WMW

[tinlonglau-blog]

情侶間的訪問

內容：情侶間的訪問 BOMBA：https://www.facebook.com/BOMBA.VDO FELIX：http://www.facebook.com/FELIX.YKC Instagram BOMBA：bomba_ig FOX : foxyu FELIX : flexible_yu KIKKO : k...

It is funny^^

[wongmanw-blog]

WOW so funny!:D

[wongmanw-blog]

Since the rugby world cup is holding this year, my fav team--- Allblacks!!!!

I believe that the allblacks boys will be the champion again!!!

Here was the haka dance before they against French. The most awesome part of this team. It should be a very special culture in New Zealand. Hope to join them one day!!!!!!!!!!!!!!

[wongmanw-blog]

Now here we go for this week’s topics:

1. What opportunities and/or challenges are online communities bringing for brands?

There are few types of online communities: (according to class lecture)

• (A)vocation communities: hobby or work • Place-based communities: geographical area • Common condition communities: demographic background; medical condition • Consumer communities: brands or fans • Concern communities: particular social, political,ideological concern

Let’s use a example to explain what opportunities and/or challenges are online communities bringing for brands.

Sportsroad

Sportsroad is a platform that people can share or comment their own experience or feeling about sports. People who are interested in sports can find different information in this website.

So how does it provide a opportunity for a brand?

Companies like Nike or Adidas can share some event information or stories at the special column. It will be a channel to promote their brand indirectly. But on the other side, some vicious slander may exist online and destroy their brand image. Bot you can also said that some positive comments from users or loyalty customers will have a positive effect  to the brand.

Example that a positive comment of a Reebok shoes:

[wongmanw-blog]

Any of the characteristics of big data

In my own understand, i define Big Data is a large and complex data set for analysis.

So, one of the characteristic is Complexity. When large volumes of data come from multiple sources, data management will be very complex and hard.

I found some information online which is about the characteristics of big data.

The authority calls it “5Vs”

Volume refers to the vast amounts of data generated every second. Just think of all the emails, twitter messages, photos, video clips, sensor data etc. we produce and share every second. We are not talking Terabytes but Zettabytes or Brontobytes. On Facebook alone we send 10 billion messages per day, click the "like' button 4.5 billion times and upload 350 million new pictures each and every day. If we take all the data generated in the world between the beginning of time and 2008, the same amount of data will soon be generated every minute! This increasingly makes data sets too large to store and analyse using traditional database technology. With big data technology we can now store and use these data sets with the help of distributed systems, where parts of the data is stored in different locations and brought together by software.

Velocity refers to the speed at which new data is generated and the speed at which data moves around. Just think of social media messages going viral in seconds, the speed at which credit card transactions are checked for fraudulent activities, or the milliseconds it takes trading systems to analyse social media networks to pick up signals that trigger decisions to buy or sell shares. Big data technology allows us now to analyse the data while it is being generated, without ever putting it into databases.

Variety refers to the different types of data we can now use. In the past we focused on structured data that neatly fits into tables or relational databases, such as financial data (e.g. sales by product or region). In fact, 80% of the world’s data is now unstructured, and therefore can’t easily be put into tables (think of photos, video sequences or social media updates). With big data technology we can now harness differed types of data (structured and unstructured) including messages, social media conversations, photos, sensor data, video or voice recordings and bring them together with more traditional, structured data.

Veracity refers to the messiness or trustworthiness of the data. With many forms of big data, quality and accuracy are less controllable (just think of Twitter posts with hash tags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of content) but big data and analytics technology now allows us to work with these type of data. The volumes often make up for the lack of quality or accuracy.

Value Then there is another V to take into account when looking at Big Data: Value! It is all well and good having access to big data but unless we can turn it into value it is useless. So you can safely argue that 'value' is the most important V of Big Data. It is important that businesses make a business case for any attempt to collect and leverage big data. It is so easy to fall into the buzz trap and embark on big data initiatives without a clear understanding of costs and benefits.

[wongmanw-blog]

For your blog exercise this week, please summarize your understanding / comments / critiques and/or supplement examples / cases related to any of the following topics:

1. Diffusion of innovations model

Diffusion of innovations is actually a theory created by Everett Rogers in his book in 1962. This model explain how, why and at what rate Innovation spread and adoption through communication channel in social system.

Adoption is an individual process detailing the series of stages one undergoes from first hearing about a product to finally adopting it.

Diffusion signifies a group phenomena, which suggests how an innovation spreads.

The following is the definition described by Roger.

Adopter category and Definition

Innovators

Innovators are willing to take risks, have the highest social status, have financial liquidity, are social and have closest contact to scientific sources and interaction with other innovators. Their risk tolerance allows them to adopt technologies that may ultimately fail. Financial resources help absorb these failures. 

Early adopters

These individuals have the highest degree of opinion leadership among the adopter categories. Early adopters have a higher social status, financial liquidity, advanced education and are more socially forward than late adopters. They are more discreet in adoption choices than innovators. They use judicious choice of adoption to help them maintain a central communication position.

Early Majority

They adopt an innovation after a varying degree of time that is significantly longer than the innovators and early adopters. Early Majority have above average social status, contact with early adopters and seldom hold positions of opinion leadership in a system 

Late Majority

They adopt an innovation after the average participant. These individuals approach an innovation with a high degree of skepticism and after the majority of society has adopted the innovation. Late Majority are typically skeptical about an innovation, have below average social status, little financial liquidity, in contact with others in late majority and early majority and little opinion leadership.

Laggards

They are the last to adopt an innovation. Unlike some of the previous categories, individuals in this category show little to no opinion leadership. These individuals typically have an aversion to change-agents. Laggards typically tend to be focused on "traditions", lowest social status, lowest financial liquidity, oldest among adopters, and in contact with only family and close friends.

The adoption curve :

There are many elements affecting the rate of adoption.

Perceived attributes of innovations

Relative advantage, Compatibility , Complexity , Trialability , Observability 

Types of innovative decisions

Optional? Collective? Authority?

Communication channels 

eg. Mass media? Interpersonal? 

Nature of social system 

eg. Norms, level of interconnectedness, etc. 

Change agents’ characteristics and efforts

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- In what ways can you relate to this course?

New media is totally connecting our daily life, such as social media, phone etc.

- What topic(s) in our 13-week schedule are you interested in?

Youtube. Coz i like using video to express my though and Youtube is actually a new media which is controversial.

- What question(s) do you have about new media culture (or the above topics that you are interested) now?

Culture of Youtube? Limitation?