doi: 10.56294/sctconf2024912

 

Category: Finance, Business, Management, Economics and Accounting

 

ORIGINAL

 

Analyzing the impact of brand resonance on consumer purchase intentions for fast moving consumer goods: an empirical study

 

Análisis del impacto de la resonancia de marca en las intenciones de compra de los consumidores de bienes de consumo inmediato: un estudio empírico

 

R. Chakkaravarthy Kumaresan1 *, S. Chandramohan1 *

 

1Alagappa Institute of Management, Alagappa University. Karaikudi, Tamil Nadu. India.

 

Cite as: Chakkaravarthy Kumaresan R, Chandramohan S. Analyzing the impact of brand resonance on consumer purchase intentions for fast moving consumer goods: an empirical study. Salud, Ciencia y Tecnología - Serie de Conferencias. 2024; 3:912. https://doi.org/10.56294/sctconf2024912

 

Submitted: 05-02-2024                   Revised: 27-04-2024                   Accepted: 11-06-2024                 Published: 12-06-2024

 

Editor: Dr. William Castillo-González

 

ABSTRACT

 

In the dynamic and highly competitive landscape of the Fast-Moving Consumer Goods (FMCG) industry, a multitude of companies are in a constant race, each one striving to outdo the others. This paper aims to delve deeper into the role of brands within this context. It seeks to explore and understand the significance of brands and how they can be leveraged to succeed in the FMCG sector. An extensive examination of the prevailing studies regarding this topic unveils a distinct void in research, especially regarding the notion of brand resonance. Despite its importance, there seems to be a lack of comprehensive research on this topic. It seeks to understand what elements play a crucial role in creating brand resonance and how it, in turn, impacts consumer purchasing decisions. To achieve this, the study employs a quantitative research approach. Data was collected through structured questionnaires, designed to gather relevant information from the respondents. The collected data was then analyzed using robust statistical tools. Confirmatory Factor Analysis (CFA) served to validate the measurement model, while Multiple Regression Analysis was utilized to grasp the connections among the variables. The findings of the study suggest a favourable correlation between brand resonance and intentions to purchase. These findings offer valuable insights for marketers operating in the FMCG sector. Understanding the factors that contribute to brand resonance can help them devise effective branding strategies, ultimately leading to increased sales and market share. Moreover, the study proposes potential paths for future investigation in this domain, thereby enriching the current understanding of the subject matter.

 

Keywords: Brand Resonance; Brand Equity; Brand Engagement; Fast Moving Consumer Goods; Purchase Intention.

 

RESUMEN

 

En el dinámico y altamente competitivo panorama de la industria de los bienes de consumo de rápida rotación (FMCG), multitud de empresas se encuentran en una carrera constante, cada una de ellas esforzándose por superar a las demás. Este artículo pretende profundizar en el papel de las marcas en este contexto. Pretende explorar y comprender la importancia de las marcas y cómo pueden aprovecharse para triunfar en el sector de los bienes de consumo de alta rotación. Un examen exhaustivo de los estudios existentes sobre este tema revela un claro vacío en la investigación, especialmente en lo que respecta a la noción de resonancia de marca. A pesar de su importancia, parece haber una falta de investigación exhaustiva sobre este tema. El estudio pretende comprender qué elementos desempeñan un papel crucial en la creación de resonancia de marca y cómo ésta, a su vez, influye en las decisiones de compra de los consumidores. Para lograrlo, el estudio emplea un enfoque de investigación cuantitativo. Los datos se recogieron mediante cuestionarios estructurados, diseñados para recabar información relevante de los encuestados. A continuación, los datos recogidos se analizaron mediante sólidas herramientas estadísticas. El Análisis Factorial Confirmatorio (AFC) sirvió para validar el modelo de medición, mientras que el Análisis de Regresión Múltiple se utilizó para captar las conexiones entre las variables. Los resultados del estudio sugieren una correlación favorable entre la resonancia de la marca y la intención de compra. Estos resultados ofrecen valiosas perspectivas para los profesionales del marketing que operan en el sector de los bienes de consumo de alta rotación. Comprender los factores que contribuyen a la resonancia de la marca puede ayudarles a diseñar estrategias de marca eficaces que, en última instancia, conduzcan a un aumento de las ventas y de la cuota de mercado. Además, el estudio propone posibles vías de investigación futura en este ámbito, enriqueciendo así los conocimientos actuales sobre la materia.

 

Palabras clave: Resonancia de Marca; Valor de Marca; Compromiso de Marca; Bienes de Consumo de Alta Rotación; Intención de Compra.

 

 

 

INTRODUCTION

The field of brand studies has seen considerable growth and diversification. The main goal of these past studies was to offer businesses valuable knowledge on how the power of brands can shape consumer purchasing habits. However, they have largely neglected to explore relationship branding within the FMCG sector.(1,2,3) This omission highlights the reluctance of researchers in the FMCG sector, which stems from consumers’ low purchase involvement. Despite this hesitation, the FMCG sector remains a promising domain for both domestic and foreign players, despite its low profit margins due to intense competition and the entry of new players. It is characterized by a huge inventory turnover and high frequency of sales, encompassing personal care products, packed food products, dairy products, beverages, and cosmetics.(4,5,6) Among these categories, personal care products stand out as they are more frequently bought and used by a larger number of people compared to others. Nonetheless, previous researchers have explored various branding concepts within the FMCG sector, albeit with limited contributions to both theory and practice. Today, there has been a significant shift in consumer purchasing patterns, preferences, and behaviours within the FMCG sector, largely attributed to the widespread use of the internet and the digital era in India. Consequently, marketers are eager to understand the nuances in brand management of FMCG products, as competition among FMCG players intensifies.

Indian consumers are price-conscious when buying any FMCG products. This price sensitivity is compounded by their interest in trying new products, driven by the low risk and switching costs involved. Moreover, customer retention has become a serious business compared to merely attracting a mass customer base. In response to this dynamic market, each player in the industry is intensifying efforts to establish a strong brand presence. Despite India’s enormous market potential for every product, only a few researchers have concentrated on developing countries, particularly India. As discussed earlier, most researchers have shown little interest in analysing the significance of brand attributes on FMCG products. This oversight has led to undervaluing the importance of the brand among FMCG product consumers. However, while the influence and effects of brand equity on the buying behaviour of FMCG products have been examined, the aspect of brand resonance remains largely unexplored.(7,8,9)

Brand resonance explains how much customers are attached to a brand, developing as the outcome of psychological and emotional relations with the brand. To reach the brand resonance stage, customers must pass through stages such as brand awareness, brand loyalty, and brand image. It represents the peak point in the process of brand building and serves as a valuable tool to manage and develop sustainable customer relations. Despite its significance for brands in maintaining long-term relationships with customers, brand resonance has not been thoroughly studied. After a thorough analysis of previous literature, a fascinating gap emerged: the influence of brand resonance on customer purchase intentions for FMCG products. This research initiative aimed to overcome shortcomings in previous literature and contribute additional knowledge to the field of brand management. Based on the identified research gap, the subsequent questions were devised to guide the research efficiently:

RQ1: is there a relationship between brand resonance and customer purchase intentions for fast-moving consumer goods?

RQ2: if RQ1 is confirmed, which specific elements of brand resonance influence customer purchase of fast-moving consumer goods more?

The study aims to address these inquiries and make significant contributions to the literature and managerial implications. Following the identification of research gaps, the upcoming literature review section will provide a snapshot of the existing literature related to the identified gap. Subsequently, the methodology section will outline data collection and sampling methods, followed by the analysis and findings sections presenting the results. Finally, the discussion and conclusion section will detail the implications, concluding comments, and limitations of this research.

 

Literature review

The present research aims to enhance brand management practices among FMCG marketers, thereby gaining a strategic advantage over their rivals. The research problem has been formulated to explore how brand resonance influences the purchase intention of FMCG products.(10) It also aims to identify the factors that comprise brand resonance, with a particular focus on determining which factor holds greater influence. Emphasizing the aforementioned research problem, a thorough literature review is conducted.(27,28)

Brand is a strategic tool to differentiate product offers from other competitors, and the retention of existing customers is achieved by creating a good brand image.(20,6) Brands can influence purchasing decisions, leading customers to prefer one brand over another based on recommendations from loyal and satisfied customers.(8) A brand serves as a recognizable element that acts as a cue for customers when they are making purchasing decisions within a specific product category. Brands offer buyers and consumers different benefits, which can be of two types. The first type of benefit is emotional, stemming from the connection between the brand and the customer, while the second type is based on the experience of repeated use. Rizwan(25) Based on these benefits, customers form attitudes toward the brand Positive thoughts and feelings toward the brand lead to a positive attitude, prompting consumers to take favourable actions. These benefits not only differentiate a brand but also shape consumer attitudes and behaviours.

Repeated purchases of brand products could be the result of certain consumer attitudes or behaviours. The customer’s attitudinal attachment to the brand is founded on their positive experiences with it. According to Foroudi et al.(14) attitudinal attachment and behavioural loyalty are significant antecedents of branding. Behavioral loyalty develops over time as a result of habitual activity. Due to factors like switching costs, product features, and benefits, these customers tend to be risk-averse. Consequently, they stay loyal to a specific brand even when faced with competition. Huang et al.(16) While these factors contribute to brand loyalty, research has further explored their impact on brand purchase intention.

Ahmad et al.(1) has investigated the impact of behavioural loyalty and attitudinal attachment of customers on brand purchase intention. He also validated and recognized both factors as significant determinants of customer buying behaviours. Customer buying intent is a subjective action resulting from attitudes and behaviour towards the brand. Hussain et al.(18) Positive attitudes among consumers stem from their trust in the brand, as they believe it will consistently provide quality products at a fair price. Trust is developed after they perceive that the product is of quality at a reasonable price. Chakraborty(7) While Behavioral loyalty can exist without emotional attachment, the latter plays a crucial role in the FMCG sector.

Behavioral loyalty is created without any emotional attachment to the brand. Attitudinal attachment refers to the emotional connection or affectivity that consumers have toward the brand.(22) In the fast-moving consumer goods (FMCG) sector, products are often differentiated in only a few aspects, with the brand serving as a key element to distinguish them from competitors.(29) The emotional aspect of a brand can enhance customer attitudes and loyalty, thereby increasing purchase intentions. This emotional connection not only influences buying intentions but also fosters the formation of brand communities

Communities formed around a brand serve as focal points for both individuals seeking to explore a new product category and those considering switching to another brand within the same category. Members of these communities provide feedback and often become brand ambassadors. The human decision-making process is mostly influenced by attitudes and behaviour.(2) Attitudes are formed from the evaluation of the brand by the customer. A brand possesses a bundle of values associated with it. These values comprise quality product, features, emotional belief, and trust.(19) These communities, driven by shared brand values, play a significant role in shaping consumer attitudes and behaviours.

Consumers’ cognitive knowledge of the value gained from the brand is responsible for the purchase decision. While these decisions are rational, they are also influenced by the emotions and values experienced by the consumer. Purchase intention exhibits customers’ interest and effort to buy specific brand products.(4) It is an outcome derived as a consequence of customer motivation to obtain brand products. When a product meets customer expectations and provides satisfaction, customers perceive value in both its physical attributes and emotional aspects, ultimately leading to a purchase.(23) This cognitive knowledge, combined with emotional factors, influences consumers’ purchase decisions.

The decision-making process is a cognitive approach by customers. Customers are attracted by brands that aspire to create sustainability in all aspects of the products offered. To understand customers’ purchasing behaviours, it’s essential to assess how they evaluate brands and what motivates them to invest effort in acquiring specific products.(11) Purchase intention is recognized as the bridge between willingness to purchase and the actual purchase. It is an urge triggered by the emotional response to take a subjective action and accomplish the buying process.(12) Customers may buy the same brand products if their experience and perceived value are good enough. This purchase intention, driven by perceived value and satisfaction, attracts new customers and retains existing ones.(9)

New customers are often curious and interested in trying a brand due to the attraction it holds for them. Existing customers are retained by the brand because of the expectation that it would assure quality products and maintain customer trust.(31) Given a choice, customers tend to prefer their favourite brands. Customer loyalty could be affected by situational factors and the value of products offered.(17)

After reviewing the literature, it was found that there is a gap in research regarding brand resonance and purchase intention. Therefore, the research focused on exploring the impact of brand resonance on customer buying intent for fast-moving consumer goods. The study developed a regression model for analysing the effects of brand resonance components on the purchase intention based on the previous literature. The construct of latent variables and observed variables is derived from previous studies. Furthermore, the questions associated with the observed variables, which are adopted from previous studies, are modified to suit the FMCG sector.

 

METHOD

Data is a crucial element in any research. To obtain the appropriate data, we designed a questionnaire with utmost dedication and expert suggestions. This questionnaire was developed by adopting methodologies from previous literature. The next step involved identifying a sample from the population. For this research, the respondents were FMCG customers residing in Madurai city. We approached the sample respondents with a polite note, requesting them to fill out the questionnaire. We ensured that only the respondents who had ample time were asked to fill and complete it without any rush. We also provided assurance about maintaining the confidentiality of their data and obtained their consent. A convenient sampling method was used to select the sample respondents. We gave the respondents a brief explanation about the research. Later, we scrutinized the filled questionnaires and only considered the completely filled ones for the study. In total, 400 questionnaires were considered for this research. Table 1 depicts the questions adopted from previous literature for the major constructs used in this research.

 

Table 1. Model constructs

Constructs

Reference

Brand resonance

Raut(24)

Becerra(5)

Ahmad(1)

Vo(30)

Foroudi(14)

Husain(17)

Behavioural loyalty

• I am very interested in buying products from this brand in the future.

• I always prefer this brand when I decide to make a purchase.

• I consider myself loyal to the brand.

• I check for the availability of the brand when I need to buy the product.

Attitudinal Attachment

• I feel that the brand is the best and special.

• I am so proud to buy products from this brand.

• I am emotionally attached to the brand.

• The brand makes me feel good.

Sense of Community

• I easily engage with people who buy this brand.

 • I try to identify people who buy the same brand as I do.

• I feel very satisfied when discussing the brand I use within a group.

• I sense a good relationship with the members of the group who use the same brand.

Purchase Intention

• I always select my preferred brand.

• I prefer this brand, even though there may be various other brands to choose from.

• I buy the product of this brand without considering the price.

                                                                                                                                          

Analysis and findings

 

Table 2. Profile of respondents

Variables

Frequency

 %

Gender

Male

153

38,25

 

Female

247

61,75

Age

20 to 30

120

30,0

 

31-40

112

28,0

 

41-50

 94

23,5

 

Above 50

 74

18,5

Educational qualification

Ph.D.

 26

6,5

 

Postgraduate

 95

23,75

 

Undergraduate

186

46,5

 

Diploma

 44

11,0

 

ITI

 21

5,25

 

HSC

 18

4,5

 

Others

 10

02,5

Family income – monthly

Less than rs.10 000

 24

06,0

 

Rs.10 001 – Rs.30 000

112

28.0

 

Rs.30 001 – Rs.60 000

196

49,0

 

More than 60 000

 98

17,0

Marital status

Unmarried

 76

19,0

 

Married

324

81,0

 

Table 2 furnishes a thorough summary of the demographic variety among the participants in our study. A meticulous descriptive analysis was undertaken to grasp the composition of our respondents. It was found that a significant majority, precisely 61,75 %, of the respondents were female. This statistic emphasizes the crucial role women hold in the acquisition of fast-moving consumer goods, spotlighting their impact and authority in this market.

In terms of age distribution, we observed that 28,0 % of the respondents belonged to the Gen Y category, which includes individuals aged between 31 and 40 years. Additionally, 30,0 % of the respondents were classified as part of the Gen Z group. The dominance of these two groups in our respondent pool is particularly noteworthy. Individuals from Gen Y and Gen Z are known to play various roles in the purchasing process, including those of the buyer, decision maker, initiator, and influencer. Their notable presence in our study offers valuable insights into present market trends and consumer behaviour.

Our analysis also included a segmentation of respondents based on their family income. We found that the middle-income group, defined as families with an income range between Rs.30 000 and Rs.60 000, constituted the majority of our respondents at 49,0 %. This finding suggests that individuals in the middle-income bracket are frequently making purchases, possibly as a means to enhance their standard of living using their surplus income.

In summary, our analysis of the demographic diversity of our study participants, as presented in table 2, provides a rich and detailed understanding of the current market dynamics and consumer behaviour in the fast-moving consumer goods sector. This information is invaluable in shaping effective marketing strategies and making informed business decisions.

 

Reliability and Validity Tests

 

Table 3. Reliability and validity

Construct

Items

Estimates

AVE

CR

Cronbach’s  alpha

Behavioral Loyalty

BL 1

0,835

0,662

0,886

0,885

 

BL 2

0,841

 

 

 

 

BL 3

0,825

 

 

 

 

BL 4

0,749

 

 

 

Attitudinal Attachment

AA 1

0,701

0,502

0,801

0,801

 

AA 2

0,691

 

 

 

 

AA 3

0,713

 

 

 

 

AA 4

0,729

 

 

 

Sense of Community

SC 1

0,822

0,673

0,891

0,891

 

SC 2

0,835

 

 

 

 

SC 3

0,807

 

 

 

 

SC 4

0,816

 

 

 

Purchase Intention

PI 1

0,751

0,543

0,776

0,775

 

PI 2

0,862

 

 

 

 

PI 3

0,568

 

 

 

 

In our study, we undertook a rigorous process to verify the reliability of the constructs used. This was accomplished through scrutiny of both the Cronbach’s alpha value and the composite reliability (CR) value. These statistical metrics are employed to evaluate the internal consistency or reliability of a test score across a set of test items. In our scenario, both the Cronbach’s alpha value and CR exceeded the recommended threshold of 0,7. This result is a strong indicator of the reliability of the constructs used in our study. Reliability and validity in shown in table 3.

Following that, we evaluated the convergent validity of the constructs. Convergent validity pertains to the extent to which two measures of constructs that should theoretically be associated are indeed correlated. To accomplish this, we utilized the average variance extracted (AVE) and standard estimates. The AVE quantifies the proportion of variance captured by the construct compared to the variance attributable to measurement error. Analytical experts recommend that the AVE should exceed the threshold value of 0,5, and the standard estimates should be greater than 0,7.(13) Our constructs met these criteria, further validating their reliability.

 

Table 4. Discriminant validity

Factors

Behavioral Loyalty

Attitudinal Attachment

Sense of Community

Purchase Intention

Behavioural Loyalty

0,813

Attitudinal Attachment

0,215

0,709

Sense of Community

0,241

0,036

0,820

Purchase Intention

0,643

0,216

0,320

0,737

 

We also verified the discriminant validity of the constructs. Discriminant validity examines whether concepts or measurements that are expected to be independent are indeed unrelated. This assessment involved contrasting the square root of the average variance extracted (AVE) with the correlation values of all constructs. It was observed that the square root of the AVE for all constructs was lower than the correlation values between them. This indicates that there is minimal correlation between different constructs, which is a good sign of discriminant validity.

In the end, by means of this thorough process, we have substantiated the reliability, convergent validity, and discriminant validity of the constructs employed in our study. This rigorous validation process adds robustness to our research findings and provides a solid foundation for further analysis. Discriminant validity in shown in table 4.

 

Confirmatory Factor Analysis

 

Table 5. Model Fit indices

Model Fit measures

Suggested value

Measurement model

CMIN

≤3,00

1,197

Goodness of fit (GFI)

≥0,90

0,969

Adjusted Goodness of fit (AGFI)

≥0,80

0,956

Normalized fit index (NFI)

≥0,90

0,965

Comparative fit index (CFI)

≥0,90

0,994

Root mean square error of approximation (RMSEA)

≤0,10

0,022

 

In their seminal work Hair et al.(15) proposed a guideline that the CMIN (Chi-square/degrees of freedom) value in a measurement model should be less than 5. This is a critical criterion for assessing the goodness of fit of the model. In our research, we are pleased to report that our measurement model successfully meets this criterion, demonstrating the robustness of our model.

Furthermore Anderson et al.(3) have posited that the Root Mean Square Error of Approximation (RMSEA) should be less than 0,10 for a model to be deemed acceptable. This is another important standard that helps in determining the acceptability of a model. We are glad to inform that our measurement model also adheres to this standard, further validating the accuracy of our model.

In addition to these, we also examined the Goodness of Fit Index (GFI) and the Adjusted Goodness of Fit Index (AGFI) values. These indices provide additional measures of the model’s fit. The GFI evaluates the proportion of variance and covariance collectively accounted for by the model, whereas the AGFI modifies the GFI considering the degrees of freedom. Both these values for our model fall within the acceptable ranges, adding another layer of confidence in our model’s fit and accuracy. Model Fit indices in shown in table 5.

 

Figure 1. Measurement model

 

Based on these findings from the confirmatory factor analysis, which includes meeting the criteria set(15,3) as well as achieving acceptable GFI and AGFI values, we concluded that our measurement model is accurate and fits well. This conclusion provides a solid foundation for our research and lends credibility to our findings. It also underscores the meticulousness of our research design and the rigor of our analytical approach. We believe that this thorough validation of our measurement model will contribute significantly to the robustness and reliability of our research outcomes in shown in figure 1.

 

Multiple Regression Analysis

In the course of our research, we employed a statistical technique known as multiple regression analysis. This approach proves especially valuable when aiming to comprehend the dynamics of the connection between a dependent variable and multiple independent variables. The dependent variable represents the outcome we seek to predict or explain, whereas the independent variables are those we believe exert influence on our dependent variable.

Before we could proceed with the multiple regression analysis, it was necessary to evaluate the multicollinearity of the constructs, as suggested. Multicollinearity occurs when two or more independent variables in a regression model exhibit high correlation. In such instances, it becomes challenging to discern the individual effects of the independent variables on the dependent variable.

To assess multicollinearity, we examined two key metrics: the Variance Inflation Factor (VIF) and Tolerance values. The VIF quantifies the extent to which multicollinearity elevates the variance of the estimated regression coefficient. On the other hand, Tolerance is a measure of the influence of one independent variable on all other independent variables; it is calculated as the reciprocal of the VIF.

To ensure the validity of our regression model, we needed to mitigate multicollinearity. As a general guideline, VIF values should ideally fall below 5, while Tolerance values should be above 0,2. If these conditions are met, we can be confident that multicollinearity is not significantly distorting our regression results. Therefore, we made sure to adhere to these guidelines in our analysis. This rigorous approach allowed us to conduct a robust and reliable multiple regression analysis.

 

Table 6. Collinearity Diagnostics Top of Form

Collinearity Diagnosticsa

Model

Dimensions

Collinearity Statistics

 

 

Tolerance

VIF

1

1

0,922

1,085

 

2

0,953

1,049

 

3

0,966

1,035

a. Dependent Variable: Purchase Intention

 

Collinearity diagnostics

As illustrated in table 6, both the tolerance and Variance Inflation Factor (VIF) values adhere to the established norms. These values are crucial in determining the presence of multicollinearity among the constructs. Multicollinearity denotes a scenario in which two or more predictor variables within a multiple regression model display high correlation. In this case, the adherence of both tolerance and VIF values to the accepted criteria suggests that there is no multicollinearity among the constructs. This is a positive indication as the absence of multicollinearity ensures that each construct contributes uniquely to the understanding of the dependent variable, thereby enhancing the reliability of the model.

 

Table 7. Model summary

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

0,532a

0,283

0,277

2,61391

a. Predictors: (Constant), Attitudinal Attachment, Sense of Community, Behavioral Loyalty

b. Dependent Variable: Purchase Intention

 

Alongside the tolerance and VIF values, another significant statistic in regression analysis is the R-squared (R2) value. This value indicates the proportion of variance in the dependent variable explained by the independent variable(s) in the model. Essentially, it quantifies how much the independent variable clarifies the dependent variable. A higher R2 value signifies a superior fit of the model and implies that the independent variable(s) can account for a greater portion of the variance in the dependent variable.

In summary, the information provided in table 6 and 7, including the tolerance, VIF, and R2 values, offers valuable insights into the validity and reliability of the constructs used in the model. This rigorous statistical analysis strengthens the robustness of our research findings and provides a solid foundation for further analysis

In the extensive data analysis provided in table 7, a significant statistical measure stands out: the R-squared (R²) value, recorded at 0,283. This R² value is a commonly employed metric in regression analysis, indicating the proportion of variance in a dependent variable explained by one or more independent variables.

In this context, the R² value of 0,283 indicates that roughly 28,3 % of the variance in purchase intentions can be accounted for by brand resonance factors. This is a substantial percentage, considering the multitude of factors that can influence purchase intentions.

It’s important to note that while this does not imply causation, it does highlight a significant correlation between brand resonance and purchase intentions. The remaining 71,7 % could be attributed to other factors not included in this model, which might include elements such as personal preference, economic conditions, marketing efforts, and more.

To sum up, the data in table 7 provides valuable insights into the role of brand resonance in shaping purchase intentions, underscoring its importance in strategic decision-making for businesses aiming to enhance customer engagement and drive sales. However, it also points to the need for further research to uncover the other factors contributing to purchase intentions. Purchase Intention, Attitudinal Attachment, Sense of Community, Behavioral Loyalty in shown in table 8.

 

Table 8. ANOVA

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

 

 

Regression

1 067,363

3

355,788

52,073

0,000b

Residual

2 705,677

396

6,833

 

 

Total

3 773,040

399

 

 

 

a. Dependent Variable: Purchase Intention

b. Predictors: (Constant), Attitudinal Attachment, Sense of Community, Behavioral Loyalty

 

Table 9. Co-efficient of Explanatory variables

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

3,153

0,671

 

4,702

0,000

Behavioral Loyalty

0,373

0,035

0,467

10,530

0,000

Sense of Community

0,082

0,027

0,134

3,083

0,002

Attitudinal Attachment

0,061

0,034

0,077

1,789

0,074

a. Dependent Variable: Purchase Intention

 

The data presented in table 9 provides us with the results of a regression analysis. The unstandardized coefficient values, which are significant at a 1 % level, indicate that an increase of one unit in behavioural loyalty is associated with a corresponding increase of 0,3373 units in purchase intention. This relationship suggests a positive correlation between behavioural loyalty and purchase intention.

This finding aligns with the results of previous studies, such as the one conducted, which also reported a positive impact on purchase intention. In their study, the impact was quantified as 0,082, reinforcing the notion that behavioural loyalty can significantly influence purchase intention.

Nevertheless, it’s noteworthy that within the scope of this specific study, the impact of attitude was determined to be statistically insignificant at the 5 % significance level. This suggests that while attitude may play a role in shaping purchase intentions, its impact is not strong enough to be statistically significant in this specific analysis.

Following the multiple regression analysis, the study’s hypotheses were evaluated. Hypothesis H1, along with sub-hypotheses H1a and H1b, were accepted, indicating that the data supports these propositions. However, Hypothesis H1c was not supported by the data and was therefore rejected.

In summary, these findings underscore the importance of behavioural loyalty in driving purchase intentions, while also highlighting the need for further research to understand the role of other factors, such as attitude, in this complex relationship. This nuanced understanding can provide valuable insights for businesses seeking to enhance customer loyalty and drive purchase intentions.

 

DISCUSSION AND CONCLUSIONS

The research at hand was undertaken with the specific aim of addressing the gaps and unexplored areas in the existing body of literature. The main objective of the study was to determine the influence of brand resonance on purchase intention, a subject that has sparked considerable debate and discussion within the academic community.

Upon conducting a thorough analysis, the study revealed that not all elements of brand resonance contribute equally to the influence on purchase intention. This is a significant finding, as it challenges the commonly held belief that all components of brand resonance play a substantial role in shaping purchase intentions.

The results of the multiple regression analysis further shed light on this phenomenon. It was discovered that among the various factors of brand resonance, behavioural loyalty emerged as the most dominant. This suggests that behavioural loyalty plays a pivotal role in influencing purchase intentions, more so than other factors of brand resonance.

In contrast, other factors such as sense of community and attitudinal attachment were found to have a minimal and almost negligible effect on purchase intention. This discovery highlights the intricacy of the correlation between brand resonance and purchase intention, emphasizing the necessity for a nuanced comprehension of the multitude of factors involved.

Interestingly, these findings align with the research conducted. Their study also concluded that attitudinal attachment did not significantly affect purchase intention. This consistency across studies lends further credibility to the findings of the present research.

 

Implications

This research, to a certain degree, fills a crucial void in the existing body of literature. Earlier studies have primarily concentrated on the factors influencing brand resonance and its function as a mediator or moderator in conjunction with other elements of brand equity. The insights gleaned from this study are of immense value to marketers, especially in understanding the critical role of behavioral loyalty within the context of brand resonance.

Companies need to pay close attention to consumer behavior at various stages of the purchasing process. They must undertake promotional activities aimed at bolstering the behavioral aspects associated with purchasing their preferred brand. In a fiercely competitive marketplace, where consumers are spoilt for choice, they tend to stick with brands they are already using.(26)

New entrants to the market face the daunting task of persuading customers to switch to their brand. To overcome this challenge, these new players should engage in promotional activities that highlight what sets their brand apart and why it is superior to others.(21)

Marketers must take these findings into account when devising strategies to attract new customers and retain existing ones. A nuanced understanding of these dynamics can help them craft more effective marketing strategies that resonate with consumers and foster brand loyalty.

 

Limitations

A common constraint of this study, as with many others, is its geographical limitation to a specific locale, in this case, the city of Madurai. Furthermore, the study’s sample size, which consists of 400 participants, is relatively modest. This could potentially restrict the applicability of the study’s findings to a larger, more diverse population. The study primarily concentrates on the relationship between brand resonance and purchase intentions, thereby excluding the consideration of other potential factors that may influence purchase intentions. This focus could potentially overlook other significant influences on purchase intentions.

 

Concluding Comments

The core aim of this research was to explore and authenticate the impact of brand resonance on consumers’ purchase intentions, particularly within the fast-moving consumer goods (FMCG) sector. The study underscored the pivotal role of behavioural loyalty, a component of brand resonance, in shaping these purchase intentions. This focus provided marketers with crucial insights that can be leveraged to enhance their marketing strategies and customer engagement efforts.

However, it’s crucial to acknowledge that this study’s scope was restricted to analyzing the influence of brand resonance on consumer purchasing behaviour. While this focus yielded valuable findings, it also meant that other potential factors influencing purchase intentions were not explored in this research. These factors could include elements such as price sensitivity, product availability, consumer attitudes, and more.

Given this, we recommend that future research endeavours broaden the scope to investigate the effects of these yet-to-be-explored factors on purchase intentions. Such research could provide a more comprehensive understanding of consumer behaviour in the FMCG sector, thereby equipping marketers with a more holistic view of the factors driving purchase decisions. This, in turn, could enable them to devise more effective and targeted marketing strategies to boost sales and customer loyalty. In conclusion, while this study has made significant strides in understanding the role of brand resonance in influencing purchase intentions, there is still much to be uncovered in this fascinating field of study.

 

ACKNOWLEDGEMENT

I want to convey my heartfelt appreciation to the Alagappa Institute of Management and Alagappa University for providing me with an excellent learning environment and steadfast assistance during my educational path. Furthermore, I offer my sincere gratitude to the Ministry of Education, the Ministry of Social Justice and Empowerment, and the University Grants Commission for their financial support through the prestigious Doctoral Fellowship Program of the NFSC, generously sponsored by the Government of India.

 

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FINANCING

The first author of this article is financed through a doctoral fellowship by the National Scheduled Castes Finance and Development Corporation. This corporation is a department under the Ministry of Social Justice and Empowerment, Government of India. The financial support received has greatly assisted the authors in carrying out the research effectively.

 

CONFLICT OF INTEREST

Both authors of this article affirm that they have no conflicts of interest with any parties regarding this research.

 

AUTHORSHIP CONTRIBUTION

Conceptualization: R. Chakkaravarthy Kumaresan, S. Chandramoha.

Data curation: R. Chakkaravarthy Kumaresan, S. Chandramoha.

Formal analysis: R. Chakkaravarthy Kumaresan, S. Chandramoha.

Research: R. Chakkaravarthy Kumaresan, S. Chandramoha.

Methodology: R. Chakkaravarthy Kumaresan, S. Chandramoha.

Drafting - original draft: R. Chakkaravarthy Kumaresan, S. Chandramoha.

Writing - proofreading and editing: R. Chakkaravarthy Kumaresan, S. Chandramoha.