Abstract
The integration of artificial intelligence into electronic commerce has revolutionized consumer behavior due to its capability in supporting cutting-edge features for conducting business online. It pertains to contextual commerce that facilitates customers to connect and buy goods wherever they are. This study aimed to examine the influence of artificial intelligence applications on the operations of contextual commerce. The conceptual framework was based on the UTAUT theory. The sample was the users of contextual commerce who were familiar with its usage. An online questionnaire was used to collect the data, and variance-based structured equation modeling was applied for data analysis. The four technological acceptance constructs derived from UTAUT were tested and confirmed as antecedents for contextual commerce. Furthermore, the inclusion of brand anthropomorphism as the antecedent was also supported. The empirical findings of the study explain the consumer attitude toward the significant use of artificial intelligence in contextual commerce.
TopIntroduction
The advancement of artificial intelligence is influencing us in many aspects of our life (Javaid et al., 2022). The business world jumped on this bandwagon effect to gain its benefits (Enholm et al., 2022; Loureiro et al., 2021; Sestino & De Mauro, 2022). What makes it effective is its use by businesses in achieving business performance. The advancement of AI keeps growing with the development of neural networks, machine learning, and deep learning (Kraus et al., 2020; Naim, 2022; Shaikh et al., 2022). The direct impact of artificial intelligence usage on business performance (Wamba-Taguimdje et al., 2020). The current literature has an abundance of studies relating the use of artificial intelligence with business operations (Di Vaio et al., 2020; Lee et al., 2019; Rana et al., 2022). Electronic commerce was no exception with the integration of artificial intelligence into its operations. Many of them are integrated with tools engineered by advanced use of predictive data analytics in different industries. For example, hospitality (Mariani, 2019), supply chain management (Gunasekaran et al., 2017), and fashion retailing (Shi et al., 2020).
Electronic commerce is becoming the main platform of business transactions in the post-Corvid era(Li et al., 2021). One of the new waves is contextual commerce (González et al., 2021). Contextual commerce refers to customers who would make purchases while conducting non-shopping activities, such as jogging, and watching drama. In another word, it means buying in context. While it is still a new concept to many, it has been progressing to be the new online initiative for many firms that are tapping on its potential to reach out the ever-demanding customers. It is about a new online experience for consumers moving away from merely buying goods. This suits the trend of on-demand-oriented customers who choose products they need and purchase instantly and without any hassle while they are working on their other tasks. With the increasing use of AI, consumers are enjoying many useful functionalities embedded in online shopping apps. such as electronic product fulfillment (Zhang et al., 2021), smart tourism (Samara et al., 2020), and e-chatbot (Moriuchi et al., 2021).
The implementation of contextual commerce has been highly supported by online retailers. It brings about the innovative idea of engaging the customers more deeply. Similarly, consumers are expected to enjoy the “on-the-move” features in providing more convenience to their shopping experience (Ho, 2022). However, there is little coverage of the literature on the new and innovative features of electronic commerce. The current literature on electronic commerce is highly devoted to customer acceptance, consumer needs, and functionalities (Rosário & Raimundo, 2021). With contextual commerce, many features are controlled by systems intelligence and automation (Khrais, 2020). The shopping journey is highly driven by artificial intelligence. Therefore, the acceptance of the consumers in this avenue is lacking and warrants further exploration.
Key Terms in this Chapter
Brand Anthropomorphism: The act of anthropomorphism to transform a brand with human-like attributes to enhance customer engagement with the brand.
Effort Expectancy: The effort dedicated to familiarizing and learning to use technologies or tools.
Facilitating conditions: The infrastructure and facilities supporting the operations of technology deployment.
Performance Expectancy: The expected task accomplishment with the use of technologies or tools.
Contextual Commerce: It refers to customers who would make purchases while conducting non-shopping activities, such as jogging, and watching drama.
Self-Efficacy: Self-assurance of own abilities in conducting a behavior or task to completion.
Social Influence: The affection and support from other people while conducting a task and behavior.
Artificial Intelligence: Machine ability to conduct activities typically needing human intelligence with the support of machine learning, natural language processing, dynamic messaging, and other elements.