This is the summary of an article by Marco Janssen. You can get the pdf of the behavioral targeting article here: Targeting Individuals to Catalyze Collective Action in Social Network.

A lot of the challenges that our society faces today are problems that require collective action, such as climate change. It is very important to study collective action within large heterogeneous groups. Governments can help provide collective action, but there’s more to the rules and laws implemented to cause a behavioral change. Some studies have shown that an individual has a greater chance in participating in a collective action through social influence, such as reputation and social pressure.

Is it possible to target the right, influential individuals so they can have a social influence on others leading to cooperative behavior? There have been studies related to spreading influence in the context of social networks and viral marketing and the primary role of peer pressure. This is the summary of a paper presenting a model of agents to solving a collective action problem, and describe which individuals to nudge.

Model Description

The model is composed of agents that make decisions on which behavior to adopt, and each behavior corresponds to a certain personal reward (individual part). An agent’s action is also influenced by neighbors in the network (social influence part). The initial behavior is A, and the main question is what conditions are necessary for an agent to adopt behavior B. Individual utility is defined as how far away or close to the preference of an agent is to the behavior. Social influence, on the other hand, increases when there are more similar behavior around the neighborhood.

Model analysis is in the investigation of the effects of homophily, or the similarity of agent attributes in a certain network. Plus, interventions are also include, such a incentives which make a certain behavior more attractive than the already is, increasing the probability that an agent will make that decision.

Model Analysis

The following are some of the model’s initial results. The default case used in model analysis is one wherein the agents can only obtain global feedback, which means that each agent has an equal share of the initial behavior. Investigation on the effects of local information is the next step, and it has been shown that adoption rate increases where homophily is at a high level.

Another batch of experiments investigates 4 types of interventions. It has been shown that those agents who are not socially influenced by the behavior of others are the ones more likely to be affected by these interventions. Those who have lots of connections are least affected because of the larger influence of peer pressure.

Conclusion

Improvement of this model can be done by including other human behavior assumptions including the process of deriving information and product preference changes, for example, due to changes in price. Testing the model on empirical data is also challenging.

Some of the interesting findings in this simplistic model include the following: the desired behavior can be increased to 400 percent if feedback on the adoption is given only to the agents in their social network, instead of on a global level. Second, the most effective strategy for increasing adoption rates is by targeting agents that are least likely to be affected by social influence in decision-making.

This is the summary of an article by Bernard J. Jansen and Simone Schuster. You can get the pdf of the behavioral targeting article here: Bidding on the Buying Funnel for Sponsored Search and Keyword Advertising.

Search engine marketing comes in various terms, such as search engine advertising, pay-per-click and keyword advertising. It is used by companies for product and service promotion on SERPs, or search engine results pages and its effective use.

Among the major search engines, this is the central business model used. The paradigm used by SEM is primarily called the buying funnel, a staged process that describes how consumers decide how they buy. The first stage is Awareness, the second stage is Research, third stage is Decision, and final stage is Purchase.

The buying funnel is widely accepted, so it is very important to investigate if this is the accurate way of explaining how consumers behave in response to keyword advertising campaigns. Is the buying funnel an advertising paradigm or is it really how consumers behave? Should a SEM campaign target the Purchase stage right away, or should each stage be targeted? These are some of the open questions.

Buying Funnel

The foundations of the buying funnel comes from information processing theory which is the main theory for all consumer behavior models. It is used to model how consumers can be reached by advertisers, and it is a nice fit to decision making among consumers.

Awareness is a consumer’s conscious need and desire to address that need with a service or product. Research is an information seeking process which includes looking for the right product governed by several factors, including affordability, necessity, etc. Decision is when a consumer comes up with a purchase set or a list of possible products. Purchase is when the consumer has actually made a decision to purchase or not purchase a certain good.

According to this paper, there is a few published empirical research regarding the buying funnel among literature related to SEM. This research has both practical and academic implications and so is a very worthwhile pursuit.

Research questions

The research question is: For online consumers, do the purchase and search interactions with keyword advertisements follow the buying funnel stages? Specifically, the study aims to determine if it is possible to tell in what stage of the buying funnel an online consumer is based on his or her search query. The following are the hypotheses to the research quesion.

First Hypothesis: There is a significant difference among the queries of each buying funnel stage based on the average number of impressions. Whenever an advertisement appears on a search engine results page after a user submits a search query, that’s called an ad impression.

The second to sixth Hypotheses are all similar to the above hypothesis, where the significant differences on each stage of the buying funnel are based on average number of clicks, average cost per click, average sales revenue per query, average number of orders, and average number of items ordered, respectively.

Methods and Results

Data is obtained from daily keyword advertising information from a nationwide retail chain that has a sales presence in both brick and mortar and online stores, spanning four years amounting to a little less than 7 million records and 40,000 keyword phrases. These key phrases are then classified according to one of the four stages found in the buying funnel.

Based on the occurences of classified queries, 51 percent are found to belong to the Research stage, followed by 28 percent for Awareness, 17 percent for Decision and finally 4 percent for Purchase. One research has previously concluded that most online consumers use search engines to research about products or services.

The hypothesis testing shows that indeed the buying funnel represents the behavior of actual consumers online. All of the hypotheses have significant differences, and so the buying model is really a workable model. On the other hand, the results also show that the buying funnel only works well for the classification of the focus of online ecommerce queries and not a good model to describe the movement of a consumer from one stage to the following stage.

This is the summary of an article by Ethan Liever and Chad Syverson for the Oxford Handbook of the Digital Economy. You can get the pdf of the behavioral targeting article here: Online vs Offline Competition.

Jeff Bezos, founder, president and CEO of Amazon.com, understood the advantages of his online business selling books. Consumers can easily look for the books and products they want to buy, they can order from any location and most of the purchases do not have sales tax. He also understood its limitations. Consumers have to wait before they receive their orders. They can’t inspect the products they will be receiving beforehand. Amazon had to find unique ways of letting consumers know about their products before buying.

Brick-and-mortar book companies are now thinking, how should they use online marketing? What fundamental aspects of marketing do they need to change or maintain? This article discusses the competition between the offline and online segments of a market.

Onlines Sales versus Offline Sales

During the year 2008, Offline sales amounted to 18.7 trillion dollars, while that of online sales is 3.7 trillion dollars. That’s just around 16 percent of all sales for online. However, online sales have grown faster. Between the years 2002 to 2008, online sales grew by 120 percent while offline sales grew by 30 percent. Apparently, online sales will definitely rise as more and more people engage in e-commerce.

business-to-business (B2B) e-commerce dominates over business-to-consumer (B2C) e-commerce, but B2C is growing faster at 174 percent between the years 2002 to 2008.

Who Sells Online?

For manufacturing, 54 percent of online sales come from Transportation Equipment. For electroning shopping and the industry of mail-order houses, 47 percent are done through online sales. There are certain businesses such as dentistry which are not fit for online sales but the logistics of these businesses including billing and advertising can be done online. Across manufacturing industries, share of online sales are heterogeneous and most businesses adopted internet technology but not the extent of fundamentally changing their business.

Who buys online?

Internet users are those with higher income, younger, and are more educated. The higher the income, the more likely it is for an individual to use the Internet, although that ends after the 70,000 to 90,000 dollar annual income threshold. After that, there is no significant relationship between internet use and income. Education is also an important factor. You are 8 to 9 percent less likely to use the Internet if you did not graduate from highschool. Having a college degree increases the probability to 8 to 8 percent. In addition, the older you are, the less likely you are to be online.

Low income persons have less probability of buying something online, and the same is true for people who have no high school diploma. Racially, blacks are less likely to purchase than Whites, while Asians are not significantly more likely to buy goods online than Whites.

Difference Between Offline and Online Channel

E-commerce technology keeps customers from inspecting the goods they want to purchase beforehand. There is also a delay between purchase and consumption, but search costs among consumers is reduced. Furthermore, supply chain costs are reduced and/or services are improved due to new e-commerce technologies. Taxes are also different for offline and online sales.

information asymmetries for online purchases are due to several reasons, with the most obvious one being the fact that consumers can’t inspect the products prior to purchase. Therefore, the market becomes inefficient. There have been efforts to reduce asymmetries, including free shipment, but the delay of receiving the purchase still poses some problems. Another approach to asymmetry alleviation is by using third-party certification, where firms establish a quality reputation for their products.

Another interesting difference is market geography. Online sellers provide for the notion of “death of distance” according to Kolko (2000). There is some evidence that people living in more isolated cities tend to use the internet more. But there are studies that volume of exchanges decreases as distance increases.

There are no sales taxes for most online transactions. Chicago consumers that buy online can save up to 10.25 percent in sales tax for the year 2009, for example. If, however, sales taxes are added to online purchases, studies have shown that online retail sales could be reduced by a quarter.

How E-commerce Affects Market Outcomes

Lower distribution costs and reduced search costs are said to be the two main factors to reduce online market prices. It has been shown in several studies that this is the case. One example is that consumers who purchased cars with the help of an online service paid 2 percent less. However, online marketing is not as frictionless as when it was originally thought of to be during its early commercial applications. One market outcome is that lower-cost businesses may tend to grab larger shares off higher cost competition.

This is the summary of an article by Patricia E.M. Covington and Meghan Musselman. You can get the pdf of the behavioral targeting article here: 2010 Privacy and Data Security Developments.

The decade ending 2010 is great for new laws and regulations made for protecting online consumers in terms of secure data and privacy. It started with the GLB act of November 1999 for detailing sharing and collection o fpersonal information among financial institutions. Regulators such as the Federal Trade Comission or FTC also created new laws to punish companies that were unable to protect the information of their consumers. Online activities, data security and privacy seem to be top priority for FTC in the coming years.

Importance of Online Marketing

Wireless internet, social networks, and smart mobile devices have radically changed how consumers spend their time on the Internet. Online shopping has also become more prevalent and there are significant increases in loan production, which is primarily due to improvements in mobile technology, and businesses don’t want to be technologically left behind.

In fact, businesses engage in tracking the online behaviors of consumers to produce targeted ads. FTC and the Congress have prioritized behavioral advertising because this has caused some privacy concerns among consumers who didn’t want to be tracked. From 2009-2010, FTC held Privacy Rountables in search of better technologies for collection user information. Jon Leibowitz, FTC Chairman, stated that consumers are no longer aware that their information is being used because the user agreements are too complex for them to bother reading. FTC hopes that self-regulation will make forward progress and will not be regulating behavioral advertising in the meantime.

Data Pass

Data Pass is a recent issue. As a consumer checks out during an online purchase, he is offered a discount or any offer that gets him to pay recurring fees. A consumer usually doesn’t recognize that this offer comes from a third party who already has his credit card info. In response to this anomaly, Visa has initiated by not allowing third parties to get credit card information. Another response comes from Senator John Rockefeller who legislated a data pass regulation, called the Restore Online Shoppers’ Confidence Act.

Data Security

During 2010, the FTC has continued its campaign to prohibit practices that are unfair to data security. For example, a Twitter settlement was made which was the first action by FTC towards a social networking site. FTC pointed out incidents during 2009 where users have gained access to private Twitter accounts and have made unauthorized tweets. Twitter was unable to protect the information and the system which contains it. As a result, Twitter was asked to maintain “a comprehensive information security program”.

This is the summary of an article by Alan J. Bush, et al. You can get the pdf of the behavioral targeting article here: Sports Celebrity Influence on the Behavioral Intentions of Generation Y.

Sports celebrities are role models in today’s media culture. Advertisers have taken advantage of this, and many of our spokespersons today are famous athletes such as Michael Jordan, Tiger Woods, Kobe Bryant and Shaq. However, there is a lack of research regarding the influence of athletes on a target market. There is also some doubt that sports celebrities are really that effective for meeting the strategic demands of advertisers.

For advertisers, teenagers are one of the most important and challenging targets markets.
Nowadays, they belong to what is called the Generation Y, or those born between 1977 to 1994. Research is being done to understand what motivates this generation and what their behaviors are.

The objectives of this study are the following. First, explore the concept of sports celebrities being role models for Generation Y. Second, investigate if sports celebrities really influence the behaviors and intentions of this generation. Third, explore the influence of sports celebrities on female members of this generation.

Consumer socialization

Ward, 1974, describes consumer socialization as the process by which young people acquire attitudes, skills and knowledge that are important to their roles as marketplace consumers. There are socialization agents that influence young people’s behaviors, motivations and attitudes. Role models, in its conceptual definition, clearly include peers, teachers, parents, and the like. Sports celebrities, on the other hand, offer indirect contact, where their influences comes from their individual outstanding achievements.

Vicarious role model: the celebrity athlete

The entertainment and sports market is huge and fast growing. Advertisers have used celebrity athletes to endorse their products for various reasons. One theory that explains why this preference exists is the following. Sports heroes are highly dynamic, attractive and have many likable attributes. However, consumer socialization may provide the theoretical groundwork to determine the effectiveness of the influence of a sports celebrity athlete.

Hypotheses

The following are the hypotheses of the study. First hypothesis states that teenagers’ athlete role model influence has a positive relationship with complaint behavior and product switching. Second hypothesis states that Teenagers’ athlete role model influence has a positive relationship with favorable word-of-mouth behavior. Third hypothesis states that teenagers’ athlete role model influence has a positive relation to brand loyalty. Finally, the fourth hypothesis states that the athlete role model influence of female teenagers’ has a more positive relationship to complaint behavior and product switching, positive word of mouth behavior and brand loyalty than male teenagers.

Summary of Methodology

Teenagers from Generation Y were the sample for this study, because of their huge population, the fact that they are currently acquiring brand loyalties and product preferences, and because they will spend huge amounts of money in the future. All in all, there were 218 teenagers who participated in the study, with 54 percent male and 46 female.

Role model influence was assessed using the Rich (1997) role model scale, which determines the level of agreement a respondent has on statements regarding an athlete’s behavior which the respondent may want to emulate.

Intentions and behaviors are measured using a purchase intentions scale by Zeithaml, Berry and Parasuraman (1996). A 12-item 7-point scale test composed of several questions related to purchase and behavioral intentions. Confirmatory factor analysis was also used for assessing the multidimensionality of the scale used for behavioral intentions.

Results, Discussion and Implication

Results for the first hypothesis show that there is no significant relationship between athlete role models and complaining behavior or product switching. For the second hypothesis, there is a positive relation between favorable word-of-mouth communication and the role models. There is also a positive relation for hypothesis 3, with regards to brand loyalty.

For the fourth hypothesis, there is only partial support. For product switching and complaint behavior, girl and boy teenagers don’t differ much. However, female teenagers agree that athlete role models influence then to talk about good things about a product or brand, recommending and encouraging relatives and friends to purchase the product.

Most interestingly, this research shows that for adolescents, celebrity sports role models are important to them when they make choices and when they talk positively to a certain brand or product.

This is the summary of an article by Marious Koufaris. You can get the pdf of the behavioral targeting article here: Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior.

Behaviors of consumer on the Internet are unique in terms of the two main players. Online consumers are also computer users, while the businesses are now virtual stores. This kind of online transaction has been given a name, NEOs, or Net-enabled organizations. This study focuses on B2C or business to consumer e-commerce and the purchases in this framework that are unplanned. Study is also done for customer retention and how these two are influenced by the perceptions of an online consumer toward a Web store.

Theoretical Framework and Study Measures

Do online consumers have a different way of thinking and acting in comparison to offline consumers? Well, one of the differences between these two is that online consumers have lower customer loyalty than offline counterparts because they have more power, are more demanding, etc. The vendor has less power than the consumer. Another difference is that online consumers are afraid of online risks such as vendors asking for your credit card number for all the wrong reasons.

What attitudes or metrics influence how users behave in an e-commerce environment? Definitely, online consumers have similarities with offline consumers, but they have their own unique concens because online marketing is a different kind of environment.

Customer Intention to Return

One of the primary goals of companies is to convince their clients to return and establish costumer retention. NEOs seem to have a harder time developing customer loyalty because it is very easy to switch to another vendor, and would even go back to buying from physical stores if they find the experience not so delightful and convenient. This study approximates long periods of data taking for accurate customer loyalty measurement by conducting a survey to measure behavioral intention.

Why Customers Return

Online consumers have a double identity of being a computer user and a traditional shopper. As a traditional shopper, an online consumer’s desire to return will be based on traditional marketing / psychological variables, such as emotional responses, pleasure, arousal and dominance, to the environment. Another model is called flow, which is described as consumers being absorbed with what they are doing. Flow has also been studied in terms of computer environments.

Hypotheses of the Study

Flow can be measured in terms of shopping enjoyment. However, the importance of this factor has been challenged by previous researches, but some researches also point out that enjoyment may determine the loyalty of an online customer. The first hypothesis states that intention to return has a positive relation to shopping enjoyment. Intention to return is also hypothesized to be positively related to concentration and perceived control.

Hypothesis two states that Perceived usefulness and ease of use of the Web store has a positive relation to intention return. The third hypothesis states that consumers that have higher shopping enjoyments are more likely to make unplanned purchases, while those with higher perceived control are less likely to do so. High likeliness to make unplanned purchases is also related to consumers with higher concentrations.

Product involvement is the motivational state of a person to an object activated by its relevance or importance. Hypothesis 4 states that there is a positive relation between product involvement and concentration and shopping enjoyment. The fifth hypothesis states that there is a positive relation between perceived skills and perceived control, shopping enjoyment and concentration.

Product search mechanisms are called for because consumers want to have more control and easily find what they are looking for. Hypothesis 6 states that the use of value-added search mechanisms has a positvie relation to concentration, shopping enjoyment and perceived control. Finally, hypothesis seven states that the level of challenges of a Web store is positively related to concentration, shopping enjoyment and perceived control.

Discussion

Results show that both shopping experience and enjoyment are significant variables to determine the intention of return of an online consumer. How much a consumer believes a Web store is useful also determines his future visits, as well as an emotional resoponse to the store. Consumers don’t have to expect to enjoy while they shop online, but when they do they are most likely to come back. This means that online consumers are closer to offline consumers than we previously thought.

There are surprising results as well, such as no relationship between unplanned purchases and perceived control, shopping enjoyment and concentration. Perhaps there are other variables that are in play here that actually have a significant relationship with unplanned purchases.

Other results show that consumers are more likely to enjoy shopping online if they feel comfortable and confident with the Web store.

As a practical implication, online web stores should provide hedonic and utilitarian value to their stores, catering to not only increasing customer convenience but also making sure that emotional experiences such as shopping enjoyment are incorporated as well to retain customers.

Opinions about behavioral targeting are divided. Some are pro-behavioral targeting while others are anti-behavioral targeting. A recent study on individuals that understand and work in the online ad industry was conducted. However, these individuals may or may not fully understand the whole concept of behavioral targeting. The study shows that around 25 percent of individuals in the USA are not in favor of online behavioral tracking. Around 40 percent of these individuals delete the cookies in their cache or web histories. Only 11 percent are in favor of behavioral targeting.

Behavioral Targeting is not a new concept

Did you know that behavioral targeting already existed more than a hundred years ago? When Montgomery Ward catalog came out in 1872, the concept of behavioral targeting started. When a catalog arrives in front of your door, you are already their target customer, and you wonder how they are able to send you a catalog that you wanted. This is similar to the concept of online targeting.

Pro-cookies and for behavioral targeting

Individuals who are pro-cookies say that tracking cookies are a good thing. There is no need to delete it in the trail, mail or cache. They know that cookies do not contain any personal identification information. Cookies do not contain any material to know who you are; name, age, address, etc. The advertisers do not actually care about any personal information about you. All they care about is the profile of their target customer and the ads that they can flash in front of you.

Anti-cookies and not for behavioral targeting

On the other hand, there are people who think tracking cookies is not a good thing. There is a need to delete it in their mail or cache. These people are scared of the possibility of knowing any personal information about them. They do not want their online behavior to be followed or be tracked. Target online advertisement is an idea that they are not comfortable with.

Self-assessment for behavioral targeting

Are you now thinking if you are a pro or anti behavioral targeting? There may be a lot of things going on your head right now. You may be puzzled if you are going to delete those cookies in your email or cache or you should retain it. It all depends on your desire to be followed on your online activity or not. You may be bothered for security reasons. You just need to weigh the pros and cons, and read more on details about behavioral targeting.

This is an article which reviews practitioner and academic literature regarding several consumer segmentation trends, written by Brownwyn Higgs and Allison C Ringer. You can get the pdf of the behavioral targeting article here Trends in Consumer Segmentation.

Consumer generated media, such as product review sites and social networking sites have allowed online consumers the chance to influence what kind of content they want to see online. This, in turn, influences how online business deals with its transactions. In addition, there are more intelligent consumers now than ever before, with the particular characteristic that they want to see content and purchase goods and services based on their individual needs. Demand has indeed become individualized, and so mass-customization is born.

Communication channels that have interactive features are now everywhere, and they continue to grow in number and sophistication. Interactivity allows consumers to talk to marketers and vice versa. These transactions are documented and used to improve how markets respond.

Overview of Market Segmentation

Market segmentation was developed during the mid 20th century because at that time, purchasing and demographic data, and even distribution and advertising channels was only made for consumer groups. Segmentation is composed of four methods, the traditional ones, post-hoc and a-priori, and the flexible ones, componential and dynamic.

A-priori method is such that an analyst selects a segmentation base and does his or her analysis, while post-hoc does the analysis first before forming bases. On the other hand, componential segmentation focuses on making predictions and deemphasizes on partitioning, while dynamic segmentation models simulated conditions in which analyses can be done on how consumers respond to the characteristics of test products.

Segmentation is important because it aims to identify segments that vary in terms of market behavior, aspirations and purchasing power. The kinds of data that are obtained to properly segment consumers include consumption, purchasing and attitudes toward products or services. As a result, most segmentation techniques are brand-driven and tactical.

Segmentation has several limitations, including its inabilty to narrow down groups into sufficiently small custers. Another criticism of this process is that it relys heavily on one off surveys. Ideally, continuous data collection hhelps prevent certain marketing dynamics difficulties in the long run. Still, segmentation is endeared by many practitioners and there is a lot of research going to make analysis more sophisticated and improve on segmentation approaches.

Specialized Segmentation

Certain types of segmentation, including those used for advertising, have diverged in terms of development and improvement because of their unique purposes and goals. A different set of methodologies and procedures for analysis are employed as well, and unique instruments are carried out to engage in segmentation studies. Aside from advertising segmentation, CRM and direct marketing segmentation also evolve unique segmentation strands, creating new frameworks, segmentation techniques which employ highly extensive mining of data.

Finer and Hyper-segmentation

Finer segmentation is used to group markets into narrow clusters more precisely, and has been improved by advances in information technology over the years. Hyper-segmentation, on the other hand, is used to identify an individual consumer’s segment. There are two common methods for hyper-segmentation, progressive profiling and addressable marketing.

Progressive profiling involves collecting data through interactive websites, asking consumers a question or two during transactions in a continuous process, gathering rich data about individual consumers and his or her preferences. Addressable marketing, on the other hand, uses digital communication services to collect information regadrding online behaviors like advertising exposure, content involvement, site engagement and site visitation.

Behavioural Based Targeting

Behavioural-based targeting or BBT involves aggregating the market rather than partitioning it, by determining the behavior and patterns that a user forms across the web. Two types of data are used for Behavioural-based targeting: first is sample populations of site visitors and a target users list. With BBT, marketers can identify valuable information regading user concentrations across websites.

Implications and Conclusion

These segmentation trends will improve the quality of data one can get for marketing purposes, but at the expense of increased complexity in processing among others. Certainly, as marketing improves so will the number of segmentation approaches increase.

Online Marketing Düsseldorf 2007
This is a video about behavioral targeting with Frank Wagner. Frank Wagner tells how he explains to his mother what his job in behavioral marketing is all about.

www.youtube.com/watch?v=26FSZ6RRJRE

This is the summary of an article by Michele Ybarra, et al. You can get the pdf of the behavioral targeting article here: Internet Prevention Messages: Targeting the Right Online Behaviors.

The Internet is a place for 9 percent of online youth being harrassed which could lead to emotional distress and other psychosocial problems. Advocates have called for the youth to stop sending out personal information about themselves online, and refrain from talking to strangers. While it seems logical to do so, there is little evidence to either support or refute this claim.

This study investigates 5 online behaviors: downloading images through file-sharing, sexual behavior, talking to online strangers, aggressive behavior, and sharing of personal information. Four questions will be asked. First, What are the prevalence rates and characteristics of online behaviors commonly referred to as “risky”? Second, Are behaviors targeted in Internet safety and prevention messages associated with increased likelihood of online interpersonal victimization? Thirds, Do psychosocial and personal behavior problems account for these associations? Fourth, does the total number of online behaviors engaged in affect the association between specific behaviors and victimization online?

Methods

The survey was conducted by telephone towards 1500 youth in what was called the Second Youth Internet Safety Survey on June 11, 2005. English speaking youth who used the Internet during the past 6 months on a monthly basis were allowed to do the survey. The makeup of the respondents correspond to the Internet population on a national survey level.

The respondents were asked how often they performed one of 9 online behaviors that are said to make one prone to online victimization. These behaviors were determined through messages about Internet safety and documents regarding youth victimization online. One example is sexual behavior, which are divided into two types: that which involves someone you don’t know talking to you about sexual matters and intently entering an x-rated website.

Online Interpersonal Victimization

When people are harrassed online in a sexual manner, that’s called online interpersonal victimization. The respondents are asked three questions to determine whether they have been baited into talking about sexual matters. They were also asked if they had any online relationship and whether it turned sexual in any way. Aside from that, harrassment was asked to find out whether the Internet was used against them for harrassment. Bivariate analyses was done to determine how harassment, unwanted sexual allurement and online behaviors were related to each other.

Psychosocial and Behavioral Problems

Questions from the Juvenile Victimization Questionnaire were used to ask the respondents if they have been abused physically or sexually during the year prior. In addition, the Youth Self-report of the Child Behavior Checklist was used to assess child emotional and behavioral problems.

Internet Use and Demographics

The youth were asked to estimate how much time they spent online in terms of number of hours in a day and days in a week. They were also asked to assess how well they are familiar with using the Internet and how important the Internet is to them. They also answered questions regarding chat rooms, instant messaging, and blogging.

Results

The results show that 20 percent of youth mentioned they have experienced online interpersonal victimization during the previous year. Three fourths of the respondents said they experienced at least one of the online behaviors linked with increased victimization. Most common was posting personal information online and least common was talking to online strangers.

All of the nine online behaviors are found to have a significant relationship with online interpersonal victimiation. The behaviors which have the strongest association are talking about sex with a person you only know online, meeting online people, and embarrassing someone deliberately.

Commonplace Risky Online Behaviors

A lot of online behaviors are becoming commonplace, such as posting personal online information. More than 30 percent of youth have friends they don’t really know in person. It is important to recognize the risky online environment that our youth are immersed in and find ways to reduce these risks.

Meeting people online is rightfully considered as a behavior that may heighten Internet harassment. On the other hand, sharing of personal information should be given more consideration. It is also found that youth that exhibit four or more kinds of risky online behaviors are more than a tenfold times likely to be victims. Health professionals and child experts should collaborate with parents to assess the behavior of their children online.