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Empirical Study of Consumers’ Purchase Intentions in C2c Electronic Commerce

Autor:   •  March 21, 2019  •  Research Paper  •  3,297 Words (14 Pages)  •  88 Views

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Empirical Study of Consumers’ Purchase Intentions in C2C Electronic Commerce

Abstract: Electronic commerce is becoming increasingly important in business, but lack of intention to purchase has become a main barrier in the development of electronic commerce. Thus, effective measures are needed to promote consumers’ intentions to purchase in online consumer to consumer (C2C) stores. This paper postulates that five factors, the perceived ease of use of the website, perceived usefulness of the website, vendor competence, introduction and recommendations of third parties, and  vendors’ attitude toward customers, influence consumers’ intentions to purchase in online C2C stores and this intention directly leads to their action to purchase from online C2C stores. The structural equation modeling (SEM) method was used to analyze empirical data, supporting these hypotheses except for the effect of vendor competence.

Key words: electronic commerce; consumer to consumer (C2C); purchase intention; technology acceptance model (TAM); structural equation modeling (SEM)

Introduction

Consumer to consumer (C2C) electronic commerce is one of the few applications that come close to emulat- ing the success of the other two main electronic com- merce models for business to consumer (B2C) elec- tronic commerce and business to business (B2B) elec- tronic commerce. The C2C websites, eBay.com.cn and Taobao.com, have attracted many people and are very famous. C2C is growing rapidly for several reasons. First, the C2C model provides an online transaction platform for individual buyers and sellers; thus, the buyers and sellers can auction product online.  Second,

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Received: 2007-06-06; revised: 2007-12-10

Supported partially by the National Natural Science Foundation of China (No. 70731001) and the National Social Science Founda- tion of China (No. 06BJY101)

﹡﹡To whom correspondence should be addressed.

E-mail: luyb@mail.hust.edu.cn; Tel: 86-27-87556448


the C2C model has received widespread support from major Internet users because of the ease of interactions between users. Third, C2C online transactions have no time and space restrictions compared with tradition auction models, so they maximize the probability of transactions and help identify the real value of the merchandise.

Despite the fact that electronic commerce is develop- ing very quickly, online shopping is not among the main purposes for people using the Internet in China, but is ranked the 12th according to a CNNIC (China  Internet

Network Information Center) survey[1]. More  research

is needed to identify which factors influence people’s intentions to take part in online transactions, which factors influence consumers’ purchase intentions, and how to improve the sellers’ effectiveness in C2C transactions. A better understanding of these issues is critical to the effective use of electronic commerce, especially  to  know   how  consumers   feel  about C2C


electronic commerce. Sellers and C2C market provid- ers need to understand consumers’ shopping behavior in the market to improve their business.

Although there have been many recent publications discussing the issues of transaction intention and trust in B2C electronic commerce[2-6], only a few studies have attempted to explain the factors influencing the adoption of C2C[7] and there has been little research into the critical factors influencing C2C purchase in- tentions particularly in China.

The study investigated the acceptance of C2C elec- tronic commerce and empirically tested whether the technology acceptance model (TAM) can predict C2C purchase intention. This study seeks to identify factors critical to consumers’ purchase intentions in C2C elec- tronic commerce, compares the relative importance of every factor, and examines the causal relationships among the variables on C2C purchase intentions. Ex- perts and consumers were first interviewed to create  the research model and questionnaire. Data was col- lected using a questionnaire and sampling method and analyzed using structural equation modeling (SEM).

The study seeks to investigate factors influencing consumers’ purchasing intentions in C2C electronic commerce and to develop guidelines to predict con- sumer purchasing behavior in C2C electronic com- merce. The research findings are useful for sellers wanting to attract more buyers and for online busi- nesses to improve services for consumers in C2C elec- tronic commerce.

  1. Literature Review

The outlook for C2C electronic commerce depends not only on consumer acceptance of Internet technologies as viable transaction means, but on consumer recogni- tion of C2C sellers as reliable people. Thus, a compre- hensive model describing the factors that drive con- sumers  to  accept  C2C  online  transactions  would   be useful to help them better understand consumer online behavior in the emerging C2C e-commerce environment.

The major driving factors for consumers’ purchase intentions were identified by integrating two literature streams for the TAM[8-13] and the literature on trust and risk[3,6,14-17] under the nomological structure of the the- ory of reasoned action (TRA). Some reviews have al- ready  integrated these  two streams[2,18,19].  A modified


TAM model was developed and empirically validated to predict consumer acceptance of C2C electronic commerce.

In general, several technology acceptance models such as innovation diffusion theory (IDT), TRA, TAM, the theory of planned behavior (TPB), and the task/technology fit (TTF) can be used to examine the adoption behavior of C2C electronic commerce.     Lee

and  Turban’s  model[3]   presented  the  major  relation-

ships between consumer trust in e-commerce and the four groups of major potential antecedents: trustwor- thiness of the Internet merchant (ability, integrity, and benevolence), trustworthiness of the Internet shopping medium (technical competence, reliability, and me- dium understanding), infrastructural (contextual) fac- tors (effectiveness of third party functions, certification, and effectiveness of security infrastructure). Sultan   et

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