The new world of technological revolution touches every part of our lives. Suggestions from friends, relatives, and celebrities are considered as a credible source that can influence customers’ choices (Chen and Shen, 2015; Lopes and Goulart, 2021). Presently, marketing by influencers on Instagram has steadily grown into a strategy that they use to spread a message through social media personalities, especially considering the past decade. Consequently, influencer marketing is developing as a strategy involving online networking through the status of budding celebrities/influencers to market final products (Conick, 2018; Varsamis, 2018). It is also the most successful online consumer acquisition strategy, which has been growing at the fastest rate since 2017 (Weismueller et al., 2020). The origin of this type of novel marketing goes back to celebrity endorsement. In 2008, the term influencer marketing was first coined by Brown and Hayes in their annals. Promotion through social media platforms is the newest and hottest craze in the market. It has already been established that primitive/traditional marketing techniques, including TVs, National/State dailies, and magazine advertisements, prove to be dearer and raise awareness among a relatively smaller audience (Lee et al.,2021). Thus, social awareness in modern times is garnering more attention than ever.

The networks that connect people socially online are specifically designed to connect individuals and share content produced by influencers to reap better economic gains. The social network, which mainly consists of Instagram, Facebook, LinkedIn, and Twitter, is a mechanism through which people share their materials and where information relating to the services that can be offered to the customers is provided (Hernando and Martn, 2022; Bawack and Bonhoure, 2021). Influencer Marketing Hub Benchmark (2020) conducted a study between 2017 and 2019. It was found that there was an immense increase of 1500 percent in terms of searches pertaining to the buzzword Influencer Marketing solely on the Google platform. According to many studies, customers make purchasing decisions based on the environment they live in and their direct surroundings rather than on attitudes towards conventional marketing methods (Brown and Vasantha, 2019; Brown and Hayes, 2008; Sudha and Sheena, 2017). Dolan et al. (2019) argues that word-of-mouth sharing leads to consumer sales of more than 13 percent. Word-of-mouth is not a new phenomenon that emerged during the online social networking era. Word-of-mouth is an informal way of communicating with one another about the experience with certain services, products, or the features of the people providing them.

Influencer marketing is tremendously significant to consumers.  It refers to the activity of information dissemination in a society whereby a new style is employed by specific consumer groups. Instagram Fashion Influencers (IFIs) can have an astonishing effect on consumers. They have a significant economic and social impact and provide people with a medium for expressing and creating a personality. IFIs can be described as third-party endorsements that tie the effort of developing an audience through the use of social media networks (Gomes et al., 2022). Recently, influencer marketing has been of much interest, particularly within the fashion business (Lee et al., 2017). The sensitivity of buyers to the latest fashion trends has increased significantly, and it is becoming the biggest challenge to meet the ever-growing fashion demands of buyers by   fashion houses. An influencer is someone with power, expertise, stance, or familiarity with their consumers and can impact the buying behavior of others (Farivar et al., 2022). Source authenticity such as reliability and credibility influence the decision made at the consumer level to purchase fashion products. The authenticity of celebrities makes people associate themselves with the offering of a brand, making them purchase the product on the spur of the moment (Chetioui et al., 2020). A survey was conducted in the United Kingdom, Australia, Germany, France, and the United States. It was revealed that over 43 percent of respondents track the fashion style of influencers to make their purchases (Sulthana et al., 2021). Most of digital clients are generally dependent on them and they prefer to investigate digital platforms (Delbaere et al., 2021). People follow professionals; therefore, brands employ an influencer marketing approach in targeting their audiences. Brands do not specifically market their offerings on social media; rather, they build life-like narratives to appeal to the emotional side of the consumer (Mohd et al., 2021).

Existing research on the fashion business and buying intentions has provided valuable insights. However, there are still considerable lacunae necessitating more in-depth exploration. While earlier investigations have carried out detailed assessments, they have not thoroughly investigated on-the-spot choice-making processes of buyers while making real purchases. Furthermore, there is a dearth of studies based on detailed cross-sectional analyses that identify key factors among different categories of the fashion industry. Additionally, the ever-changing characteristics of the fashion sector and swiftly developing customer inclinations call for sustained studies to remain abreast of the aforementioned shifts. Subsequent research might emphasize bridging these chasms by conducting an in-depth analysis of shoppers in a real-world environment while making actual purchases to present a holistic image of buyer psychology in the fashion business.

This study derives its conceptual framework from the Theory of Planned Behavior (TPB) proposed by Ajzen in 1991. The theory explains behavioral intentions in terms of subjective norms, perceived behavior control and attitude. As stipulated in the model, subjective norms, behavioral beliefs are expected to have an impact on attitude.

TPB posits that a person’s behavioral reaction to norms, self-efficacy, and attitudes determines their behavior. The initial factors incorporated in the model included Attitude (ATT), Subjective Norms (SN), and Perceived Behavioral Control (PBC). This was later prolonged to incorporate other variables, such as self-reported past conduct, ethical norms, social approvals, and previous behavior (Ajzen, 2001). ATT refers to positive or negative appraisals of participation in a given behavior. These judgments depend on the ideas that one has arbitrated about the probable consequences or punitive actions associated with a behavior.  A positive attitude towards a behavior leads to a stronger desire to accomplish the behavior (Ajzen, 1991). SN refers to the perceived societal gravity of social groups, including peers, family or societal expectations which exerts pressure on the performance or non-performance of the act. TPB is grounded on the Theory of Reasoned Action and practices. There is an addition of an important factor namely perceived behavioral control which adds explanatory strength to it (Ajzen, 1991).  PBC refers to a person’s conviction regarding the simplicity or difficulty of a specific task performance. TPB points to the importance of an individual’s outlook, societal norms, and self-efficacy in influencing a person’s intention towards a behavioral issue (Ajzen, 2002).

An additional variable i.e. trust was added to the model to enhance predictive accuracy. It also provides a better understanding of buying intentions within the framework. Trust plays an important role in forming positive attitudes towards fashion influencers, particularly where face-to-face contact is not possible (Gomes et al., 2022). By adding trust to the model, it can provide a better understanding of the specific intricacies of the relationships between fashion influencers and their followers. This will enhance its capability to elaborate and estimate shopper buying intentions in the fashion industry.

PBC refers to the level at which an individual performs a certain type of work (Fishbein and Ajzen, 1975). The intent to accomplish a given behavior that is perceived as easy or difficult (Ajzen,1991). The TPB states that PBC directly influences an individual’s outlook and buying intentions positively (Aydin & Aydin, 2022). On the other hand, online shopping intention is poorly related to perceived behavioral control (Lim et al.,2016). Therefore, the following hypothesis is proposed:

H₁: PBC positively influences attitudes towards IFIs.

SN refers to faith regarding how the majority of people either approve or disapprove of certain conduct (Lee et al., 2021). Social pressure on an individual subject himself or herself to act in a particular manner. It is associated with thoughts about what others, particularly peers and individuals considered significant to a person, believe that he/she should or should not do a particular behavior. Lim et al. (2016) found that subjective norms have a positive influence on purchase intentions. These findings suggest that an individual's purchase plan is influenced by their family, friends, and media perception. In contrast, Lee et al. 2017 discovered that SN has no significant impact on buying intention. Building upon the preceding analysis, we formulate the following hypothesis

H₂: SN positively influences attitudes towards IFIs.

Figure 1: Conceptual Research Model

Trust plays a crucial role in shaping buyer’s mindsets towards IFIs. When a person has faith in the credibility, competence, and uprightness of IFIs (Gomes et al., 2022). They tend to have a positive outlook on the contents and endorsements made by IFIs. This positive outlook is exhibited in different ways, such as elevated involvement with IFIs posts, increased numbers of followers, and sharing of post content, followers openly accepting the suggestions of IFIs (Tiwari, 2024). Consequently, attitudes towards IFIs are greatly influenced by trust, which in turn impacts the choices made by fashion shoppers and their aggregate involvement with IFIs. This leads to the formulation of the following hypotheses:

H₃: Trust positively influences attitudes towards IFIs.

The positive or negative assessment of a specific behaviour by a person is called their attitude (Ajzen, 1991). Some articles presented in this regard have displayed empirical evidence of positive associations between a person’s attitude towards an online influencer and their intention to purchase. When intention is positive, there is an improvement in the likelihood of behaviour. The research done by Bataineh (2015) concluded that purchase intention is positively influenced by quality, useful and accurate information, credibility, and quantity. Jalilvand and Samiei (2012) identified that electronic word-of-mouth marketing plays a vital role in determining brand image and brand buying intention in consumer markets. On the other hand, some investigators found no relationship between influencer marketing and purchase intention. Lim et al. (2016) investigated and found that there was no correlation between the positive attitude of buyers and their buying intent. Based on the aforementioned discussion, the following hypothesis is developed:

H₄: Attitude towards IFIs positively influences the impact on purchase intention.

RESEARCH METHODOLOGY

The survey questionnaire was developed by adapting questions from the extant literature and following expert opinions, which ensured the content validity and relevance of the questions (Cebi Karaaslan, 2021).  Initially, suitable respondents posed challenges due to the absence of a suitable sampling frame, and very few responses were obtained by sending a web-based survey to relevant hashtags and followers of Instagram Fashion Influencers (IFIs) (Hashim et al., 2018). Finally, it was decided to select the respondents who were shopping for fashion goods at shopping malls in three major cities of Punjab, namely Amritsar, Jalandhar, and Ludhiana.  Pretesting was done on the twenty-five respondents following the IFIs. Two filter questions were asked to select suitable participants for the research. The first question confirmed that the respondent was actively and honestly following IFIs. The second question ensured that the participants had a new and updated fashion shopping experience. A total of 236 respondents answered the two filter questions correctly. This was done to check the clarity and functionality of the survey design before it was finalized. This study focused on respondents with recent buying experiences and uncovered various unapparent factors that impact consumers’ buying decisions when buying fashion goods. The shopping mall also provided a diversified demographic background covering different age groups, socio-economic backgrounds, and varied shopping stimulants. This diversity increased the selection of the cross-sectional population and helped to generalize the results (Amin et al., 2020). It assisted the researchers in concluding to decide the key factors that impact the buying behavior of fashion shoppers.

Measuring Instrument

A 5-item evolved by Tiwari (2024) and Leong et al. (2023) was used to quantify SN. A five-item scale was used to measure PBC, adapted from Tiwari (2024). Five items were derived from the works of Belanche et al. (2023) and Chopra et al. (2021) to assess attitudes towards influencers. Purchase Intention was assessed using a 6-item scale developed by Weismueller et al. (2020) and Leong et al. (2022). Responses were documented using a seven-point Likert scale, where 1 represented strong disagreement and 7 represented strong agreement.

Table 1: Sample Demographics

       Source: Primary Data

The respondents included both men (53.39%) and women (47.61%). In terms of age, 23.72% of the respondents fell within the 18-25 years age range, 32.63% within the 26-40 years age range, 29.24% within the 41-55 years age range, and the remaining respondents were above 55 years of age. Regarding income level, 37.72% and 32.20% were respectively had their incomes below Rs 5 lacs and 5 to 10 Lacs, while 30.08 % of the respondents had their incomes above 10 lacs.

This study employed PLS-SEM to examine the intricate relationship between IFIs and purchase intentions of shoppers towards fashion goods. Since it is a non-parametric technique, it does not require the assumption of multivariate normality. Therefore, covariance-based SEM (CB-SEM) was not suitable for this analysis. One of the primary reasons for opting for PLS-SEM instead of CB-SEM is the superior predictive relevance that PLS-SEM-4 provides as a methodological tool (Hair et al., 2019). The Web-Power analysis tool (Zhang & Yuan, 2018) was used to evaluate the data for multivariate normality and it was found that the minimum sample size found to be 109. The analysis revealed significant results for Mardia’s multivariate skewness and kurtosis, indicating deviations from normality. SmartPLS-4 is useful for data set that does not meet the conditions for multivariate normality (Memon et al., 2021).

Since this study used a 7-point Likert scale to measure the variables, complete multicollinearity evaluation was performed to eliminate the existence of CMB within the dataset. According to this technique, the internal variance inflation factor scores of the variables are examined against a random dependent variable, and any value above 3.3 indicates a problem of CMB (Kock & Lynn, 2012). The problem of CMB does not exist (Kock, 2015) as none of the inner VIFs for the variables under study exceeded 3.3.

Model Measurement

Measures of reliability of indicators, reliability of internal consistency, convergent validity, and discriminatory validity of the latent variables were all used to analyze the measurement model. If the item loadings cross the mark of 0.707, the indicator is deemed reliable (Arora et al.2019; Hair et al., 2019). The inner VIF values were first explored as the initial step of the process of the structural model analysis to eliminate the occurrence of the multicollinearity problem between the variables. The inner values of VIF in all of them were also less than 5, excluding the risk of significant multicollinearity problems (Hair et al., 2019; James et al., 2013).

Composite reliability and Cronbach’s alpha were computed to assess the reliability of the construct. The former measure checked the substantial reliability, whereas the latter was used to check the conventional reliability. The real dependability of a construct between these two, as suggested by Dijkstra and Henseler (2015), is Rho A (ρA). It is recommended to employ the aforementioned measurements to calculate the dependability of the construction (Hair et al., 2022). Hair et al. (2010) indicate that convergent validity in a construct is proven when its average variance extracted (AVE) does not fall short of 0.5, which implies that the variance explained by the construct is greater than 50 percent of the variance with the measured items.

Table 2: Reliability and Validity

Sources: Compiled from SmartPls-4

Discriminant validity describes the extent to which a construct is empirically different from other latent variables in the structural model. Dijkstra and Henseler (2015) noted that the Heterotrait-Monotrait (HTMT) ratio is a superior method for evaluating discriminant validity across constructs compared to the Fornell-Larcker standards (Fornell & Larcker, 1981).

Table 3: HTMT Criterion for Discriminant Validity

Sources: Compiled from SmartPls-4

Table 2 reports the measures of convergent validity and reliability, whereas Table 3 reports the discriminant validity assessed using the HTMT criteria. Table 3 shows that the square root of the AVE for the latent variable was greater than the correlation with the other constructs. This provides strong support for discriminant validity. Therefore, the HTMT was employed in the data analysis. Discriminant validity is obtained when the HTMT value is below 0.85(Hair et al., 2022).

The results of Fornell-Larcker test, as presented in table 4, state the square root of the AVE for each construct on the diagonal. These diagonal values should be greater than the elements of the off-diagonal in the respective rows and columns (Mohd Dzin & Lay, 2021). It shows the mutual relationships between items. This contrast shows that an item shares more variation with its related indicators than with any other item in the model (Swedlow et al. 2020). Thus, Table 4 provides sufficient proof of the existence of discriminant validity.

Table 5: R-square and R-square Adjusted

Sources: Compiled from SmartPls-4

The coefficient of determination (R²) was used to estimate the explanatory capacity of the model, and the path coefficients were calculated before the significance tests were performed using a bootstrapping method to test the structural connection. The minimum values for R-squared and adjusted R-squared can be grouped into five levels of fit quality. An outstanding fit is indicated by values greater than 0.90. A good fit value falls within the limits of 0.70 to 0.90. A medium fit is shown when values lie between 0.50 and 0.70. A poor model is depicted by values ranging from 0.30–0.50. Lastly, a very poor fit is indicated by values below 0.30(Arora et al., 2019). Table 6 shows the values of effect size (F ²) of latent predictors in the model. The F² value indicates the impact of the independent variables on the dependent variables. Effect sizes of 0.02, 0.15, and 0.35 are considered low, medium, and large, respectively, as suggested by Cohen (2013). To determine the model fit in PLS-SEM, the standardized root mean square residual (SRMR) value should also be considered, and a value less than 0.08 indicates a successful model fit (Hu & Bentler, 1998).

Table 6: F-Square

Sources: Compiled from SmartPls-4

Table 6 displays the results regarding the explanatory power of the estimated model. The results indicate that the explanatory power of all endogenous factors is moderately fitted. Model fit of the estimated model shows a strong relationship between the endogenous and exogenous variables. The SRMR value was 0.044. It is substantially lower than 0.5 (Hair et al., 2022); therefore, the model satisfies all the requirements of the measurement model.

Structural Model Assessment

Hypotheses were tested using the reliable PLS approach in the SmartPLS-4 software. Bootstrapping was employed with 5,000 subsamples to assess the statistical significance of coefficients. Table 7 presents the results of the structural model for the direct effects, and Figure 2 displays the model estimation results. All direct path coefficients related to purchase intention were found to be positive and significant. Table 7 indicates that all the proposed hypothesesH₁, H₂, H_3, and H_4, are well supported in the research model. Therefore, it was found that PBC, SN, and TRS significantly and positively impact attitude towards IFIs. Also, there is a significant and positive impact of attitude towards IFIs on purchase intention to buy fashion products among shoppers.

Table 7: Structural Model

Sources: Compiled from SmartPls-4

Figure 2: Model Estimation Results

This study aims to conduct a comprehensive empirical investigation into the key factors that shape consumers’ intentions while purchasing fashion items. By employing Partial Least Squares Structural Equation Modeling (PLS-SEM) software, the research seeks to uncover the complex relationships that exist between various variables that influence consumer decision-making in the fashion retail sector. The use of PLS-SEM methodology allows for a sophisticated analysis of the complex relationships that exist among selected key constructs simultaneously. This study addresses a significant gap in the literature by conducting a comprehensive empirical investigation into the key factors influencing consumer intentions in fashion purchasing. This approach enables researchers to model latent variables and their indicators, accounting for measurement error and providing a more nuanced understanding of the interplay between factors such as consumer attitudes, subjective norms, perceived behavioral control, and trust are the key determinants of purchase intention of fashion shoppers. By identifying and quantifying these relationships, the study aims to provide valuable insights for fashion retailers, marketers, and researchers, potentially informing strategies to enhance consumer engagement and optimize marketing efforts dynamically and competitively industry.

Theoretical Implications

This research identifies two theoretical implications. Firstly, consumers' attitudes toward IFIs are primarily shaped by perceived behavioral control, subjective norms, and trust, in alignment with existing research (Aydin & Aydin, 2022; Lim et al., 2016; Gomes et al., 2022; Tiwari, 2024). Among these factors, perceived behavioral control is identified as the most significant in shaping attitudes toward influencers, followed by social norms, with trust having the minimum impact. This proposes that influencers who are perceived as credible in terms of behavioral control are more likely to influence followers' attitudes and purchasing decisions, as supported by previous studies (Chetioui et al., 2020). PBC pertains to individuals' perception of their capacity to engage with or adhere to an influencer's fashion recommendations. When consumers believe that they have autonomy over their choices regarding influencer-endorsed fashion items, they tend to form more favorable attitudes toward the influencer. This perceived behavioral control allows consumers to assess influencer suggestions critically, instills confidence in their ability to accept or reject fashion advice, and supports independent decision-making in purchasing or styling fashion products. Influencers who empower their audience in making fashion-related choices are more likely to build positive attitudes and stronger relationships with their followers.

Secondly, the intentions of consumers to make purchases are notably shaped by the views of fashion influencers (Bataineh, 2015; Jalilvand and Samiei, 2012). These influencers, esteemed as style experts, exert a significant impact through their active presence on social media platforms. Their influence is largely due to their skill in crafting content that resonates with audiences, presenting products within authentic, everyday settings, and offering thorough product insights. The sense of genuineness and personal rapport that consumers perceive with influencers further strengthens their persuasive influence. In light of this impact, fashion brands and marketers are increasingly forming partnerships with influencers to effectively connect with their target demographics and stimulate their purchase intention.

Practical Implications

The results of this study have important implications for marketing practitioners, especially in the field of fashion. Gaining insight into the drivers of consumer intention and purchase considerations will improve strategic decision-making and communication strategies. Social norms can be effectively leveraged to influence consumer attitudes and intentions when brands align their messages and campaigns with the dominant values and expectations of society. Such alignment nurtures a feeling of belonging and connection among consumers, thereby deepening brand loyalty and trust. Furthermore, the influence of perceived behavioral control on purchase intentions underscores the importance of providing consumers with adequate information and support to facilitate informed choices. By ensuring product transparency, showcasing genuine customer feedback, and simplifying the buying process, marketers can boost consumer confidence and satisfaction, resulting in improved conversion rates and repeat purchases.

By understanding the mediation dynamics between social norms, perceived behavioral control, and consumption behavior, vendors will be better able to tune their strategies to drive consumer interaction and achieve desired outcomes. Investing in programs that builds a positive image of fashion influencers digitally can translate to positive purchase intent and behavior over time. This study offers three key practical implications. First, it assists marketers and promoters in the fashion industry in identifying the key factors to consider when choosing suitable influencer partners. Second, it contributes valuable insights into the relationship between influencer marketing and consumer purchase intentions, particularly within the fashion sector, where empirical research remains scarce. Third, the findings enhance understanding of the factors influencing the purchasing behavior of educated youth in Punjab, providing a foundation for designing targeted marketing strategies that effectively and profitably cater to this growing market segment.

1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
2. Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52, 27–58.
3. Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683.
4. Amin, M., Rahman, M. S., & Rahman, M. M. (2020). Social media marketing and consumer intention: A study in Bangladesh. Asian Journal of Business and Accounting, 13(1), 1–24.
5. Amin, M., Ryu, K., Cobanoglu, C., Rezaei, S., & Wulan, M. M. (2020). Examining the effect of shopping mall attributes on predicting tourist shopping satisfaction and behavioral intentions: Variation across Generation X and Y. Journal of Quality Assurance in Hospitality & Tourism, 22(3), 367–394.
6. Arora, N., Parsad, C., Tata, S. V., & Prashar, S. (2019). Influence of celebrity factors, consumer attitude, and involvement on shoppers’ purchase intention using hierarchical regression. Decision, 46(3), 179–195.
7. Aydin, H., & Aydin, C. (2022). Investigating consumers’ food waste behaviors: An extended theory of planned behavior of Turkey sample. Cleaner Waste Systems, 3, 100036.
8. Bawack, R. E., & Bonhoure, E. (2021). Influencer is the new recommender: Insights for enhancing social recommender systems. In Digital technologies and transformation in business, industry, and society (pp. 681–691). Springer.
9. Bataineh, M. (2015). The impact of perceived e-WOM on purchase intention: The mediating role of corporate image. International Journal of Marketing Studies, 7(6), 1–11.
10. Belanche, D., Flavián, C., & Pérez-Rueda, A. (2023). The role of social media influencers in consumer decision-making: A systematic review. Journal of Business Research, 145, 1–14.
11. Brown, D., & Hayes, N. (2008). Influencer marketing. Routledge.
12. Çebi Karaaslan, İ. (2021). The impact of social media marketing on purchase intention: The mediating role of customer trust. Journal of Marketing Management, 37(1–2), 1–18.
13. Çebi Karaaslan, K. (2021). Determinants of the online shopping attitudes of households in Turkey. Journal of Modelling in Management, 17(1), 119–133.
14. Chen, H., & Shen, C. (2015). Consumers’ decisions in social commerce context: An empirical. Decision Support Systems, 79, 55–64.
15. Chetioui, Y., Benlafqih, H., & Lebdaoui, H. (2020). How fashion influencers contribute to consumers' purchase intention. Journal of Fashion Marketing and Management: An International Journal, 24(3), 471–487.
16. Chopra, R., Sharma, A., & Soni, S. (2021). Influencer marketing: An exploratory study to identify the antecedents of millennial consumer behavior. Journal of Marketing Management, 37(1–2), 1–18.
17. Chung, S., & Cho, H. (2017). Fostering parasocial relationships with celebrities on social media: Implications for celebrity endorsement. Psychology & Marketing, 34(4), 481–495.
18. Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.
19. Conick, H. (2018). How to win friends and influence millions: The rules of influencer marketing. Marketing News, 52(7), 36–45.
20. Delbaere, M., Michael, B., & Phillips, B. (2021). Social media influencers: A route to brand engagement for followers. Psychology & Marketing, 38(1), 101–112.
21. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.
22. Dolan, R., Conduit, J., Frethey-Bentham, C., Fahy, J., & Goodman, S. (2019). Social media engagement behavior: A framework for engaging customers through social media content. European Journal of Marketing, 53(10), 2213–2243.
23. Farivar, S., Wang, F., & Turel, O. (2022). Followers' problematic engagement with influencers on social media: An attachment theory perspective. Computers in Human Behavior, 133, 107288.
24. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
25. Freberg, K., Graham, K., McGaughey, K., & Freberg, L. A. (2011). Who are social media influencers? A study of public perceptions of personality. Public Relations Review, 37(1), 90–92.
26. Gomes, M. A., Marques, S., & Dias, Á. (2022). The impact of digital influencers’ characteristics on purchase intention of fashion products. Journal of Global Fashion Marketing, 13(3), 187–204.
27. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
28. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
29. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.
30. Hashim, M., Baig, S. A., & Sami, A. (2018). The influence of mobile advertising message content on consumer purchase intention. Journal of Business Research, 92, 1–10.
31. Hashim, N. H., Sajali, N., & Normalini, N. (2018). The influence factors towards mobile advertising message content on consumer purchase intention. Global Business Review, 19(5), 1187–1206.
32. Herrando, C., & Martín-de Hoyos, M. J. (2022). Influencer marketing: Explaining the effects of influencer self-presentation strategies on brand responses through source credibility. International Journal of Consumer Studies. Advance online publication.
33. Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to under-parameterized model misspecification. Psychological Methods, 3(4), 424–453.
34. Ilk, Noyan, & Fan. (2022). Combining textual cues with social clues: Utilizing social features to improve sentiment analysis on social media. Decision Sciences, 53(2), 320–347.
35. Influencer Marketing Hub. (2020). Influencer marketing benchmark report 2020.
36. James, L. R., Mulaik, S. A., & Brett, J. M. (2013). Causal analysis: Models, assumptions and data. Routledge.
37. Jalilvand, M. R., & Samiei, N. (2012). The effect of electronic word-of-mouth on brand image and purchase intention: An empirical study in the automobile industry in Iran. Marketing Intelligence & Planning, 30(4), 460–476.
38. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.
39. Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580.
40. Kutz, K. O. (2024). Social media influencers' impact on consumer purchasing decisions. Proceedings of the New York State Communication Association, 2024, 1157.
41. Lee, J., Lee, H., & Choi, J. (2017). The influence of social media marketing on purchase intention: The mediating role of brand trust and image. Journal of Business Research, 70, 1–10.
42. Lee, P. Y., Koseoglu, M. A., Qi, L., Liu, E. C., & King, B. (2021). The sway of influencer marketing: Evidence from a restaurant group. International Journal of Hospitality Management, 98, 103022.
43. Leong, C. M., Tan, S. M., & Tan, S. H. (2022). The influence of social media eWOM information on purchase intention. Journal of Marketing Analytics, 10(2), 1–10.
44. Leong, C. M., Tan, S. M., & Tan, S. H. (2023). Understanding the role of social media influencers' personal and content characteristics in affecting consumers' intentions to purchase. Journal of Retailing and Consumer Services, 70, 1–10.
45. Lim, W. M., Osman, A., Salahuddin, S. N., & Romle, A. R. (2016). Social commerce research: The role of trust in online purchase intentions. International Journal of Business and Society, 17(S1), 45–64.
46. Lopes, E. L., & Goulart, J. A. (2021). Effects of social media influencer characteristics on credibility, purchase intention, and recommendation. Revista Brasileira de Gestão de Negócios, 23(4), 783–802.
47. Magwegwe, F. (2024). Theory of planned behavior and fast fashion purchasing. Current Psychology. Advance online publication.
48. McCormick, K. (2016). Celebrity endorsements: Influence of a product-endorser match on Millennials attitudes and purchase intentions. Journal of Retailing and Consumer Services, 32, 39–45.
49. Memon, M. A., Cheah, J.-H., Cham, T. H., Chuah, F., Ting, H., & T, R. (2021). PLS-SEM statistical programs: A review. Journal of Applied Structural Equation Modeling, 5(1), i–xiv.
50. Mohd Dzin, N. H., & Lay, Y. F. (2021). Validity and reliability of adapted self-efficacy scales in the Malaysian context using PLS-SEM approach. Education Sciences, 11(11), 676.
51. Sulthana, A. N., Evangelin, R., & Vasantha, S. (2021). Influence of social media marketing in post COVID-19. Design Engineering, 5(2), 23.
52. Sudha, M., & Sheena, K. (2017). Impact of influencers on consumer decision-making process: The fashion industry. SCMS Journal of Indian Management, 14(3), 14–30.
53. Swedlow, B., Ripberger, J. T., Johnson, B. B., Jenkins‐Smith, H., Silva, C. L., & Liu, L. (2020). Construct validity of the cultural theory survey measures. Social Science Quarterly, 101(6), 2332–2383.
54. Tiwari, R. (2024). Inspiration or influence: Assessing Gen-Z’s response to wellness influencers through customer inspiration theory. International Journal of Spa and Wellness, 7(3), 341–362.
55. Varsamis, E. (2018, June 13). Are social media influencers the next-generation and ambassadors? https://www.forbes.com.
56. Weismueller, J., Harrigan, P., Wang, S., & Soutar, G. N. (2020). Influencer endorsements: How advertising disclosure and source credibility affect consumer purchase intention on social media. Australasian Marketing Journal, 28(4), 160–170.
57. Zhao, X., Xu, Z., Ding, F., & Li, Z. (2024). The influencers’ attributes and customer purchase intention: The mediating role of customer attitude toward brand. SAGE Open, 14(2), 21582440241250122.
58. Zhang, Z., & Yuan, K. H. (2018). Practical statistical power analysis. ISDSA Press.