Cross Cultural Online Communication (LETCIWB204IBC)
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Cross-cultural Online Communication Summary
Introduction- Lecture 1
External communication
External communication before and after rise of internet:
Past Present
Controlled information stream directed Less controlled information stream directed
toward consumers (marketer generated toward consumers (consumer generated
content) content)
Ways for consumers to communicate with each other online → eWOM:
• Blogs
• Social media
• Brand communities (set up by the brand and consumers can go there)
• Forum
• Review website
Electronic word of mouth plays a huge role in tourism. With the emergence of user-
generated content reviews, the travel decision-making process is revolutionized. Travelers
are more dependent on online guest reviews.
Relationship e-WOM and different communication channels
Degree of Interactivity
Scope of the Sender-Receiver Asynchronous Synchronous
communication (send and receive different (at one time)
times)
Many-to-many Blogs, Twitter, group- Video-
Whatsapp conferencing
One-to-many Websites, product review Speech
sites
One-to-one Email Telephone
Whatsapp
Source credibility
Perceived expertise:
• Lay persons (someone not qualified in a subject) have little expertise
• Self-proclaimed expert has expertise
• Rated expert has a high level of expertise
Perceived trustworthiness:
• Lay persons are more trustworthy
• Self-proclaimed expert is not so trustworthy
• Rated expert is more trustworthy
Which advertising formats do people trust the most? Personal recommendations-Consumer
opinions online-Search engine ad results-Ads on social networks.
,Role of e-WOM in the consumer selection process
There is a positive correlation between review rating the degree to which a product was
bought.
eWOM is more influential than traditional WOM, because:
• There are no time constraints
• Comfort (you can find reviews any time and write and read them in your own time)
• One-to-many
• Absence of face-to-face pressure/Speed (you have more time to write your
message)
Types of e-WOM users:
• Opinion leaders
They are the opinion activators. They are contributing. 10% are opinion leaders.
They make up for 90% of the contributors.
• Opinion followers/lurkers
They are the opinion followers. 90% are opinion lurkers. They do not contribute
anything.
→ The 90-9-1 principal users: 90% of users is a lurker and makes up for 0% of the posts. 9%
is an intermittent contributor makes up for 10% of the posts. 1% is a heavy contributor and
makes up for 90% of the posts.
What motivates people who post reviews?
• Ego-focused:
- I have been disadvantaged, therefore I must have revenge
- Self-presentation
• Socially focused
- Helping others
- Desire for social interaction
What is more frequent? Positive or negative reviews?
Positive tweets get retweeted more often than negative reviews. You don’t want to be
seen as a person who spreads negative news.
The effect of the review may depend on the type of product:
• Products for which the quality of information is easy to evaluate before purchase,
like cameras → Positive reviews are seen as more valuable.
• Products for which the experience is more important than the product
characteristics, such as airlines and city trips → Negative reviews are seen as more
valuable.
Implications for organisations
• Online reputation management= Monitoring your image/reputation
• Everyone can share their experience about your company at any time → Webcare=
Reading and acting on negative messages. Can be done using humour.
• Automatic sentiment analysis= System to get data on what the overall company
sentiment is by analysing the key words of e-WOM.
, Webcare
• Directly responding to negative review and the larger audience that is watching.
• Ensure complainers stop complaining and show everybody else that the company
responds when customers are dissatisfied
→ Best method= Mediated immediacy
• Immediate= Through the same channel, public
• As soon as possible
• Personal= Use personal language and speak in the first person
Automatic sentiment analysis/ Opinion mining
• Automatic sentiment analysis= Computer evaluates subjective information to find
out what the sentiment is, e.g. Tweets.
• Takes key words from a list of words on a scale from pleasant to unpleasant and
decides if the Tweet comes across as positive or negative.
• Doesn’t look at jargon, emojis, sarcasm, abbreviations, new words, irony. Nationality
and culture have not been looked at
Predicting intercultural differences- Lecture 2
Model of influence of culture
Living in a certain culture affects how you act as an individual which affects how you use
communication. Communication in turn affects how you act as individual, and individuals
make a culture.
Edward Hall- Models
Iceberg model
• We can only see artifacts and behaviour from the outside, but under water are
beliefs, norms, values. The only way to learn about the invisible part of the iceberg is
by living in that culture.
Proxemics
• People from different cultures perceive space differently. The size of the spaces
differs. In Argentina for example they like to keep smaller distance from strangers
and acquaintances.
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