Online Video Reviews Helpfulness: Exploratory Study

被引:0
|
作者
Alsharo, Mohammad [1 ]
Sibona, Christopher [2 ]
Alnsour, Yazan [3 ]
机构
[1] Al Albayt Univ, Al Mafraq, Jordan
[2] Univ North Carolina Wilmington, Wilmington, NC USA
[3] Univ Colorado, Denver, CO 80202 USA
来源
关键词
Social commerce; social computing; online video review; online review helpfulness; YouTube; economics of information theory; WORD-OF-MOUTH; SOCIAL COMMERCE; PEOPLE;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Online reviews assist consumers in making an informed purchase decision and they became a trusted source for product information. This study aims to investigate online video reviews on YouTube to understand what are the most commonly reviewed products and what are the factors of YouTube video reviews which contribute to review helpfulness. We use qualitative and quantitative techniques as research methodologies. The results show that major categories reviewed on YouTube are video games, movies, and technology. Exploratory factor analysis revealed four important factors that may determine online video review helpfulness which are review popularity, comments, video information, and review depth. A conceptual model is introduced based on the factor analysis. The study has significant implications to research as it provides new insights regarding the role of online video reviews in purchases decision making process.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] PREDICTING HELPFULNESS OF ONLINE CUSTOMER REVIEWS: MODERATING EFFECT OF PRODUCT PRICE
    Balasubramanian, Vaishnavi
    Justus, T. Frank Sunil
    [J]. SMART-JOURNAL OF BUSINESS MANAGEMENT STUDIES, 2024, 20 (01)
  • [42] Using Dependency Bigrams and Discourse Connectives for Predicting the Helpfulness of Online Reviews
    Mertz, Matthias
    Korfiatis, Nikolaos
    Zicari, Roberto V.
    [J]. E-COMMERCE AND WEBTECHNOLOGIES, 2014, 188 : 146 - 152
  • [43] Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction
    Du, Jiahua
    Rong, Jia
    Wang, Hua
    Zhang, Yanchun
    [J]. WEB INFORMATION SYSTEMS ENGINEERING - WISE 2019, 2019, 11881 : 795 - 809
  • [44] Antecedents of Online Customers Reviews' Helpfulness: A Support Vector Machine Approach
    Mousavizadeh, Mohammadreza
    Koohikamali, Mehrdad
    Salehan, Mohammad
    [J]. AMCIS 2015 PROCEEDINGS, 2015,
  • [45] Predicting the Helpfulness of Online Customer Reviews across Different Product Types
    Park, Yoon-Joo
    [J]. SUSTAINABILITY, 2018, 10 (06)
  • [46] Assessing the Impact of Textual Content Concreteness on Helpfulness in Online Travel Reviews
    Shin, Seunghun
    Chung, Namho
    Xiang, Zheng
    Koo, Chulmo
    [J]. JOURNAL OF TRAVEL RESEARCH, 2019, 58 (04) : 579 - 593
  • [47] Predicting the helpfulness score of online reviews using convolutional neural network
    Saumya, Sunil
    Singh, Jyoti Prakash
    Dwivedi, Yogesh K.
    [J]. SOFT COMPUTING, 2020, 24 (15) : 10989 - 11005
  • [48] Improving text summarization of online hotel reviews with review helpfulness and sentiment
    Tsai, Chih-Fong
    Chen, Kuanchin
    Hu, Ya-Han
    Chen, Wei-Kai
    [J]. TOURISM MANAGEMENT, 2020, 80
  • [49] Do cultural orientations moderate the effect of online review features on review helpfulness? A case study of online movie reviews
    Kong, Juan
    Lou, Chen
    [J]. JOURNAL OF RETAILING AND CONSUMER SERVICES, 2023, 73
  • [50] Predicting the helpfulness of online reviews using multilayer perceptron neural networks
    Lee, Sangjae
    Choeh, Joon Yeon
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (06) : 3041 - 3046