ENG_AI
Author: Yustres-Duro
Date: 0101-01-01T00:00:00+00:00
Keywords: raeic122409
Publisher: raeic

RAEIC, Revista de la Asociación Española de Investigación de la Comunicación

ISSN 2341-2690

DOI: https://doi.org/10.24137/raeic.12.24.9

Submitted: 10th March 2025

Accepted: 21st October 2025

Published: 28th November de 2025

Recibido el 14 de abril de 2025

Aceptado el 21 de julio de 2025

Publicado el 3 de noviembre de 2025

Artificial Intelligence for effective communication: impact on consumer purchase intention and loyalty in fashion

Inteligencia Artificial para una comunicación eficaz: efectos en la intención de compra y lealtad del consumidor de moda

Yustres-Duro, Pilar

Universidad Camilo José Cela (UCJC)

pilar.yustres@ucjc.es

Forma de citar este artículo:

Yustres-Duro, P. (2025). Artificial Intelligence for effective communication: impacto n consumer purchase intention and loyalty in fashion. RAE-IC, Revista de la Asociación Española de Investigación de la Comunicación, 12(24), raeic122409. https://doi.org/10.24137/raeic.12.24.9

Abstract:

E-commerce has transformed the fashion sector, driving companies to innovate and incorporate artificial intelligence (AI) elements into their communication strategies, this is reflected in consumers' purchase intention and loyalty. This advancement has allowed the continued study of the Consumer Loyalty Theory from a more innovative perspective, identifying key concepts such as purchase intention and loyalty, and relating them to cognitive and affective factors derived from AI communication. The need to study e-commerce through a mixed methodology has been identified to evaluate the determining role of AI in consumer purchase intention and loyalty. The first study proposes the modelling of a structural equation based on a questionnaire (471 responses). The second study includes in-depth interviews (18) with experts in fashion e-commerce. The humanization of the digital experience and the empathetic use of advanced technology strengthen consumer loyalty in digital fashion by fostering emotional connections, transparent communication, and AI-driven personalization.

Keywords: artificial intelligence, communication, consumer purchase intention, consumer loyalty, e-commerce, fashion, retail

Resumen:

El comercio electrónico ha transformado el sector de la moda, impulsando a las empresas a innovar e incorporar elementos de inteligencia artificial (IA) en sus estrategias de comunicación, lo que se ve reflejado en la intención de compra y la lealtad de sus consumidores. Este avance ha permitido continuar el estudio de la Teoría de la Lealtad del Consumidor desde una perspectiva más innovadora, identificando conceptos clave como la intención de compra y la lealtad, y relacionándolos con factores cognitivos y afectivos derivados de la comunicación con IA. Se ha identificado la necesidad de estudiar el comercio electrónico mediante una metodología mixta para evaluar el papel determinante de la IA en la intención de compra y la lealtad del consumidor. El primer estudio propone la modelización de una ecuación estructural basada en un cuestionario (471 respuestas). El segundo estudio incluye entrevistas en profundidad (18) a expertos en comercio electrónico de moda. Por lo que se puede concluir que la humanización de la experiencia digital y el uso empático de tecnología avanzada fortalecen la lealtad del consumidor en la moda digital al fomentar conexiones emocionales, comunicación transparente y personalización impulsada por IA.

Palabras clave: inteligencia artificial, comunicación, intención de compra del consumidor, lealtad del consumidor, comercio electrónico, moda, comercio minorista.

1. INTRODUCTION

In the digital era, e-commerce has revolutionized the fashion industry, enabling brands to deliver shopping experiences that match satisfaction levels in both physical and digital environments (Gutiérrez-Rodríguez et al., 2020; Cuesta-Valiño et al., 2022). This study focuses on analysing consumer behavior within the context of fashion e-commerce, exploring key factors such as purchase intention and consumer loyalty.

E-commerce has gained popularity due to its convenience and cost-effectiveness, leading to a rise in transactions conducted exclusively online and a growing preference for omnichannel strategies (Dogra & Kaushal, 2023; Yustres Duro & Melendo Rodríguez-Carmona, 2022; Alonso-García et al., 2021). Fashion brands have incorporated this channel into their development strategies to adapt to shifts in consumer behaviour, offering products and services that meet emerging demands (Lavanda Reyes et al., 2022).

Communication through artificial intelligence (hereafter, AI) has emerged as a powerful tool for these companies, enabling more personalized and efficient interactions with customers. Through AI, brands can better understand consumer preferences and needs, thereby enhance the shopping experience and fostering loyalty. Moreover, the integration of emerging technologies presents new opportunities to personalize customer experiences and improve operational efficiency.

In this context, the study delves into Consumer Loyalty Theory (hereafter, CLT) as proposed by Dick & Basu (1994), identifying various antecedents of loyalty. Some of these antecedents, such as cognitive and affective factors, may be linked to how AI-driven communication influences purchase intention and consumer loyalty. Specifically, the trust and accessibility offered by AI communication platforms are identified as cognitive factors within this theory. Additionally, affective factors such as emotions, satisfaction, and mood states resulting from AI usage are also recognized.

Accordingly, a mixed-methods research design was proposed, combining the modelling of a structural equation with in-depth interviews conducted with experts in fashion e-commerce.

2. THEORETICAL FRAMEWORK

Within the theoretical framework, the study variables have been developed. First, the variables of purchase intention and consumer loyalty have been examined, along with the role of artificial intelligence (AI) in enabling effective communication.

2.1. CONSUMER PURCHASE INTENTION

Consumer purchase intention has been studied in both academic and business contexts. Toma et al. (2011) argue that consumers often use distrust in information as a justification for maintaining their existing purchasing habits. A consumer’s perception of a company significantly influences their purchase intention: a positive perception increases the likelihood of purchase, while a negative one diminishes it (Kang & Hustvedt, 2014). Similarly, Nielsen and Loranger (2006) emphasize that accurate information facilitates purchasing decisions by being perceived as more trustworthy. Grappe et al. (2021) further explain that sceptical individuals are less likely to adopt a positive attitude toward a company and are less inclined to make a purchase.

Corporate transparency and social responsibility (Cuesta-Valiño et al., 2024b) are also key determinants. Kang and Hustvedt (2014) examine how consumers’ perceptions of a company’s labour and social transparency foster trust and a positive attitude, thereby encouraging purchase intention. Bonifacio et al. (2024) demonstrate that ethical practices and environmental impact are prioritized over traditional factors in the purchasing process. In parallel, Leite et al. (2024) highlight that emotional involvement enhances purchase intention and promotes loyalty. Additionally, Gutiérrez-Rodríguez et al. (2024) indicate that fulfilling consumer needs and the happiness derived from acquiring a product are primary motivators of purchase intention.

Finally, Pappas (2016) notes that opportunistic behaviour by companies undermines consumer trust, thereby reducing purchase intention. In the same vein, Margalina et al. (2024) underscore the importance of trust as a key factor influencing purchase intention.

In summary, when purchase intention is driven by both emotional and rational factors, it not only encourages repeated purchasing decisions but also strengthens a lasting relationship based on satisfaction and loyalty. This leads to the first research hypothesis:

H1: Consumer purchase intention positively influences consumer loyalty.

2.2. CONSUMER LOYALTY

Consumer loyalty is understood, according to Dick and Basu (1994), as the relationship between an individual’s attitude toward a brand and their repeated purchasing behaviour. Other scholars conceptualize it as a long-term process in which the quality of offerings and effective communication play a crucial role (Olsen, 2002). For instance, Otim and Grover (2006) suggest that the success of e-commerce is closely tied to a company’s ability to foster customer loyalty. Personalization is considered an effective loyalty-building technique, as it generates positive feelings and maintains perceptions of website security (Swaid & Wigand, 2009). Casaló et al. (2008) define loyalty as a non-random behaviour, dependent on psychological processes and brand commitment. Similarly, Cyr (2014) examines online loyalty as a lasting psychological attachment to a vendor.

In this regard, Prasad et al. (2019) explain that truly loyal customers identify so strongly with a company that they exclude competitors. Panda et al. (2020) define brand loyalty as the degree of attachment to a specific brand. Likewise, Núñez-Barriopedro et al. (2021) describe consumer loyalty as a strategic variable for long-term survival. Furthermore, Guo et al. (2023) indicate that website design elements influence customer loyalty. Raman and Ramachandaran (2023) also define consumer loyalty as a key factor for long-term success. In the same vein, Zhen et al. (2015) emphasize the importance of keeping consumers satisfied and building long-term relationships. Similarly, Kancherla et al. (2023) note that brand loyalty increases consumers’ emotional attachment. Additionally, Leite et al. (2024) describe loyalty as the commitment to repurchase a brand, highlighting the importance of clear and transparent communication.

In conclusion, consumer loyalty emerges as a dynamic and multifactorial process, shaped by emotional, cognitive, and behavioural elements. Loyalty becomes a strategic asset for long-term business sustainability.

2.3. IA FOR EFFECTIVE COMMUNICATION

Effective communication through artificial intelligence (AI) plays a crucial role in shaping consumer purchase intention. In this regard, Cheng and Jiang (2021) emphasize that the quality of chatbot communication—characterized by accuracy, credibility, and competence—is essential for building strong relationships between companies and customers. Similarly, Guo et al. (2020) identify that in staffless stores, emerging technologies enhance the customer experience and increase purchase intention. Martínez Puertas et al. (2024) also note that chatbots using personalized and human-like language stimulate purchasing behaviour. Yen and Chiang (2020) corroborate these findings, highlighting that chatbot credibility and competence foster trust, thereby increasing consumer purchase intention. This leads to the second research hypothesis:

H2: Effective communication through AI positively influences consumer purchase intention.

In addition to the aforementioned, Hernandez-Ortega and Ferreira (2021) underline that effective, humanized, and empathetic communication enhances consumer loyalty. Complementarily, Fetais et al. (2022) examine how AI is used to personalize messages, create entertaining experiences, and deliver updated information, which increases consumer engagement and loyalty. In this context, Marín-García et al. (2021) emphasize that AI improves customer experience and enhances perceptions of innovation and sustainability in retail, contributing to greater consumer loyalty. This gives rise to the third research hypothesis:

H3: Effective communication through AI positively influences consumer loyalty.

Ultimately, effective communication mediated by artificial intelligence has become a key factor in building strong relationships between consumers and brands.

Illustration 1. Proposed model

Source: Own elaboration

3. METHODOLOGY

To test the aforementioned hypotheses, a mixed-methods research approach was employed. On one hand, a structural equation modelling (SEM) technique was used to confirm the relationship between purchase intention and consumer loyalty. On the other hand, in-depth interviews with experts in fashion e-commerce were conducted to examine the influence of effective AI-driven communication on both purchase intention and consumer loyalty.

3.1. STUDY 1: QUESTIONNAIRE

The first study aims to test Hypothesis 1. To this end, a questionnaire was designed. Initially, one behavioural question was included, followed by three items related to consumer loyalty and four items concerning consumer purchase intention. An additional control question was added to validate the questionnaire. Finally, three behavioural questions and four demographic questions were incorporated.

The survey employed a five-point Likert scale, where 1 indicated strong disagreement and 5 indicated strong agreement. All scales used in the questionnaire were adapted from previous research: consumer purchase intention was measured using scales from Vila & Kuster (2011) and Hsu et al. (2012), while consumer loyalty was assessed using scales from Zeithaml et al. (1996) and Molinillo et al. (2020).

The behavioural questions were based on the literature by Cuesta-Valiño et al. (2022), Lim et al. (2023), and Ahmad et al. (2023). The demographic items were informed by Theodorakis et al. (2019) and data from the Spanish National Institute of Statistics (2024).

Table 1. Total items

Construct

Items number

Source

Consumer purchase intention

3

Vila & Kuster (2011) & Hsu et al. (2012)

Consumer loyalty

4

Zeithaml et al. (1996) & Molinillo et al.  (2020)

Source: Own elaboration

3.1.1. Sample Composition and Size

This research is characterized as descriptive (Hernández Sampieri et al., 2007) and is based on primary data collected through an online questionnaire distributed among the Spanish population aged 18 to 78. The survey was conducted between April and July 2024 and targeted individuals who shop for fashion online. Additional data were collected regarding respondents’ age and gender.

By the end of July, a total of 471 valid responses were obtained, with a confidence level of 95% and a margin of error of 5%. Data collection was carried out using snowball sampling, and the distribution of the survey was monitored to ensure representation across all social strata identified in the questionnaire (gender and age). The data were analysed using SmartPLS as the tool to validate the proposed model (Henseler, 2018; Henseler et al., 2018).

Prior to the survey distribution, a pretest was conducted in March 2024 with ten men and ten women who were online fashion shoppers within the target age range. This was done to ensure clarity and allow for any necessary adjustments to optimize the questionnaire.

The objective of distributing this questionnaire was to obtain a representative sample of the Spanish population, as shown in Table 2.

Table 2. Sample information

Sample information

%

Total

Shopping preferences: physical store, digital store, or both

Physical

Digital

Both

19.11

12.74

68.15

90

60

321

Gender

Male

Female

45.01

54.99

212

259

Age

Between 18 and 27 years old

Between 28 and 43 years old

Between 44 and 59 years old

Between 60 and 78 years old

33.12

29.51

29.30

8.07

156

139

138

38

Source: Own elaboration

3.1.2. Measurement Model: Reliability and Validity

Beginning with the analysis of the questionnaire items, multicollinearity among the independent variables was assessed using the Variance Inflation Factor (VIF). Variables with VIF values exceeding 5 were removed, as they indicated high collinearity, which could compromise the accuracy and stability of the model (Hair et al., 2019). Consequently, item IC2 was excluded due to VIF values above 5 in the collinearity statistics. As shown in Table 3, all item loadings were above 0.748, thus meeting the criterion of accepting items with loadings of 0.707 or higher (Hair et al., 2011), and all items were retained.

Subsequently, Cronbach’s Alpha was calculated to assess internal consistency, yielding satisfactory values across all constructs. According to Nunnally and Bernstein (1994), acceptable levels must exceed 0.8. Finally, an additional test was conducted to evaluate internal consistency using the Average Variance Extracted (AVE), with all values accepted as they were above the threshold of 0.50.

Table 3. Constructs, indicators, Cronbach’s Alpha, AVE, Composite Reliability and External loads

Construct

External loads

Consumer purchase intention: Cronbach’s Alpha 0.821, AVE 0.739, Composite reliability 0.894

IC1 – I would buy from this website

IC3 – I intend to buy from this website

IC4 – I should buy from this website

0.884

0.926

0.761

Consumer loyalty: Cronbach’s Alpha 0.858, AVE 0.705, Composite reliability 0.905

L1 – I Will buy fashion online in the future

L2 – I consider the internet my first option for buying fashion

L3 – I encourage others to buy fashion online

L4 – I recommend buying fashion online

0.748

0.781

0.909

0.908

Source: Own elaboration

3.2. STUDY 2: IN-DEPTH INTERVIEWS

After analysing and compiling the data obtained from the questionnaire, a complementary investigation was carried out with the aim of addressing hypotheses 2 and 3. This research sought to confirm whether purchase intention and consumer loyalty were influenced by AI-based communication.

To this end, eighteen professionals from the fashion industry were identified to analyse their purchasing behaviour in relation to the previously mentioned variables.

These in-depth interviews lasted 45 minutes, and the responses were manually coded afterward to process them as efficiently as possible.

For the analysis of the in-depth interviews, a thematic coding methodology was followed (Braun & Clarke, 2006). This methodology aims to detect common patterns among the various responses from interviewees, with the goal of categorizing the information into distinct themes. After a thorough and systematic reading of the responses, the most representative phrases supporting the key pillars of the answers were identified. This approach facilitated the interpretation of the responses and enabled the identification of key findings for this study.

Table 4. Detailed profile of survey participants

ID

Age

Years of experience in the fashion industry

Gender

R1

57

30

Male

R2

46

21

Female

R3

60

37

Female

R4

53

12

Female

R5

55

28

Male

R6

42

13

Female

R7

34

10

Male

R8

51

7

Male

R9

57

24

Female

R10

38

20

Male

R11

46

15

Male

R12

52

20

Female

R13

63

35

Female

R14

28

6

Male

R15

33

12

Female

R16

43

18

Male

R17

61

40

Male

R18

29

5

Female

Source: Own elaboration

4. RESULTS

4.1. STUDY 1 RESULTS: QUESTIONNAIRE

After conducting the methodological analysis of Study 1, the results obtained from the questionnaire were examined. The structural equation model revealed that consumer purchase intention has a very strong positive influence on consumer loyalty, with a loading of 0.751. This finding highlights a significant influential relationship between the constructs analysed, confirming both its existence and the direction of the relationship.

Additionally, the structural equation model displays the various outer loadings that make up each construct. These loadings are all above 0.707 (Hair et al., 2011), indicating that the constructs are based on a solid foundation.

Also, these results show that consumer purchase intention exerts a relevant positive relationship with consumer loyalty, with a loading of 0.564. Following the presentation of the questionnaire results, Hypothesis 1 (H1) would be accepted.

Illustration 2. Results

Source: Own elaboration

4.2. STUDY 2 RESULTS: IN-DEPTH INTERVIEWS

Moving into the analysis of the in-depth interview results, the most relevant responses within each theme are highlighted. Additionally, three major themes were identified.

The fist theme is Humanization of the Digital Experience This theme emphasizes the importance of offering a human-centered digital experience. The interviews suggest the creation of personalized interactions within the shopping experience.

A standout response from Participant R3 states: “I consider it very important to personalize the customer’s needs. Not all customers are the same or need exactly the same things, so personalized attention that seeks to meet their needs and makes them feel like the centre is important for establishing a long-term relationship.”

The importance of promotions and discounts as part of the humanization strategy was also highlighted. Participant R6 noted: “…most brands offer discounts on the first purchase to attract customers, specific discounts for online purchases or on your birthday. But they could offer special promotions or give a gift to customers who have been with them for years and make frequent purchases.”

The need for an intuitive shopping experience was also emphasized. Participant R2 commented: “In my case, knowing that something ‘works’ is essential. … If they change it, I’ll have to look for an alternative, which takes time. If they keep it as it is, I’ll be able to quickly buy the new season’s product through the website when I need it.”

Another major theme identified is the use and integration of AI and augmented reality. Within this theme, suggestions were made for using these technologies to generate personalized recommendations. Participant R8 stated: “…it could also be a system where, by entering your body measurements, it recommends which types of garments available on the website would suit you best.”

An alternative for integrating AI and augmented reality is through avatars and virtual fitting rooms. Participant R12 proposed: “Introducing the end consumer into a virtual fitting room.” Participant R15 added: “It’s complicated; but simply a virtual fitting room…”

The integration of these new technologies can also be achieved through immersive experiences. Participant R17 explained that this should be done in a specific way: “Used as augmented intelligence to enhance the human factor, but not to replace it.”

Finally, AI and augmented reality were discussed from the perspective of data and personalization. Participant R3 explained: “…these technologies would allow companies in the sector to predict a customer’s needs and offer a personalized product/service when and how the consumer wants it.”

The last theme addressed in a more general way is emotional connection, the feeling of exclusivity, and brand belonging. This theme reveals how brand values and mission are expressed. Participant R8 commented: “I think it plays an important role, since a well-crafted narrative can resonate in the customer’s mind and emotions and be remembered. Moreover, if this narrative reflects good values, such as a commitment to sustainability…”

Another way to emotionally connect with customers is through stories or customer testimonials. Participant R6 explained: “…another emotion that more and more brands appeal to with notable results is nostalgia. From time to time, they release designs featuring characters, music groups, cartoons, or movies that take the consumer back in time…”

Emotional connection can also be fostered by creating a sense of belonging. Participant R4 noted: “The role of the brand is significant, as it’s a feeling associated with exclusivity or a sense of belonging that accompanies you throughout the purchasing process.”

Lastly, transparency and authenticity also contribute to emotional connection. Participant R7 stated: “From my point of view, purchase intention starts with the product. If the brand provides more information, that’s a plus, but secondary to the product and the purchasing conditions it offers the consumer.”

Following the presentation of the in-depth interview results, Hypotheses 2 and 3 (H2 and H3) can be confirmed.

5. DISCUSSIONS

The presentation of the results leads to a discussion of their implications. This section includes three types of discussions: theoretical, managerial, and social.

Firstly, the theoretical discussion reaffirms the influential relationship between purchase intention and consumer loyalty. Based on a brief survey, these two variables show a very strong positive relationship, indicating that they are key to the development of any business. This assertion is supported by the Theory of Loyalty Commitment (TLC) developed by Dick and Basu (1994).

At this point, the results of both studies conducted in this research can be linked. It becomes evident that effective customer-centred communication can be crucial for increasing both purchase intention and consumer loyalty. Concepts such as the humanization of the digital experience, the integration of new technologies in e-commerce communication, and the emotional connection between consumers and companies are addressed. These concepts are closely related to satisfaction, trust, and commitment toward a brand—key factors within the TLC. Thus, this study contributes to the existing literature on consumer behaviour by offering new perspectives on a classic behavioural theory.

From a managerial standpoint, this research sheds light on new AI-based communication techniques and other technologies that consumers demand to enhance their purchase intention and loyalty. The study shows that new technologies are becoming a fundamental component of the digital fashion world.

Personalization emerges as the cornerstone of this study. Consumers seek personalized services tailored to their preferences and life stages. Immersive, intuitive, and digital experiences are also emphasized, with suggestions for the development and normalization of avatars and virtual fitting rooms in digital shopping environments.

Traditional commerce elements like promotions and discounts are highlighted as well. These price-related factors are closely tied to the consumer’s decision-making process and contribute to loyalty.

Finally, the study addresses emotional connections between consumers and brands. Through emotional communication, brands can resonate with customers and build closeness. Communication based on values, mission, transparency, and authenticity is key to achieving this connection (Castillo-Abdul et al., 2022; Castillo-Abdul et al., 2021). Moreover, strategies that involve communicating with consumers based on their profiles—sharing relatable stories or emotional testimonials and making them feel part of the brand community—help create strong company-customer bonds. These strategies can be effectively developed using AI.

The social discussion is the most important part of the study, as it ultimately concerns people and the relationship between companies and customers. Maintaining a focus on the human component is essential.

The results of this research show a strong concern for the humanization of purchasing processes and company-customer relationships. The goal is to foster a balance between technology and the human side of business.

It is clear that advanced technologies promote efficiency and effectiveness in routine e-commerce processes. For example, automating product recommendations with AI, developing virtual fitting rooms, or personalizing a digital shopping interface after user registration are all advancements enabled by technological development.

However, ensuring that this technology complements rather than replaces human support in e-commerce will help consumers feel more confident and comfortable when navigating online stores. This, in turn, will increase their purchase intention and future loyalty.

6. CONCLUSIONS

From a theoretical standpoint, effective AI-mediated communication emerges as a key element in understanding purchase intention and consumer loyalty. The quality of communication delivered by AI systems directly influences consumer perception (Grappe et al., 2021), fostering trust (Margalina et al., 2024; Kang & Hustvedt, 2014), engagement, and emotional attachment (Leite et al., 2024). When these interactions are personalized, credible, and empathetic, they not only enhance the user experience but also strengthen psychological bonds with the brand—leading to greater willingness to purchase and sustained loyalty.

From a managerial perspective, the findings suggest that companies should strategically invest in AI technologies that prioritize personalization (Martínez Puertas et al., 2024; Fetais et al., 2022), transparency, and empathy in customer communication (Hernandez-Ortega & Ferreira, 2021). Implementing AI-driven communication not only optimizes customer service but also boosts purchase intention and reinforces loyalty (Marín-García et al., 2021). Moreover, AI in digital marketing enables the customization of messages to match consumer preferences, creating more satisfying and distinctive experiences. Therefore, AI should be viewed not merely as an operational tool, but as a strategic resource for building long-term customer relationships.

On a social level, the integration of AI into commercial communication has significant implications for how consumers interact with brands. These technologies’ ability to create more human and accessible experiences helps reduce information barriers, improve decision-making, and promote more informed and confident consumption. Additionally, the perception of innovation and sustainability (Kang & Hustvedt, 2014) associated with the responsible use of AI can enhance corporate image and generate a positive societal impact, fostering more ethical and transparent consumer relationships (Bonifacio et al., 2024).

As a future line of research, it is proposed to conduct a similar study using structural equation modelling to analyse all proposed variables, including AI-mediated communication as a mediating variable between purchase intention and consumer loyalty.

7. FUNDING

This research has no funding.

8. CONTRIBUTION

Author 1

Conceptualization

X

Data curation

X

Formal analysis

X

Investigation

X

Methodology

X

Supervision

X

Validation

X

Visualization

X

Writing - Draft

X

Writing – Review & editing

X

9. CONFLICT OF INTEREST

The author declare that there is no conflict of interest.

10. DECLARATION OF ARTIFICIAL INTELLIGENCE USAGE

The author declares not having used artificial intelligence in the preparation of this article.

11. ACKNOWLEDGMENTS

I would like to express my deepest gratitude to Dr. Laura Melendo, Dr. Pedro Cuesta-Valiño, and Dr. Juan Ramón Cuadrado-Roura for their invaluable support during my development as a researcher. In particular, I am grateful to Dr. Max Römer for encouraging me to participate in this initiative. I also extend my thanks to the International Doctoral School of UCJC for its ongoing support, especially to Dr. Miguel Ángel Pérez.

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Table of Contents

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Artificial Intelligence for effective communication: impact on consumer purchase intention and loyalty in fashion

Inteligencia Artificial para una comunicación eficaz: efectos en la intención de compra y lealtad del consumidor de moda

4. RESULTS

5. DISCUSSIONS

6. CONCLUSIONS

7. FUNDING

8. CONTRIBUTION

9. CONFLICT OF INTEREST

10. DECLARATION OF ARTIFICIAL INTELLIGENCE USAGE

11. ACKNOWLEDGMENTS

12. REFERENCES