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Association rule mining approach: evaluating pre-purchase risk intentions in the online second-hand goods market.

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F19%3A63523880" target="_blank" >RIV/70883521:28120/19:63523880 - isvavai.cz</a>

  • Result on the web

    <a href="http://economic-research.pl/Journals/index.php/oc/article/view/1737/1628" target="_blank" >http://economic-research.pl/Journals/index.php/oc/article/view/1737/1628</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.24136/oc.2019.032" target="_blank" >10.24136/oc.2019.032</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Association rule mining approach: evaluating pre-purchase risk intentions in the online second-hand goods market.

  • Original language description

    Abstract in orig.: Research background: A considerable amount of research has been conducted on the riskiness associated with online transactions in general. However, few studies have paid particular attention to the risk of online second-hand goods transactions. We, therefore, argue in this paper that, while online transactions pose several risks to consumers, the addition of second-hand goods intensifies the risks to the user. As the risk factors brought about by the online second-hand goods transactions persist, the magnitude of such risk inherent in the customer in question has not clearly emerged. Purpose of the article: This paper aims at eliciting the magnitude of risky components aligned with the tendency to connect online in search of second-hand goods. Again, providing insight into demographic variables in relation to the pre-purchasing risk factors; averting customers to connect online in search of second-hand goods stands as one of the key reasons for the present study. Methods: The research adopts a data mining algorithm, notably the Association rule mining to glean relevant patterns in the data accrued from the Czech Republic, premised on risk components governing the online buying behaviour of second-hand goods. To this end, a simple random technique was adopted to gauge the views of e-shoppers in the Czech Republic on online second-hand goods transactions; with 329 out of 411 respondents eligible for our analysis. Findings &amp; Value added: The results of the association rule technique have revealed that respondents within the gender frame are both adamant to hook-up online, in spite of the fact that they have shopped online, yet do not think of looking at second-hand goods sites because of some risky influence inherent in them, even if the respondent is an ordinary personal-user of online transactions. In all these developments, the research concludes that the second-hand industry needs to redesign the websites with much attention to reinforce stringent measures that will give better assurance of the risk factors, which will tend to avert the customer from connecting via the Internet in pursuit of second-hand goods.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50204 - Business and management

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Oeconomia Copernicana

  • ISSN

    2083-1277

  • e-ISSN

  • Volume of the periodical

    10

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    PL - POLAND

  • Number of pages

    20

  • Pages from-to

    669-688

  • UT code for WoS article

    000515826300005

  • EID of the result in the Scopus database