With the proliferation of technology and internet platforms, digital marketing has become an integral part of modern companies [1]. Chatbots have evolved as a prominent tool in digital marketing to enhance customer experience, as they offer a simple and personalized approach for businesses to communicate with their customers [2]. A chatbot is a computer program that simulates human conversation through text-based communication [3] and can be programmed to perform various tasks such as answering consumer questions, making recommendations, and assisting with transactions and negotiations [4].

ChatGPT is a family of language models developed by OpenAI that can respond to text prompts in a human-like manner [5], [6]. These models are developed using massive datasets [7]. According to the Web of Science database, there are approximately 26,000 manuscripts with the term artificial intelligence in their titles since 1970, but only 13 of them have the word "ChatGPT" in their titles [8]. Therefore, the apparent lack of published studies on the topic of chatbots creates challenges that researchers must now face to investigate this important subject.

ChatGPT has been used in various natural language processing applications, including translation, software programming, medicine, writing, and content creation [1], [9–11]. The latest version of ChatGPT, "ChatGPT-3", released in 2020, is one of the largest language models ever produced, with 175 billion parameters. In digital marketing, there is a growing body of research examining the impact of chatbots on customer experience [10]. However, many of these studies have focused on the technical aspects of chatbots, such as accuracy and performance, and have not fully considered the impact of other factors on customer experience, such as familiarity and comfort with technology, personalization, communication, accuracy, and convenience.

The number of studies conducted on the use of conversational AI models in marketing and customer experience is limited. Previous studies have focused on the impact of these models on customer satisfaction but have not examined the moderating role of factors such as business type, familiarity and comfort with technology, gender, age, and education level. This study fills this gap by examining the moderating role of these factors in the relationship between customer experience with ChatGPT and Overall Satisfaction (OS) with digital marketing. Previous studies have also examined the impact of conversational AI models on other aspects of customer experience, such as perceived usefulness, perceived interaction, and perceived anthropomorphism. These studies have shown that conversational AI models can have a positive impact on these aspects of customer experience. However, more research is needed to understand other factors that influence the impact of conversational AI models on customer experience.

In this study, a survey questionnaire was used to collect data from a sample of consumers who have used chatbots in digital marketing as part of a quantitative research approach. To test the proposed hypotheses and examine the moderating effects of familiarity and comfort with technology and company type, the data was analyzed using moderated regression analysis and ANOVA. The findings of this study will help researchers to understand better the aspects that drive customer experience in digital marketing and also provide insights into how organizations may use chatbots to improve customer satisfaction. This research will also impact the design and development of chatbots in digital marketing as it provides recommendations on how to customize chatbots for different types of organizations.

The present research seeks to answer the following research questions, summarized in Fig.1:

I. How does ChatGPT influence customer experience in digital marketing?

II. In digital marketing, how do familiarity and comfort with technology influence the link between personalization, communication, accuracy, convenience, and OS with ChatGPT among customers? III. How does business type influence the link between personalization, communication, accuracy, convenience, and overall customer satisfaction?

IV. Do demographic factors (gender, age, and education level) influence overall consumer satisfaction?

This research has the following five objectives:

I. To examine the relationship between service quality (personalization, communication, accuracy, and convenience) and OS among customers.

II. To examine the role of familiarity and comfort with technology in moderating the relationship between service quality and OS.

III. To investigate how business type moderates the relationship between service quality and OS.

IV. To examine whether familiarity and comfort with technology mediate the relationship between business type and OS.

V. To provide insights and recommendations for businesses on how to improve overall customer satisfaction by enhancing service quality and effectively utilizing technology.

VI. To examine variations in OS based on gender, age, and education level.

This section reviews previous studies related to the current research topic and explains how the research hypotheses were formed. The findings from previous studies are divided into four subsections to focus on the study's objectives coherently. These subsections include measuring customer experience and satisfaction, comfort and familiarity with technology, the impact of business type, the impact of demographics, and research hypotheses.

The popularity of chatbots as a tool for improving customer experience in digital marketing has increased. These chatbots provide businesses with a quick and personalized way to communicate with customers and can automate customer service, freeing up human agents to focus on more complex issues [11]. Additionally, chatbots can collect data that can be used to improve marketing and personalize the customer experience [12]. ChatGPT is a language model that can be used to train chatbots to be more engaging and informative. This model has been trained on a massive dataset of text and code so that it can understand and generate text messages similar to a human.

Since ChatGPT was released in November 2020, and its latest version, ChatGPT-3, was released in November 2022, it is logical that the research area in this subject still has many research gaps that require pioneering efforts by researchers to fill [13]. This section of the research includes a review of previous studies on this topic and identifies some research gaps that the present study can address through the following subsections.

This section delves into the literature concerning three primary axes: defining overall customer satisfaction and customer experience, understanding how ChatGPT enhances customer experience, and determining how to measure overall customer satisfaction and customer experience within the context of this study on ChatGPT in digital marketing.

Overall customer satisfaction is a metric that indicates the extent to which a customer's products, services, and overall experience meet their expectations [14], [15], while customer experience is defined as the overall perception and understanding of a brand.

According to the literature, chatbots can offer various benefits in terms of improving customer experience in digital marketing [16]. Chatbots can enhance customer interactions by providing quick support and reducing response times, eliminating the need for customers to wait for a human representative [17]. This can assist customers in identifying items or services that best suit their needs by offering tailored suggestions and increasing customer satisfaction [18], [14]. ChatGPT can be utilized in various ways to enhance chatbots. For instance, it can be employed to create more natural and engaging conversations, provide more accurate and targeted responses to queries, and generate more personalized marketing messages. As chatbots become more sophisticated, ChatGPT is likely to play a significant role in improving customer experience in digital marketing.

A review of the literature indicates that customer experience comprises four primary elements that should be measured: Perceived Personalization (PP), Perceived Communication (PR), Perceived Accuracy (PA), and Perceived Convenience (PC) [11], [15], [16]. Additionally, measuring OS is essential for statistical analysis and for examining the components of customer experience [18]. Customer satisfaction can be measured through three main components: expressing satisfaction [3], recommending to others [15], and the intention to use the service again in the future [12].

This section examines the impact of customers' comfort and familiarity with technology on their OS with ChatGPT in digital marketing.

Customers desire chatbots that provide appropriate and relevant responses that are accurate and helpful, as well as a user interface that is easy and convenient. However, not all artificial intelligence chatbots are equally easy to use.

Customers' comfort and familiarity with technology can influence how they interact with chatbots [11]. Conversely, several studies have shown that customers' comfort and familiarity with technology have a significant impact on their satisfaction with chatbots, indicating that their satisfaction with chatbots is only notable for customers who have previously interacted with chatbots [9], [19]. The question this study aims to answer is: To what extent do ease and convenience affect the relationship between customer experience and OS?

This section investigates how the type of business influences the relationship between customer experience and overall customer satisfaction regarding ChatGPT in digital marketing. In other words, it seeks to answer the question: does the type of business play a moderating role in how customer experience affects overall customer satisfaction?

Thousands of businesses globally leverage ChatGPT to engage with their customers across a wide range of industries [1], [13], [20]. Major corporations have adopted ChatGPT-powered platforms in sectors such as retail and e-commerce (e.g., Amazon, Walmart, Alibaba, eBay, ASOS, IKEA, and Target) [13], technology and communications (e.g., Google, Microsoft, Facebook, Apple, IBM, Intel, Samsung, LG, Sony, Panasonic, Cisco, Dell, HP, Oracle, SAP, Ericsson, Nokia, and Huawei) [4], and transportation and travel (e.g., Uber, Booking, Pfizer, Novartis, Johnson & Johnson, and Glaxo) [10], [11], [23]. The purpose, functionality, and optimal use of AI chatbots vary significantly, depending on the industry and specific application.

The type of business can also influence the customer experience of using a chatbot. Different types of businesses may have distinct customer characteristics and expectations that affect how chatbots are used [7], [24]. For instance, a chatbot used in a retail company may differ from one used in a healthcare business [17]. The company type can also influence the design and type of chatbot, such as the language used [22].

This study aims to assess the moderating effect of business type on the relationship between customer experiences and OS regarding ChatGPT in digital marketing, a gap in the existing literature.

This section examines previous studies on how three demographic characteristics (gender, age, and education level) moderate the relationship between customer experience with ChatGPT and OS in digital marketing.

Based on the literature review and the sources mentioned in the previous sections, the following hypotheses will be tested to achieve the objectives of this research:

Hypothesis 1. "Familiarity and comfort with technology" positively moderates the relationship between the components of "customer experience" (PP, PR, PA, PC) and "OS."

Hypothesis 2. "Type of business" positively moderates the relationship between the components of "customer experience" (PP, PR, PA, PC) and "OS."

Hypothesis 3. "Gender" positively moderates the relationship between the components of "customer experience" (PP, PR, PA, PC) and "OS."

This research aims to examine the impact of ChatGPT on customer experience in digital marketing.

The target population for this study is the statistical population of customers who have interacted with ChatGPT in digital marketing. The sample size was estimated for a survey with a 95% confidence level and a 5% margin of error. Questionnaires were sent to individuals who met the following criteria: 1) aged 18 or over, 2) engaged with ChatGPT in digital marketing, 3) able to read and understand English, and 4) willing to participate in the study.

A multi-step process was undertaken to ensure the validity of the questionnaire as the primary research tool. This questionnaire was developed after reviewing prior research and consulting with over 15 digital marketing clients. The initial questionnaire was presented to a panel of experts for validation to ensure it aligned with the study's objectives. Before finalization and distribution, a pilot test was conducted with a sample of 30 digital marketing clients to assess the quality and clarity of each questionnaire item.

The survey questionnaire comprises four sections. The first section collects demographic information such as age, gender, and education level. The second section employs a 5-point Likert scale to measure familiarity and comfort with technology. The third section uses a 5-point Likert scale to evaluate the consumer experience with ChatGPT in digital marketing, specifically assessing PP, relevance, accuracy, and ease of use. The fourth section, using an open-ended question, explores the moderating effect of business type on customer experience."

Survey data was analyzed using descriptive statistics such as mean, standard deviation, frequency, and percentage to explore the demographic characteristics and distribution of variables within the sample. To test research hypotheses and examine the moderating effects of business type, familiarity, and comfort with technology on customer experience, inferential statistics, including correlation analysis, multiple regression analysis, and moderated regression analysis, were employed. Statistical Package for the Social Sciences (SPSS) was used to evaluate the data.

Participant privacy and confidentiality were safeguarded by utilizing a secure online survey platform, storing data in a password-protected file, and using anonymous participant identifiers. Participants were informed about the study's purpose, the voluntary nature of their participation, and their right to withdraw at any time 1 without penalty. Informed consent was obtained from all participants prior to commencing the study.

This section of the present study presents and discusses the results through the following three subsections: data description, results of hypothesis testing, and discussion.

Table 2 displays the demographic characteristics of the collected data. The descriptive statistics of the variables can be described as follows:

The average ratings for PP, relevance, accuracy, ease of use, and OS in digital marketing indicate a high degree of customer experience when using ChatGPT. The mean score for familiarity and comfort with technology was 96.3, suggesting that individuals had a moderate level of technological familiarity and comfort.

Significance levels: p > 0.001 (), p > 0.01 (), and p > 0.05 ().

Fig. 2 summarizes the results of the hypothesis tests.

Hypothesis 4. the results of the moderated regression analysis revealed that familiarity and comfort with technology significantly moderated the relationship between PP (β = 0.32, p > 0.01), perceived relevance (β = 0.30, p > 0.01), PA (β = 0.29, p > 0.01), perceived ease of use (β = 0.29, p > 0.01), and OS. The interaction effects between the independent variables and familiarity and comfort with technology were significant, indicating that PP, relevance, accuracy, and ease of use had a greater impact on OS among participants who were more familiar and comfortable with technology. The weight of the influence of customer experience elements can be compared in Table 3.

Hypothesis 5. An ANOVA was conducted to assess whether business type moderated the relationship between PP, relevance, accuracy, ease of use, and OS. Results indicated a significant moderating effect of business type, suggesting that the impact of these factors on OS varied across different industries. Specifically, the interactions between the independent variables (PP, relevance, accuracy, and ease of use) and business type were significant (F values ranging from 3.01 to 3.62, p > 0.05), implying that the influence of these factors on satisfaction was not uniform across all business types. Mean scores and standard deviations for each business type (healthcare, travel and tourism, finance, retail, technology, and education) revealed distinct levels of satisfaction.

Hypothesis 6. An ANOVA was employed to examine whether gender served as a moderator in the relationship between ChatGPT and customer experience. The analysis revealed no significant interaction effect between gender and ChatGPT (F = 0.34, p < 0.05), indicating that gender did not influence the impact of ChatGPT on customer experience. This suggests a consistent effect of ChatGPT on customer satisfaction across both genders.

Hypothesis 7. An ANOVA was employed to assess the moderating role of age in the impact of ChatGPT on customer experience in digital marketing. Findings revealed a significant moderating effect of age on the link between ChatGPT and customer experience (F = 8.54, p > 0.001). The mean customer experience scores were considerably higher for younger customers compared to older consumers, indicating a more pronounced positive impact of ChatGPT on customer experience for younger consumers.

Hypothesis 8. ANOVA was utilized to examine the influence of education level on the relationship between ChatGPT and customer experience in digital marketing. Results demonstrated a significant moderating effect of education level on the association between ChatGPT and customer experience (F = 4.63, p > 0.01). Participants with higher education levels exhibited significantly higher mean customer experience scores compared to those with lower education levels, suggesting a more beneficial impact of ChatGPT on customer experience for individuals with higher levels of education.

This section of the present study discusses the answers to the research questions by comparing its results and suggestions with those of previous related studies. While the benefits of chatbots are evident, the impact of other aspects on customer experience in relation to ChatGPT and their OS in digital marketing has not been fully explored in the previous literature. In this regard, the present study aimed to investigate the moderating role of five variables: customers' comfort and familiarity with technology, business type, gender, age, and education level.

Customers' comfort and familiarity with technology may influence how they interact with chatbots and their overall usage. For instance, they are more likely to use and find chatbots useful [11]. The question addressed in the present study is to what extent ease influences the relationship between customer experience and OS. The results reported that familiarity and comfort with technology play a significant role in moderating customer experience with ChatGPT and OS in digital marketing. A higher level of familiarity and comfort with technology improved the impact of ChatGPT on customer experience. These results of the current study are consistent with the results of most previous studies, whether general studies on chatbots or recent specialized studies on ChatGPT (e.g., references [17], [27], [28]).

In contrast, a study conducted by Trivedi and Jain [29] showed that other factors, such as the chatbot's ability to understand customer needs and provide accurate information, are more important in determining satisfaction. Also, the study by Gnewuch et al. [30] showed that the impact of customers' comfort and familiarity with technology on their satisfaction with chatbots is only significant for customers who have previously been familiar with chatbots. Their findings suggest that businesses should focus on other factors when designing and deploying chatbots, such as the chatbot's ability to understand customer needs and provide accurate information. Here, there is no contradiction between the results of the present study and the results of this group of other studies. Instead, the results of the current study determine the fundamental essence of the impact of chatbots on customer experience, which is confirmed by most previous studies, as mentioned earlier.

This part of the study focuses on the academic and practical contributions of ChatGPT to the relationship between customer experience and OS in digital marketing, as well as implications for future research.

Zeynab Dehnavi conceptualized and designed the study, collected and analyzed the data, and wrote the manuscript. Zeynab was responsible for interpreting the results and ensuring the accuracy and integrity of the work. The author approved the final version of the manuscript.

No specific funding was received for this study.

The data used in this study is available from the corresponding author upon reasonable request.

The author declares no conflicts of interest concerning the research, authorship, and publication of this paper.