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Call for a Special Issue
The Cultural Impact in Platform Competition
Deadline: October 31, 2020
Guest Editors
Makoto Nakayama, DePaul University, United States, mnakayama(at)
cdm.depaul.edu
Simon Poon, University of Sydney, simon.poon(at)sydney.edu.au
Lee Chei Sian, Nanyang Technological University,
LeeCS(at)ntu.edu.sg
Panagiotis Stamolampros, University of Leeds,
P.Stamolampros(at)leeds.ac.uk
Yun Wan, University of Houston Victoria, United States,
wany(at)uhv.edu
Theme
User-generated content (UGC) has been widely utilized in the
cross-cultural
context with the globalization of online platform portals. For
this special
issue, we define UGC as what consumers post online including
consumer
product reviews at sites like Amazon and Yelp, posts on social
media such
as Facebook and Twitter, blogs, pictures, and images found in the
online
environment. The diversity of global consumers’ cultural
backgrounds both
enriches the utility of UGC on multi-national social media
platforms and
increases the complexity of platform competition strategy (Alt
&
Zimmermann, 2019). While some studies reported cross-cultural
created
differences in ethnic restaurant reviews (Chik et al., 2016;
Nakayama &
Wan, 2019b), we are still in the early stage of understanding how
cross-cultural influences are transpiring on the broader spectrum
of UGC,
such as how to channel their influence as a differential factor
into
service or product recommendation and customization in platform
competition. Thus, empirical investigations are needed to explore
innovative methods and frameworks on the cultural data analytics
of UGC as
well as on the integration of cultural factors into platform
competition
strategies. In this special issue, we encourage submissions that
apply a
cross-disciplinary perspective beyond the extant national culture
frameworks, such as Hofstede (Hofstede, 1980; Minkov &
Hofstede, 2012) and
intercultural communication (Gudykunst, 2005).
Central issue and Themes
Today, global social media platforms such as Facebook and Yelp are
accessible not only via PC and smartphone but also via automobiles
and an
increasing number of IoT devices. Such platforms constantly
collect and
analyze UGC, then interpret them into recommendation or customized
products
and services back into the platform. Though this process has been
replicated across the globe in different languages by ecommerce
portals,
they are more or less applying the same algorithms and standard
practices
across platforms. However, consumers from different countries are
not a
monolithic group. Their cultural backgrounds heavily influence
their
preferences, usage, and evaluation of products and services.
Globally
standardized recommendation and customization algorithms without
incorporating culture elements could contribute or directly lead
to
strategic failures, such as the exit of the Chinese market by
Amazon (Liao,
2019). Meanwhile, recent studies found consumers with different
cultural
backgrounds place different emphases on food quality, waiter
service,
ambiance, and price fairness in their reviews (Nakayama & Wan,
2018,
2019a). In other words, comparable Yelp five-star sushi
restaurants in
Tokyo and New York could be different because Japanese and US
reviewers
have different emphasis on evaluation attributes. Failure to
account for
cross-cultural differences on the provided ratings may also
distort the
information content of UGC for firms and customers (Stamolampros
et al.,
2019).
Today’s economy is driven strongly by optimizing the product mix
to the
target consumer profiles on a global scale. When cultural factors
influence
the consumer in the evaluation of products and services, we should
expect
such influence being properly utilized, presented, and informed to
other
consumers through recommendation systems, widgets on social media
outlets,
and review websites. With the big data analytical methods being
systematically used in the cross-cultural perspective of UGC to
help
analyze such influence (Akter & Wamba, 2016), we expect future
studies
would not only identify cultural influence but also measure them
quantitatively. We encourage submissions that apply a
cross-disciplinary
perspective on the cultural impact of UGC with topics such as:
• Theoretical frameworks to analyze national culture influence on
UGC
• Cultural induced textual/sentimental characteristics of UGC
• Sentiment analyses of UGC across national, regional or ethnic
cultures
• Cross-cultural analyses of UGC for subjective goods such as
hospitality
products and services
• Diversity of UGC reactions and consumers’ cultural background
• Review summarization strategy considering cultural variations of
UGC
• Adaption strategy of UGC across national, regional or ethnic
cultures
into platform competition
• Biases in social media due to national, regional or ethnic
cultures
• Review of national, regional or ethnic cultural impact on UGC
• Algorithm design in digital platforms considering cultural
factors
• Culture in designing recommendation and review systems
• Management of platform content and functionalities in different
cultures
• Analyses of the competitive impact of cultural issues in digital
platforms
• Cross-cultural differences on the effect of UGC on corporate
performance
• Antecedents of the credibility of UGC in different cultures
• Firm responses to negative UGC across national, regional or
ethnic
cultures
Submission
Electronic Markets is a Social Science Citation Index
(SSCI)-listed journal
(IF 3.553 in 2018) in the area of information systems. We
encourage
original contributions with a broad range of methodological
approaches,
including conceptual, qualitative and quantitative research.
Please also
consider position papers and case studies for this special issue.
All
papers should fit the journal scope (for more information, see
www.electronicmarkets.org/about-em/scope/) and will undergo a
double-blind
peer-review process. Submissions must be made via the journal’s
submission
system and comply with the journal's formatting standards. The
preferred
average article length is approximately 8,000 words, excluding
references.
If you would like to discuss any aspect of this special issue, you
may
either contact the guest editors or the Editorial Office.
References
Akter, S., & Wamba, S. F. (2016). Big data analytics in
E-commerce: a
systematic review and agenda for future research. Electronic
Markets,
26(2), 173–194. doi:10.1007/s12525-016-0219-0
Alt, R., & Zimmermann, H.-D. (2019). Electronic Markets on
platform
competition. Electronic Markets, 29(2), 143–149.
doi:10.1007/s12525-019-00353-y
Chik, A., Vásquez, C., & Vasquez, C. (2016). A comparative
multimodal
analysis of restaurant reviews from two geographical contexts.
Visual
Communication, 16(1), 3–26. doi:10.1177/1470357216634005
Gudykunst, W. B. (2005). Theorizing about intercultural
communication. Sage.
Hofstede, G. (1980). Culture’s consequences: International
differences in
work-related values. London: Sage.
Liao, S. (2019). Amazon admits defeat against Chinese e-commerce
rivals
like Alibaba and JD.com - The Verge. The Verge.
https://www.theverge.com/2019/4/18/18485578/amazon-china-marketplace-alibaba-jd-e-commerce-compete.
Accessed 30 December 2019
Minkov, M., & Hofstede, G. (2012). Hofstede’s fifth dimension:
New evidence
from the World Values Survey. Journal of cross-cultural
psychology, 43(1),
3–14.
Nakayama, M., & Wan, Y. (2018). Is culture of origin
associated with more
expressions? An analysis of Yelp reviews on Japanese restaurants.
Tourism
Management, 66. doi:10.1016/j.tourman.2017.10.019
Nakayama, M., & Wan, Y. (2019a). Cross-Cultural Examination on
Content Bias
and Helpfulness of Online Reviews : Sentiment Balance at the
Aspect Level
for a Subjective Good. In HICSS 52 (Vol. 6, pp. 1154–1163).
Nakayama, M., & Wan, Y. (2019b). The cultural impact on social
commerce: A
sentiment analysis on Yelp ethnic restaurant reviews. Information
and
Management. doi:10.1016/j.im.2018.09.004
Stamolampros, P., Korfiatis, N., Kourouthanassis, P., &
Symitsi, E. (2019).
Flying to Quality: Cultural Influences on Online Reviews. Journal
of Travel
Research, 58(3).496-511
https://doi.org/10.1177/0047287518764345
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