RESEARCH REPORT EXAMINING THE ROLE OF AI—- ENABLED VOICE ASSISTANTS IN AFFECTING CONSUMER MOTIVATIONS FOR ONLINE SHOPPING: THE MEDIATINGFACTORS ROLE AWE EXPERIENCE, PRICE VALUE, SALES PR
INTRODUCTION 0
THEORETICAL BACKGROUND HH HH TH HH HH HH HH ni 15
AI is revolutionizing corporate strategies and accelerating digital transformation, with smart speakers emerging as the fastest-growing AI consumer devices since smartphones (Simms, 2019) Business leaders are exploring ways to leverage these technologies to enhance sales and improve customer shopping experiences Voice-assistant software, like Alexa and Siri, operates in a continuous listening mode, ready to respond to activation keywords (McLean & Osei-Frimpong, 2019) Upon detecting the keyword, the system records the user's voice and processes it using natural language processing and machine learning on a main server The server then generates a response that is relayed back to the voice assistant for playback to the user, facilitating natural conversations (Hoy, 2018; Schweitzer et al., 2019).
Voice assistants are increasingly replacing traditional search engines because they can effectively respond to complex customer inquiries For example, users can request voice assistants to find "the best UV protection mask under 50 thousand VND."
Recent studies reveal that voice assistants are becoming a popular tool for consumers to place online orders, reflecting a significant trend in shopping behavior (Klaus & Zaichkowsky, 2020) This shift highlights the growing prevalence of jeans in the market as consumers increasingly rely on technology for their purchasing decisions.
The use of voice assistants for Internet shopping represents a paradigm shift (Klaus &
Recent studies highlight significant shifts in consumer behavior influenced by artificial intelligence, particularly voice assistants Consumers are prioritizing product qualities and benefits as key search criteria, rather than searching for brands directly Instead, they engage with brands through voice assistants, which retrieve information that impacts purchasing decisions Additionally, there is a notable shift from visual stimuli to auditory cues in consumer preferences While some research has explored the psychological aspects of decision-making in the context of AI, the precise role of voice assistants in providing accurate information and supporting consumer tasks remains unclear.
Marketers can enhance consumer interactions and improve their value propositions by leveraging innovative strategies This study aims to address the existing gap in research by applying the MCI framework as a comprehensive theoretical model specifically focused on voice assistants.
Source: Apple's Siri Is The Most Popular Virtual Assistant In The World: Report (Mathur, 2019)
Motivated customer innovativeness refers to the various motivations driving individuals to explore new products and experiences (Midgley & Dowling, 1978) Innovative customers often seek out cutting-edge technology due to their desire for uniqueness (Hwang et al., 2019) The Technology Acceptance Model (TAM) identifies perceived usefulness and perceived ease of use as key factors influencing consumers' willingness to adopt new technologies (Davis, 1989) However, critics argue that TAM alone cannot fully explain technology adoption, leading to the development of the Motivated Customer Innovativeness (MCI) concept, which encompasses both internal and external factors affecting innovative purchasing behavior (Hwang et al., 2019) MCI is characterized by four theoretical dimensions: functional, hedonic, social, and cognitive motivations, each of which predicts customers' purchasing intentions (Hwang et al., 2024).
AI-enabled Voice Assistants (VAs) are innovative technologies that simplify voice-based product shopping These assistants offer a unique perspective, encouraging customers to engage in shopping and other purchase-related activities for various reasons.
The broaden-and-build theory (B&BT) is a key model for understanding the unique aspects of positive emotions such as joy, interest, contentment, and love (Fredrickson, 2001) These emotions expand an individual's thought-action repertoire, encouraging activities like play and exploration that lead to the discovery of innovative actions, ideas, and social connections Additionally, positive emotions contribute to the development of various resources—physical, intellectual, social, and psychological (Huppert et al., 2004)—which serve as reserves that enhance coping abilities and increase the likelihood of successful survival in challenging situations.
Artificial intelligence (AI), including voice assistants and virtual reality, is increasingly perceived as possessing a semblance of a real mind that can express wisdom and emotions According to Russell and Norvig (2016), AI systems can be classified into two types: those that demonstrate human performance-rationality, which think and act rationally, and those that exhibit human reasoning-behavior, which mimic human thought and action By blending these two dimensions, AI-enabled services are leveraging emotions to enhance and transform consumer experiences (Huang et al., 2019).
The S-O-R model, proposed by Mehrabian and Russell (1974), illustrates how environmental stimuli (S) elicit emotional reactions (O) that subsequently drive consumer behavioral responses (R) This model suggests that exposure to external stimuli alters the inner state of individuals, leading to specific behaviors Its application has been extensive in various online contexts (Sharma et al., 2021; Sharma, Fadahunsi, et al., 2022), highlighting the role of environmental cues as external stimuli that interact with the organism as a mediator, ultimately affecting consumer responses.
In their 1974 study, Mehrabian and Russell identified 17 psychological processes that shape consumer behavior, highlighting the significant impact of emotions, expectations, and cognitive processes on motivated consumer innovativeness Utilizing the S-O-R model, the research explored how consumers interact with the features of online fashion portals (stimuli) and how this interaction fosters an engaging experience (organism), ultimately influencing electronic word-of-mouth (eWOM) and purchase intentions (response).
The emotion of amazement, often referred to as the awe effect, plays a significant role in the acceptance of AI-enabled products, virtual reality (VR), and augmented reality (AR) Awe is characterized by perceived vastness and the need for accommodation, where individuals may feel small and helpless in the face of powerful emotional stimuli This sense of confusion and surprise arises when encountering experiences that surpass previous understanding Such awe can inspire creativity and innovative behavior, enhancing social and physical activities Ultimately, the emotions of awe are transforming consumer experiences through AI-enabled services.
A core premise in consumer behavioral research 1s that value maximization (Zeithaml et al.,
Value plays a crucial role in consumer decision-making, as defined by prospect theory, which highlights the perceived gains or losses relative to a baseline (Kahneman & Tversky, 1979) Consumers tend to engage in behaviors that maximize their benefits, viewing price value as a balance between what they give and what they receive Zeithaml (1988) describes perceived value as a consumer's evaluation of a product's usefulness based on their perceptions of the costs and benefits involved When making choices, consumers carefully consider the advantages and costs associated with a product, leading to a nuanced understanding of price value in relation to the value-added (VA) services they receive.
Voice assistants enhance user experience by providing personalized information and targeted recommendations based on previously shared data, such as past decisions and purchases, through advanced algorithms and analytics tools (Huang, 2018) Moreover, their hands-free functionality enables users to make quicker decisions, ultimately saving time and effort (Rhee & Choi, 2020).
Sales promotion is an essential tactic for marketers aiming to achieve sales goals and enhance profitability This strategy leverages short-term incentives and motivational techniques to influence consumer purchasing behavior and encourage brand switching from competitors.
RESEARCH HYPOTHESIS DEVELOPMENT ng HH, 20
Purchase intentions indicate a consumer's readiness to make a purchase soon (Kang et al., 2020), and there is substantial evidence linking these intentions to various aspects of MCI However, the relationship between purchase intentions and virtual assistants (VAs) remains underexplored in existing literature Specifically, there is a lack of clarity regarding the psychological factors that drive the use of VAs and subsequently influence purchase intentions Research by Lin and Filieri (2015) emphasizes the importance of understanding psychological motivations in technology adoption, highlighting that consumer innovation fosters continued technology usage Furthermore, Cao (2021) asserts that consumers' eagerness for new technology-based services significantly affects their purchase intentions.
20 findings about how MCI facets—functional, hedonic, social, and cognitive—affect purchase intentions can be found in the body of existing research (Seyed Esfahani & Reynolds, 2021a; Vandecasteele & Geuens, 2010).
This study examines how functional, hedonic, social, and cognitive incentives influence online buyers' purchase intentions when using AI-enabled VA technology.
3.1.1 Functional MCI and Purchase Intentions The functional MCI alludes to “consumer innovativeness motivated by the functional performance of innovations and focuses on task management and accomplishment improvement” (Vandecasteele & Geuens, 2010) The functional MCI depicts the functional or utilitarian benefits such as enhanced efficiency, greater production, and risk (Vandecasteele & Geuens,
2010) conceived by the customer that provokes the use of the innovative product (Venkatraman
Virtual assistants (VAs) enhance user experience by utilizing machine learning to perform tasks autonomously, offering significant functional advantages (McLean & Osei-Frimpong, 2019) Customers are drawn to online shopping primarily due to the efficiency and convenience provided by VAs, which improve speed, compatibility, and accessibility, ultimately saving time during the purchasing process (Moriuchi, 2019).
Voice assistant (VA) technology enables customers to complete various tasks effortlessly, enhancing the satisfaction of accomplishing tasks like making purchases without the need for typing or physically holding a device (McLean & Osei-Frimpong, 2019) Research indicates that functional Multi-Channel Integration (MCI) significantly influences purchase intentions (Chopra, 2019; Seyed Esfahani & Reynolds, 2021b) Therefore, the functional benefits provided by VAs are likely to enhance online buyers' purchasing intentions Based on this literature, we propose the following hypothesis.
Hypothesis la: Functional MCI to use Al-enabled VA has a positive impact on Consumer Purchase intentions.
3.1.2 Hedonic MCI and Purchase Intentions Hypothesis 1b: Hedonic MCI to use AT-enabled VA has a positive impact on Consumer Purchase intentions.
Hedonic consumer innovativeness refers to the drive for affective or sensory enjoyment that motivates individuals to explore new and innovative products (Vandecasteele & Geuens, 2010) This type of consumer behavior is fueled by the desire for excitement and pleasure, leading customers to seek out experiences that provide sensory stimulation and satisfaction.
The number 21 symbolizes emotions like pleasure, as noted by Hwang et al (2019) Consumers are drawn to products that are perceived as unusual by others, which enhances their enjoyment (Hwang et al., 2019) Prior studies have highlighted the role of modern technologies, such as virtual assistants, in fostering hedonic experiences (McLean & Osei-Frimpong).
Research indicates that hedonic motivations significantly influence purchase intentions, particularly in the context of using sophisticated devices like virtual assistants (VAs) Studies have shown that when customers derive enjoyment and fulfillment from using these technologies, they are more inclined to engage in various activities, including shopping Consequently, we assert that the positive emotions associated with hedonic experiences—such as excitement, joy, and pleasure—can enhance consumers' intentions to purchase goods.
3.1.3 Social MCI and Purchase Intentions Social MCI is defined as "consumer innovativeness motivated by the self-assertive social desire for differentiation" (Vandecasteele & Geuens, 2010) Brown and Venkatesh (2005) define socially motivated consumer innovativeness (SMCD as the usage of innovative items to improve one's social standing or to attract others According to the literature, customers' adoption of AI- enabled novel products is influenced by their sense of belonging Several consumers believe that utilizing a certain technology, such as the VA, elevates their social standing (McLean & Osei- Frimpong, 2019) Researchers have pointed out the symbolic component in socially driven consumer innovativeness illustrated in the creation of social identity through the adoption of innovative items (Roehrich, 2004), such as VA for shopping activities These customers would like to achieve their social connection goals (Vandecasteele & Geuens, 2010) while appearing trendy and smart to get social values Customers, for example, buy smartwatches to show off their social recognition, and this desire stimulates their purchase intentions (Patel et al., 2023). People's behavior is determined by what motivates them (Hwang et al., 2019), hence we assert that socially driven consumer innovativeness is likely to increase the purchase intentions of online customers.
Hypothesis 1c: Social MCI to use Al-enabled VA has a positive impact on Consumer Purchase intentions.
3.1.4 Cognitive MCI and purchase intentions Venkatraman and Price (1990, p.294) specify cognitively motivated consumer innovativeness as a need for fresh sensations that excite cognitive abilities Such customers want
Consumers leverage innovative technologies to enhance their cognitive capabilities, such as exploration, perception, and creativity (Hwang et al., 2019) They engage in tasks that require careful selection of words and sentences, demonstrating the importance of cognitive skills in utilizing virtual assistants (VAs) Additionally, effective task management is crucial, as tasks should be controlled and executed at designated times (Sharma et al., 2021) This underscores the role of cognitive functions in optimizing the performance of VAs (Vandecasteele & Geuens, 2010).
Hypothesis 1d: Cognitive MCI to use Al-enabled VA has a positive impact on Consumers' Purchase Intentions
This study explores the impact of MCTI on customers' feelings of astonishment, highlighting the significance of awe in recent marketing research and consumer psychology Cutting-edge technologies have been shown to enhance consumers' sense of wonder, as they evoke complex emotions when individuals encounter something vast that exceeds their previous knowledge.
A study from 2007 highlights that AI-enabled virtual assistants (VAs) can evoke feelings of self-transcendent awe in buyers, potentially transforming their experiences Customers may find themselves captivated by the various incentives—functional, hedonistic, social, and cognitive—that drive the use of interactive AI technology (Mishra et al., 2022).
3.2.1 Functional MCI and awe experience Functional motivation to use innovative products can be explained by the customer's desire for convenience, time-saving, and accuracy (Hwang et al., 2019) These customers are drawn to the goods because of their practicality and functionality Researchers have demonstrated awe in the requirement for accommodations and proposed that customers experience awe as a result of virtual technology (Quesnel & Riecke, 2018) In addition, amazement is a powerful feeling that someone has when they are confronted with a lot of stimuli (Hinsch et al., 2020) Customers are likely to be amazed by the functional gains that Al-enabled VA technology offers (McLean et al.,
2021) Therefore, it is suggested that:
Hypothesis 2a: Functional MCI to use Al-enabled VA has a positive impact on the Awe experience.
3.2.2 Hedonic MCI and awe experience People who seek enjoyment or excitement experiment with novel and innovative technologies (Mishra et al., 2022) According to Hwang et al (2019), hedonism was classified as a psychological incentive Previous research has noted that the awe is triggered by the psychological drive or schema's astounding impact (Hinsch et al., 2020) Shoppers may also enjoy using AlI-enabled technology to explore the fashion portals (Kautish & Khare, 2022). Exposure to new technologies can cause amazement (Guo et al., 2018) as well as excitement or delight (McLean & Osei-Frimpong, 2019) For instance, customers can utilize voice search on their cellphones to find the newest things with ASOS's Enki app Customers who use AT-enabled
Virtual assistants (VAs) in the purchasing process can captivate customers, generating a sense of excitement and wonder (McLean & Osei-Frimpong, 2019) This suggests that online consumers are likely to experience amazement when engaging with VA technology for entertainment purposes.
Hypothesis 2b: Hedonic MCI to use Al-enabled VA has a positive impact on the Awe experience.
3.2.3 Social MCI and awe experience According to Quesnel and Riecke (2018), the feeling of awe falls into the range of self- transcendent encounters with wellness tools and an affective state of social interconnectedness. Previous research has established the awe sensation that is generated by social motives. According to the author, social cues were a major factor in the awe experience that people had for commercial objects Since fashion products are commercial, we incorporate this idea into our research The social impact's immensity can be explained by awe (Hinsch et al., 2020). Furthermore, as an emotion that delves into the underlying psychology of a person's social presence or status, awe can be grounded in the social component A greater social status is conferred upon users of Al-enabled VA, as it is regarded as prestigious (Mishra et al., 2022). Therefore, we contend that the employment of VA by socially driven online consumers may cause astonishment or the "wow" effect in them Drawing from the existing literature's discussions, we propose that:
Hypothesis 2c: Social MCI to use Al-enabled VA has a positive impact on the Awe experience.
3.2.4 Cognitive MCI and awe experience
METHODOLOGY HH HH TH TH HH HH HH TT TT TT 28
Data were collected during the period between February and March 2024 A Google Form was used to construct an online questionnaire then distributed to Vietnamese consumers via
Vietnam's rapidly growing internet landscape, characterized by a remarkable 25% increase in e-commerce market value, reaching approximately 20.5 billion U.S dollars in 2023, makes it an ideal setting for this study With around 57 million e-commerce users in 2022, large businesses are increasingly investing in information technology, recognizing social media and e-commerce platforms as effective sales channels Notably, 78% of websites now feature online customer interaction tools, with many employing AI-driven chatbots To gather insights, a convenience sampling method was utilized, allowing for quick and cost-effective data collection from easily accessible participants, resulting in 300 valid responses Demographic details of the respondents are outlined in the accompanying table.
Are you familiar with AI-enabled
How often do you use Al-enabled
The measurement scales for the constructs in this study were derived from a thorough literature review, ensuring their reliability and validity Specifically, the MCI related to ATI-enabled voice assistant services includes functional MCI (7 items), hedonic MCI (6 items), social MCI (6 items), and cognitive MCI (5 items), based on the work of Vandecasteele & Geuens (2010) Additionally, the awe experience was measured using six items adapted from Chirico & Gaggioli (2019), Chirico et al (2018), and Yaden et al (2019) Price value was assessed with four items from Dodds et al (1991), Jarvenpaa & Todd (1996), and Sirdeshmukh et al (2002) Finally, sales promotion (4 items), E-WOM (5 items), and purchase intentions (6 items) were sourced from Kotler et al.
The questionnaire utilized a seven-point Likert scale, ranging from "Strongly disagree" (1) to "Strongly agree" (7), to assess responses Originally developed in English, the survey was subsequently translated into Vietnamese to ensure clarity and comprehension for Vietnamese participants.
This study utilized partial least squares structural equation modeling (PLS-SEM) to analyze data, a robust methodology recognized in information systems and marketing research for examining causal relationships among multiple independent and dependent variables (Hair et al., 2014; Chen et al., 2021; Gu et al., 2019) The process involved evaluating the measurement model to confirm the reliability and validity of the constructs, followed by assessing the structural model to test the hypotheses Data analysis was performed using SmartPLS software version 4.
To assess the measurement model, we utilized key metrics including item loadings, Cronbach's alpha, composite reliability, average variance extracted (AVE), and the Fornell-Larcker criterion, as outlined by Hair et al (2017) This comprehensive evaluation aimed to validate the reliability of our scale and ensure its effectiveness in measuring the intended constructs.
To ensure item and construct reliability, Cronbach's alpha and composite reliability values should exceed the threshold of 0.7 Additionally, average variance extracted (AVE) values must be greater than 0.5 to confirm convergent validity For validating discriminant validity, the Heterotrait-Monotrait ratio (HTMT) should remain below 0.85, and the square root of the AVE for each construct must surpass the correlation between constructs, in accordance with the Fornell-Larcker criterion.
The structural model was analyzed using path coefficients and significance levels, with p-values derived from a bootstrap analysis of 5000 subsamples to ensure the reliability of the findings (Hair et al., 2017) To assess the model's predictability, we calculated the R-squared (R²) value.
In information systems and marketing studies, R² values of 0.75, 0.50, and 0.25 are typically categorized as substantial, moderate, and weak, respectively However, research on consumer behavior, particularly regarding customer satisfaction, considers an R² value of 0.20 to be significant (Hair et al., 2017).
4.4 Fuzzy Set Qualitative Comparative Analysis (fsQCA)
Qualitative comparative analysis (QCA) is a set-theoretic method that incorporates Boolean algebra and fuzzy set theory to evaluate causal relationships A more advanced version, fsQCA, enhances consistency evaluation and accommodates continuous and interval-scale variables, distinguishing itself from traditional regression methods Key features of fsQCA include the recognition of asymmetrical interactions, where different causes may lead to the same outcome or its absence, the concept of equilibrium, which allows multiple configurations to achieve identical results, and the acknowledgment of causal complexities, where various combinations of causal factors can produce the same effect.
Unlike traditional regression methods, fsQCA posits that outcomes arise from various combinations of causal factors rather than a single cause (Woodside, 2013) This method allows for a deeper empirical and theoretical evaluation of the optimal variable combinations for specific results (Hughes et al., 2019) Researchers advocate for the use of asymmetric configural analysis to better understand complex situations, particularly those involving human behavior, which often does not follow a symmetrical pattern (Schmitt et al., 2017).
We utilized fsQCA to examine the intricate relationships between various types of Marketing Communication Initiatives (MCIs) and purchase intentions, including functional, hedonic, social, and cognitive MCIs Additionally, we explored how awe experiences, perceived price value, and sales promotions influence purchase intentions, aiming to highlight the significance of these factors in customers' online purchases when engaging with AI-based Virtual Assistant services.
In fSQCA, two key measures are utilized to evaluate fit parameters: consistency and coverage Consistency reflects how effectively causal combinations lead to similar outcomes, highlighting the strength of subgroup connections On the other hand, the coverage index measures the percentage of variance explained, similar to R² in traditional regression Specifically, raw coverage indicates the proportion of outcomes explained by a causal combination, while unique coverage delineates the portion of the outcome attributed exclusively to a single causal combination, without overlap from others.
All reliability and validity measurements met acceptable standards, with Cronbach's alpha and composite reliability exceeding the 0.70 threshold Additionally, all item loadings surpassed the 0.7 criterion, and the Average Variance Extracted (AVE) results were above the 0.50 benchmark Consequently, the reliability and validity of the items and constructs were confirmed.
Table 2: Factor loadings of measures
AW CMCI | EWOM | FMCI | HMCI | PI PV SMCI | SP AWI 0.901
Table 3: Assessment of reliability and convergent validity (n00)
Constructs Items OL CR AVE Cronbach’s a
PI6 0.906 Note: FL: Factor Loadings; CR: Composite reliability; AVE: Average Variance Extracted; SD: Standard Deviations.
Table 4 demonstrates that the square root of each construct's Average Variance Extracted (AVE) surpasses its highest correlation with other constructs, confirming the discriminant validity of the latent variables as per the Fornell-Larcker criterion Additionally, the HTMT pairs of latent variables are all below the 0.85 threshold, as shown in Table 5.
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Functional MCI (FMCI), Hedonic MCI (HMCI), Social MCI (SMCI), and Cognitive MCI (CMCI) are key concepts that influence consumer behavior Awe experiences (AW) significantly enhance purchase intentions (PI) by creating a strong emotional connection Additionally, the perceived value (PV) of a product is often boosted through effective sales promotions (SP) and positive electronic word of mouth (E-WOM) Understanding these dynamics is crucial for marketers aiming to increase consumer engagement and drive sales.
Table 5: Discriminant validity - Heterotrait-monotrait ratio of correlations (HTMT)
Construct AW CMCI |EWOM | FMCI | HMCI) PI PV | SMCI SP
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This paper makes important theoretical contributions by exploring the intersection of marketing and artificial intelligence, particularly through the lens of consumer behavior and purchase intention (Vlašié et al., 2021) It assesses factors such as sale promotion, pricing value, awe experience, and electronic-word-of-mouth (eWoM) related to the motivation behind using AI-enabled voice assistants (VAs) Additionally, the study employs consumer innovativeness theory to examine the adoption of VAs in online shopping, enhancing our understanding of why consumers embrace advanced technologies, especially those powered by AI (Vandecasteele & Geuens).
Despite limited research on consumer motivations for using AI-enabled voice assistants (VAs) in online shopping, it is crucial to understand how these factors influence purchase intentions Previous studies have highlighted the significance of VA usage in e-commerce, yet they have not thoroughly explored the motivations driving this technology adoption This research contributes valuable insights to the existing literature by examining the impact of motivational innovativeness on purchase intention and the awe experience within the VA context Utilizing the MCI scale developed by Vandecasteele and Geuens (2010), this study represents a pioneering effort to analyze these dynamics in the realm of AI-enabled VAs.
The proposed framework integrates B&BT theory, the S-O-R model, and consumer innovativeness theory to elucidate the relationship between purchase intention, price value, sales promotions, awe experiences, and eWOM behaviors in the context of MCI for AI-enabled virtual assistants (VAs) This study enhances the understanding of these theories within the online purchasing landscape, illustrating how MCI components—functional, hedonic, social, and cognitive—shape online buyers' behaviors Additionally, by applying Roger's diffusion of innovation theory, we emphasize the crucial role of consumer innovativeness in influencing online shopping behaviors related to AI-enabled VAs, highlighting the need for further research in this area.
This study reveals that functional, hedonic, social, and cognitive motivating goals significantly influence consumer innovation in online shopping through the use of virtual assistant (VA) technology By employing consumer innovativeness theory, B&BT theory, and the S-O-R model, we highlight how MCI-inspired awe experiences enhance purchase intentions via perceived pricing value, sales promotions, and electronic word-of-mouth (eWOM) The findings underscore the pivotal role of awe experiences in shaping online shoppers' behaviors toward AI-enabled VAs Given the scarcity of research on AI technology focusing on awe as a theoretical framework, this area warrants further academic exploration.
Guo et al (2018) highlighted that commercial products evoke feelings of awe, a concept our study supports by showing that voice assistants elicit similar emotions in online shoppers Recognizing the significance of emotional experiences in technology (Venkatesh, 2000), our research introduces the concept of awe into the literature on voice assistants and shopping, providing valuable theoretical insights Furthermore, this study reveals that perceived price value enhances online customers' willingness to purchase when utilizing voice assistant technology, as noted by Beneke et al.
Price plays a crucial role in consumer purchasing decisions, representing the economic sacrifice required for a transaction Additionally, sales promotions significantly influence online shoppers' use of virtual assistant (VA) technology, with more attractive promotions leading to higher consumer engagement Furthermore, electronic word-of-mouth (eWOM) behavior enhances purchase intentions among online shoppers utilizing VA technology This relationship is well-documented in existing literature and underscores the importance of eWOM in technology-related research Our study highlights how price value, sales promotions, and eWOM behaviors collectively impact the purchasing intentions of online consumers using advanced AI technology.
VA technology for shopping purposes.
The study highlights potential applications for developers of AI-enabled VA interfaces for e- retailers We can further offer advice to e-retail shop marketing managers The findings indicated
Functional, hedonic, social, and cognitive MCI are crucial factors influencing online buyers' purchase intentions, price perceptions, sales promotions, e-WOM, and awe experiences Therefore, VA interface engineers for online stores must prioritize these elements Marketing managers in e-retail must promote the use of virtual assistants (VAs) to enhance the online shopping experience AI-enabled VAs should be versatile, tailored, intriguing, and effective to attract consumers Collaboration between marketing managers and product teams is essential for developing effective product designs and promotional strategies By focusing on user-friendliness, customization, and appealing features, businesses can enhance functional, hedonic, social, and cognitive MCI, ultimately driving better online purchasing outcomes.
To enhance performance and customer satisfaction, e-retailers must leverage AI-enabled virtual assistant services, as functional MCI significantly impacts PI according to PLE-SEM and fsQCA findings With just one tap, customers can efficiently execute multiple tasks, saving time while effortlessly expressing their preferences for sizes, designs, and styles of desired items.
Awe experiences, price value, sales promotions, and electronic word-of-mouth (e-WOM) play crucial roles in mediating the relationship between marketing communication initiatives (MCIs) and purchase intentions, prompting marketing managers to focus on these factors To effectively evoke awe, promotional strategies like influencer marketing should leverage reputable influencers to showcase AI-enabled virtual assistants (VAs) on various e-commerce platforms, highlighting their appeal and ability to generate curiosity It is advisable for consumers to utilize devices of different sizes—smartphones, tablets, and computers—for seamless navigation and observation at any time Additionally, creating a human-like interface enhances the user experience, as humanized AI can integrate functional, hedonic, and cognitive aspects of MCIs with emotional and social intelligence AI-driven intelligent agents can provide valuable insights into online shopping behavior, customer journeys, and overall satisfaction, ultimately fostering loyalty through tailored products and services.
Recent studies indicate that AI-enabled voice assistants significantly enhance the online shopping experience, providing a foundation for interface designers to integrate emotional assessment themes The awe experienced by users influences key factors such as price value, sales promotions, and electronic word-of-mouth (eWOM), ultimately affecting purchase intentions Customers are more inclined to engage with e-stores that offer convenience and time savings through these technologies, prompting marketing managers to collaborate with sales teams to establish mutually beneficial pricing strategies Additionally, leveraging selling subsidies from platforms like Shopee and Lazada can reduce costs effectively To maintain customer loyalty, e-stores must balance customer experience with product pricing, ensuring satisfaction through AI-enabled services Marketing managers are advised to prioritize sales promotions that enhance the awe experience, utilizing innovative strategies like lower prices during promotional seasons, live streaming, and celebrity endorsements to attract customers Moreover, AI-enabled VAs facilitate quick access to discounts and promotions, further enhancing customer satisfaction and encouraging repeat purchases eWOM also serves as a crucial link between awe experiences and purchase intentions, allowing firms to engage with user-generated content effectively.
Engaging with online communities enhances trust and customer interaction for e-tail firms, as noted by 56 online consumers Retailers should motivate shoppers to share their experiences across multiple platforms, including feedback on product searches with virtual assistants (VAs) Creating an interface that facilitates this sharing can significantly boost consumer engagement and brand credibility.
The VA app offers significant advantages for e-retailers by enabling advertisers to leverage electronic word-of-mouth (eWOM) as a powerful marketing strategy This approach fosters engaging interactions with online shoppers while integrating both emotional and factual content related to their experiences with fashion products As a result, customers can access more precise information about brand stylists, designers, and personalized products through these platforms.
This research explores how AI-enabled voice assistants can evoke feelings of awe in online customers, influencing their purchase intentions through factors like price value, sales promotions, and electronic word-of-mouth (e-WOM) The study highlights the complex relationship between these elements, examining the functional, hedonic, social, and cognitive dimensions of customer innovativeness in the context of voice assistant services It suggests that the positive emotions associated with awe can lead to significant psychological changes, motivating online purchases driven by innovative technology Additionally, the perceived value proposition, including pricing and promotional strategies, enhances the connection between voice assistants and consumer buying decisions, while positive e-WOM further amplifies this effect These findings provide actionable insights for online retailers, encouraging them to leverage voice assistants and emphasize their value to boost consumer motivation for online shopping.
The current study presents significant theoretical and practical implications, yet it has notable limitations that warrant consideration Primarily, the generalizability of the findings is restricted due to the data being exclusively collected from Vietnam, suggesting a need for future research to explore the theoretical model in diverse locations to enhance its applicability Additionally, the reliance on convenience sampling may not accurately represent the broader population of Vietnamese consumers The study's focus solely on AI-enabled services for online shopping limits its scope; researchers are encouraged to extend the theoretical framework to other AI technologies such as chatbots and augmented reality Furthermore, while the research utilized the MCI theory to analyze e-tail paradigms, investigating the relevance of the TAM or UTAUT theories concerning AI-enabled voice assistants and various online shopping environments would be advantageous Lastly, despite existing research indicating that awe is predominantly a positive emotion in nature, future investigations should also consider the potential negative aspects of awe experiences, as well as examine how voice assistants affect purchasing decisions across different product categories.
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