The impact of artificial intelligence on re-purchase intentions: the mediation approach Текст научной статьи по специальности «СМИ (медиа) и массовые коммуникации»

DOI: 10.17323/2587-814X.2024.3.87.107

The impact of artificial intelligence on re-purchase intentions: the mediation approach

Raed N. Alkaied ©

E-mail: Raedalkaied@bau.edu.jo

Shadi A. Khattab

E-mail: Shadikhattab@bau.edu.jo

Ishaq M. Al Shaar ©

E-mail: I.shaar@bau.edu.jo

Mohammed K. Abu Zaid CD

E-mail: Mohammed_abu_zaid@bau.edu.jo

Sakher A.I. Al-Bazaiah ©

E-mail: bazaiah1@bau.edu.jo

Al Balqa Applied University, Salt, Jordan

Abstract

Purchases made on online platforms have heavily incorporated artificial intelligence (AI) to shape consumer purchasing behavior. To investigate re-purchase intentions, this study combines AI, social media engagement, conversion rate optimization, brand experience and brand preference. A survey was conducted with a questionnaire sent to 355 people who had at least once purchased or used services offered online from any site associated with aviation. The questionnaire was analyzed using structural equation modeling. Utilizing Amos V.22, the study hypotheses were assessed. The empirical results show that social media engagement, brand experience, brand preference and conversion rate optimization were all impacted by AI. Conversion rate optimization and social media interaction also have an impact on brand preference and experience. Re-purchase intention is influenced by brand preference and brand experience. Additionally, the association between AI and re-purchase intention was mediated by social media engagement, brand experience, conversion rate optimization and brand preference. The study will support airline companies in developing AI and creating more effective branding and marketing campaigns to increase customer intention to re-purchase. This study discovered that the use of AI in

I marketing significantly improved brand preference, which subsequently affected consumers' desire to make additional purchases. Furthermore, to improve long-term commercial performance and brand attractiveness, the airline should focus brand-building efforts on AI. Thus, the airline ought to make greater investments in AI and booking service technology, both to draw in new business and to strengthen existing ones.

Keywords: artificial intelligence, conversion rate optimization, social media engagement, brand experience, brand preference, re-purchase intention

Citation: Alkaied R.N., Khattab S.A., Al Shaar I.M., Abu Zaid M.K., Al-Bazaiah S.A.I. (2024) The impact of artificial intelligence on re-purchase intentions: the mediation approach. Business Informatics, vol. 18, no. 3, pp. 87-107. DOI: 10.17323/2587-814X.2024.3.87.107

Introduction

Every facet of our real lives - both individually and collectively - has been impacted by technology, including both the real and virtual worlds [1]. Among its most crucial concerns was the various methods for increasing public awareness. According to predictions made by [2], the economic impact of artificial intelligence (AI) would increase from $20.82 billion in 2020 to $15 trillion in 2030.

One of the most significant technical advancements is AI, whose applications have completely transformed a wide range of societal fields [3, 4]. AI technologies are described as "natural genetic predisposition genetic inheritance or learned skills that form the essence of individual personalities" [5]. AI technologies rely on predefined computer programs, algorithms, and function similarly to the human mind in making decisions [6, 7].

Businesses may improve the customer experience by identifying innovation, developing strategies and identifying long-term solutions through business automation that leverages AI. Critical decisions can also be made with AI in a corporate environment that is surprisingly competitive and unstable [2].

According to [8], AI chatbot content recommendations are now a part of marketing AI activities. They also help to boost customer engagement on social media platforms, give online users a personalized experience and increase the likelihood that suggested goods and services will be purchased [2, 9]. For instance, Amazon is leading the way in utilizing AI technology and extending its use beyond object recognition, language understanding and conversation to include search and suggestion. This increases the conversion rate toward product purchases

by personalizing and refining the recommendations of related and complementary products in real-time [10]. AI-driven digital businesses are attempting to interact with clients on social media in an effort to build, maintain and nurture enduring client relationship [11]. As a result, it is important to recognize the growing significance of online shopping. By 2025, the worldwide e-commerce market is expected to reach $1.2 trillion, growing at a rate three times faster than traditional commerce [12]. Marketers use customer engagement to get customers' attention by providing them with valuable knowledge [13]. Through their customer experience, marketers want to keep their goods or services at the front of consumers' minds. Social networking is one of the best platforms for connecting with customers. Customers should be able to use social media to interact with businesses [14]. Clients that are happy with the goods and services will write content for social media platforms. Companies may alter their current goods and services in response to negative customer feedback on social media [15]. These days, it's commonplace to see creative marketing. Marketers can expedite intelligent marketing by utilizing AI [9]. In order to boost the rate of conversion (from user to customer), businesses can also track customer opinions on social media and utilize that data to tailor marketing initiatives for each individual customer [16].

Social media consumer conversion is a continuous process rather than a one-time occurrence. The association between conversion rate and customer purchase intentions is not well understood [9]. Businesses are utilizing AI to forecast customer behavior as more and more consumers make purchases online. With a major influence on consumer decision-making, AI has been a crucial part of the digital transformation. AI technolo-

gies can be leveraged to entice consumers to make exciting new purchases [17].

AI technology has the potential to enhance user experiences in interactive environments and foster faster response times for products and services [18].

According to [19], intelligent service bots have become increasingly prevalent in gauging customer experiences with products and services in recent times. One of the primary motivations for implementing AI is to enhance customer experience, as AI technologies are becoming a more significant aspect of our daily lives and form the foundation for novel value propositions and unique consumer experiences. Delivering improved customer experiences is therefore essential for strengthening the bond between consumers and brands as well as for promoting brand distinctiveness [20].

Thus, businesses employ hardware, software, networks and AI for a range of objectives, including improving customer experience and fostering continuous harmonization and collaboration among stakeholders [21]. Based on whether the AI offers the services that customers have asked for, the consumer experience will differ. According to [22], customers felt more intellectual and sensory experiences when AI offered the services, and vice versa, when humans supplied the services, customers felt more emotional experiences. Even though AI and human services differ in how they are experienced, AI services are crucial to giving clients an enjoyable journey [23].

If customers receive the proper experience from the company, they feel content and joyful thanks to AI procedures [24]. A pleasant customer experience will create positive value for the company in terms of brand preference, helping companies achieve excellence and competitive advantage [25]. Research on how AI affects brands is scarce and dispersed, despite the significance of AI for consumer-brand connections. While improvements in technology may save customers time and effort during transactions, errors and a lack of human support can still generate dissatisfaction [20]. Therefore, it's still unknown how AI will affect branding.

Most marketers lack a strong knowledge of AI and how it may assist both organizations and consumers, despite expanding research in this field [26]. A road map for effective AI initiatives is necessary, according to comprehensive AI frameworks and empirical research, particularly in the area of digital marketing [27]. Furthermore, the literature that has already been written has not looked at AI in the setting of interactive marketing, where sellers and buyers work together to impact marketing choices that encourage active consumer participation, commu-

nication and interaction [18]. Accordingly, the real value of AI is not in the technology per se, but rather in the way it is applied to build robust, interactive buyer-seller interactions that are based on generating value together and keeping commitments [28]. There are few studies assessing how AI and digital innovations affect social media customer involvement. More investigation into the ways AI-powered marketing tools affect consumer views, opinions, and actions is advised by [29].

1. Literature review and hypothesis development

1.1. Artificial intelligence

Nowadays, online platforms are used for the majority of the shopping process. When it comes to buying things online, trust and client awareness are crucial factors. Organizations are working to get the most out of the enhanced trust and intent that customers have toward specific products and services as a result of the experience that AI has given them [30]. Studying client habits, purchasing patterns, behaviors and choices are only a few of the activities and functions where AI in marketing has been demonstrated to be widely applied [7]. Personalizing advertising messaging [31] in addition to tailoring items and other offerings to fit client demands and managing and altering prices in real-time in response to customer demand, rivals and supply chains [32].

AI provides virtual experiences to customers who are sitting in the right places, helping them make a final purchase decision. Because AI is a cutting-edge technology that selects the best choice from a range of options provided with a variety of facts through exchange and combinations, it saves customers money [7]. AI-pow-ered augmented reality applications let consumers view things in new ways and facilitate enhanced decision-making [33]. Businesses have integrated most AI-ena-bled technology to offer clients the best and most customized solutions [34].

Thanks to AI's cutting-edge technologies, customers can easily understand their purchase preferences. Previous research [30, 35, 36] indicates that AI aims to develop software with human-like problem-solving capabilities that enhances ability to make decisions about purchase intention. Studies show that people who visit websites with the integration of AI feel more confident when making judgments about what to buy, which lowers the risk [37].AI is a lightweight technology that helps consumers make informed purchasing decisions. Because consumers are more interested in AI's promise and capabilities, they are using it widely [30]. AI's ability to manage the massive

amount of relevant, high-quality data that consumers may access, and that is tied to their purchase activities, determines both its usefulness and efficacy [38, 39].

Because AI is an advanced technology, customers often discover the best virtual experience when they make purchases from online retailers. Customers' virtual experience is important whenever it concerns their purchasing intentions, and studies have indicated that positive virtual experiences affect consumers' intentions to purchase [33].

1.2. Artificial intelligence and social media engagement

Engaging customers and building customer loyalty is critical for providers who value face-to-face communication with consumers. Few studies have examined customer engagement from a technological perspective, even though several social and technological factors have been demonstrated to support customer engagement [40]. Research [40, 41] suggests that AI can enhance moral consumer behavior. Businesses that use AI may change the social media buying experience to give shoppers a social media platform experience, as AI has greatly changed consumer behavior [42].

By using AI to forecast consumer behavior and interact with customers on social media platforms, these businesses may increase the effectiveness of their online marketing campaigns and use it to make more analytical judgments [2, 17]. For instance, AI closes the gap between companies and customers by gathering and evaluating data about goods and services [43]. This changes the online buying experience. AI also offers solutions for a range of problems pertaining to social networking. For instance, evaluating the vast volume of data produced by social media platforms may cause stress for sales staff [2].

To address these issues, businesses utilizing AI may employ a range of AI-based methods for predictive analysis in marketing [9]. The use of AI in social media platforms is one of the outside variables that is believed to motivate consumers to engage with these platforms more. For instance, users can join any community of interest on e-commerce platforms like Facebook, Taobao and Etsy. From there, they can engage with other users, follow other buyers and sellers who share their interests, look up information about products and/or share their own related buying experiences [12]. According to [27], there is a connection across AI and dynamic marketing when real-time technologies are utilized to build personalized, response-focused relationships between buyers and sellers. If businesses provide several options for evaluating the qualities of their products or services, in-

tegrating AI increases the likelihood that customers will engage on social media platforms [44]. Thus, it can be assumed that:

H1: AI has a positive effect on social media engagement.

1.3. Artificial intelligence and conversion rate optimization

AI has been used in purchasing procedures to give customers more dependable, individualized services [10]. Wang and Lei [18] claim that artificial intelligence AI technology can manage interactions between consumers and goods or services as well as quickly responding to client demands in interactive environments. As part of AI marketing efforts, chatbots, content features and buyer sales recognition are artificially becoming autonomous [8].

Online social networking platforms give businesses the ability to interact with a wide variety of customers and customize their products to meet their needs [45]. To improve digital marketers' ability to use AI to raise visitor conversion rates, it is crucial to study the buying habits of customers on social networking sites [9]. Businesses on social networking platforms also use AI to entice consumers and win them over as devoted customers [2]. In order to elevate the rate of conversion (from user to customer), businesses can also track consumer behavior on social media and use that data to craft customized promotional campaigns for each individual customer [9, 16]. AI encompasses more than just conversation, language comprehension and object identification; it also includes consumer recommendation and research. This increases the rate at which products are purchased by improving the suggestion of related and complementary products in a more customized and real-time manner [10].

The relationship between the client and the business may be strengthened by AI-based social media initiatives that increase consumer involvement, feedback, and conversion [15]. AI could encourage people to buy goods and services by improving their interaction with social media adverts [2]. Because AI on social networking platforms allows users to examine items or services through the platform, it also encourages prospective consumers to buy a certain product or service. Companies can utilize AI to differentiate their goods or services from competitors' offerings and entice consumers to purchase them. The [46] have reported that prior research has furnished empirical proof of the affirmative correlation between social networking sites and consumer conversion rate. Thus, it can be assumed that:

H2: AI has a positive effect on conversion rate optimization.

1.4. Social media engagement and brand experience

According to [47], there are two types of consumer engagement: uncontrolled (word-of-mouth) and controlled (corporate-sponsored). By sharing knowledge with others, such as through sharing across online platforms, consumers can contribute to the improvement of customer experiences with brands [14]. Satisfactory brand buying experiences can be facilitated by customer engagement [48, 49]. According to research by [20], mobile applications for customer interaction have a favorable impact on customer equity and increase the likelihood that current consumers will make another purchase. A variety of studies [50-52] have also investigated the connection between brand experiences and customer engagement, concluding that there is a substantial impact from consumer involvement. Based on the explanation provided by [51] regarding how customer engagement functions as a mediator to enhance the brand experience and encourage repeat purchases, thus, it can be assumed that:

H3: Social media engagement has a positive effect on brand experience.

1.5. Conversion rate optimization and brand experience

Interaction with customers is important to every business. The shopping experience for customers in a virtual environment is mediated by technology. With the introduction of augmented reality, mixed reality and virtual reality technology, a new environment integrating virtual and physical elements at various levels has emerged. The customer experience environment is changing into new kinds of hybrid experiences as a result of the growth of mobile and wearable devices as well as highly dynamic physical-virtual interactions [9, 53]. Since shoppers of these businesses are more likely to express their favorable experiences with the brand in question, marketers must engage customers and offer a unique social media experience. This is because the most satisfied customers are those who are more involved on social media [2, 11].

72% of businesses prioritize improving the customer experience and appealing to customers throughout the buying process is a marketing trend. Businesses are concentrating on offering value-added ideas to create the greatest possible customer experiences in the digital age [53].

The consumer experience is being drastically changed by emerging technologies including the Internet of Things (IoT), chatbots, bots, augmented reality (AR), virtual reality (VR), mixed reality (MR) and virtual assistants, which are usually powered by AI. Concerns about privacy for clients who would rather buy goods and services online and through social media are crucial when trying to find a consistent way to incorporate the client experience. Instead, marketers must comprehend how digital technologies affect the customer experiences [54].

The cognitive component ingrained in the customer's relation with the brand is satisfaction. Positive remarks affect other users' cognitive processes [55]. Key clients may be drawn to interactive involvement and end up giving products or services favorable reviews [56]. Different client categories will likely require different approaches to customer interaction; after all, in the digital age, consumer engagement is essential [57]. For marketers to comprehend customer segmentation, they must create strong social media analytics. These analyses' findings show how marketers may use social media platforms to sway consumers and raise conversion rates, which in turn have a big impact on customer happiness. For businesses to increase sales, it is essential to comprehend users' attitudes regarding digital media [58]. Thus, it can be assumed that:

H4: Conversion rate optimization has a positive effect on brand experience.

1.6. Brand experience

and brand preference

According to [50, 59], brand experience is defined as "the consumer's subjective responses (sensations, feelings and perceptions) and behavioral responses elicited by brand-related stimuli that are part of the brand's design, identity, packaging, communications and surrounding environment." Four categories can be used to categorize brand experience: sensory, intellectual, emotional and behavioral. The stimulation that a brand provides through the senses of sight, sound, smell, taste and touch is known as the sensory brand experience [60]. The emotions evoked by a brand are known as the emotional brand experience. According to [61], behavioral brand experience encompasses actual experiences, behaviors and brand interactions, whereas intellectual brand experiences refer to a company's capacity to elicit thought from consumers.

Perceptions of brand qualities by consumers influence their preferences, which in turn affect their intent and brand selections. As a result, according to [62], brand preference is a pattern of behavior that represents customers' views about the brand. Customers like a spe-

cific brand whenever they have positive thoughts regarding it, and their perceptions of a brand's features impact their preferences, which in turn determine their intentions and selection of brands [20].

Brand experience has an impact on brand loyalty and affection, according to [59].

Positive brand experiences help customers form strong bonds with brands and grow to love them [63]. Brand experience has an impact on brand preferences, according to [20]. The notion of brand experience was validated by [64], who found that brand experience is a key indicator of brand preference. It has been noted that brand preference and memorable brand experiences are related. A memorable brand experience positively influences brand preference, which then positively impacts usage intentions, word-of-mouth and readiness to pay more, according to the findings of a study conducted by [65]. Therefore, it can be assumed that:

H5: Brand experience has a positive effect on brand preference.

1.7. Brand preference and re-purchase intention

When comparing a company's products to those of other companies, consumers' preferences toward certain products determine their brand preference. In terms of capturing customers' hearts so they will re-purchase the company's brand, it can be said that this preference for a brand is the first phase of branding [66].

Re-purchase intention is the consumer's plan to carry out the behavioral act of purchasing a brand again [67, 68]. It is the process by which clients choose to re-purchase services or goods from the same company [69]. This probably happens because customers can buy the same thing again. Re-purchase intentions, according to [70], refers to a customer's willingness to make additional purchases from the same merchant or supplier, whereas repurchases are, in theory, actual actions. According to Sullivan and Kim [71], reactionary intention to re-purchase can be understood as a consumer's wish to reevaluate the brand in light of their present circumstances. The intention to re-purchase is of particular relevance to marketers since it may result from the influence of prior purchases. Re-purchase intention is likely to be lower if consumers' perceptions of price, experience, brand and fulfillment differ from what they paid and received [70].

Customers are more likely to repeat purchases when they have a preferred brand. Only when consumers feel positive about a brand will they choose to re-purchase it and replicate their experience [23]. Additionally, research indicates that customers' decisions to buy

a product are influenced by their information processing, which is reflected in their choice of a brand [70]. According to [62], re-purchase intention was positively impacted by brand preference. The [66] claim that a product's identity as a brand and preference are responsible for its resurgence in popularity. Research from Ho & Chow [20] has shown that brand preference affects consumers' likelihood to make more purchases. According to [64], brand preference and re-purchase intentions were positively correlated statistically. The [65] investigation confirmed that brand preference affects repurchase intentions. Thus, it can be assumed that:

H6: Brand preference has a positive effect on re-purchase intentions.

1.8. Brand experience and re-purchase intentions

Consumer brand experience precedes actual purchase because favorable brand experiences have a positive and significant impact on consumer purchase intentions, and prior experiences become memorable during brand purchase [72, 73]. The positive feelings that consumers have for a brand can impact their intention to make a purchase if they are feeling good about it [74]. This suggests that consumers' behavioral intentions may grow as a result of their brand experience. According to [62], a favorable brand experience can affect the propensity to re-purchase. According to [70], the re-purchase intention is positively impacted by brand experience. Therefore, it can be assumed that:

H7: Brand experience has a positive effect on re-purchase intentions.

2. Methodology 2.1. Procedures and respondents

Data was gathered from Jordanian users of the internet who have at least once made a purchase or used services available online from any website relevant to aviation. An additional eight weeks were added to the data gathering period.

The data was gathered using convenience sampling, which is a non-random sampling technique [75]. Based on their actual usage of the web services for the websites of the aviation companies, the study's respondents were selected. A non-probability sampling design was used in this investigation, meaning that there are no odds associated with any member of the study population being selected as a sample subject [76]. The questionnaire was created in English, therefore with the assistance of two multilingual specialists, we translated it first into Arabic

before translating it back into English. The individuals who participated were informed that they might opt out of the study at any moment and that participation in it was entirely optional. Pens were used by the participants to rate the questions. The participants' answers to the surveys were gathered directly. A survey was disregarded and the next one was chosen if it was not completed correctly. Surveys filled out by participants who had no prior e-commerce experience were disqualified. An amount of 800 questionnaires together with cover letters were given out, and 387 respondents brought the completed forms back. Thirty-two questionnaires were eliminated due to incomplete information. In the end, 355 replies were considered for study. 44.4% of respondents responded.

2.2. Measures

A five-point Likert scale, with 1 denoting "strongly disagree" and 5 denoting "strongly agree," was used to rate each scale item.

Artificial Intelligence: [2, 45] produced a 6-item scale that we used to gauge technological advancements in AI. "Multiple types of data about customers, such as sales, purchases, or demographic and behavioral data," was the sample item.

Social Media Engagement: An instrument consisting of five items was created by [2, 14] to gauge consumer participation on social media. "Using social networking websites sparks my curiosity about brands" was the sample item.

Conversion Rate Optimization: Utilizing a 4-item scale created by [2, 46], conversion rate optimization was examined. "I am influenced to buy products and services by web-based promotions and messages on social networking sites" was the sample item.

Brand Experience: We used a 5-item measure that was created by [20, 24]. The sample item was, "the experience of using AI".

Brand Preference: A 5-item scale created by [20, 77] was used to measure brand preference. The sample item was, "preferred brand over any other brand."

Re-purchase Intention: We used a 4-item measure that was created by [2, 20]. To gauge technological advancements in AI. To gauge the re-purchase intentions of the consumer. "I plan to keep using the site that I frequently use for booking flights" was the sample item.

2.3. Reliability and validity

Factor analysis, average variance extracted (AVE) and composite reliability (CR) are computed using AMOS. One method for condensing a large range of variables

into a smaller number of factors is a confirmatory factor analysis. Using this method, all variables' highest common variance is extracted, and the results are combined into a single score. Confirmatory factor analysis also known as a CFA, was carried out to examine the multiple-item measures' discriminant validity, convergent validity and reliability. According to [78] recommendation, the analysis's findings supported each measuring scale's convergent validity. The theoretical constructs appear to have convergent validity, as indicated by the statistically significant (p < 0.05) factor loadings of 0.60 to 0.90 for all indicators in their respective constructs, as presented in Table 1 [79]. Furthermore, every constructs average variance extracted (AVE) is greater than the minimal value of 0.5 that is advised [80]. The average variance extracted (AVE) results were used to evaluate the discriminant validity. According to the results of Table 1, the square roots of AVE are greater than correlations, which suggests that the discriminant validity is satisfactory [80]. The [81] suggested using composite reliability (CR) to assess the dependability of the measures. Table 1 shows the discriminant and convergent validity as well as the reliability across all reflection measures based on CR values that are over the 0.70 threshold.

Cronbach's alpha is calculated using the average correlations between the concept-measuring items. The internal consistency reliability increases with Cronbach's alpha's proximity to 1 [76]. The Cronbach's coefficient is employed to assess the reliability of each concept. It is a metric for assessing a multi-item scale's internal consistency. According to the SPSS results, all alpha coefficient values are higher than 0.7, indicating that the measuring scales' reliability is sufficient [82].

3. Results 3.1. Descriptive statistics

The mean, standard deviations, and correlation matrix are the primary descriptive statistics that were utilized to characterize the study constructs. The study model constructs' mean scores ranged from 3.46 to 3.80, as shown in Table 2. Furthermore, the correlations showed that the research variables had a strong link and ranged from 3.46 to 3.80.

3.2. Research model and hypotheses

The links between the constructs were estimated by the application of structural equation modeling (SEM). Amos V.22 was used to calculate SEM estimates. Regarding the proposed connections, the path that leads from AI to Social Media Engagement has a coefficient

Table 1.

Confirmatory factor analysis & reliabilities

Construct Items Loading factor Z-value CR AVE a

AI1 0.719

AI2 0.868 15.523

Artificial AI3 0.892 15.833 0.881 0.557 0.898

Intelligence AI4 0.668 11.994

AI5 0.633 11.345

AI6 0.654 11.739

SME1 0.777

Social Media Engagement SME 2 0.803 14.679

SME 3 0.716 13.066 0.850 0.532 0.858

SME 4 0.63 11.32

SME 5 0.71 13.013

CRO1 0.813

Conversion Rate Optimization CRO2 0.82 16.009 0.856 0.598 0.855

CRO3 0.724 13.986

CRO4 0.732 14.171

BE1 0.603

BE2 0.735 10.932

Brand Experience BE3 0.876 12.181 0.861 0.557 0.852

BE 4 0.822 11.766

BE 5 0.663 10.159

BR1 0.814

BR2 0.726 14.915

Brand Preference BR3 0.816 17.39 0.897 0.635 0.897

BR4 0.792 14.723

BR5 0.833 17.783

RI1 0.773

Re-purchase RI2 0.788 15.291 0.827 0.546 0.930

Intention RI3 0.698 13.308

RI4 0.691 13.145

of 0.449 (p < 0.01) regarding the linkages. Therefore, the positive correlation implies that H1 is validated. Furthermore, the results confirm hypothesis H2 by demonstrating that the relationship among AI and Conversion Rate Optimization (P = 0.305, p > 0.01) follows the expected direction. Furthermore, the findings demonstrate that Brand Experience is positively and significantly impacted by Social Media Engagement (P = 0.317, t = 5.519, p < 0.01) and positively and significantly impacted by Conversion Rate Optimization (P = 0.346, t = 5.592, p < 0.01). As a result, theories H3 and H4 are validated. Additionally, according to the findings, Brand Experience significantly and favorably influences Brand Preference (P = 0.684, t = 9.419, p < 0.01). Re-purchase Intention is favorably correlated with both Brand Preference and Brand Experience, supporting hypotheses H6 and H7.

3.3. Mediating test

5000 bootstrap samples were chosen, with a 95% confidence level. According to the study model, there are four ways that indirect impacts can manifest.

♦ H8 AI -> Social Media Engagement -> Brand Experience -> e-purchase Intention

♦ H9 AI —► ocial Media Engagement -» Brand Experience —* Brand Preference -» Re-purchase Intention

♦ H10 AI Conversion Rate Optimization Brand Experience Re-purchase Intention

♦ H11 AI -> Conversion Rate Optimization -> Brand Experience -* Brand Preference -»• Re-purchase Intention The product of the route coefficients between AI and

Re-purchase Intention was used to determine the indirect effect of AI on Re-purchase Intentions. Significant indirect effects of AI on Re-purchase Intentions were discovered from the study model for the four paths. To be more precise, there is an indirect effect by means of Conversion Rate Optimization and Brand Experience (P = 0.020, p < 0.01), Social Media Engagement and Brand Experience (P = 0.027, p < 0.01), and Social Media Engagement, Brand Experience and Brand Preference (P = 0.068,p < 0.01). Lastly, there is an indirect effect through Conversion Rate Optimization, Brand Experience and Brand Preference (P = 0.050, p < 0.01). Therefore, the influence of AI on Re-purchase Intentions was mediated by Social Media Engagement, Brand Experience, Brand Preference and Conversion Rate Optimization.

Bootstrap approaches were used to evaluate the indirect effect of AI on Re-purchase Intention.

Table 2.

Means, standard deviations, and correlations for the study variables

Study variables Mean Std. dev 1 2 3 4 5 6

Artificial Intelligence 3.80 0.665 0.746

Social Media Engagement 3.53 0.715 0.315* 0.729

Conversion Rate Optimization 3.46 0.833 0.225* 0.313* 0.773

Brand Experience 3.83 0.687 0.282* 0.307* 0.366* 0.746

Brand Preference 3.56 0.709 0.282* 0.411* 0.433* 0.611* 0.796

Re-purchase Intention 3.65 0.657 0.335* 0.392* 0.333* 0.620* 0.754* 0.740

Notes: *p < 0.01; square root of AVE is on the diagonal

Fig. 1. Structural model with parameter estimates [2, 9, 51].

4. Discussion

The [45] reported that the results showed how AI technology affects social media participation. This implies that in order for businesses to remain competitive in the present business environment, they have used social media campaigns to transform their offline operations into online ones and generate website traffic that eventually converts into actual customers. Businesses that use AI technology have a good correlation with social media user engagement [83]. This result supports the hypothesis that social media integration of AI could enable marketers to interact with prospective clients to promote the products and services they offer [84]. The study discovered an effect on businesses' adoption of AI to raise conversion rates. This lends credence to the idea that social networking sites might boost a business's amount of sales. Social media marketing powered by AI may increase consumer feedback and engagement as well as the customer-

business relationship's conversion rate. AI improves user engagement with social media marketing, which encourages users to buy goods or services. This outcome is in line with the findings of [2, 85].

Customer conversion rates have been shown to affect the brand experience. Businesses may better understand client segmentation and increase conversion rates on online platforms with the help of social media analytics. Social media marketers can tailor their commercial practices and professional activities by leveraging AI technologies to learn customer attitudes. To provide a positive experience and increase sales volume; this outcome is in line with [2, 85].

According to [70], this study demonstrated the relationship between social media engagement and brand experience, indicating that consumers who are more active in social networks are more likely to interact with the brand. Our results align with earlier studies that have demonstrated positive consumer evaluations of goods

Table 3.

Path analysis for the constructs of the study

Relation Coefficients Z-value* Support/ nonsupport

Artificial Intelligence Social Media Engagement 0.449 7.108* Support

Artificial Intelligence Conversion Rate Optimization 0.305 4.998* Support

Path Social Media Engagement Brand Experience 0.317 5.159* Support

Conversion Rate Optimization Brand Experience 0.346 5.592* Support

Brand Experience Brand Preference 0.684 9.419* Support

Brand Experience Re-purchase Intention 0.204 3.405* Support

Brand Preference Re-purchase Intention 0.754 10.597* Support

Explained variance proportion R2 of Conversion Rate Optimization 0.093

Explained variance proportion R2 of Social Media Engagement 0.202

Explained variance proportion R2 of Brand Experience 0.251

Explained variance proportion R2 of Brand Preference 0.467

Explained variance proportion R2 of Re-purchase Intention 0.82

Notes: *p < 0.01

hibit positive behavioral intentions when they perceive a high degree of brand experience. This highlights the significance of an unforgettable brand experience in the context of consumer behavior.

As previously said in the literature, brand experience aids in encapsulating a brand's behavioral, emotional, social, pragmatic, sensory, intellectual and lifestyle elements [70]. The consumer will develop preferences and make judgments about what to buy through this interactive experience [74, 86].

Table 4.

Indirect effects of SCC on NPP through KS and IC

Indirect effect P 95% Bootstrap CI

Lower limit Upper limit p

AI SME ->• BE RI 0.027 0.011 0.069 0.003

AI SME ->• BE ->■ BR RI 0.068 0.032 0.132 0.001

AI CRO ->■ BE ->■ RI 0.020 0.008 0.047 0.003

AI -> CRO BE -> BR RI 0.050 0.020 0.105 0.001

(AI) Artificial Intelligence; (SME) Social Media Engagement; (CRO) Conversion Rate Optimization; (BR) Brand Experience; (RI) Re-purchase Intention.

and services on social networking platforms are given by those who are happy with their purchases [2].

Consistent with earlier research [20, 62, 70], the results show that brand experience influences brand choice and that a favorable brand experience can boost consumer-based brand preference. As a result, customers' brand preference may be increased by a favorable brand experience. Consumers mostly base their brand choice on their experiences. Customers are more inclined to like a brand when they have had numerous positive interactions with it. According to an earlier study [62], consumers will ex-

The findings indicate that the re-purchase intention is significantly influenced by brand preference and brand experience. Because consumer preferences and brand experiences are sustainable ideas that represent unreasonable elements related to the customer who engages with the brand and goes over the limits of rational assumptions, this means that if customers like the product and have a stimulating interaction with it, they are more likely to intend to re-purchase it. Customers will thus have a strong desire to buy without using reason [70].

The findings showed that the impact of AI on the intention to re-purchase is mediated by brand experience. The findings further indicate that the impact of AI on intention to re-purchase was mediated by brand preference. This is understandable given that AI offers a novel consumer experience that increases brand preference, customer satisfaction and product re-purchases [20].

The findings showed that the impact of AI on brand experience and propensity to re-purchase is mediated by social media engagement and conversion rate optimization. The majority of the research on social networking sites engagement has been on online brand communities and social media [51]. This demonstrates how crucial consumer engagement with AI technology is to brand marketing. Using AI technology for marketing brands necessitates higher social media engagement since it makes it easier for brand marketers to convey their experiences to consumers, which leads to the formation of positive brand experiences and re-purchase intentions [51]. Using AI, marketers can also raise visitor conversion rates [9]. Organizations on social networking sites also employ AI as a technique to entice consumers and win them over as devoted customers [2].

5. Theoretical implications

According to this study, businesses may assess the relative value of each element of their products and services and how it affects customer satisfaction on social media platforms by utilizing AI-enabled solutions. The rising use of social media platforms has resulted in a huge increase in consumer interaction, suggesting that social media sites are becoming a new marketplace for establishing relationships with customers to sell products and services. According to [45], AI tracks and analyzes user habits on social media platforms. Technology is playing a bigger role in customer engagement. To improve consumers' re-purchase intention, AI and consumer behavior should be taken into account while implementing a customer engagement plan.

According to this study, AI can improve social media platforms' capacity to attract new clients for Airline companies. Acknowledge the importance of AI, which efficiently manages data processing for specialized services through automation. Studies on customer contact on social media platforms and AI-powered automated business responses are still in their early stages. However, there is a gap in the way companies use real-time data to offer personalized customer service when interacting with clients [2].

Because AI can successfully generate brand preference and purchasing commitment, this study validates Al's overall effectiveness. As a result, by offering a thorough framework that clarifies the connections between AI and branding, our research adds to the body of knowledge on marketing and branding. This study's discussions will be beneficial to marketing academics who wish to apply this approach to other domains.

The study validates the impact of AI marketing methods on brand experience and preference, as well as the correlation among brand preference and re-purchase intention. A few studies have been conducted on AI preference for brands. By elucidating the function of AI in customer interactions within the setting of airline services, this study seeks to close this knowledge gap. By showing the predictive power of AI marketing techniques on brand preference, this work advances the field of service research and offers researchers with an interest in deploying AI to customer decision-making and behavior valuable information. Furthermore, the partial mediation arrangement of brand experience between AI marketing tactics and brand preference shows how these strategies predict brand preference and re-purchase intentions in airlines both directly and indirectly via brand encounters [20].

6. Practical implications

There are various implications of this study for academics and professionals. First, social media platforms are gradually being used by customers in Jordan. These days, the majority of consumers would rather buy goods and services via the Internet and on social networking platforms than leave the comfort of their homes. Additionally, social media and online channels let companies boost sales. Companies may easily keep an eye on what their clients are doing on social media [10]. As a result, they implement efficient communication strategies that aid in raising the conversion rate. According to [2], organizations must deal directly with their clients to ascertain their requirements and

expectations. Although it is essential to businesses, customer involvement on social media is insufficient to help them. Managers will benefit from the current research's understanding of the technological context and its effects on behavior and society. Third, incorporating social media platforms facilitates the analysis of client feedback and the conversion of those responses into actual sales [1]. Social media networks with AI capabilities can assist executives in forecasting customer behavior patterns within the aviation sector. The suggested structure enables managers to influence consumers on social media platforms, hence increasing sales capacity. This report encourages managers to increase social networking conversion rates by utilizing the newest digital technology.

Fourth, companies may boost sales and develop a computerized digital system that assesses and analyzes the social media user experience by integrating AI into their social media marketing campaigns. Social media sites are a useful tool for marketing goods and services internationally. The study's findings also demonstrate that after returning customers get used to online buying, they alter their decision-making processes [2]. Therefore, by providing vouchers for savings and promotions and cultivating customer re-purchase behaviors, airlines may entice frequent travelers.

Fifth, this study discovered that the use of AI in marketing significantly improved brand preference, which subsequently turn affected consumers' desire to make additional purchases. According to these findings, AI marketing initiatives should be seen as a vital instrument for enhancing the brand image in addition to being a means of improving the consumer experience [20]. To improve long-term commercial performance and brand attractiveness, the airline should focus brand-building efforts on AI. Thus, the airline ought to make greater investments in AI and booking service technology, both to draw in new business and to strengthen existing ones.

An airline that is reluctant to use AI may need to reevaluate its investment strategies because the firstmover advantage is still quite significant. Since this study shows how consumers appreciate AI activities after realizing their values, verification of AI campaigns is a crucial sign of the return on investment. Therefore, managers must make sure AI can provide accurate, dependable and efficient airline-related services. Managers can use AI to send clients tailored marketing communications about services and goods at the right time.

In order to meet customer requests, AI assistants and agents should be designed with the capacity to provide knowledgeable customer support and guidance. Airlines' practitioners can also consider improving the AI interface.

7. Limitations and future research

This study has several limitations.

First, the 355 valid samples were obtained by online questionnaires from individuals who had made travel reservations through websites, suggesting that our knowledge of AI brand interactions may be restricted.

Secondly, a cross-sectional approach was employed to gather data from the participants. A longitudinal study might be pertinent to evaluate the suggested model to investigate consumers' intention to re-purchase because habits are amassed over time. To increase validity, future research might employ a bigger sample size.

Third, the study's focus is on the airline business. The results could apply to other businesses or philosophies, even if they are probably most helpful in the context of airlines. This study could be repeated in the future and expanded to include different sectors or nations. This study is quantitative in style; future research may use mixed or qualitative methodologies.

Lastly, producing a response rate that is higher than 44.4%.

To generalize these findings, researchers can also analyze consumer behavior across national borders and industry sectors through cross-cultural studies.

Conclusion

The study explores the mediating role of social media engagement, conversion rate optimization, brand experience and brand preference in the relationship between AI and re-purchase intentions. It also empirically evaluates a model for the implementation of AI in re-purchase intentions and its role in improving conversion rate optimization. The study discovered that through social media interaction, brand experience, brand preference and conversion rate optimization, AI significantly influences re-purchase intentions indirectly. In a similar vein, it has been discovered that social media interaction and conversion rate optimization significantly affect brand experience. Additionally, the re-purchase intention is significantly impacted by brand experience. ■

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About the authors

Raed Naser Alkaied

Instructor and Head of Department, Faculty of Business, Management Information System Department, Al Balqa Applied University, Salt 19117, Jordan, PO Box 206; E-mail: Raedalkaied@bau.edu.jo ORCID: 0000-0002-6288-7503

Shadi Ahmed Khattab

Associate Professor, Faculty of Business, Management Information System Department, Al Balqa Applied University, Salt 19117, Jordan, PO Box 206;

E-mail: Shadikhattab@bau.edu.jo ORCID: 0000-0002-0824-1437

Ishaq M. Al Shaar

Professor, Faculty of Business, Department of Business Administration, Al Balqa Applied University, Salt 19117, Jordan, PO Box 206; E-mail: I.shaar@bau.edu.jo ORCID: 0000-0001-6036-4189

Mohammed Khair Abu Zaid

Professor, Faculty of Business, Planning and Project Management Department, Al Balqa Applied University, Salt 19117, Jordan, PO Box 206;

E-mail: Mohammed_abu_zaid@bau.edu.jo ORCID: 0000-0002-6687-1285

Sakher A.I. Al-Bazaiah

Associate Professor, Faculty of Business, Department of Business Administration, Al Balqa Applied University, Salt 19117, Jordan, PO Box 206;

E-mail: bazaiah1@bau.edu.jo ORCID: 0000-0002-6648-8091

Влияние искусственного интеллекта на намерения совершить повторную покупку: медиативный подход

Алькаид Р.Н.

E-mail: Raedalkaied@bau.edu.jo

Хаттаб С.А.

E-mail: Shadikhattab@bau.edu.jo

Аль Шаар И.М.

E-mail: I.shaar@bau.edu.jo

Абу Заид М.Х.

E-mail: Mohammed_abu_zaid@bau.edu.jo

Аль-Базаия С.А.И.

E-mail: bazaiah1@bau.edu.jo

Университет прикладных наук Аль-Балка, Эс-Салт, Иордания

Аннотация

Покупки, совершаемые на онлайн-платформах, в значительной степени основаны на искусственном интеллекте (ИИ), который формирует покупательское поведение потребителей. Для изучения намерений повторных покупок в данном исследовании использованы данные об ИИ, вовлеченности в социальные сети, оптимизации коэффициента конверсии, опыте использования бренда и предпочтениях бренда. Был проведен опрос с помощью анкеты, разосланной 355 респондентам, которые хотя бы раз покупали или пользовались услугами, предлагаемыми онлайн на любом сайте, связанном с авиацией. Гипотезы исследования были проанализированы с использованием Amos V.22. Эмпирические результаты показывают, что ИИ повлиял на вовлеченность в социальные сети, восприятие бренда, предпочтения к бренду и оптимизацию коэффициента конверсии. Оптимизация коэффициента конверсии и взаимодействие с социальными сетями также влияют на предпочтения и опыт использования бренда, которые, в свою очередь, влияют на намерение совершить повторную покупку. Кроме того, связь между искусственным интеллектом и намерением повторной покупки обусловлена вовлеченностью в социальные сети, опытом работы с брендом, оптимизацией коэффициента конверсии и предпочтениями бренда. Результаты исследования могут помочь авиакомпаниям в разработке ИИ и создании более эффективных кампаний по брендингу и маркетингу, чтобы повысить желание клиентов совершать повторные покупки. Исследование также показало, что использование ИИ в маркетинге значительно улучшило предпочтения бренда, что впоследствии повлияло на желание потребителей совершать дополнительные покупки. Кроме того, для улучшения долгосрочных коммерческих показателей и привлекательности бренда авиакомпаниям рекомендуется сосредоточить усилия по созданию бренда на ИИ и, таким образом, увеличить инвестиции в ИИ и технологии бронирования, как для привлечения новых клиентов, так и для удержания существующих.

Ключевые слова: искусственный интеллект, оптимизация коэффициента конверсии, вовлечение в социальные сети, опыт работы с брендом, предпочтения бренда, намерение совершить повторную покупку

Цитирование: Alkaied R.N., Khattab S.A., Al Shaar I.M., Abu Zaid M.K., Al-Bazaiah S.A.I. (2024) The impact of artificial intelligence on re-purchase intentions: the mediation approach // Business Informatics. 2024. Vol. 18. No. 3. P. 87-107. DOI: 10.17323/2587-814X.2024.3.87.107

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Об авторах

Раед Насер Алькаид

преподаватель, заведующий кафедрой, факультет бизнеса, департамент информационных систем управления, Университет прикладных наук Аль-Балка, Эс-Салт 19117, Иордания, а/я 206; E-mail: Raedalkaied@bau.edu.jo ORCID: 0000-0002-6288-7503

Шади Ахмед Хаттаб

доцент, факультет бизнеса, департамент информационных систем управления, Университет прикладных наук Аль-Балка, Эс-Салт 19117, Иордания, а/я 206;

E-mail: Shadikhattab@bau.edu.jo

ORCID: 0000-0002-0824-1437

Исхак М. Аль Шаар

профессор, факультет бизнеса, департамент информационных систем управления, Университет прикладных наук Аль-Балка, Эс-Салт 19117, Иордания, а/я 206;

E-mail: I.shaar@bau.edu.jo

ORCID: 0000-0001-6036-4189

Мохаммед Хайр Абу Заид

профессор, факультет бизнеса, департамент информационных систем управления, Университет прикладных наук Аль-Балка, Эс-Салт

19117, Иордания, а/я 206;

E-mail: Mohammed_abu_zaid@bau.edu.jo

ORCID: 0000-0002-6687-1285

Сахер А.И. Аль-Базаия

доцент, факультет бизнеса, департамент информационных систем управления, Университет прикладных наук Аль-Балка, Эс-Салт 19117, Иордания, а/я 206; E-mail: bazaiah1@bau.edu.jo ORCID: 0000-0002-6648-8091