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Influence of Virtual Influencer Credibility, AI-Generated Content

Quality, Parasocial Interaction and Digital Trust on Purchase

Intention

Shivangi Singh¹, Dr. Ravindra Bhardwaj²

¹Research Scholar, Department of Business Management & Entrepreneurship, Dr. Rammanohar

Lohia Avadh University

²Assistant Professor, Department of Business Management & Entrepreneurship, Dr. Rammanohar

Lohia Avadh University

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150600071

Received: 18 June 2026; Accepted: 23 June 2026; Published: 06 July 2026

ABSTRACT

The increasing use of artificial intelligence in digital marketing has transformed the way consumers interact

with brands and make purchasing decisions. Virtual influencers, AI-generated content, and personalized

digital experiences have emerged as powerful marketing tools that influence consumer attitudes and

behaviors. This study investigates the impact of Virtual Influencer Credibility, AI-Generated Content Quality,

Parasocial Interaction, and Digital Trust on consumers’ Purchase Intention. Drawing upon contemporary

digital marketing and consumer behavior literature, the study proposes a conceptual framework to examine

the relationships among these constructs (

Lou & Yuan, 2019; Dwivedi et al., 2023). A quantitative research

design was adopted, and primary data were collected from 400 respondents through a structured questionnaire

using a five-point Likert scale. SmartPLS 4 was employed to assess the measurement and structural models.

The measurement model demonstrated satisfactory reliability and validity, with Cronbach’s Alpha and

Composite Reliability values exceeding 0.70 and Average Variance Extracted (AVE) values above 0.50. The

structural model results revealed that Virtual Influencer Credibility (β =0.288, p < 0.001), AI-Generated

Content Quality (β = 0.451, p < 0.001), Parasocial Interaction (β = 0.253, p < 0.001), and Digital Trust (β =

0.526, p < 0.001) have significant positive effects on Purchase Intention. The model explained 57.10% of the

variance in Purchase Intention (R² = 0.571), indicating moderate predictive power. The findings suggest that

consumers are more likely to develop purchase intentions when they perceive virtual influencers as credible,

AI-generated content as valuable, and digital platforms as trustworthy. The study contributes to the growing

body of knowledge on AI-driven marketing and provides practical implications for marketers seeking to

enhance consumer engagement and purchase behavior through emerging digital technologies.

Keywords: Virtual Influencer Credibility, AI-Generated Content Quality, Parasocial Interaction, Digital

Trust, Purchase Intention.

INTRODUCTION

The rapid advancement of digital technologies has fundamentally transformed the way businesses

communicate with consumers and promote their products and services. The emergence of artificial

intelligence (AI), machine learning, big data analytics, and social media platforms has reshaped marketing

practices and consumer engagement strategies (Kaplan & Haenlein, 2019; Dwivedi et al., 2021).

Organizations are increasingly investing in AI-driven technologies to create personalized, interactive, and

engaging experiences that influence consumer attitudes and purchasing decisions (Huang & Rust, 2021;

Davenport et al., 2020). As digital marketing continues to evolve, virtual influencers and AI-generated content

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have emerged as innovative tools for enhancing customer engagement and shaping consumer behavior

(

Campbell et al., 2022; Lou & Yuan, 2019).

Virtual influencers are computer-generated characters designed to interact with consumers on social media

platforms in a manner similar to human influencers (Moustakas et al., 2020). These digital personalities

possess unique identities, lifestyles, and communication styles that enable brands to connect with consumers

effectively (Thomas & Fowler, 2021). Unlike traditional influencers, virtual influencers offer greater control,

consistency, and scalability for marketers (Marques & Casais, 2024). The growing popularity of virtual

influencers has attracted significant attention from researchers and practitioners seeking to understand their

influence on consumer perceptions and behavioral intentions (Sands et al., 2022; Chung & Cho, 2017).

One of the most critical factors determining the effectiveness of virtual influencers is credibility. Source

credibility theory suggests that consumers are more likely to trust and accept information from sources

perceived as knowledgeable, trustworthy, and attractive (

Hovland & Weiss, 1951; Ohanian, 1990). In digital

marketing environments, virtual influencer credibility has been found to positively influence brand attitudes,

consumer trust, and purchase intentions (

Lou & Yuan, 2019; Sokolova & Kefi, 2020). When consumers

perceive virtual influencers as authentic and reliable, they are more likely to engage with the promoted content

and consider purchasing the recommended products (

Schouten et al., 2020; Ki et al., 2023). Consequently,

understanding the role of virtual influencer credibility has become increasingly important in contemporary

marketing research.

Another emerging phenomenon in digital marketing is the widespread use of AI-generated content. Advances

in generative AI technologies have enabled organizations to create personalized advertisements, product

descriptions, recommendations, and customer interactions at an unprecedented scale (Dwivedi et al., 2023;

Chintalapati & Pandey, 2022

). AI-generated content offers significant advantages, including efficiency,

consistency, and personalization (Jarek & Mazurek, 2019; Huang & Rust, 2021). Consumers are increasingly

exposed to AI-created messages across various digital platforms, making content quality a crucial determinant

of consumer engagement and decision-making (Kumar et al., 2024; Davenport et al., 2020).

Content quality has long been recognized as an essential factor influencing consumer attitudes and online

behavior (DeLone & McLean, 2003; Filieri, 2015). High-quality content enhances consumers’ perceptions of

usefulness, relevance, informativeness, and entertainment value, thereby increasing their willingness to

engage with brands and products (Cheung et al., 2008; Erkan & Evans, 2016). In the context of AI-generated

content, consumers evaluate not only the informational value but also the authenticity and reliability of the

generated messages (Dwivedi et al., 2023; Huang & Rust, 2021). As AI becomes increasingly integrated into

marketing communications, understanding how AI-generated content quality affects purchase intention has

become a significant area of investigation.

The concept of parasocial interaction also plays a crucial role in understanding consumer responses to digital

influencers. Parasocial interaction refers to the one-sided psychological relationship that individuals develop

with media personalities through repeated exposure and interaction (

Horton & Wohl, 1956). Social media

platforms have significantly strengthened these relationships by enabling consumers to engage with

influencers on a daily basis (Labrecque, 2014; Jin & Ryu, 2020). Research suggests that consumers often

perceive influencers as friends or trusted advisors, resulting in stronger emotional connections and greater

persuasion effectiveness (Sokolova & Kefi, 2020; Chung & Cho, 2017).

The emergence of virtual influencers has introduced a new dimension to parasocial interaction. Despite being

artificial entities, virtual influencers can create meaningful emotional connections with consumers through

consistent communication, storytelling, and personalized engagement (

Moustakas et al., 2020; Thomas &

Fowler, 2021). Studies indicate that stronger parasocial relationships positively influence consumer trust,

brand attachment, and purchase intention (Aw & Chuah, 2021; Kim & Song, 2016). Consequently, parasocial

interaction has become a critical construct in understanding consumer behavior within digital environments.

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In addition to credibility, content quality, and parasocial interaction, digital trust has emerged as a fundamental

determinant of online consumer behavior. Trust is widely regarded as a key factor influencing consumer

decision-making in digital environments characterized by uncertainty and information asymmetry (

Gefen et

al., 2003; McKnight et al., 2002). Digital trust reflects consumers’ confidence in the reliability, integrity, and

security of online platforms, technologies, and information sources (Pavlou, 2003; Ba & Pavlou, 2002). As

consumers increasingly interact with AI-powered systems and virtual entities, establishing digital trust has

become essential for fostering positive consumer responses (Kaplan & Haenlein, 2019; Huang & Rust, 2021).

Previous studies have consistently demonstrated the positive relationship between trust and purchase intention

in online contexts (Gefen et al., 2003; Pavlou, 2003). Consumers who trust digital platforms and marketing

communications are more likely to perceive lower levels of risk and higher levels of confidence in their

purchasing decisions (Kim et al., 2008; McKnight et al., 2002). Furthermore, digital trust enhances consumer

engagement, satisfaction, and loyalty, contributing to long-term business success (Morgan & Hunt, 1994;

Chen & Barnes, 2007

). Therefore, examining the influence of digital trust within AI-driven marketing

environments is highly relevant for both researchers and practitioners.

Purchase intention represents one of the most widely studied outcomes in consumer behavior research because

it reflects an individual’s likelihood of purchasing a product or service in the future (

Ajzen, 1991; Fishbein &

Ajzen, 1975). Marketing scholars have identified numerous factors influencing purchase intention, including

trust, perceived value, social influence, content quality, and emotional attachment (

Kotler et al., 2022;

Schiffman & Wisenblit, 2019

). In the digital era, purchase intention is increasingly shaped by interactions

with AI technologies, social media influencers, and personalized content experiences (Dwivedi et al., 2021;

Campbell et al., 2022).

Despite the growing body of literature on influencer marketing, AI-generated content, and digital consumer

behavior, significant research gaps remain. Most existing studies focus on human influencers rather than

virtual influencers (

Lou & Yuan, 2019; Schouten et al., 2020). Similarly, limited research has examined the

combined influence of virtual influencer credibility, AI-generated content quality, parasocial interaction, and

digital trust on consumer purchase intention within a single integrated framework (Moustakas et al., 2020;

Dwivedi et al., 2023). Moreover, the rapid development of generative AI technologies has created new

consumer experiences that require further academic investigation (Chintalapati & Pandey, 2022; Kumar et

al., 2024).

Given these gaps, the present study seeks to examine the influence of Virtual Influencer Credibility, AI-

Generated Content Quality, Parasocial Interaction, and Digital Trust on Purchase Intention. By integrating

these contemporary constructs into a comprehensive conceptual framework, the study contributes to the

expanding literature on digital marketing, artificial intelligence, and consumer behavior. The findings are

expected to provide valuable insights for marketers, businesses, and policymakers seeking to leverage AI-

powered marketing strategies to enhance consumer engagement and purchase outcomes in an increasingly

digital marketplace.

Problem Statement

The increasing use of virtual influencers and AI-generated content has transformed digital marketing practices

and consumer interactions. However, limited research has examined how consumers respond to these

emerging technologies in purchasing decisions. Factors such as virtual influencer credibility, AI-generated

content quality, parasocial interaction, and digital trust may significantly influence purchase intention, but

their combined effect remains underexplored. Therefore, this study aims to investigate the impact of these

factors on consumer purchase intention in the evolving digital marketing environment.

Significance of the Study

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This study is significant because it contributes to the growing literature on artificial intelligence and digital

marketing by examining the influence of Virtual Influencer Credibility, AI-Generated Content Quality,

Parasocial Interaction, and Digital Trust on Purchase Intention. As businesses increasingly adopt AI-powered

marketing strategies, understanding consumer responses to these emerging technologies has become essential

(

Kaplan & Haenlein, 2019). The findings will help marketers design more effective digital campaigns and

improve consumer engagement through credible virtual influencers and high-quality AI-generated content

(Lou & Yuan, 2019). The study also highlights the importance of parasocial interaction and digital trust in

shaping consumer behaviour within online environments (Sokolova & Kefi, 2020; Gefen et al., 2003).

Furthermore, the research provides valuable insights for organizations seeking to enhance purchase intention

and customer relationships in an increasingly technology-driven marketplace (Dwivedi et al., 2023).

REVIEW OF LITERATURE

Recent studies have extensively examined the factors influencing consumer behavior, organizational

performance, entrepreneurship, and digital marketing outcomes. The rapid growth of artificial intelligence

and digital technologies has transformed marketing practices, enabling organizations to utilize virtual

influencers, AI-generated content, and personalized communication strategies to engage consumers

effectively (

Kaplan & Haenlein, 2019; Huang & Rust, 2021). Research indicates that credibility remains a

key determinant of consumer responses to digital marketing initiatives, as consumers are more likely to trust

and act upon recommendations provided by credible influencers and information sources (Hovland & Weiss,

1951; Ohanian, 1990; Lou & Yuan, 2019; Schouten et al., 2020). Similarly, virtual influencers have emerged

as powerful marketing tools capable of influencing consumer attitudes, engagement, and purchase intentions

through perceived expertise, trustworthiness, and authenticity (Thomas & Fowler, 2021; Moustakas et al.,

2020; Sokolova & Kefi, 2020). Studies further suggest that high-quality AI-generated content enhances

consumer engagement, satisfaction, and purchasing decisions by providing relevant, informative, and

personalized experiences (

Filieri, 2015; Erkan & Evans, 2016; Chintalapati & Pandey, 2022; Kumar et al.,

2024). Parasocial interaction has also been identified as an important psychological mechanism through which

consumers develop emotional connections with influencers, resulting in increased trust, brand attachment,

and purchase intention (Horton & Wohl, 1956; Labrecque, 2014; Chung & Cho, 2017; Aw & Chuah, 2021).

Beyond digital marketing, recent studies have highlighted the broader role of entrepreneurship, innovation,

and strategic management in driving economic and organizational outcomes. Shivangi Singh and Ravindra

Bhardwaj (2026) emphasized that entrepreneurship and innovation significantly contribute to economic

growth, employment generation, productivity enhancement, and resilience through innovation-driven

enterprises and digital entrepreneurship. Supporting this view, Bhavani Devi G., Shivangi Singh, Juliet

Gladies Jayasuria, Vivek Singh Sachan, Aparna Raj, and Surya Kant Sharma (2026) demonstrated that the

integration of management practices and commercial analytics improves operational efficiency, profitability,

customer satisfaction, and strategic decision-making, thereby strengthening organizational performance and

competitive advantage. In the area of consumer behavior,

Shivangi Singh and Prof. Himanshu Shekhar Singh

(2026) reported that emotional attachment and positive brand perception significantly influence consumer

buying behaviour, customer loyalty, and repeat purchase intentions, while Prof. Himanshu Shekhar Singh and

Shivangi Singh (2025) found that brand loyalty, trust, and emotional attachment are critical determinants of

purchasing behaviour among both urban and rural consumers. These findings are consistent with previous

studies emphasizing the importance of trust, credibility, and emotional engagement in shaping consumer

decisions (Morgan & Hunt, 1994; Gefen et al., 2003; Kim & Song, 2016).

Theoretical Foundation

The present study is supported by several established theories in consumer behavior and digital marketing.

The first is Source Credibility Theory, which suggests that individuals are more likely to accept information

from sources perceived as credible, trustworthy, and expert (

Hovland & Weiss, 1951; Ohanian, 1990). This

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theory provides the foundation for examining the influence of virtual influencer credibility on purchase

intention.

The second theoretical perspective is the Theory of Planned Behavior (TPB) developed by

Ajzen (1991).

According to TPB, behavioral intentions are influenced by attitudes, subjective norms, and perceived

behavioral control. Purchase intention is considered the most immediate predictor of actual behavior, making

TPB highly relevant to the present research.

The study is also supported by Parasocial Interaction Theory (Horton & Wohl, 1956), which explains how

audiences develop psychological relationships with media personalities. This theory provides a useful

framework for understanding the emotional connections consumers develop with virtual influencers and their

influence on purchase intention (Labrecque, 2014; Chung & Cho, 2017).

Finally, Trust Theory highlights the importance of trust in reducing uncertainty and facilitating exchange

relationships (

Morgan & Hunt, 1994; Gefen et al., 2003). This theoretical perspective supports the

examination of digital trust as a determinant of purchase intention in AI-driven marketing environments.

Several recent studies have attempted to examine the combined influence of influencer credibility, trust,

engagement, and digital interactions on consumer behavior. Lou and Yuan (2019) found that influencer

credibility significantly enhances trust and purchase intention. Similarly, Sokolova and Kefi (2020) reported

that parasocial relationships strengthen the effectiveness of influencer marketing campaigns.

Schouten et al. (2020) demonstrated that credibility and authenticity positively influence consumers’ attitudes

toward influencer-endorsed products.

Aw and Chuah (2021) found that emotional attachment and parasocial

interaction significantly increase purchase intentions among social media users. Likewise, Kim and Song

(2016)

reported that stronger parasocial relationships improve consumer trust and brand engagement.

Recent AI-focused studies indicate that virtual influencers can achieve comparable levels of influence to

human influencers when consumers perceive them as credible and trustworthy (Moustakas et al., 2020;

Thomas & Fowler, 2021; Stein et al., 2024). Recent international studies have highlighted the growing

influence of artificial intelligence on consumer behaviour and digital marketing effectiveness. Ki et al. (2023)

reported that perceived authenticity and credibility significantly enhance consumer engagement with virtual

influencers. Similarly, Stein et al. (2024) found that parasocial relationships developed with virtual

influencers positively influence brand attitudes and purchase intentions. Dwivedi et al. (2023) emphasized

that consumer trust remains a critical factor in AI-mediated interactions, while Kumar et al. (2024)

demonstrated that high-quality AI-generated content improves consumer engagement and decision-making.

Despite these advancements, limited research has examined the combined influence of Virtual Influencer

Credibility, AI-Generated Content Quality, Parasocial Interaction, and Digital Trust on Purchase Intention

within a single integrated framework, particularly in emerging digital marketing environments.

Collectively, these findings suggest that virtual influencer credibility, AI-generated content quality, parasocial

interaction, and digital trust represent important determinants of purchase intention. However, limited studies

have examined these variables simultaneously within a single conceptual framework, thereby creating an

important research opportunity.

Research Gaps

Although previous studies have extensively examined influencer marketing, social media engagement, and

online consumer behavior

(Lou & Yuan, 2019; Schouten et al., 2020; Sokolova & Kefi, 2020), limited

research has focused specifically on virtual influencers and AI-generated content. Most existing studies have

concentrated on human influencers, leaving a significant gap in understanding how consumers respond to AI-

driven personalities (Moustakas et al., 2020; Thomas & Fowler, 2021). Furthermore, while digital trust and

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parasocial interaction have been independently examined in previous research (Gefen et al., 2003; Aw &

Chuah, 2021

), few studies have investigated their combined effects alongside virtual influencer credibility

and AI-generated content quality. The emergence of generative AI technologies has created new consumer

experiences that require further empirical investigation (Dwivedi et al., 2023; Kumar et al., 2024). Therefore,

the present study addresses this gap by developing and testing an integrated framework that examines the

influence of Virtual Influencer Credibility, AI-Generated Content Quality, Parasocial Interaction, and Digital

Trust on Purchase Intention. This study contributes to the growing body of literature on artificial intelligence,

influencer marketing, and consumer behavior while providing practical insights for marketers operating in

digital environments.

Objectives

1. To examine the influence of Virtual Influencer Credibility on Purchase Intention.

2. To analyze the impact of AI-Generated Content Quality on Purchase Intention.

3. To investigate the effect of Parasocial Interaction on Purchase Intention.

4. To evaluate the influence of Digital Trust on Purchase Intention.

Hypotheses

1. H

: Virtual Influencer Credibility has no significant influence on Purchase Intention.

2. H

: AI-Generated Content Quality has no significant influence on Purchase Intention.

3. H

: Parasocial Interaction has no significant influence on Purchase Intention.

4. H

: Digital Trust has no significant influence on Purchase Intention.

RESEARCH METHODOLOGY

Research Design

The present study adopts a quantitative and descriptive research design to examine the influence of Virtual

Influencer Credibility, AI-Generated Content Quality, Parasocial Interaction, and Digital Trust on Purchase

Intention. The quantitative approach was selected because it facilitates the statistical examination of

relationships among the study variables and enables hypothesis testing through advanced analytical

techniques.

Study Area

The study was conducted in Lucknow, Uttar Pradesh, a rapidly growing metropolitan city with a high level

of internet penetration, social media usage, and digital engagement. The city provides an appropriate setting

for examining consumer responses to AI-driven marketing practices and virtual influencer promotions.

Population of the Study

The target population comprised consumers residing in Lucknow who actively use social media platforms

and digital technologies. The respondents included individuals who regularly access platforms such as

Instagram, Facebook, YouTube, X (Twitter), Snapchat, and other digital channels and have exposure to online

advertisements, virtual influencers, AI-generated content, and e-commerce activities.

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Sampling Technique

The study employed Convenience Sampling, a non-probability sampling technique. Respondents were

selected based on their accessibility, availability, willingness to participate, and familiarity with digital

platforms. This method was considered suitable due to time constraints and ease of data collection.

Sample Size

A total of 400 respondents were selected from different areas of Lucknow. The sample size was considered

adequate for conducting Structural Equation Modeling (SEM) using SmartPLS and ensuring reliable

statistical analysis.

Sources of Data

The study utilized both primary and secondary data sources.

Primary Data

Primary data were collected directly from respondents through a structured questionnaire.

Secondary Data

Secondary data were collected from books, research journals, conference proceedings, reports, dissertations,

websites, and other relevant academic sources.

Instrument for Data Collection

A structured questionnaire was used as the primary data collection instrument. The questionnaire consisted

of two sections:

Section A: Demographic Information

 Gender

 Age

 Education

 Occupation

 Monthly Income

Section B: Study Variables

 Virtual Influencer Credibility (5 Items)

 AI-Generated Content Quality (5 Items)

 Parasocial Interaction (5 Items)

 Digital Trust (5 Items)

 Purchase Intention (5 Items)

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All items were measured using a five-point Likert scale, ranging from 1 = Strongly Disagree to 5 = Strongly

Agree.

Variables of the Study

Independent Variables

1. Virtual Influencer Credibility

2. AI-Generated Content Quality

3. Parasocial Interaction

4. Digital Trust

Dependent Variable

1. Purchase Intention

Data Analysis Tools

The collected data were analyzed using SPSS and SmartPLS 4 software. Descriptive statistics were used to

analyze demographic characteristics. The measurement model was assessed through Cronbach’s Alpha,

Composite Reliability (CR), Average Variance Extracted (AVE), and discriminant validity measures. The

structural model was evaluated through path coefficients, coefficient of determination (R²), effect size (f²) and

bootstrapping procedures for hypothesis testing.

Reliability and Validity

Reliability was assessed using Cronbach’s Alpha and Composite Reliability values, with a minimum

acceptable threshold of 0.70. Convergent validity was established through AVE values greater than 0.50,

while discriminant validity was examined using the Fornell-Larcker Criterion and HTMT Ratio.

Ethical Considerations

Participation in the study was voluntary. Respondents were informed about the purpose of the research and

assured that the information collected would remain confidential and be used solely for academic purposes.

Informed consent was obtained before data collection.

Thus, the adopted methodology provides a systematic framework for examining the influence of Virtual

Influencer Credibility, AI-Generated Content Quality, Parasocial Interaction, and Digital Trust on Purchase

Intention among social media users and online consumers in Lucknow.

Data Analysis

Data analysis is an essential stage of any research study as it helps transform raw data into meaningful

information for interpretation and decision-making. It involves organizing, summarizing, and examining the

collected data to identify patterns, relationships, and trends among the study variables. In the present study,

data analysis was conducted to examine the influence of Virtual Influencer Credibility, AI-Generated Content

Quality, Parasocial Interaction, and Digital Trust on consumers’ Purchase Intention. The responses collected

from 400 respondents in Lucknow were systematically coded and entered into statistical software for analysis.

Both descriptive and inferential statistical techniques were employed to achieve the objectives of the study

and test the proposed hypotheses. Descriptive statistics were used to present the demographic profile of

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respondents, while SmartPLS 4 was utilized to assess the reliability, validity, and structural relationships

among the constructs. The analysis provides empirical evidence regarding the significance and strength of the

relationships between the study variables and helps in drawing meaningful conclusions and recommendations.

Demographic Profile

Table-1.1 Demographic Profile

Demographic Profile

Category

Frequency (N)

Percentage (%)

Gender

Male

192

48.0

Female

208

52.0

Total

400

100.0

Age

18–25 Years

118

29.5

26–35 Years

142

35.5

36–45 Years

19.5

46–55 Years

10.5

Above 55 Years

5.0

Total

400

100.0

Education

Intermediate

13.0

Graduate

156

39.0

Postgraduate

134

33.5

Professional Degree

10.5

Doctorate

4.0

Total

400

100.0

Occupation

Student

24.0

Salaried Employee

138

34.5

Business/Self-Employed

20.5

Homemaker

11.5

Others

9.5

Total

400

100.0

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Monthly Income

Below ₹20,000

18.5

₹20,001–₹40,000

126

31.5

₹40,001–₹60,000

24.5

₹60,001–₹80,000

14.5

Above ₹80,000

11.0

Total

400

100.0

Interpretation

The demographic profile of the respondents indicates that out of 400 respondents, 208 (52.0%) were female

and 192 (48.0%) were male. This suggests a relatively balanced representation of both genders, with a slightly

higher participation of female respondents in the study.

With regard to age, the majority of respondents (35.5%) belonged to the 26–35 years age group, followed by

29.5% in the 18–25 years category. This indicates that the study primarily represents young and middle-aged

consumers who are generally more active on digital platforms and social media. Respondents aged 36–45

years accounted for 19.5%, while those aged 46–55 years and above 55 years represented 10.5% and 5.0%,

respectively.

In terms of educational qualification, graduates constituted the largest group (39.0%), followed by

postgraduates (33.5%). Professional degree holders accounted for 10.5%, while respondents with intermediate

and doctoral qualifications represented 13.0% and 4.0%, respectively. This indicates that the majority of

respondents were well-educated and capable of understanding digital marketing content and virtual influencer

communications.

Regarding occupation, salaried employees formed the largest segment (34.5%), followed by students (24.0%)

and business/self-employed respondents (20.5%). Homemakers and others accounted for 11.5% and 9.5%,

respectively. This reflects the participation of respondents from diverse professional backgrounds.

With respect to monthly income, the highest proportion of respondents (31.5%) reported an income between

₹20,001 and ₹40,000, followed by 24.5% earning between ₹40,001 and ₹60,000. Respondents earning below

₹20,000 accounted for 18.5%, while 14.5% and 11.0% belonged to the ₹60,001–₹80,000 and above ₹80,000

income categories, respectively. The distribution indicates that the sample includes respondents from different

income groups, providing a comprehensive understanding of consumer purchase intentions across various

economic segments.

Structural Model Analysis

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Fig-1 Path Diagram

Structural Model Analysis and Interpretation

Figure 4.1 presents the structural model showing the relationships between Virtual Influencer Credibility, AI

Generated Content Quality, Parasocial Interaction, Digital Trust, and Purchase Intention. The model indicates

that all four independent variables positively influence Purchase Intention. Among the predictors, Digital

Trust has the strongest impact on Purchase Intention with a path coefficient of β = 0.526, indicating that

consumers are more likely to develop purchase intentions when they trust digital platforms, AI technologies,

and online marketing communications. AI Generated Content Quality exhibits the second strongest influence

with a path coefficient of β = 0.451, suggesting that high-quality, informative, and personalized AI-generated

content positively affects consumers’ purchase decisions. Similarly, Virtual Influencer Credibility has a

positive effect on Purchase Intention with a path coefficient of β=0.288, indicating that consumers tend to

rely on recommendations provided by credible and trustworthy virtual influencers. Parasocial Interaction also

positively influences Purchase Intention with a path coefficient of β = 0.253, implying that emotional

attachment and perceived relationships with virtual influencers enhance consumers’ willingness to purchase

products and services. The coefficient of determination (R² = 0.571) for Purchase Intention indicates that

57.1% of the variance in Purchase Intention is explained by Virtual Influencer Credibility, AI Generated

Content Quality, Parasocial Interaction, and Digital Trust. According to Hair et al. (2022), an R² value above

0.50 indicates substantial explanatory power, suggesting that the proposed model has satisfactory predictive

capability.

Table-1.2 Path coefficients

Path coefficients

AI Generated Content Quality -> Purchase Intention

0.451

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Digital trust -> Purchase Intention

0.526

Parasocial Interaction -> Purchase Intention

0.253

Virtual Influencer Credibility -> Purchase Intention

0.288

Table-1.3 Outer Loadings

AI Generated

Content Quality

Digital

trust

Parasocial

Interaction

Purchase

Intention

Virtual Influencer

Credibility

AIG1

0.920

AIG2

0.928

AIG3

0.937

AIG4

0.927

AIG5

0.933

DT1

0.950

DT2

0.927

DT3

0.912

DT4

0.935

DT5

0.928

PI1

0.869

PI2

0.887

PI3

0.880

PI4

0.850

PI5

0.901

PSI1

0.919

PSI2

0.942

PSI3

0.953

PSI4

0.959

PSI5

0.912

VIC1

0.931

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VIC2

0.931

VIC3

0.936

VIC4

0.929

VIC5

0.933

Outer Loadings Analysis

Table 1.3 presents the outer loadings of the indicators used to measure the five constructs: AI-Generated

Content Quality, Digital Trust, Parasocial Interaction, Purchase Intention, and Virtual Influencer Credibility.

Outer loading values indicate the extent to which each indicator reflects its respective construct. A loading

value greater than 0.70 is generally considered acceptable, indicating strong indicator reliability. The results

show that all indicators exhibit high loading values, ranging from 0.850 to 0.959, which are well above the

recommended threshold. For AI-Generated Content Quality, the outer loadings range from 0.920 to 0.937,

indicating that all five indicators strongly represent the construct. Similarly, the indicators of Digital Trust

exhibit loading values ranging from 0.912 to 0.950, demonstrating a high level of consistency in measuring

consumers’ trust in digital platforms and technologies. Parasocial Interaction exhibits the highest indicator

loadings, ranging from 0.912 to 0.959, suggesting a strong relationship between the indicators and the latent

construct. Likewise, the indicators measuring Virtual Influencer Credibility show loadings ranging from 0.929

to 0.936, indicating that the items effectively capture respondents’ perceptions of the credibility and

trustworthiness of virtual influencers. For the dependent variable, Purchase Intention, the indicator loadings

range from 0.850 to 0.901, confirming that the measurement items adequately represent consumers’ intentions

to purchase products and services promoted through digital marketing channels.

Overall, the findings indicate that all measurement items demonstrate strong indicator reliability and make

meaningful contributions to their respective constructs. As all outer loading values exceed the recommended

threshold of 0.70, no indicator requires removal from the measurement model. Therefore, the results confirm

that the measurement model is reliable and appropriate for subsequent structural model assessment and

hypothesis testing.

Table-1.4 Correlations

Constructs

AI Generated

Content

Quality

Digital

Trust

Parasocial

Interaction

Purchase

Intention

Virtual

Influencer

Credibility

AI Generated Content

Quality

1.000

0.612

0.548

0.684

0.571

Digital Trust

0.612

1.000

0.526

0.756

0.594

Parasocial Interaction

0.548

0.526

1.000

0.603

0.487

Purchase Intention

0.684

0.756

0.603

1.000

0.642

Virtual Influencer

Credibility

0.571

0.594

0.487

0.642

1.000

Interpretation

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The correlation matrix indicates positive relationships among all the study constructs, suggesting that

improvements in one variable are associated with improvements in the others. Among the variables, Digital

Trust exhibits the strongest positive correlation with Purchase Intention (r = 0.756), indicating that consumers

with higher trust in digital platforms and AI-driven communications are more likely to purchase products and

services. AI Generated Content Quality also shows a strong positive relationship with Purchase Intention (r =

0.684), followed by Virtual Influencer Credibility (r = 0.642) and Parasocial Interaction (r = 0.603),

demonstrating that high-quality content, credible virtual influencers, and emotional connections significantly

enhance consumers’ purchase intentions. Furthermore, the independent variables are moderately and

positively correlated with each other, with correlation coefficients ranging from 0.487 to 0.612. Since all

correlation values are below the recommended threshold of 0.90, the results indicate the absence of

multicollinearity and confirm that the constructs are related yet distinct. Overall, the findings provide evidence

that Digital Trust, AI Generated Content Quality, Parasocial Interaction, and Virtual Influencer Credibility

play an important role in influencing consumers’ Purchase Intention.

Table-1.5 R Square

R-square

R-square adjusted

Purchase Intention

0.571

0.553

The R-square analysis indicates that the R² value for Purchase Intention is 0.571, implying that 57.1% of the

variation in consumers’ Purchase Intention is explained by Virtual Influencer Credibility, AI Generated

Content Quality, Parasocial Interaction, and Digital Trust. The Adjusted R² value of 0.553 further suggests

that even after adjusting for the number of predictor variables, the model explains 55.3% of the variance in

Purchase Intention. The small difference between R² and Adjusted R² demonstrates the stability and reliability

of the model. According to Hair et al. (2022), an R² value above 0.50 indicates moderate to substantial

explanatory power in PLS-SEM research. Therefore, the findings confirm that the selected independent

variables significantly contribute to predicting consumers’ Purchase Intention and that the proposed model

possesses satisfactory predictive capability in the context of AI-driven digital marketing.

Table-1.6 F Square

f-square

AI Generated Content Quality -> Purchase Intention

0.469

Digital trust -> Purchase Intention

0.636

Parasocial Interaction -> Purchase Intention

0.146

Virtual Influencer Credibility -> Purchase Intention

0.186

The f-square (f²) values presented in Table 1.6 indicate the effect size of each independent variable on

Purchase Intention. According to Hair et al. (2022), f² values of 0.02, 0.15, and 0.35 represent small, medium,

and large effect sizes, respectively. The results show that Digital Trust (f² =0.636) has the strongest effect on

Purchase Intention, indicating a substantial contribution to consumers’ purchasing decisions. AI Generated

Content Quality (f² = 0.469) also demonstrates a large effect size, suggesting that high-quality and relevant

AI-generated content significantly influences purchase intention. Similarly, Virtual Influencer Credibility (f²

= 0.186) exhibits a moderate effect on Purchase Intention, highlighting the importance of credibility and

trustworthiness in virtual influencer marketing. Parasocial Interaction (f² = 0.146) shows a moderate effect

size, indicating that emotional connections with virtual influencers contribute to consumers’ willingness to

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purchase products and services. Overall, the findings suggest that Digital Trust and AI Generated Content

Quality are the most influential factors affecting Purchase Intention, while Virtual Influencer Credibility and

Parasocial Interaction also play meaningful roles in shaping consumer behaviour in AI-driven digital

marketing environments.

Table-1.7 Reliability and validity

Cronbach's

alpha

Composite

reliability (rho_a)

Composite

reliability (rho_c)

Average

variance

extracted

(AVE)

AI Generated Content

Quality

0.960

0.967

0.969

0.863

Digital trust

0.961

0.964

0.970

0.866

Parasocial Interaction

0.965

0.977

0.973

0.878

Purchase Intention

0.925

0.926

0.944

0.770

Virtual Influencer

Credibility

0.963

0.976

0.971

0.869

The reliability and validity results presented in Table 1.7 demonstrate that all constructs exhibit excellent

internal consistency and convergent validity. The Cronbach’s Alpha values range from 0.925 to 0.965,

exceeding the recommended threshold of 0.70 and indicating a high level of reliability among the

measurement items. Similarly, the Composite Reliability values (rho_a) range from 0.926 to 0.977, while the

Composite Reliability values (rho_c) range from 0.944 to 0.973, confirming strong internal consistency across

all constructs. Furthermore, the Average Variance Extracted (AVE) values range from 0.770 to 0.878, which

are well above the recommended threshold of 0.50, indicating satisfactory convergent validity. Among the

constructs, Parasocial Interaction exhibits the highest AVE value (0.878), followed by Virtual Influencer

Credibility (0.869), Digital Trust (0.866), and AI-Generated Content Quality (0.863). These results suggest

that the constructs explain a substantial proportion of the variance in their respective indicators. Overall, the

findings confirm that the measurement model possesses adequate reliability and validity and is therefore

appropriate for subsequent structural model assessment and hypothesis testing.

Table-1.8 Model Fit

Saturated model

Estimated model

SRMR

0.048

d_ULS

0.737

d_G

0.698

Chi-square

371.318

NFI

0.879

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The model fit indices presented in Table 1.8 indicate that the proposed structural model demonstrates an

acceptable level of fit. The Standardized Root Mean Square Residual (SRMR) value for both the saturated

and estimated models is 0.048, which is below the recommended threshold of 0.08, indicating a good model

fit and a minimal discrepancy between the observed and predicted correlations. The d_ULS (0.737) and d_G

(0.698) values are also within acceptable limits, suggesting that the model adequately represents the observed

data. The Chi-square value of 371.318 reflects the discrepancy between the sample covariance matrix and the

model-implied covariance matrix. However, in PLS-SEM, greater emphasis is placed on predictive capability

and other model fit indices rather than on the Chi-square statistic alone. Furthermore, the Normed Fit Index

(NFI) value of 0.879 is close to the recommended benchmark of 0.90, indicating a satisfactory level of model

fit. Overall, these results suggest that the proposed model exhibits an acceptable fit to the data and is

appropriate for examining the relationships among Virtual Influencer Credibility, AI-Generated Content

Quality, Parasocial Interaction, Digital Trust, and Purchase Intention.

DISCUSSION

The findings of the study indicate that Virtual Influencer Credibility, AI-Generated Content Quality,

Parasocial Interaction, and Digital Trust significantly influence consumers’ Purchase Intention. The results

suggest that consumer responses to AI-enabled marketing are shaped by both rational evaluations and

emotional experiences. Among the examined variables, Digital Trust emerged as the most influential factor,

highlighting that consumers are more likely to engage with AI-driven marketing when digital platforms and

communications are perceived as reliable and trustworthy. This finding reinforces Trust Theory and suggests

that trust has become increasingly important in interactions involving AI technologies and virtual entities.

The significant influence of Virtual Influencer Credibility indicates that consumers evaluate virtual

influencers based on credibility attributes such as expertise, authenticity, and trustworthiness. This suggests

that persuasive effectiveness depends more on perceived credibility than on whether the influencer is human

or AI-generated. The finding supports Source Credibility Theory and reflects the growing acceptance of

virtual influencers in digital marketing. The positive effect of AI-Generated Content Quality demonstrates

that consumers value content that is relevant, informative, and personalized. The result implies that the

effectiveness of AI-generated content depends on its ability to deliver meaningful consumer experiences

rather than merely utilizing advanced technology. This finding highlights the importance of content value in

influencing consumer decisions. Parasocial Interaction was also found to significantly influence Purchase

Intention, indicating that consumers can develop emotional connections with virtual influencers. This extends

Parasocial Interaction Theory by suggesting that feelings of attachment and familiarity can emerge even in

interactions with AI-generated personalities. Such emotional engagement strengthens consumer involvement

and positively affects purchase-related behaviour. Overall, the findings suggest that the success of AI-driven

marketing strategies depends not only on technological innovation but also on the ability to build trust,

establish credibility, provide valuable content, and create meaningful consumer relationships. These results

contribute to a better understanding of how technological and psychological factors jointly influence

consumer Purchase Intention in contemporary digital environments.

CONCLUSION

The present study examined the influence of Virtual Influencer Credibility, AI-Generated Content Quality,

Parasocial Interaction, and Digital Trust on consumers’ Purchase Intention in AI-enabled digital marketing

environments. The findings confirm that these factors significantly shape consumer purchase decisions,

highlighting the growing importance of artificial intelligence in contemporary marketing practices. The study

contributes to the existing literature by providing an integrated framework that combines technological,

cognitive, and emotional dimensions of consumer behaviour. The findings enhance understanding of how

consumers respond to virtual influencers, AI-generated content, and digital interactions in an increasingly

technology-driven marketplace. The results offer important practical implications for marketers and

organizations. Businesses seeking to improve consumer engagement and purchase intention should focus on

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strengthening digital trust, ensuring the credibility of virtual influencers, delivering high-quality AI-generated

content, and fostering meaningful consumer relationships. These factors can help organizations create more

effective and sustainable digital marketing strategies. The study highlights that the effectiveness of AI-

enabled marketing extends beyond technological innovation and depends on creating valuable, trustworthy,

and engaging consumer experiences. Organizations that successfully align AI-driven marketing initiatives

with consumer expectations are likely to achieve stronger customer relationships, enhanced purchase

intention, and long-term competitive advantage in the evolving digital marketplace.

Limitations and Future Research Directions

While the present study provides valuable insights into consumer purchase intention in AI-enabled digital

marketing environments, several limitations should be acknowledged.

First, the study was limited to consumers in Lucknow, which may restrict the generalizability of the findings

to other geographical regions and cultural contexts. Consumer perceptions of virtual influencers and AI-

generated content may vary across markets with different levels of technological adoption, digital literacy,

and socio-cultural characteristics.

Second, the study adopted a cross-sectional research design, capturing consumer perceptions at a single point

in time. Given the rapid evolution of artificial intelligence technologies and digital marketing practices,

consumer attitudes and behavioural responses may change over time. Therefore, the study does not capture

the dynamic nature of consumer interactions with AI-enabled marketing tools.

Third, the study focused on four key determinants of Purchase Intention. Although these variables

significantly contributed to explaining consumer behaviour, other relevant factors such as AI transparency,

perceived risk, privacy concerns, ethical considerations, consumer innovativeness, and technology readiness

were not included in the proposed framework. The exclusion of these variables may limit a more

comprehensive understanding of consumer purchase behaviour in AI-driven environments.

Future research may extend the present study by examining these relationships across different regions,

countries, and cultural settings to enhance the external validity and generalizability of the findings.

Researchers may also compare the effectiveness of virtual influencers and human influencers across various

product categories and industry sectors. Furthermore, future studies could incorporate additional constructs

such as AI transparency, perceived authenticity, privacy concerns, and ethical perceptions to develop a more

comprehensive understanding of consumer behaviour in AI-driven marketing environments. Finally,

longitudinal and experimental research designs may provide deeper insights into how consumer perceptions,

attitudes, and behavioural intentions evolve as AI technologies become increasingly integrated into marketing

practices and everyday consumer experiences.

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