电子商务论文哪里有？本文探讨了影响国际买家在中国跨境电子商务平台上购买电子产品的可能因素。价理论框架被用来说明变量之间的关系。这项研究回顾了OPU对PI的影响不如对OPV的影响。影响OPU的变量是PDU，该变量不受支持，但支持DR issupported。另一方面，OPV得到了MS、PO和CON的所有实用利益的支持。
1.Introduction
1.2. Problem Statement
Based on a recent literature review on purchase decision or intention, it was found thatmore studies have been dedicated to traditional e-commerce rather than CBEC (Giuffrida et al.,2017; Mou et al., 2019). CBEC and delivery are much more complicated and risky than eitherthe traditional offline market or local e-commerce due to the high information symmetrybetween international sellers and buyers (Song, Yan & Zhang, 2019). In lieu to this, there weremany studies that now involve international studies due to positive effects of expansion. Oneparticular one was South Korea where it has taken some interest on various authors because despite being two times smaller than Japan, its trade ratio is higher than Japan (CECRC, 2016).Compared to a traditional domestic e-commerce, cross-border e-commerce (CBEC) has beengaining an increasing interest for many countries, especially China as sellers want to know howto sell more, build more, engage more, list more or position more. In light of the recent Covid-19outbreak, the encouragement to do CBEC has been stronger than ever. This is because manygovernment safety measures have been put in place where consumers and businesses alike are todo trading with minimal amount of social contact. While it is hoped that the situation will beresolved in the future, it has undoubtedly pushed and encouraged a lot of business to adopt andexpand their business with CBEC if they were to stay relevant.
3. Model Construction and Hypotheses
3.1. Research model
The research model is shown in Figure 1. This study proposes a research model ofpurchase intention in CBEC based on the overall perceived value and overall perceiveduncertainty. The dependent variable in this model is consumer purchase intention. The subjectivelikelihood that a customer will make a purchase from an online seller is referred to as purchaseintention. When it comes to e-commerce, once a customer develops positive behaviours, they aremore likely to make a purchase.
4. Data Analysis and Discussion
4.1. Participants
For the pilot test, we used 25 responses to validate scale measures. Subsequently, themain data collection period was done and 247 responses were collected but dropped 22 responsesthat clearly showed repeated selection of the same responses. Finally, 225 responses wereconfirmed usable and used to facilitate analysis of the research model. Table 1. reports thedemographics of the 225 respondents.
Responses results showed that the gender demographics were pretty equal where thereare 53.3 percent female and 46.7 percent male. On age demographics, there is a clear majority ofparticipants aged between 21 to 30 years old with 65.3%. This is closely followed by participantsaged under 21 and aged between 31 to 40, both 13.3 percent and 16.0 percent respectively.
In regards to country demographics, we found that the majority of responses come fromAsia, Malaysia in particular with 49.3 percent in total, typically half of the countriesdemographics. The high response rate from Malaysia is both expected and unexpected. It isexpected mainly because English is also a main language in Malaysia. Most participants wouldhave no issue answering the questionnaire. It is however also unexpected because Malaysia isnot a large country with population ranging to around only 32 million as of year 2019. Even so,these responses are consistent with the research looking for English-speaking internationalbuyers.
4.2. Reliability and validity test
Thanks to its emphasis on analyzing statistical data, SPSS has been chosen formanipulating and deciphering survey data. SPSS can analyse data for descriptive and bivariatestatistics, numerical result forecasts, and group identification predictions. Data transformation,charting, and direct marketing are also included in the package. SPSS is capable of analysing andmodifying a wide range of data types including practically all structured data formats.Spreadsheets and plain text files are supported by the software. Once data has been collected,keep the excel file ready with all data inserted using the right tabular forms. Once data isimported, the SPSS will analyze it when variables are inserted correctly. To do so, differentvariables including both categorical (e.g. age, gender) and continuous (e.g. Likert scale items)are created.
To test the reliability and validity of the measurement model, SPSS software was used toconduct an exploratory factor analysis. The test showed that all items are able to load onto theirexpected constructs and are shown on Table 2. For scale reliability, we can see that all Cronbachα are greater than 0.7 whereas composite reliability (CR) have some lower values. This is similarwhen average variance extracted (AVE) has some values lower than 0.5. While factor analysis isneeded to determine that there are no cross cultural, the originator of the instrument wasdeveloped using Western sample. For Asians, the items may not fully load into the constructs assuggested by the developers to replicate the findings, possible causing some weaker results.These weaknesses has been noted and further discussed in the limitation section of this paper.
5. Results
5.1. Findings
This paper examines the factors that can influence cross-border e-commerce (CBEC)purchase intention on China electronic products among international consumers. Due to thecomplex nature of CBEC compared to traditional local e-commerce which is known to besimpler, there should be a higher sense of uncertainty faced by online buyers. These uncertaintiescould stem from the distance and complex interactions between buyers and sellers on productdescription due to cultural and language difference. Even so, CBEC provides beneficial valuethat many online buyers could appreciate such as cheaper and more products offerings than theirdomestic markets. Based on the valence theoretical framework, it is conceptualized thatuncertainties of CBEC as negative valences whereby the benefits as positive valences. Theresearch model was empirically tested using survey data collected from 225 international CBECrespondents.
We classified uncertainties as a factor that influences purchase intention based onresearch into information asymmetry and risk in online commerce (Dimoka et al., 2012; Kim &Krishnan, 2015). In CBEC settings, risk perceptions such as delivery risk and product descriptionuncertainty have been regarded as significant uncertainties due to different consumer protectionlaws and complications in monitoring delivery across borders (Cho and Lee, 2017; Kim et al.,2017). We introduced this uncertainty under negative valence based on literature review (Cui etal., 2019). On the other hand, placed under positive valence, it was hypothesised that utilitarianbenefits of product offering, monetary savings, and convenience would have positive effects onnet utility and thus increase purchase intention (Cui et al., 2019; Mou et al., 2019). Positivevalence increases overall value, which was found to be the most important factor in purchaseintentions. It appears to be the primary driver of consumer engagement in CBEC, as it is morestrongly related to purchase intention than overall perceived uncertainty. This could be because apositive consumer experience with the e-commerce platform, such as ease of site navigation andpayment procedures, combined with a positive reviewer experience, such as successful delivery,pricing, and product availability, reduces some of the uncertainty inherent in e-commercetransactions, allowing for increased perceptions of overall net value.
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