Keywords

Tone of Voice, Advertisement, Engagement, Trust, Creativity, AI-Generated Content.

Introduction

The modern advertising scene has gotten increasingly competitive, requiring good content authoring to catch client attention and increase sales. According to research conducted by the Yaghtin (2020) 90% of B2B marketers consider content to be an essential component of their marketing strategy. Furthermore, according to report, Yaghtin (2020) 70% of marketers spend in content marketing, with lead generation and customer acquisition as the major aim. Writing compelling content is essential for gaining client attention and creating a memorable brand experience. A well-crafted message distinguishes a company from its rivals and communicates to customers its distinct value offer. Furthermore, engaging, and relevant content builds trust with the target audience, resulting in long-term connections that improve sales and brand loyalty. According to Aberdeen Group (2015) research, firms with strong content marketing strategies have a 6.7 times greater conversion rate than those without. This emphasizes the importance of creating helpful content that inspires consumer action and conversion. Furthermore, modern clients have short attention spans and are bombarded with material on a regular basis. According to research by the Nielsen Norman Group (2020), just 20% of the material on a web page is actually read by visitors. This highlights the value of clear and compelling language to draw in customers and effectively convey important messages. The subject of content writing for advertising is the main topic of this study. The author makes the case that AI-Generated advertising Material can improve consumers' perceptions of advertising by drawing on earlier research. It might be able to boost customer engagement and trust by choosing the right tone of voice for AI-Generated advertising contents, it may be feasible to boost client trust and involvement. Despite the expanding usage of AI-Generated Advertising Content in marketing, there has been little study on its impact. Notably, there has been no empirical investigation on the relationship between the tone of voice of AI-Generated Advertising Content and client trust and engagement. As a result, the purpose of this study is to look at the impact of tone of voice on reader trust and customer engagement in the context of AI-Generated advertising Content. As a result, the primary study question is: What influence does tone of voice have on consumer trust and engagement in AI-Generated Advertising Content?

Literature Review

Artificial intelligence (AI) has revolutionized how businesses develop and distribute information to their customers. Barcelos, (2018) defines AI-generated content as the use of machine learning algorithms and natural language processing (NLP) to create material that mimics human writing. This technology has grown in popularity in recent years because it enables businesses to generate big amounts of material fast and affordably (Brown, 1992).

AI-generated content has been proved in studies to be just as successful as human-written content in captivating audiences and generating conversions. Mazumdar (2021), for example, discovered that AI-generated text beat human-written language in marketing efforts across many sectors. Similarly, Wolny (2014) research showed that AI-generated email subject lines beat human-written subject lines in terms of open and click-through rates. The capacity of AI-generated content to customize material at scale is a fundamental benefit. AI algorithms may develop content that is personalized to the particular interests and needs of clients by evaluating customer (Patrick, 2021). This can assist businesses in producing more relevant and interesting content that connects with their target audience. Despite the potential benefits of AI-generated content, there are questions about how it may affect content quality and authenticity. Critics contend that AI-generated material lacks the originality and emotional intelligence of human authors and may propagate prejudices (Zhao, 2022). Artificial intelligence (AI) breakthroughs have resulted in the creation of technology that can create written material for advertising reasons. Machine learning algorithms evaluate data and generate textual output using natural language processing techniques to create AI-generated content.

Several studies have investigated AI-generated content's capacity to match the quality of human-generated content in advertising. According to Haleem (2022), a business that employs AI-generated content for marketing, AI-generated language can surpass human authors in terms of engagement, click-through rates, and other key performance measures. Similarly, Bozidar (2021) annual report discovered that AI-generated content has greater open and click-through rates than human-generated content. While AI-generated content can be beneficial in increasing client engagement, certain studies indicate that it may not yet equal the quality of human-generated material. Zaki (2023) for example, contends that AI-generated material may lack the inventiveness and refinement of human-written content, as well as the emotional depth and complexity that is generally associated with human language. Despite these reservations, the usage of AI-generated content for advertising is growing, with many organizations investing in this technology to increase client engagement and revenue (Luo, 2019). According to Hu (2018) AI-generated content is particularly useful for delivering tailored messages at scale, and it may assist organizations in optimizing their content for certain audiences and platforms. Overall, the literature demonstrates that AI-generated content in advertising has the ability to match or even exceed the quality of human-generated material. While there are worries about AI-generated content's lack of originality and emotional depth, businesses are increasingly turning to this technology to promote consumer engagement and revenue.

Nielsen Norman Framework

The author's goal in this study is to use the Nielsen Norman framework as a basis to examine linguistics and contribute to the scientific community. The Nielsen Norman Group has developed a methodology for determining if a website's tone of voice is acceptable for its offerings and consumers. They identified four major tone of voice characteristics and created a web-specific tool for content strategists to construct basic tone profiles for a company's online presence. The group performed a two-part study to investigate the impact of tone of voice on users' perceptions of a company's friendliness, trustworthiness, and attractiveness. They discovered that the tone of one's voice had demonstrable effects on users Tables 1 & 2.

Table 1 Tone of Voice Dimensions
Tone of voice dimension	Explanation
Formal vs. casual	Is the writing formal? Informal/Casual?
Funny vs. serious	Is the writer trying to be humorous? Or is the subject approached in a serious way?
Enthusiastic vs. matter of fact	Does the writer seem to be enthusiastic about the subject? Is the organization excited about the service or product, or the information it conveys? Or is the writing dry and matter of fact?
Respectful vs. irreverent	Does the writer approach the subject in a respectful way? Or does (s)he take an irreverent approach

Table 2 Hypothesis Testing
Trust	Mean difference	P: SIG.	Hypothesis Accepted (x) or rejected (-)
H1	-0.132	0.15	X
H2	0.046	0.321	-
H3	0.291	0.000	X

The following hypotheses are formulated based on the tone of voice dimensions introduced by the Nielsen Norman Group.

H₁: There is a difference in customer engagement among the four levels of tone of voice (Formal, Casual, Enthusiastic, Funny) based on the Nielsen Norman Group framework.

H₂: The four levels of tone of voice are expected to show differences in trusting the ads.

H₃: There exists a positive correlation between trust and customer engagement with the ads.

Trust in Advertising

The degree to which customers believe in the trustworthiness and dependability of advertising messages is referred to as trust in advertising. It is concerned with customers' readiness to depend on and accept the information offered in advertising, as well as their impressions of advertisers' honesty and authenticity. Advertising must be trusted by its intended audience in order to be effective. According to a 2020 Edelman (2020) poll, 71% of customers said they would only buy from businesses they trust. As a result, it is critical to comprehend how readers interpret. and have trust in the advertising content. The platform on which the advertisement is placed is another important component that impacts trust in advertising. Readers, for example, may be more inclined to believe advertisements featured on well-known media platforms, such as newspapers or websites (Soh, 2023). This is due to the fact that these platforms have a reputation for offering trustworthy and high-quality material, which might extend to the advertising presented on them.

Engagement in Advertising

Engagement in advertising is consumer's cognitive, emotional, and behavioral involvement with a focal brand, advertisement, or marketing communication (Anubha, 2021). Reader engagement is a crucial aspect in deciding advertisement success. Engagement, according to to Kim (2009), is a multifaceted construct that includes cognitive, emotional, and behavioural dimensions. They propose that cognitive engagement relates to attention and information processing, emotional engagement comprises affective reactions, and behavioural engagement refers to behaviours made in response to the advertising. Product recall and purchase intent are increased through engaging commercials. This shows that high-engagement commercials are more likely to be remembered and affect customer behavior. Specific approaches for increasing emotional involvement in commercials include comedy. Another study discovered that personalization, such as adapting adverts to specific interests and preferences, can boost both cognitive and emotional engagement Figures 1& 2.

Research Methodology

Research Model

The following hypotheses have been formulated based on the dimensions of tone of voice outlined by the Nielsen Norman Group.

H₁: The four levels of tone of voice in AI-generated ad content result in varying levels of customer trust and engagement.

1. Using a formal tone of voice has a greater positive impact on engagement compared to a humorous tone of voice.

2. A casual tone of voice results in a more favorable impact on engagement than an enthusiastic tone of voice.

3. Employing a humorous tone of voice yields a more positive impact on engagement compared to a casual tone of voice

Research Method

This study's research methodology includes an online poll done using SurveyMonkey to illustrate the use and significance of the linguistics of AI-generated content for advertising. Though most studies employ questionnaires or interviews to acquire information about respondents' values, attitudes, evaluations, opinions, feelings, preferences, expectations, status, occupation, education, income, and behavior. As such, this study falls under the category of descriptive research. Furthermore, because the study contains four variables that are tested using the same respondents, the obtained data is subjected to repeated measures ANOVA.

The questionnaire includes two sorts of questions: those about demographic information and those on AI-generated content. Participants must respond to statements using a 5-point Likert scale and provide specific information about the measurement scales. The Nielsen Norman Group (n.d.) framework is used and extended upon to assess tone of voice in an online situation. The dependent variable is tone of voice, which is divided into four separate types: formal, casual, enthusiastic, and funny.

Measurement Scales: Measurement Scales for Tone of Voice & Trust

Participants must utilize a 5-point Likert scale to answer statements, with detailed information about the measurement scales, to collect replies. The Nielsen Norman Group (n.d.) framework has been used and extended upon to analyze the tone of voice in an online setting. The dependent variable, tone of voice, is divided into four categories: formal, casual, enthusiastic, and humorous. Using AI and each of the four aforementioned tone of voice categories, the author created four separate sorts of advertising content for the same product. These adverts require respondents to submit comments.

Measurement Scales for AI Content Engagement

The author used multidimensional questions in their survey that encompassed cognitive, emotional, and behavioral characteristics to successfully evaluate engagement in material and adverts. The degree of attention and processing that the audience pays to the material is referred to as cognitive engagement, whereas emotional engagement refers to the emotive emotions induced by the advertising. Finally, behavioral engagement refers to the audience's activities in reaction to the material, such as sharing or commenting on it. The inclusion of such multidimensional questions in the survey provides for a thorough assessment of the audience's engagement with the material. Beyond simple measures like click-through rates, it allows the author to gain a more sophisticated insight of how the audience sees and responds to the advertisement. Incorporating cognitive, emotional, and behavioral factors also offers a more complete picture of the audience's involvement and can aid in identifying areas for development.

Furthermore, by examining the survey results, the author can receive insight into the advertisement's success in evoking the intended amount of involvement. This can help determine how to optimize future content and advertisements to better engage the target audience.

AI-Generated advertising content categorized into four distinct types: Formal, Casual, Enthusiastic, and Funny.

Results

Descriptive Statistics

The study began by collecting demographic information such as age, gender, and education. Following that, participants scored the tone of voice of the AI-generated content in terms of engagement and consumer trust on a 5-point Likert scale. Out of the 360 remaining responders, 244 (68%) were female, while 116 (32%) were male. The bulk of responders were college students between the ages of 18 and 30 who had previously used AI for various writing skills.

Hypothesis Testing

H₁: The four levels of tone of voice in AI-generated ad content result in varying levels of customer trust and engagement.

This hypothesis was investigated using a univariate test. The findings show a large and statistically significant variation in consumer trust across the four degrees of tone of voice.

A: Using a formal tone of voice has a greater positive impact on engagement compared to a humorous tone of voice.

After testing the hypothesis, it was shown that AI produced commercials with a formal tone of voice have a higher positive influence on client trust than casual ads (P: 0.15, Mean difference: -0.132). As a result, the theory is accepted.

B: A casual tone of voice results in a more favorable impact on engagement than an enthusiastic tone of voice.

As indicated by a mean difference of 0.046, using an enthusiastic tone has a somewhat stronger positive impact on customer satisfaction than using a casual tone. Statistical examination, however, demonstrates that this difference is not significant (p = 0.321), leading to the rejection of this hypothesis.

Employing a Humorous Tone of Voice Yields a More Positive Impact on Engagement Compared to a Casual Tone of Voice

The results demonstrate that a humorous and informal tone of voice has a substantial influence on customer engagement (p-value: 0.000, mean difference: 0.291). A humorous tone of voice, for instance, has a stronger beneficial influence on consumer engagement than a casual tone of speech. As a result, the theory is thought to be correct.

Discussion

According to the findings of the hypothesis testing, the tone of voice employed in AI-generated ad content has a substantial influence on client trust and engagement. This implies that businesses should carefully evaluate the tone of voice they employ in their advertisements since it has a significant impact on how people perceive their brand. The discovery that a formal tone of speech has a higher positive influence on consumer trust than a casual tone shows that customers are more inclined to believe advertisements and material that present themselves professionally. This is critical for companies who want to establish themselves as a trustworthy brand and create long-term connections with their consumers. On the other hand, the discovery that a humorous tone of voice is more successful than a casual tone in improving consumer engagement implies that advertisers should not be hesitant to insert a little of humor into their ad material to make it more engaging. This is especially critical for commercials aimed at younger, more tech-savvy audiences, who are more inclined to respond to funny and engaging ads. It is also worth mentioning that there was no significant difference in consumer engagement when employing a casual or enthusiastic tone of voice. This implies that advertisements should have a tone of voice suited for their brand and target demographic rather than trying to be too enthusiastic or informal. In conclusion, the findings imply that businesses should be conscious of the tone of voice they employ in their AI-generated ad material and match it to their brand and target demographic to maximize its influence on consumer behavior.

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