Exploring the Impact of Ripple Effect in Creating Brand Perception in Service Industry with Special Reference to Insurance Sector

Exploring the Impact of Ripple Effect in Creating Brand Perception in Service Industry with Special Reference to Insurance Sector

Dr Geeta Nema¹, Ms Aashi Choudhary²

International Institute of Professional Studies, Devi Ahilya University, Indore

DOI: https://doi.org/10.51244/IJRSI.2025.120500184

Received: 05 June 2025; Accepted: 09 June 2025; Published: 23 June 2025

ABSTRACT

The ripple effect describes the phenomenon where individuals shape their opinions and decisions not only through their own experiences but also by observing or hearing about the experiences of others. This study investigates the influence of the ripple effect on brand perception in insurance industry. The research explores how different aspects of customer interaction—such as changes in premiums, policy modifications, customer service quality, digital engagement and claims processing—contribute to shaping brand image. Additionally, it assesses the role of demographic variables like age, gender, income, education and occupation in either reinforcing or moderating these effects. The findings offer valuable insights for insurance brands seeking to build trust, foster loyalty and boost customer engagement by addressing both direct interactions and second-hand perceptions.

The study involved 130 participants, who were selected using a structured questionnaire approach. Data analysis was carried out using SPSS and Smart PLS 4 software, employing a range of statistical techniques to fulfil the research objectives. To ensure internal consistency, reliability analysis was conducted using Cronbach’s Alpha, with all constructs exceeding the threshold value of 0.70. Demographic insights and levels of awareness were derived through frequency and percentage analysis. Chi-square tests assessed the relationship between demographic factors and brand perception, indicating significant links for all variables except gender. Structural path modelling in Smart PLS 4 was then utilized to explore the connections between constructs such as trust, perceived quality and customer loyalty, thereby supporting the influence of the ripple effect on brand perception.

The study demonstrates that the ripple effect significantly shapes brand perception, especially when moderated by demographic variables. These insights are beneficial for insurance companies aiming to enhance customer satisfaction and strengthen brand engagement.

Keywords: Ripple effect, Brand perception, Insurance sector, Consumer behaviour

INTRODUCTION

In today’s highly competitive and interconnected market environment, establishing a strong and favourable brand image is essential for the long-term success of businesses, especially within the service industry. The ripple effect describes how a single event, action, or experience can extend its impact across a wider audience or network, ultimately shaping public opinion about a brand.

The term “Ripple effect” is often used to describe the phenomenon where a single action, decision, or event causes a series of indirect, widespread consequences, similar to the ripples created when a stone is dropped into a body of water. In the context of branding, the Ripple effect refers to the way individual customer experiences, reviews, or marketing efforts spread across a broader audience, influencing other’s perceptions of the brand. Positive interactions, such as excellent customer service or high-quality offerings, can generate a favourable Ripple effect, leading to increased trust and loyalty. Conversely, negative experiences can result in a damaging ripple, causing harm to the brand’s reputation and eroding customer trust. This study examines how the ripple effect contributes to brand perception in the service sector, emphasizing the crucial role of customer experiences, word-of-mouth communication and social media interactions in defining a brand’s identity and reputation.

The service sector, also known as the tertiary sector, encompasses businesses and organizations that provide intangible goods or services to consumers. Services are characterized by their intangibility, heterogeneity and reliance on human interactions. In this sector, customer experiences, service quality and trust play significant roles in shaping brand perception and customer loyalty. The Ripple effect in the service industry describes how the actions, choices, or experiences of individual customers or service providers can trigger far-reaching and often indirect outcomes. This can happen through several avenues, such as word-of-mouth, online reviews and social media interactions.

In the financial and insurance service sector, companies like Life Insurance Corporation of India (LIC) and private insurers experience the Ripple effect in shaping public perception. A positive Ripple effect can be seen when satisfied policyholders share their experiences with timely claims, reliable service and long-term benefits. This trust often leads to recommendations from customers to their social networks, reinforcing LIC’s brand image.Private insurance providers, such as HDFC Life, ICICI Prudential and Max Life, are also influenced by the Ripple effect in shaping their brand perception. A private insurer that offers smooth service, fast claims processing, or unique insurance products can create a positive Ripple effect, attracting new clients through word of mouth or online reviews.

This study seeks to understand how the Ripple effect shapes brand perception in the service industry, emphasizing key customer touchpoints, word-of-mouth influence and digital engagement. It will explore how favourable experiences—like efficient problem-solving or exceptional service—can generate positive ripples that foster trust and loyalty. On the other hand, unfavourable encounters may lead to negative ripple effects, harming the brand’s reputation. It will examine how demographic variables such as age, income level and professional background affect individuals’ perceptions and their susceptibility to the Ripple effect. Understanding these differences can help businesses tailor their strategies to better serve diverse customer segments.

Ultimately, this study aims to provide actionable insights into how companies in the service sector can manage and harness the Ripple effect to strengthen their brand image. By focusing on the elements that shape customer experience, businesses can enhance satisfaction, boost loyalty and achieve sustained success in an increasingly competitive marketplace.

LITERATURE REVIEW

A literature review in a research study is a summary and analysis of existing research on a specific topic. It identifies gaps, trends and relevant theories, helping to justify the need for the current study. It demonstrates how your study contributes to and aligns with existing scholarly discussions

For the present work following studies are considered –

Aboalganam and Alzghoul (2025) investigated the impact of digital marketing on the public image of insurance companies in Jordan, highlighting how service quality acts as a mediator and brand trust as a moderating factor. Their study, featured in Insurance Markets and Companies, emphasizes the crucial role of digital interaction in influencing customer perception.

Mathur, N. (2024) has highlighted how positive customer reviews build trust, enhance brand visibility and influence purchasing decisions. They serve as digital word-of-mouth, boosting referrals and customer loyalty. Encouraging satisfied customers to become advocates through referral programs increases lifetime value. Additionally, reviews improve SEO by incorporating relevant keywords and provide insights for product development.

Kunal Rajesh Lahoti, Shivani Hanji, Pratik Kamble and Kavita Vemuri (2023) utilized a game-based platform to examine how loss-framed messages and individual risk preferences influence insurance purchasing behaviour. The research indicated that younger participants were more responsive to loss-framed messaging, especially based on the insurance type. Those with higher risk aversion were less likely to purchase health and accident insurance. In contrast, older individuals with greater risk tolerance were more inclined to buy accident insurance. Interestingly, the study found no significant correlation between risk preferences and decisions regarding life insurance. These findings emphasize the impact of factors such as age, income level, family responsibilities and risk appetite on insurance choices, highlighting the effectiveness of framing and behavioural nudging strategies.Top of FormBottom of Form

Zhou (2023) explored the evolving role of influencer marketing in shaping brand perception in the digital age, emphasizing the critical importance of authenticity. The study highlights that genuine influencer—particularly micro-influencers—are effective in establishing trust and positively influencing brand image. Notable examples such as Glossier and Athletic Greens showcase the success of authentic influencer collaborations. In contrast, partnerships lacking authenticity, such as the Snap Spectacles campaign, have shown how poor alignment can damage a brand’s reputation. The research underscores the need for brands to prioritize authentic influencer relationships to strengthen brand perception.

Olorunsola, V. O., Saydam, M. B., Ogunmokun, O. A., & Ozturen, A. (2022) investigated the impact of corporate social responsibility (CSR) and internal marketing (IM) on enhancing employee engagement. Their findings suggest that when employees are actively engaged, they are more likely to deliver customer-oriented services. This chain reaction, or “ripple effect,” leads to elevated service quality, greater customer satisfaction and a strengthened brand image. The study highlights the importance of integrating CSR and IM into organizational strategies to foster a culture centred on customer needs and to enhance overall business performance.

Mei-Ling Wang (2021) identified that in the insurance industry, customer satisfaction plays a key role in fostering loyalty, with trust and commitment serving as important influencing factors. The study emphasizes the importance of maintaining a balance between attracting new customers and strengthening existing client relationships. Consistently evaluating and adapting to customer expectations is essential for maintaining satisfaction and improving retention rates.

Objectives and Hypothesis:

For the present study, the following objectives are established:

To Study Awareness About Ripple Effect Among Sample Respondents:

To explore how demographic factors—such as gender, age, education, income and occupation influence customer perceptions towards Ripple effect.

To Study the Variables Contributing to Ripple effect in insurance sector.

To investigate the role of demographic variables in understanding the Impact of Ripple effect in Forming Customer Brand Perception in the Insurance Sector.       

In order to achieve the objectives of the study, the following hypothesis is formulated –

H₀: Ripple effect does not significantly contribute in forming customers brand perception in insurance sector.

RESEARCH METHODOLOGY

Research Methodology is the framework of entire research. This study employed two research designs—exploratory and cross-sectional descriptive—to provide a comprehensive view of how the ripple effect influences brand perception in the insurance industry. Sample population is comprised of insurance service consumers across three specific age brackets: Young Adults: 20–35 years, Middle-Aged Adults: 36–50 years, Older Adults: 51–65 years. The data is collected from 130 respondents through structured questionnaire. The study employed purposive sampling, a non-probability technique that allowed the researcher to choose participants relevant to the study’s objectives. Quantitative data gathered via the questionnaire was analysed using SPSS and Smart PLS 4 including statistical tests like Reliability Statistics, Frequency and Percentage Analysis, Descriptive Statistics, Chi-Square Test, One-Way ANOVA and Smart PLS 4. Smart PLS 4 was used to conduct Path Analysis through Partial Least Squares Structural Equation Modelling (PLS-SEM), ideal for studying complex variable relationships in exploratory research.

Findings and Interpretation:

The findings of data analysis are presented as under:

Reliability Statistics: Reliability is the measure of internal consistency.

Table 1: Reliability Statistics
Cronbach’s Alpha	N of Items
.931	36

The Cronbach’s Alpha value for 36 items taken in the questionnaire is 0.931, which suggests that the items in the questionnaire reliably measure the underlying construct related to customer brand perception.

Frequency Percentage:

Frequency and percentage analysis were used to classify and evaluate the responses, identifying how many participants were aware of the ripple effects and to what extent. Key variables such as gender, age, educational background, income and profession were included to better understand how they shape customer perceptions of the Ripple effect in the insurance industry.

Table 2: Frequency Distribution of Gender
		Frequency	Percent	Valid Percent	Cumulative Percent
	Male	66	50.8	50.8	50.8
	Female	64	49.2	49.2	100.0
	Total	130	100.0	100.0

Out of 130 respondents, 50.8% were male and 49.2% were female, indicating a nearly balanced representation. Such balance enhances the generalizability of insights related to gender-based perception patterns.

Table 3: Frequency Distribution of Age
		Frequency	Percent	Valid Percent	Cumulative Percent
	20 – 35 years	89	68.5	68.5	68.5
	36 – 50 years	28	21.5	21.5	90.0
	51 – 65 years	13	10.0	10.0	100.0
	Total	130	100.0	100.0

The majority of respondents were aged between 20 and 35 years (68.5%), followed by those in the 36 to 50 age group (21.5%) and 51 to 65 years (10%). This indicates that younger adults made up the largest portion of the sample. Their feedback offered important insights into how digital experiences, peer feedback and social influence affect brand perception—central aspects of the ripple effect—particularly among tech-oriented individuals in the early stages of their careers.

Table 4: Frequency Distribution of Qualification
		Frequency	Percent	Valid Percent	Cumulative Percent
	High school	7	5.4	5.4	5.4
	Undergraduate	39	30.0	30.0	35.4
	Postgraduate	74	56.9	56.9	92.3
	Doctorate	10	7.7	7.7	100.0
	Total	130	100.0	100.0

A significant portion of respondents were postgraduates (56.9%), followed by undergraduates (30%), doctorates (7.7%) and high school graduates (5.4%). Educated consumers are likely to critically assess both positive and negative experiences, influencing their perception more analytically.

Table 5: Frequency Distribution of Annual Income

		Frequency	Percent	Valid Percent	Cumulative Percent
	up to 4 lakhs	61	46.9	46.9	46.9
	4 lakhs – 8 lakhs	43	33.1	33.1	80.0
	Above 8 lakhs	26	20.0	20.0	100.0
	Total	130	100.0	100.0

Nearly 47% of respondents had an income up to ₹4 lakhs, while 33.1% earned between ₹4–8 lakhs and 20% earned above ₹8 lakhs. Income influences risk preferences and expectations from insurance providers. Lower-income groups may be more sensitive to premium changes or policy shifts (key ripple triggers), while higher-income consumers may place greater value on service quality, brand reputation and digital presence.

Table 6: Frequency Distribution of Occupation

		Frequency	Percent	Valid Percent	Cumulative Percent
	Student	56	43.1	43.1	43.1
	Service	35	26.9	26.9	70.0
	Businessman	21	16.2	16.2	86.2
	Housewife	12	9.2	9.2	95.4
	Retired	6	4.6	4.6	100.0
	Total	130	100.0	100.0

Respondents were mostly students (43.1%), followed by those in service (26.9%), business (16.2%), housewives (9.2%) and retired individuals (4.6%). This occupational diversity supports analysis across lifestyle-based expectations and experiences.

In order to further check the significant association among the demographic variables, Non- Parametric Chi- square test was applied on the demographics. To explore the relationship between demographic factors and brand perception in the insurance sector, five null hypotheses were developed and evaluated using the chi-square test. The results of the same are presented as under:

H₀₁: Gender does not significantly affect customer perception towards the ripple effect in the insurance sector.
H₀₂: Age does not significantly affect customer perception towards the ripple effect in the insurance sector.
H₀₃: Qualification does not significantly affect customer perception towards the ripple effect in the insurance sector.
H₀₄: Annual income does not significantly affect customer perception towards the ripple effect in the insurance sector.
H₀₅: Occupation does not significantly affect customer perception towards the ripple effect in the insurance sector.

Table 7: Chi- Square Test Statistics
	Gender	Age	Education	Income	Occupation
Chi-Square	.031a	74.785b	89.877c	14.138b	61.615d
df	1	2	3	2	4
Asymp. Sig.	.861	.000	.000	.001	.000

Among all the demographic variables examined, only gender returned a p-value greater than 0.05 (specifically 0.861), indicating no statistically significant influence on how customers perceive ripple effects in the insurance context. A p-value above 0.05 suggests that differences in perception between male and female participants are not substantial enough to be statistically valid. In essence, both genders demonstrate similar levels of awareness and reaction to ripple-related experiences such as premium hikes, policy adjustments and service encounters. Therefore, while other demographic factors show significant influence, gender does not.

Smart Pls 4 Path Analysis Model:

A path analysis model was constructed based on data gathered through a structured questionnaire that assessed respondents’ perceptions across various facets of insurance services. These included reactions to premium adjustments, policy revisions, claims processing, overall service quality, digital interactions and levels of brand engagement. The collected indicators were categorized into broader outcome constructs for a clearer analytical framework:

• Contributors to the Ripple Effect
• Trust and Claims Management
• Customer Experience and Service Quality
• Brand Reputation and Word-of-Mouth Influence
• Brand Engagement and Digital Presence
• Customer Loyalty and Overall Brand Perception

The primary objective of this analysis was to explore the extent to which key demographic variables—namely Gender, Age, Education, Annual Income and Occupation—impact customer responses to the ripple effect and influence their perception of insurance brands.

To conduct the analysis, Smart PLS 4, a robust tool for Partial Least Squares Structural Equation Modelling (PLS-SEM), was employed. This software enabled the calculation of path coefficients, which represent the strength and direction of relationships between demographic variables and the identified outcome categories. Additionally, R-squared (R²) values were derived to quantify the proportion of variance in each dependent variable that can be attributed to the demographic inputs.

Through this methodological approach, the research was able to pinpoint which demographic segments exert the most influence on customer attitudes and behaviour in response to subtle service-related triggers—collectively referred to as the ripple effect. The findings provide valuable insights into the differential impact of these triggers across customer groups.

For instance, age and income may shape how a customer perceives a delay in claim settlement or responds to changes in policy terms. Similarly, higher educational attainment might correlate with greater sensitivity to transparency and digital engagement. These nuanced insights are critical for insurance companies aiming to enhance customer satisfaction and retention. By understanding the specific triggers and response patterns associated with each demographic group, insurers can craft more personalized strategies—focusing on trust-building, service refinement and brand loyalty reinforcement tailored to diverse customer profiles.

Interpretations

Gender → Variables Contributing to Ripple Effect (0.000)

Gender has no influence which suggests that gender does not significantly impact how customers perceive factors like market conditions, policy changes, or customer interactions that generate ripple effects in behaviour or attitudes.

Gender → Trust & Claims Handling (0.024)

Very weak positive relationship which implies that gender plays a minor role in shaping trust in insurance claims or interactions with providers.

Gender → Customer Experience & Service Quality (0.296)

Moderately strong positive effect. Gender significantly influences how customers perceive service quality and their overall experience.

Gender → Reputation & Word of Mouth (0.274)

Moderate effect. This highlights a gender-based difference in how trust or social influence is spread.

Gender → Brand Engagement & Digital Presence (0.080)

Weak effect. Gender shows limited influence on how customers interact with a brand digitally.This suggests that online engagement is becoming more uniform across gender lines.

Gender → Loyalty & Overall Perception (0.297)

Moderate effect. This indicates a clear gender difference in brand loyalty and overall brand evaluation.

Interpretations

Age Group → Variables Contributing to Ripple Effect (0.168):

Age has a moderate impact on how customers perceive external or environmental changes.Different age groups may respond differently to shifts in market conditions, policy changes, or service interactions.

Age Group → Trust & Claims Handling (0.208):

Age influences how customers view the handling of claims and the trustworthiness of the brand. Older customers may place a higher value on trust and reliability compared to younger customers.

Age Group → Customer Experience & Service Quality (0.171):

Age moderately affects how service quality and overall customer experience are assessed. Preferences may differ by age, with younger individuals favouring accessibility and older customers prioritizing attentiveness.

Age Group → Reputation & Word of Mouth (0.261):

A notable positive effect suggests that age influences how customers perceive brand reputation. Age also plays a role in the likelihood of sharing experiences, such as through online reviews or referrals.

Age Group → Brand Engagement & Digital Presence (0.265):

A strong relationship indicates that digital engagement varies significantly across age groups. Younger individuals tend to engage with brands more frequently in digital spaces.

Age Group → Loyalty & Overall Perception (0.270):

This is the strongest path in the model. Age has a significant influence on brand loyalty and overall brand perception, suggesting that age-specific loyalty strategies may be necessary.

Interpretations

Level of Education → Variables Contributing to Ripple Effect (0.372):

This is the strongest relationship in the model. Education significantly impacts how individuals perceive the indirect or secondary effects of policy changes, service experiences and market conditions.

Level of Education → Reputation & Word of Mouth (0.307):

Education moderately influences how individuals form and share opinions about a company’s reputation.

Level of Education → Trust & Claims Handling (0.296):

Education plays a notable role in shaping perceptions of trust and the claims process. Educated customers may be more critical of the claims process or develop more informed opinions based on their understanding.

Level of Education → Customer Experience & Service Quality (0.295):

This relationship is almost as strong as trust and reputation. Consumers with higher education levels tend to value service quality more and are likely to base their trust or loyalty on their customer service experiences.

Level of Education → Brand Engagement & Digital Presence (0.264):

Education influences how consumers engage with a brand online and respond to digital marketing which reflect higher digital literacy or more sophisticated expectations among more educated individuals.

Level of Education → Loyalty & Overall Perception (0.032):

This is the weakest relationship involving education. While education impacts earlier stages like trust and reputation, its direct effect on brand loyalty and overall perception is relatively minor.

Interpretations

Annual Income → Brand Engagement & Digital Presence (0.309):

This is the most significant relationship, indicating that individuals with higher incomes are notably more engaged in digital interactions with the brand.

Annual Income → Variables Contributing to Ripple Effect (0.261):

Income has a considerable impact on how individuals perceive secondary factors such as market fluctuations, policy changes, or premium increases. Higher-income individuals are often more financially savvy, which makes them more attuned to the ripple effects within the insurance sector.

Annual Income → Trust & Claims Handling (0.259):

Wealthier individuals tend to expect more efficient, transparent claims handling and may scrutinize the trustworthiness of providers more carefully.

Annual Income → Reputation & Word of Mouth (0.252):

This suggests that reputation and peer influence play a more significant role for higher-income groups, are more likely to engage in discussions, rely on reviews and share their experiences, thereby affecting the brand’s reputation.

Annual Income → Customer Experience & Service Quality (0.221):

Income has a moderate influence on customer experience and service quality. Higher-income individuals often have elevated expectations regarding service and quality and their experiences with the company have a significant impact on their perceptions.

Annual Income → Loyalty & Overall Perception (0.055):

This is the weakest direct relationship. Loyalty tends to be more driven by trust, service quality and digital engagement.

Interpretations

Occupation → Variables Contributing to Ripple Effect (0.180):

This is the most prominent relationship for occupation, suggesting that a person’s profession greatly influences how they perceive broader aspects of insurance, such as policy terms, market conditions and premium changes.

Occupation → Loyalty & Overall Perception (0.178):

This is also a significant relationship, indicating that one’s occupation has a meaningful influence on their loyalty to an insurance provider.

Occupation → Reputation & Word of Mouth (0.112):

Occupation has a moderate influence on how individuals perceive and engage with brand reputation. Professionals in communication-heavy or network-oriented roles may place more importance on peer recommendations and public opinion and are more likely to share their experiences with others.

Occupation → Customer Experience & Service Quality (0.105):

Occupational roles affect how people evaluate service quality.Individuals in fast-paced or client-focused jobs may expect quicker, clearer and more personalized services and judge insurers based on these factors.

Occupation → Brand Engagement & Digital Presence (0.072):

Although this path is weaker, it remains relevant. Certain occupations may promote greater digital fluency, leading individuals to engage with insurers online more frequently or have higher expectations for user-friendly digital interfaces.

Occupation → Trust & Claims Handling (0.041):

This represents the weakest influence of occupation. Trust in claims processes seems to be less determined by one’s professional background and more influenced by personal experiences with the insurance provider.

CONCLUSION

This study aimed to examine the impact of the ripple effect on shaping brand perception in service sector. As services become increasingly intangible and experience-driven, the influence of indirect factors—such as customer opinions shared through social media or word of mouth—has grown significantly.

In this context, the ripple effect refers to the broader influence that consumer interactions and opinions have on shaping the perception of a brand, even among individuals who have not directly engaged with the brand. The study explored how factors such as brand awareness, trust, loyalty and perceived service quality are impacted by these indirect experiences. In today’s digital era, where both positive and negative experiences are amplified through online platforms, understanding the ripple effect has become crucial for service organizations looking to manage their brand image, maintain customer satisfaction and foster loyalty.

The study’s findings underscore the importance of the ripple effect in shaping various aspects of brand perception. The analysis revealed a positive correlation between brand trust and indirect customer experiences, suggesting that consumers often rely on others’ experiences to gauge a brand’s reliability, even when they haven’t personally interacted with the brand. Additionally, brand loyalty was found to be strongly influenced by shared sentiments across social networks and communities. The study also highlighted the significant role that ripple effects play in shaping customer expectations of service quality.

One of the most significant insights from the study was the increased sensitivity of service sector customers to peer reviews and shared opinions, indicating that social proof and emotional contagion are key drivers of brand perception. Furthermore, brand awareness was shown to be significantly enhanced by ripple effects, particularly in the digital space. These findings reinforce the notion that indirect interactions can profoundly influence brand equity and consumer behaviour in service-oriented industries.

To ensure the robustness and validity of the study’s conclusions, a comprehensive statistical approach was used. Cronbach’s Alpha was employed to assess the reliability of the data, with all measured constructs meeting the standard threshold for internal consistency, confirming the dependability of the survey instruments. Frequency and percentage analysis provided valuable insights into demographic patterns and the distribution of responses, which helped in understanding the sample composition. Descriptive statistics further enriched the analysis by detailing central tendencies and variabilities within the data.

The Chi-square test was applied to identify significant associations between categorical variables such as age, occupation and brand perception. Smart PLS-4 Path Analysis model  mapped the relationships among the dimensions of the ripple effect and various brand perception factors. The path model showed strong path coefficients and significant t-statistics, validating the direct and indirect effects hypothesized in the study. Collectively, these analytical techniques affirmed the reliability of the data and the strength of the relationships identified, providing a comprehensive view of how the ripple effect shapes brand perception.

The statistical and empirical findings of the study reinforce the argument that the ripple effect is a crucial driver of brand perception in service industries. In sectors where tangible proof of value is limited, consumers often rely on the narratives constructed by others to form their judgments. This highlights the importance of managing not only direct service encounters but also the wider ecosystem of opinions and experiences that circulate among current and potential customers. The ripple effect can be both an opportunity and a challenge for service brands: it can magnify positive customer experiences to fuel brand growth or spread dissatisfaction if not managed effectively.

Thus, understanding the dynamics of the ripple effect allows service providers to design strategies that encourage customer advocacy, transparency and positive emotional engagement. This study emphasizes that, in the modern service economy, managing the perceptions shaped by ripple effects should be given as much strategic attention as traditional marketing or direct customer engagement. By leveraging these insights, service organizations can enhance their brand credibility, improve service delivery and maintain a competitive edge in an ever-evolving marketplace.

The societal implications of the study involve a range of benefits that extend well beyond the realms of business and marketing. By shedding light on the ripple effect in brand perception, the research empowers consumers to make more informed choices by recognizing the influence of external factors such as social media and word-of-mouth. This awareness fosters conscious consumption and encourages businesses to adopt greater transparency and ethical practices, ultimately enhancing corporate accountability.

Additionally, the findings can inform policymakers in strengthening regulations that ensure fair advertising and ethical standards, particularly in sectors like insurance. Companies may also respond by improving customer service and fraud prevention, resulting in stronger consumer protections. Positive brand perceptions contribute to ethical workplace environments, attracting skilled employees and boosting morale. The amplified role of social media in shaping public discourse necessitates responsible corporate engagement online. Lastly, awareness of the ripple effect may drive businesses toward more sustainable and inclusive practices, promoting greater corporate social responsibility and contributing to the broader social good.

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