Keywords

Fake Reviews, Deceptive Marketing, Issues, Misleading Practices, Online Platform.

Introduction

The increasing prevalence of counterfeit reviews and misleading marketing tactics has become a significant issue in the ever-changing realm of e-commerce and digital advertising. The growing dependence on online evaluations as a main information source for consumer decision-making has led to a critical examination of the credibility of review systems and the reliability of online platforms. Deceptive evaluations, frequently produced by dishonest enterprises or individuals aiming to manipulate opinions, have the potential to alter consumer perceptions, deceive purchasing choices, and undermine confidence in digital marketplaces. In addition, deceptive marketing methods go beyond fabricated evaluations and include a range of strategies such as astroturfing, misleading advertisements, and undeclared sponsorships, among others. These methods not only engage in customer deception but also foster unfair competition, tarnish the reputations of honest enterprises, and give rise to ethical and legal difficulties. With the continuous advancement of technology, there are constantly new opportunities for deceit and manipulation, which provide continual difficulties for regulators, businesses, and consumers. To tackle the problems associated with fake reviews and deceptive marketing practices, it is necessary to adopt a comprehensive strategy that involves cooperation and endeavours to counteract fake reviews and deceptive marketing. This strategy should prioritise improving transparency, accountability, and integrity in online commerce, while also safeguarding consumer trust and protecting their interests.

Fake Reviews

When someone posts false or incorrect feedback about a business, product, or service online, usually on a review site or an e-commerce site, they are trying to trick people or change how people think about the business. These reviews could have been written by people or organisations with a personal stake in the outcome, like rivals trying to hurt a rival's image, companies wanting to promote their own goods or services, or hired freelancers who were paid to write positive reviews Figures 1 & 2.

Some Important Signs of Fake Reviews are

1. A lot of the time, fake reviews make up or exaggerate claims about a product or service's features, performance, or customer experiences that aren't true.

2. The people who write fake reviews often hide their real identities by using fake names, accounts, or private profiles. This makes it impossible to confirm their identities.

3. Patterns of manipulation can be seen in fake reviews, such as a sudden influx of positive reviews in a short amount of time, the use of the same words or phrases in multiple reviews, or ratings that seem strangely uniform.

4. Fake reviews might be biassed in favour of the product or service being reviewed, not provide a critical analysis, or only talk about the good things about the product or service without stating any problems or shortcomings (Pamnani, P. S., 2023).

5. People who write fake reviews might not have actually used or interacted with the product or service they are criticising, which can lead to weak or wrong opinions.

6. Fake reviews are very common and cause a lot of problems for customers, companies, and online platforms. For buyers, fake reviews can cause them to make bad choices about what to buy, waste time and money, and lose faith in online reviews. If businesses are found lying, they could lose their credibility, have their reputations hurt, and even be sued. Keeping the integrity of their review systems and keeping users' trust are hard things for online sites to do. To deal with the problem of fake reviews, people often use technological solutions, like AI algorithms that look for patterns of fraud, along with legal measures and community-based moderation strategies (Priyadi, U., 2023). Stakeholders want to make the online market more trustworthy for customers by fighting fake reviews and encouraging honesty and openness in online feedback.

Deceptive Marketing

Deceptive marketing is the use of false or dishonest practices to promote products or services, alter consumer perceptions, and increase sales. This notion refers to a variety of strategies and practices used by businesses to create a misleading or exaggerated picture of their services, conceal crucial facts, or exploit consumer vulnerabilities for economic gain (Srivastava, et al. 2023). Deceptive marketing strategies may contravene consumer protection laws and regulations, erode customer trust and relationships, and ruin the company's brand.

False advertising refers to making false or misleading statements about a product or service in advertisements, promotional materials, or marketing communications. False advertising can involve misrepresenting product features, performance, benefits, or pricing in order to trick consumers into making purchasing decisions based on incorrect information. In a bait-and-switch scheme, firms entice clients with an appealing offer or promotion, only to upsell or substitute a different, generally more expensive, product or service after the consumer is engaged. This strategy uses deceit and manipulation to persuade customers to buy something. Deceptive marketing may include the omission or concealment of crucial terms, conditions, or fees related with a product or service. Businesses may hide such information in fine print or use misleading wording to obscure unfavourable clauses, causing consumers to make ignorant purchases.

Some deceptive marketing methods involve inflating product benefits, efficacy, or consumer happiness using inflated claims or manufactured testimonials. Businesses attempt to increase the perceived worth or credibility of their offerings by providing fake or deceptive endorsements. Deceptive marketing can use psychological concepts and persuasion tactics to exploit consumer vulnerabilities and influence behaviour (Wildan, 2023). Scarcity, social proof, and fear-based appeals are examples of strategies used to encourage impulse purchases or generate a sense of urgency.

By supporting openness, honesty, and ethical standards in marketing tactics, stakeholders hope to create a fair and trustworthy marketplace that protects consumers' interests and fosters healthy competition. Efforts to prevent misleading marketing include governmental oversight, enforcement of consumer protection legislation, industry self-regulation, and consumer education campaigns. Deceptive marketing tactics can have major ramifications for customers, firms, and the market as a whole. Consumers may suffer financial losses, dissatisfaction with products or services, and a loss of faith in brands and marketers. Businesses that engage in deceptive marketing run the danger of damaging their reputation, incurring legal liabilities, and facing regulatory punishments. Furthermore, dishonest marketing weakens the competitive marketplace's integrity and reduces trust in the marketing ecosystem as a whole.

Review Literature

Alsubari et al. (2022) investigated the utilisation of supervised learning methods to detect fraudulent reviews. The study employs data analytics techniques to differentiate authentic and counterfeit reviews, providing valuable insights into the identification of deceptive internet reviews. Azimi et al. (2022) examines the variables that impact the efficacy of counterfeit reviews. The study examines the correlation between review aspects and consumer variables in order to assess the efficacy of false reviews. It illuminates how these factors contribute to the spread of misleading reviews in consumer decision-making processes. Barbado et al. (2019) introduces a thorough framework for identifying fraudulent reviews, with a specific focus on online stores that sell consumer goods. The framework presents methodologies and approaches for detecting fraudulent reviews, with the goal of improving the genuineness and dependability of online consumer feedback in the electronics sector. Cao et al. (2021) specifically examines the identification of fraudulent reviewer collectives in digital review platforms. The study investigates techniques for detecting collaborative endeavours among fake reviewers with the intention of manipulating ratings and misleading consumers. The study focuses on the problem of identifying coordinated fraudulent review operations, offering valuable information on how to improve the credibility of online review platforms. Gourkar (2023) examines the impact of deceitful and unscrupulous marketing on consumer behaviour. The study examines the impact of deceptive marketing strategies on consumer perceptions, attitudes, and purchase choices. The study is expected to investigate several deceptive marketing approaches and their effects on consumer trust and brand reputation. Its objective is to enhance the comprehension of ethical considerations in marketing strategies.

In their study, He et al. (2022) analyse the intricacies of the counterfeit reviews market. This study presumably investigates the motivations, processes, and parties involved in the creation and use of deceptive reviews. The study illuminates the economic and behavioural dimensions of counterfeit reviews, offering valuable insights into their frequency and influence on customer choices and market dynamics. Hunt (2015) explores the convergence of fraudulent internet reviews and consumer legislation. The text probably examines the legal structures and regulations surrounding dishonest acts in online reviews, as well as the difficulties in implementing laws that safeguard consumers in the digital domain. The study is expected to offer valuable insights into the legal ramifications and measures for addressing fraudulent reviews and protecting consumer welfare. The study conducted by Kauffmann et al. (2020) introduces a comprehensive strategy for effectively harnessing big data analytics within commercial social networks. The project centres on utilising sentiment analysis and false review detection as means to improve marketing decision-making processes. The article is expected to showcase the actual implementation of these analytics tools in enhancing marketing tactics and tackling issues like fraudulent reviews on social platforms, using a case study approach. Martínez Otero (2021) analyses the regulatory strategies employed in the United States, United Kingdom, and European Union to combat the issue of fraudulent reviews on internet platforms. The text probably examines and highlights the differences and similarities in the legal systems and methods used to combat bogus reviews in different regions. The study offers valuable insights into the regulatory framework concerning fraudulent reviews, including comments on the efficacy and obstacles of legislative interventions in various countries.

Ott et al. (2012) examines the degree of dishonesty present in online review sites. The paper employs computational approaches to suggest techniques for estimating the prevalence of fraudulent or misleading reviews across diverse online communities. The paper seeks to utilise extensive datasets to analyse and offer insights into the magnitude and consequences of fraudulent practices in online review systems. This research intends to enhance our comprehension of the reliability of online customer feedback. Peng et al. (2016) investigates the way in which consumers interpret dishonest activities in online reviews specifically in the Chinese market. The study is anticipated to examine consumer attitudes, beliefs, and behaviours about counterfeit reviews using empirical research. The research enhances our comprehension of online consumer behaviour in China by examining the influence of deceptive online reviews on consumer trust and decision-making. Román et al. (2019) examines multiple facets of perceived deceit in online consumer evaluations. The study presumably investigates the variables that impact customers' perceptions of deceit in reviews, as well as the effects of these beliefs on their attitudes and behaviours. In addition, the chapter may explore the moderators that influence the connection between perceived deceit and its consequences. The chapter provides vital insights into the intricate dynamics of online customer behaviour and trust in digital marketing environments by exploring these factors. Rynarzewska (2019) examines the ethical implications associated with reviewers who are offered incentives. The study presumably explores the moral perceptions and attitudes of persons who engage in review incentives, providing insight into how these practices are seen by both reviewers and consumers. The study delves into the subtleties of prejudiced evaluations and their consequences, offering valuable perspectives on the morality of online customer feedback and its influence on trust in review platforms. In their study, Wu et al. (2020) present a thorough examination of the current body of research concerning fraudulent internet reviews. This study consolidates significant discoveries from prior research and suggests areas where further exploration is needed, providing guidance for future studies in this domain. The research enhances comprehension of the phenomena of phoney reviews by examining the existing knowledge and proposes potential areas for additional investigation in academia and practice.

Research Methodology

This research is based on descriptive research design. The problem identified that how fake reviews & deceptive marketing practices create issues. For this sake, entertainment industry, product based industry, service industry & education industry based primary survey has done through structured questionnaire. The convenience random sampling taken place to ease for collection of data. After pilot study, only 110 respondents finally opted for analysis. Correlation, anova test applied for finding relation. Secondary data from various online sources has been captures. SPSS used for tools & techniques Tables 1-3.

Table 1 Anova for Significant Issues Related to Fake Reviews and Deceptive Marketing Practices
Factors related to related to fake reviews and deceptive marketing practices	Issues related to related to fake reviews and deceptive marketing practices				F-Value	P-Value
	Entertainment Industry	Product Based Industry	Service Industry	Education Industry
Trust Erosion	13.61	19.33	21.76	20.76	1.643	<0.001**
	(4.102)	(1.982)	(1.457)	(1.229)
Ethical Dilemmas	18.29	22.61	19.83	23.02	1.385	<0.003**
	(4.217)	(2.387)	(2.478)	(2.092)
Algorithmic Manipulation	19.79	23.69	22.45	21.95	2.084	<0.001**
	(5.223)	(4.678)	(2.379)	(1.763)
Legal and Regulatory Concerns	21.75	21.37	21.68	24.76	2.732	<0.002**
	(5.006)	(0.119)	(3.852)	(1.456)
Reputation Damage	23.35	22.44	23.77	23.65	1.546	<0.001**
	(5.017)	(2.087)	(2.731)	(2.876)
Overall Issues	96.79	109.44	109.49	114.14	1.789	<0.002**
	(27.546)	(18.763)	(26.791)	(15.123)
** @ 1% level of significance ; ( ) Stand._ Dev.

Table 2 Chi_Square Test for Goodness Fit on Fake Reviews & Deceptive Marketing Contribution
	Frequency	Percent (%)	Chi_Sq_V	P_Value
Lowest	16	14.54%	29.681	<0.002**
Medium	33	30.00%
Highest	61	55.45%
Total	110	100.0
**@1% significance level

Table 3 Karl_Pearson Corre._Coefficient
	Trust Erosion	Ethical Dilemmas	Algorithmic Manipulation	Legal and Regulatory Concerns	Reputation Damage
Trust Erosion	0.854**	.923**	.912**	.944**	.889**
Ethical Dilemmas		.897**	.867**	.889**	.914**
Algorithmic Manipulation			.895**	.796**	.877**
Legal and Regulatory Concerns				.785**	.787**
Reputation Damage					.884*
** @1% level of significance

Objectives of the Study

1. To identify issues related to fake reviews and deceptive marketing practices.

2. To analyse issues related to fake reviews and deceptive marketing practices.

3. To suggest findings & conclusion.

Hypothesis of the Study

H1: There are no significant issues related to fake reviews and deceptive marketing practices.

H2: Overall factors for issues related to fake reviews and deceptive marketing practices.

H5: No major differences amongst fake reviews & deceptive marketing factors.

Null hypothesis “there are no significant issues related to fake reviews and deceptive marketing practices “not accepted at 1% of significance level as the P-Value is less at 0.01 on all the factors of fake reviews. So, a significant difference in factors among fake reviews & deceptive marketing.

The null hypothesis “overall factors for issues related to fake reviews and deceptive marketing practices “ is rejected at 1% (since the P-value is less at the 1% significance level). As a result, the degrees of fake reviews and deceptive marketing contributions continue to vary significantly. As a result, it is concluded that the deceptive marketing contribution is unevenly divided, with the majority of phoney reviews falling within the moderate (30%) category.

The Karl Pearson correlation coefficients for a number of variables related to confidence, ethics, algorithms, the law, and reputational damage are shown in this table. This number, which can be anywhere from -1 to 1, shows how strong and which way the linear link between two variables is. If the coefficient is 1, there is a perfect positive linear relationship. If it is -1, there is a perfect negative linear relationship, and if it is zero, there is no linear relationship. There are strong positive correlations between different aspects of "trust erosion, ethical dilemmas, algorithmic manipulation, legal concerns, and reputation damage" in the table. This means that if one thing changes, it's likely that other things will also change. It lists "Trust Erosion, Ethical Dilemmas, Algorithmic Manipulation, Legal and Regulatory Concerns, and Reputation Damage" as the factors. The correlation coefficients between each pair of factors are shown by the numbers in the table. As an example, the association coefficient between "Trust Erosion and Ethical Dilemmas" is 0.854 (marked **), which at the 1% level is very strong and positive. It looks like "Trust Erosion and Ethical Dilemmas" are strongly connected in a straight line. If the correlation value is close to 1, it means that the variables are strongly connected in a straight line. As an example, "Trust Erosion and Ethical Dilemmas" have a strong positive association with a coefficient of 0.923. If the correlation value is close to -1, it means that the variables are strongly connected in a negative linear way. If the correlation value is close to 0, it means that the variables don't have a strong linear relationship with each other. It says ** @1% level of significance, which means that the correlation coefficients are significant at the 1% level. This means that the relationships seen are very unlikely to happen by chance. All of the correlation values are positive and very high, which means that when one variable goes up, it tends to make the other variable go up too. All of these pairs of variables have a positive linear relationship with each other. The correlation coefficients are usually very high, which means that the factors are strongly linked in a straight line. There is a strong positive linear link between "Legal and Regulatory Concerns and Algorithmic Manipulation" and other variables, as shown by the correlation coefficient of 0.785. The null hypothesis that says "Fake reviews and misleading marketing factors are not very different" is not true in this case.

Findings of the Study

1. The internet age presents a variety of issues and concerns with phoney reviews and misleading marketing methods. Below are several crucial aspects:

2. Counterfeit reviews erode confidence in digital platforms and compromise consumer judgement processes. If consumers are unable to trust the credibility of reviews, they may develop doubt about product recommendations and have reluctance in making purchases.

3. Deceptive marketing methods, such as the dissemination of fabricated reviews or the manipulation of ratings, mislead consumers by providing them with incorrect information regarding products or services. Such outcomes can result in consumer discontent, disillusionment, and significant monetary setbacks.

4. Businesses who partake in fraudulent evaluations obtain an unjust edge over competitors who depend on authentic client feedback. This disparity in the marketplace produces an imbalanced environment and impedes equitable competition.

5. Companies who are discovered participating in deceitful marketing methods face the potential consequences of harming their reputation and brand perception. Fabricated reviews can generate adverse publicity, which can erode the reputation and trustworthiness of a business in the eyes of consumers.

6. Engaging in fraudulent reviews and misleading marketing tactics can potentially infringe upon consumer protection laws and regulations. Companies that are proven to engage in such tactics can be subject to legal ramifications, such as monetary penalties, litigation, and regulatory penalties.

7. Certain enterprises employ automated bots or software tools to produce a substantial number of counterfeit reviews, taking advantage of weaknesses in the algorithms of online review platforms. This manipulation compromises the integrity of review mechanisms and hinders platforms' ability to accurately identify and address fraudulent content.

8. The presence of fraudulent evaluations gives rise to ethical concerns regarding the principles of truthfulness, openness, and moral uprightness in the field of marketing. Companies must carefully contemplate the ethical ramifications of their conduct and give precedence to cultivating sincere connections with customers founded on trust and sincerity.

Conclusion

The spread of fake reviews and dishonest marketing methods has become a major problem in the fast-changing worlds of online shopping and digital marketing. People are turning more and more to online reviews as their main source of information when making decisions. As a result, the reliability of review systems and the dependability of online platforms have been questioned. Consumers' opinions can be skewed by fake reviews, which are often posted by dishonest companies or people who want to change how people see things. This can lead to bad buying choices and less trust in online markets. Also, dishonest marketing includes more than just fake reviews. It includes astroturfing, misleading advertising, and sponsorships that aren't revealed, among other things. Not only do these actions trick customers, but they also make the market less fair, hurt the reputations of honest companies, and bring up moral and legal issues. As technology keeps getting better, new ways to trick and cheat people open up. This makes things harder for lawmakers, businesses, and customers alike. Fighting fake reviews and dishonest marketing requires a multifaceted approach that involves working together. The main goals of the fight against fake reviews and dishonest marketing should be to improve honesty, openness, and accountability in online business while protecting consumers' trust and interests. It looks at what these practices mean for customers, companies, and the market as a whole, as well as the difficulties and chances of solving these problems well. The point of this paper is to help us learn more about how fake reviews and misleading advertising affect online shopping and shopper welfare. Solving these issues necessitates the cooperation of multiple parties, such as corporations, consumers, regulators, and internet platforms. To offset the adverse effects of phoney reviews and misleading marketing techniques, it is beneficial to implement measures such as enhanced detection algorithms, stricter policy enforcement, and transparency initiatives. These actions contribute to creating a more reliable and equitable online marketplace.

References

Alsubari, S. N., Deshmukh, S. N., Alqarni, A. A., Alsharif, N., Aldhyani, T. H., Alsaade, F. W., & Khalaf, O. I. (2022). Data analytics for the identification of fake reviews using supervised learning. Computers, Materials & Continua, 70(2), 3189-3204.

Indexed at, Google Scholar, Cross Ref

Azimi, S., Chan, K., & Krasnikov, A. (2022). How fakes make it through: the role of review features versus consumer characteristics. Journal of Consumer Marketing, 39(5), 523-537.

Indexed at, Google Scholar, Cross Ref

Barbado, R., Araque, O., & Iglesias, C. A. (2019). A framework for fake review detection in online consumer electronics retailers. Information Processing & Management, 56(4), 1234-1244.

Indexed at, Google Scholar, Cross Ref

Cao, C., Li, S., Yu, S., & Chen, Z. (2021). Fake reviewer group detection in online review systems. In 2021 International Conference on Data Mining Workshops (ICDMW) (pp. 935-942). IEEE.

Indexed at, Google Scholar

Gourkar, V. (2023). Effects of deceptive and unethical marketing on consumer behaviour (Doctoral dissertation, Dublin, National College of Ireland).

Google Scholar

He, S., Hollenbeck, B., & Proserpio, D. (2022). The market for fake reviews. Marketing Science, 41(5), 896-921.

Indexed at, Google Scholar, Cross Ref

Hunt, K. M. (2015). Gaming the system: Fake online reviews v. consumer law. Computer law & security review, 31(1), 3-25.

Indexed at, Google Scholar, Cross Ref

Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2020). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management, 90, 523-537.

Indexed at,  Google Scholar, Cross Ref

Martínez Otero, J. M. (2021). Fake reviews on online platforms: perspectives from the US, UK and EU legislations. SN Social Sciences, 1(7), 181.

Indexed at, Google Scholar, Cross Ref

Ott, M., Cardie, C., & Hancock, J. (2012). Estimating the prevalence of deception in online review communities. In Proceedings of the 21st international conference on World Wide Web (pp. 201-210).

Google Scholar, Cross Ref

Peng, L., Cui, G., Zhuang, M., & Li, C. (2016). Consumer perceptions of online review deceptions: an empirical study in China. Journal of Consumer Marketing, 33(4), 269-280.

Indexed at, Google Scholar, Cross Ref

Pamnani, P. S. (2023). A Study of Stress Management of Professional Courses Students in Commerce and Management in Pune. Kaav International Journal of Economics, Commerce & Business Management, 10(4), 55-60.

Cross Ref

Priyadi, U. (2023). Institutional Economics Lens: A Comprehensive Review on the Interdisciplinary Integration between Islamic Studies and Economics. Kaav International Journal of Economics, Commerce & Business Management, 10(4), 47-54.

Cross Ref

Román, S., Riquelme, I. P., & Iacobucci, D. (2019). Perceived deception in online consumer reviews: Antecedents, consequences, and moderators. In Marketing in a digital world (pp. 141-166). Emerald Publishing Limited.

Google Scholar, Cross Ref

Rynarzewska, A.I. (2019). It’s not fake, it’s biased: insights into morality of incentivized reviewers. Journal of Consumer Marketing, 36(3), 401-409.

Google Scholar, Cross Ref

Srivastava, S., Gupta, M., & Mehra, P. (2023). An Analysis of Measures for Effective Training and Development Programmes. Kaav International Journal of Economics, Commerce & Business Management, 10(4), 61-66.

Cross Ref

Wu, Y., Ngai, E.W., Wu, P., & Wu, C. (2020). Fake online reviews: Literature review, synthesis, and directions for future research. Decision Support Systems, 132, 113280.

Indexed at,  Google Scholar, Cross Ref

Wildan, M.A. (2023). Employee Performance Lens on Rapid Changing in Information Technology 4.0. Kaav International Journal of Economics, Commerce & Business Management, 10(2), 9-16.

Cross Ref

Received: 13-Feb-2024, Manuscript No. AMSJ-24-14495; Editor assigned: 14-Feb-2024, PreQC No. AMSJ-24-14495(PQ); Reviewed: 29-Mar-2024, QC No. AMSJ-24-14495; Revised: 25-May-2024, Manuscript No. AMSJ-24-14495(R); Published: 28-Jun-2024