Determinants of Consumer Online Purchasing Intention: An Empirical Study in Tunisia

Determinants of Consumer Online Purchasing Intention: An Empirical Study in Tunisia

Wadie Nasri
Copyright: © 2022 |Pages: 16
DOI: 10.4018/IJCRE.309689
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Abstract

The purpose of this paper is to understand consumers' online purchasing intentions. This study extends the technology acceptance model (TAM) by adding subjective norm, perceived enjoyment, perceived risk, and prior internet experience in the development of a theoretical model to study customers' intentions within the context of online purchasing. An empirical approach was based on an online survey of 252 people in Tunisia, undertaken during August/September 2020. The data were analyzed using structural equation modeling. The results of the study indicated that perceived usefulness, perceived enjoyment, and prior internet experience are significant positive predictors of consumers' online purchasing intentions. Nevertheless, perceived risk appeared to exert significant negative influences respectfully on consumers' online purchasing intentions. These results help the practitioners to consider the important factors in their strategies when they make strategic decisions as key factors affecting online purchasing intentions.
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Introduction

E-commerce is becoming a reality, reinventing consumers’ path to purchase, disrupting business models, and creating growth opportunities for large and small retailers. E-commerce is a type of industry where buying and selling of product or a service is conducted over electronic systems such as the Internet. One of the most common uses of Internet technology is business to consumer (B2C) e-commerce or e-retailing. The scope of e-commerce is very broad and includes different types of online businesses, such as Internet banking, online ticketing, electronic payments, online shopping and online purchasing.

In e-commerce, companies sell goods and services to individual consumers over the Internet (Lee & Tan, 2003). For consumers the advantages of Internet retailing include saving of time, better prices, more product choices, faster order processing, availability of goods 24 hours a day, 365 days per year, from almost any location, quicker delivery products, etc. (Turban & King, 2006).

Tunisia, with its 11,818,619 million inhabitants, is on the smaller side of e-commerce markets both in its region and in Africa as a whole. The total number of Internet users in Tunisia stood at 8,170,000 million in December 2020, comprising 68.4% of the Tunisian population. A report from the UN Conference on Trade and Development ranked Tunisia fourth among African countries and 79th globally in e-commerce. The overall value of e-commerce sales and online services in Tunisia reached 271.5 million dinars in 2019 that is a 21% increase compared to 2018. However, it is important to note that the Tunisian Institute of Consumption does not consider cash-on-delivery transactions, which are estimated to account for 70% of e-commerce sales, as online orders. Many web-based companies were established Tunisia: Carrefour Tunisie, Founa, Wamia, Jumia, Le marché.tn, Babymam, Try and Buy, Parashop and Paraclic. In spite of the rapid worldwide growth of the business to customer (B2C) e-commerce over the past few years, the rate of adoption of e-commerce varies from country to country, because of differences in personal characteristics of the consumers, different cultures, different framework of laws and different technical infrastructures (Cao & Mokhtarian, 2005).

The understanding of the factors that influence consumer intention towards purchasing online in Tunisia is critical for companies, to foster user acceptance and usage. Over the past years, the Technology Acceptance Model (TAM) has been one of the most used and cited models (Wang, 2018). The TAM originated from cognitive psychology theories, such as the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975) and the Theory of Planned Behavior (TPB) (Ajzen, 1991). The TAM has been proven as an adequate and valid theory to explain the behavioral use of information technology and communication services (Davis, 1989). The TAM has been used by researchers as a base model in gauging users’ perspectives on the adoption and acceptance of information technology (Svendsen et al., 2013).

The selection of the TAM model is based on its statistical power to explain users’ intentions to use information technology (Rahman et al, 2018). It is better at explaining variance than the TPB and the expectation disconfirmation theory (Ongena et al., 2013). The TAM, with standard measurements of the constructs, helps researchers to conduct studies with smaller sample sizes; its predictive power has also been proven robust (Luo et al., 2011).

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