The document summarizes a research study that examined how two types of social interactions - word-of-mouth learning (WOML) through online consumer reviews and observational learning (OL) through summary purchase statistics - influence consumer purchase behavior of digital cameras on Amazon.com. The research found that both WOML and OL positively impact sales, but the influence of OL decreases over the product lifetime as more information becomes available. The rating in consumer reviews matters less for highly-ranked products that see herd behavior, while detailed reviews dampen the effect of OL. The number of reviews serves as a key OL signal. The findings have implications for how sellers should approach attracting consumer reviews.
Accenture, comScore and dunnhumbyUSA collaborated on a study to help CPG executives better understand the link between consumers’ usage of brand websites and their brand purchases in retail stores.
מוטי משיח על הטיפול בקנאביס רפואי בקרב הסובלים מפוסט טראומהמוטי משיח
מצגת באנגלית מאת דוקטור מוטי משיח העוסקת ביעילות הטיפול בקנאביס רפואי אצל אנשים אשר סובלים מפוסט טראומה.
A presentation by Dr. Moti Messiah talking about medical efficacy of medical marijuana in people who suffer from PTSD.
Accenture, comScore and dunnhumbyUSA collaborated on a study to help CPG executives better understand the link between consumers’ usage of brand websites and their brand purchases in retail stores.
מוטי משיח על הטיפול בקנאביס רפואי בקרב הסובלים מפוסט טראומהמוטי משיח
מצגת באנגלית מאת דוקטור מוטי משיח העוסקת ביעילות הטיפול בקנאביס רפואי אצל אנשים אשר סובלים מפוסט טראומה.
A presentation by Dr. Moti Messiah talking about medical efficacy of medical marijuana in people who suffer from PTSD.
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Personal ratings or social proof - Vortrag GOR 2019.pdfChristian Bosau
Electronic word of mouth (eWOM) highly influences costumers.
Main questions of this study: Are total ratings (= social proof) more powerful than personal ratings (= direct experience)? Can a powerful brand buffer the effect of the ratings?
Two kinds of eWOM are compared regarding positive and negative effects.
MRI examined online buying styles of consumers, and identified 5 distinct types of online shoppers. This presentation focuses on two types of consumers, "Social Cyber-Shoppers" and "Selective Cyber-Shoppers", in the context of social shopping.
Proximity BBDO Sweden presents: Digital Day 2011, Feb 10.
NICK ORSMAN
ROI and Social Media -- why are companies investing in social media, how do companies measure their investment and what are some of the best examples of using social media to meet marketing objectives.
The presentation can be seen:
http://www.youtube.com/watch?v=Z9TBeCVjReM
Get to know Proximity BBDO Sweden here:
http://www.facebook.com/ProximityBBDOSweden
http://www.proximity.se
Defense contractors who are able to adjust their marketing and sales to what matters most to their prospects can shorten their sales cycle, increase sales and repeat purchases, and attain greater profitability by becoming a preferred vendor.
What is phrasing - An explorative approach to improved user manipulation BSI
As part of the content development of digital contact points such as websites, blogs and social media hubs, content is developed, prepared and published exclusively from the internal perspective of the companies. Ideally, in close cooperation with different departments, content development focuses on identified keywords and factual insights. Companies try, if at all, to put themselves in the position of the user in order to increase the demand for the corresponding content. As a result, it can be stated that companies see what they want to see.
Who is the better marketer? A comparison between marketing executives and dru...BSI
We were asked to present the current status of the marketing industry in the form of a presentation at various conferences and internal workshops. The provocative discussion of the current circumstances in our industry was explicitly desired. After a long consideration we chose the comparison between a fictitious marketing board and a drug dealer. This comparison should compare both positions on the basis of different criteria. The distance to the consumer and the detachment of the industry from agencies, consultancies and their customers was only a side effect. This article was discussed with us by agencies and companies for months. The view of pure amusement and self-reflection up to angry bewilderment surprised us. The central statement of this article was not to be insulting or even offensive. Rather the lacking proximity to the customer, to the circumstances of the life and purchase phases and deepened needs of users were brought up for discussion. The contrast and the exaggerated version were only conveyed playfully, without referring to one or a few brand companies. The value of the work within brand-leading companies should continue to be at the forefront of all efforts to take account of change in the marketing industry, better understand direct insights into the needs of customers and pay far more attention to them. At the same time, it should be attempted to change the leadership of marketing today on the basis of new approaches and to define the effect patterns better and more specifically.
We ask you to understand the contribution as a humorous discussion and not to process it too narrowly. Thank you.
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Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Personal ratings or social proof - Vortrag GOR 2019.pdfChristian Bosau
Electronic word of mouth (eWOM) highly influences costumers.
Main questions of this study: Are total ratings (= social proof) more powerful than personal ratings (= direct experience)? Can a powerful brand buffer the effect of the ratings?
Two kinds of eWOM are compared regarding positive and negative effects.
MRI examined online buying styles of consumers, and identified 5 distinct types of online shoppers. This presentation focuses on two types of consumers, "Social Cyber-Shoppers" and "Selective Cyber-Shoppers", in the context of social shopping.
Proximity BBDO Sweden presents: Digital Day 2011, Feb 10.
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The presentation can be seen:
http://www.youtube.com/watch?v=Z9TBeCVjReM
Get to know Proximity BBDO Sweden here:
http://www.facebook.com/ProximityBBDOSweden
http://www.proximity.se
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3. Does Action Speak Louder than Words in
Social Interactions?
— Online Consumer Review and Purchase Behavior
Yubo Chen & Jinhong Xie
University of Arizona University of Florida
2nd International WOM Marketing Conference
May 18-19, 2006, Barcelona, Spain
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
4. Agenda
Introduction
Research Issues
Research Design and Data
Results
Conclusions and Managerial Implications
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
5. Online Consumer Review
Online consumer product review: Increasing
Popularity
What is the function of online consumer review?
How does it differ from third-party review?
How consumer reviews influence purchase behavior
(product sales)?
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
6. Consumer vs. Third-party
Product Review
Consumer review: free sales assistants (Chen and
Xie 2004)
– provide matching information to consumers, particularly
novices
Third-party product review (e.g., CNET.com): focus
more on quality information (Chen and Xie 2005)
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
7. A Broader Perspective
— Social Interactions
Two Types of consumer social interactions (learning)
– Word-of-mouth Learning (WOML)
• Detailed product Information (e.g., online consumer reviews)
– Observational Learning (OL)
• Summary statistics of the actions of previous adopters (e.g.,
market share, sales, sales rank)
• A subset of previous adoption (e.g., imitate neighbors )
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
8. Research Issues
Observational
Learning (OL)
Consumer
Product Lifetime Purchase
(Product
Sales)
Word-of-mouth
Learning (WOML)
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
9. Research Design and Data
Research Setting: Digital Camera at Amazon.com
Social Interactions Data
– WOML: customer reviews
• Valence: average customer rating
• Information Volume: # of reviews, overall # of review words
• Credibility: # of reviews
– OL: summary stats of previous purchase
• Recommendation stats
• Sales rank
• # of reviews
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
10. Research Design and Data
Sales Data
– transformation from sales rank (Chevalier and Goolsbee 2003,
Chevalier and Mayzlin 2006)
Product Lifetime Data: CNET.com
– 120 models reviewed in CNET between 03/09 and 05/07
Other Control Variable : price, # of sellers
Data collected on three days in 09/05
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
11. Main Effect of WOML and OL
+
Recommendation Stats
+*
Sales Rank
+*
# of Reviews
Consumer
+ Purchase
Review Rating (Product
Sales)
+*
# of Overall Review Words
—*/+*/—*
Lifetime / # of Sellers / Lowest Price
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
12. Main Effect of WOML and OL
Both types of social interactions impacts
consumer purchase.
Consumer review content (information
volume) significantly increases purchase,
regardless the review rating and product rank/
recommendation
Matching Function of Consumer Review
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
13. Interaction between WOML and OL
— How OL Moderates the Impact of WOM Valence
Recommendation Stats
Sales Rank
—*
# of Reviews
—*
Consumer
+/— Purchase
Review Rating (Product
Sales)
# of Overall Review Words
Lifetime / # of Sellers / Lowest Price
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
14. How OL Moderates the Impact of WOM Valence
The impact of review rating depends on OL information.
Consumers more likely to ignore review rating for highly ranked/
recommended products
Herd Behavior/ Information Cascade (Banerjee 1992, Bikhchandani et. al 1992)
Ratings are more important for low ranked/ recommended items
How consumer review influences OL’s effect (on herd behavior)?
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
15. Interaction between WOML and OL
— How WOM Info Volume Moderates the Impact of OL
Recommendation Stats
Sales Rank
+*/— +*/—
—*
—* # of Reviews
Consumer
Purchase
# of Overall Review Word (Product
Sales)
Review Rating
Product Lifetime / # of Sellers / Lowest Price
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
16. How WOM Info Volume Moderates the Impact of OL
WOM Info Volume decreases the impact of
recommendation stats / sales rank.
# of Reviews is more used as the OL (action stats) by
consumers.
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
17. Moderating Effect of Product Lifetime
Recommendation Stats
—*
Sales Rank
—*
# of Reviews
+/—
Consumer
Product Lifetime Purchase
— (Product
Review Rating Sales)
+
# of Overall Review Words
# of Sellers / Lowest Price
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
18. Moderating Effect of Product Lifetime
The impact of OL is decreasing with the product
lifetime.
– Available product information increases along the product
life cycle, which decreases the impact of OL
Late consumers (novices) tend to focus less on
review rating.
– Consumer reviews provide matching information for
novices
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
19. Conclusions
Both types of social interactions impact consumer purchase.
Consumer review rating info is more likely to be ignored for highly ranked/
recommended products.
Consumer review content (info volume)
– increases purchase significantly, regardless the review ratings and
product OL status
– dampens the impacts of OL and shatters information cascade
# of consumer reviews mainly works as a OL statistics and signals
previous purchase situation.
The impact of OL is decreasing along the product life cycle.
Late consumers (novices) tend to focus less on review rating.
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
20. Managerial Implications
OL information can benefit mass market products but hurt
niche products.
Only providing overall consumer rating is NOT enough, and
might even hurt sellers (through limited # of reviews).
Attracting detailed review content (even mixed or negative) is
essential for sellers, particularly for niche products.
Implications might apply for offline WOM.
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?
21. Thank You!
Yubo Chen
Assistant Professor of Marketing
Eller College of Management
University of Arizona
Email: yubochen@eller.arizona.edu
http://www.eller.arizona.edu/~yubochen
Yubo Chen & Jinhong Xie: Does Action Speak Louder than Words in Social Interactions?