This study has the purpose of measuring the usability level of the image recognition feature on the Shopee and Lazada applications using the UTAUT method to measure the usability level. The variables used in this study are performance expectancy, effort expectancy, social influence, facilitating conditions, usability expectancy, and use behavior. Meanwhile, four moderate variables were involved: age, gender, experience, and voluntariness of use. This study performs structural equation modeling (SEM) using partial least square (PLS) to investigate the relationship between variables. Based on the R-square value, it can be concluded that the UTAUT method is better at evaluating the usability of the image recognition feature in Lazada than in the Shopee application. The reason is the R-square value of the dependent variable usability expectancy in the Lazada application has the highest R-square value of 0.957. This result means that the BI variable can be explained by the independent variables performance expectancy, effort expectancy, and social influence of 95.7%.
Top1. Introduction
Amidst the COVID-19 pandemic, people often use e-commerce to meet their daily needs. In Rizaty (2021), according to 2021 Bank Indonesia Annual Meeting information, the projected e-commerce transactions in Tanah Air are to touch 403 trillion in 2021. It is an increase of 51.6% from the previous year, which was 266 trillion. Nowadays, most online shopping applications compete to put Artificial Intelligence (AI) technology forward to help users. Artificial Intelligence is a simulation of the intelligence possessed by humans, modeled in machines, and programmed to be able to think like humans (Dicoding, 2015 in Ayunda and Rusdianto (2021). There are several Artificial Intelligence on e-commerce platforms, such as Image Recognition, chatbox, recommendation engine, search engine, smart logistics, etc(Jacobides, Brusoni, & Candelon, 2021).
Various theoretical models have been devised to predict adoption and use of technology. Unified Theory of Acceptance and Use of Technology or UTAUT is a framework devised by
Venkatesh et.al.to predict technology acceptance in organizational settings. UTAUT advances on the basis of integrating the dominant constructs of eight prior prevailing models that range from human behavior, to computer science. The eight models are: Theory of Reasoned Action (Ajzen & Fishbein, 1975), Technology Acceptance Model (Davis, 1989), Motivational Model (Davis, Bagozzi, & Warshaw, 1992), Theory of Planned Behavior (Ajzen, 1991), Combined TAM and TPB (Taylor & Todd, 1995), Model of PC Utilization (MPCU) (Thompson, Higgins, & Howell, 1991), Innovation Diffusion Theory (Moore & Benbasat, 1991) and Social Cognitive Theory (Compeau, Higgins, & Huff, 1999).
Numerous academics have worked at UTAUT since it was founded, including (San Martín & Herrero, 2012), (Pitchayadejanant, 2011) and (Lu & Hsiao, 2010). UTAUT and the Technology Acceptance Model were combined by (Mardikyan, Besiroglu, & Uzmaya, 2012) and (Lin, Tsai, Joe, & Chiu, 2012) respectively. Pappas, Giannakos, Pateli, and Chrissikopoulos (2011) used a UTAUT and Social Cognitive Theory combo. According to Gruzd, Staves, and Wilk (2012) the UTAUT components were generally a good place to start when examining scholarly social media use and behavior. According to Gruzd et al. (2012) and Helena Chiu, Fang, and Tseng (2010) performance expectancy, effort expectancy, enabling factors, and social influence have an impact on total use intention. However, how potential vs early users perceive these antecedents differs substantially.
Image Recognition on the Shopee and Lazada is still relatively new, leading to some frequent problems with its use. Based on the Google Play report, many users feel that the Image Recognition feature in the Shopee and Lazada is not sufficiently accurate to identify products. The discrepancy can reduce the effectiveness and usability of the Image Recognition feature. A low level of usability can reduce the user's trust level in the applications. A system has a high level of usability if users can find or get what they need and understand the setup used (Shneiderman & Leavitt, 2006). The GAP between users and the Image Recognition feature, where there are limited features in meeting user expectations, makes researchers interested in measuring the usability level of the Image Recognition feature on the Shopee and Lazada.