Publications

@inproceedings{238e314391924b088e3d77b2ad27c944,

title = {Exploiting Heterogenous Web Data – A Systematic Approach on the Example of Nintendo Switch Games},

author = {Sandra Boric and Christine Strauss},

doi = {10.1145/3487664.3487674},

year  = {2021},

date = {2021-01-01},

booktitle = {iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence},

pages = {69--73},

publisher = {Association for Computing Machinery (ACM)},

series = {ACM International Conference Proceeding Series},

abstract = {This paper shows how heterogenous web data can be retrieved from global yet regionally tailored online platforms such as Amazon. A systematic data retrieval approach was applied to obtain data from regional Amazon and Nintendo websites. The data retrieval uses a 3x3 criteria setting: Three attributes (genre, age rating, player-count), three forms of analysis (distribution, reception, price), and three countries (Germany, U.S.A., Japan). A streamlined choice of Amazon-entries is suggested, and further criteria were set to allow comparisons between different regions. 196 Nintendo Switch games and 15 game genres were analysed. The results show which attributes accumulate the highest numbers of Amazon-ratings and rating scores in which country, and which genres have the highest Amazon- and Nintendo-prices. An uncovering of rating and pricing similarities and differences is of value to game research scholars and game developer studios and aids in a targeted catering to customers in different regions.},

note = {Publisher Copyright: textcopyright 2021 ACM. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.; 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 ; Conference date: 29-11-2021 Through 01-12-2021},

keywords = {Age Rating, Amazon, Game Genre, Germany, Japan, Nintendo, Nintendo Switch, Pricing, Rating score, U.S.A., Video Game Industry},

pubstate = {published},

tppubtype = {inproceedings}

}