An investigation into how Generation Z perceive AI-powered chatbots for fashion e-commerce: assessing functional and relational aspects

Rational

With the expansion of fashion e-commerce and the rising application of AI in the chatbot function, the research question of “Does the additional feature of AI chatbots affect Generation Z consumers' shopping experience or behaviour on fashion e-commerce?” is asked.

According to Li et al. (2020), more than 92% of online consumers have engaged with these technologies.

Aim

To investigate and analyze the impact of AI chatbots on fashion ecommerce website on Generation Z consumers.

Objectives

  • To evaluate current fashion e-commerce AI chatbot strategy.

  • To develop a research model to evaluate variables influencing Generation Z’s actual intention to adopt AI chatbots when shopping on fashion e-commerce.

  • To Understand the perception of Generation Z consumers toward AI chatbots.

  • To measure the influencing factors of Generation Z consumers’ actual intention to employ AI chatbots.

Literature Review

Revolutionizing AI technology application in chatbots

AI harbours the potential to navigate the delicate balance between the effectiveness and efficiency of services by fostering an integration of human and technological capabilities (Marinova et al., 2017; Wilson and Daugherty, 2018).

Different types of chatbots

The objective of all chatbots is to mimic human conversation closely enough that users cannot easily discern whether they are interacting with a machine or a person.

Chatbots represent an emerging digital innovation with significant potential to transform the e-commerce landscape by facilitating intelligent interactions with customers through both text and voice-based communication. According to Araújo and Casais (2020).

The rule-based approach v.s. The generative model

The rule-based approach

  1. Characteristics: Operates on a set of predetermined rules.

  2. Response Mechanism: Triggers specific responses based on the recognition of certain keywords in user input.

  3. Content: Manually crafted around conversational patterns.

  4. Advantages: Effective for a wider array of inquiries.

  5. Limitations: Lacks flexibility, struggles with spelling or grammatical errors, mainly handles single-turn interactions.

The generative model

  1. Characteristics: Leverages machine learning and deep learning.

  2. Response Mechanism: Considers both current and previous messages to craft personalized responses.

  3. Advantages: Adapts to user language, including errors, offering more nuanced and human-like interactions.

  4. Challenges: Complex in development and training.

The influence of chatbots on human frontline employees (HFLE)

Professional Service Roles (PSRs)

These roles involve complex cognitive tasks combined with emotional and social tasks. They require a high degree of flexibility, out-of- the-box thinking, and creative problem-solving. Examples include divorce lawyers, PhD supervisors, and surgeons.

Chatbots v.s. Human frontline employees (HFLE)

  • Lack of the ability to engage in out-of-the-box thinking and to feel or respond with real emotions. (c.f., Rafaeli et al., 2017)

  • Limited to flexibility within predefined boundaries and cannot easily explain the rationale behind their decisions.

  • Despite their ability to optimize decisions based on mathematical structures, they lack key dimensions of social intelligence necessary for PSRs (c.f., Frey and Osborne, 2017; Metzler et al., 2015).

Subordinate Service Roles (SSRs)

  • Excel in tasks that require emotional-social capabilities, flexibility, and creativity.

  • Ability to understand and respond to complex emotional and social cues is critical in PSRs.

These roles are characterized by low pay, low education requirements, minimal training, limited decision-making discretion, low engagement, and low motivation. Tasks are more routine, and employees might engage in surface acting.

Chatbots v.s. Human frontline employees (HFLE)

  • Provide better service in SSRs due to their consistency and lack of human biases.

  • Programmed to display surface-acted emotions effectively, potentially outperforming humans in routine service encounters.

Chatbots are praised for delivering rapid, personalized digital services and providing a cost-effective customer service solution.

Juniper Research (2019) highlights that the banking sector alone could see annual cost savings exceeding US$8 billion by 2022, thanks to chatbot implementation.

A case study by Heo and Lee (2018) on South Korea’s Naver TalkTalk chatbot showcases the capacity of chatbots to significantly improve consumer satisfaction, positioning them as a promising new channel for business communication.

Comparison of AI chatbot applications across fashion brands

(Morozova, 2017; Huber, 2017)

Consumers Perceptions on AI chatbots