioning value on customer lifetime value (CLV), businesses can
justify premium pricing by emphasising the higher quality of their luxury products or services. This could
involve investing more in quality assurance, product development, and branding campaigns that highlight
their superior service quality. Given the positive impact of quality positioning value on CLV, luxury busi-
nesses should purposefully position their products or services in the market’s high-quality, premium category.
This could involve increasing premium appeal through exceptional design, exclusive benefits, and a
compelling brand story that appeals to the luxury target market. Because of the positive association
between time value and CLV, luxury businesses should optimise the time value of their items. This could
imply instituting time-limited exclusivity for new things or experiences to increase urgency and per-
ceived worth.
Luxury businesses should strive for pricing transparency, given the known role of information value in
price sensitivity. Clear communication about the product or service’s worth, craftsmanship, exclusivity,
and premium qualities helps justify higher costs and build confidence with high-end clients. Client seg-
mentation can benefit from CLV dimensions such as psychological value, social value, financial value, and
functional value. Luxury companies can use these insights to create individualised marketing approaches
and customised experiences for their most significant customers.
10. Research limitations
According to the dearth of accurate statistics on price-sensitive customers in Egypt’s luxury business, the
study’s sample size may be limited. This may have an impact on the findings’ generalisability because
the sampled customers may not fully represent the greater customer base in terms of demographics,
tastes, or behaviours. The research, as a cross-sectional study, gives a glimpse of the impact of price
sensitivity on customer lifetime value at a certain point in time. This method may overlook any temporal
changes in these variables. As a result, the data may not fully reflect how price sensitivity and customer
lifetime value change in reaction to changing market conditions or consumer trends.
Table 7. Model evaluation metrics.
SSO SSE Q²R square
Customer lifetime value 1910 1578.382 0.174 0.401
Note: SRMR = 0.100, d_ULS = 3.252, d_G = 1.001, Chi-Square = 1018.312, NFI = 0.551.
Source: Calculations based on sample collected through surveys using SmartPLS.

COGENT BUSINESS & MANAGEMENT 11
The study focuses primarily on the Egyptian market, which may limit the findings’ applicability to
other cultural or geographical contexts. Due to differences in cultural norms, economic situations, and
attitudes towards luxury spending, consumer behaviour, particularly price sensitivity and valuation of
luxury items, can vary dramatically across markets.
Obtaining the requisite data may be difficult due to the luxury nature of the firms surveyed in the
exploratory study (real estate, vehicles, and luxury hotels). Corporate sensitivity and confidentiality issues
may limit the scope and depth of available data for investigation.
11. Future research recommendations
Future research could examine broadening the geographical scope of the study beyond the Egyptian
market to include additional markets. This would aid in understanding cultural disparities in price sensi-
tivity and customer valuation of luxury goods, boosting the findings’ generalisability.
Given that luxury brands are consumed by people from all socioeconomic backgrounds in Egypt,
future studies should involve a diversified sample from all socioeconomic backgrounds. This would pro-
vide a more comprehensive view of price sensitivity and client lifetime value across different consumer
categories in the luxury industry.
In forthcoming studies, a longitudinal approach can be employed to analyse fluctuations in price
sensitivity and customer lifetime value across time. This approach will illustrate the alterations in these
factors as they react to evolving market dynamics and shifting consumer preferences.
Subsequent research endeavours may delve into other factors that exert an impact on customer life-
time value within the luxury sector. This could yield a more all-encompassing understanding of the driv-
ers behind long-term client value in this particular industry.
At the end, the utilisation of more sophisticated analytical tools, such as logistic regression modelling,
has the potential to enhance forthcoming research efforts. This could yield more intricate insights into
the relationships and trends present in the data.
Author approval
The full manuscript has been read and approved by all authors. Each listed author fulfils the require-
ments for authorship, and each author attests that the manuscript represents honest work.
Author contribution
S.A.A is the author of this research work and contributed to the planning of the research model, the literature review,
the methodology and all the remaining sections of the manuscript and drafting it; W.K is the author of this work and
contributed to the formulation and renement of the research model’s hypotheses, and reviewing the manuscript; N.A
is the author of this research and contributed to measurement development review, editing and introduction.
Disclosure statement
No potential conict of interest was reported by the author(s).
About the authors
Sarah Ahmed Awaad, Assistant lecturer in Marketing major, Arab Academy for Science, Technology & Maritime
Transport, Cairo.
Wael Kortam, Professor of Marketing, British University in Egypt.
Nihal Ayad, Lecturer of Marketing, Arab Academy for Science, Technology & Maritime Transport, Cairo.
Data availability
The data that support the ndings of this study are available from the corresponding author, Sarah Ahmed Awaad,
upon reasonable request.

12 S. A. AWAAD E TAL.
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