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A FORAGING MODEL FOR HUMAN BEHAVIOR IN E-COMMERCE Dissertation Proposal by Bjarne Berg-Saether Committee members: Dr. Antonis Stylianou, Chairperson Dr. John Brzorad Dr. Heather Lipford Dr. Sungjune Park Dr. Dmitry Shapiro Dr. Kexin Zhao. Agenda. Introduction Approach
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A FORAGING MODEL FOR HUMAN BEHAVIOR IN E-COMMERCE Dissertation Proposal by Bjarne Berg-Saether Committee members: Dr. Antonis Stylianou, Chairperson Dr. John Brzorad Dr. Heather Lipford Dr. Sungjune Park Dr. Dmitry Shapiro Dr. Kexin Zhao
Agenda • Introduction • Approach • Literature review • Optimal Foraging Theory • Human Cognitive Factors • The Economic Perspective • Developing a Behavioral Foraging Model • Theoretical Model and Hypotheses • Theoretical Components • Methods • The Experiment • Instrument • Sample Population • Questions and Answers
Introduction Human interaction with the Internet has been studied from a variety of standpoints. Some have looked at technology adoption, optimal usage and cost/benefit functions, while others have examined the Internet from social, psychological, and interface standpoints. However, few have examined Internet usage at the individual level from a combined biological and economic perspective (Pirolli, 2003). It is likely that research into human foraging behavior on the Internet will lead to improved understanding of the reasons why some e-commerce sites succeed and other fail, how participants in the marketplace select a certain number of patches (web sites) as their local foraging area and often limit their searches to these patches. It may also provide better understanding of how humans expand and contract their foraging areas (collection of web sites) and how they decide when to stop foraging and start executing purchase transactions.
Introduction & Example of Information Foraging In this example we are searching for tickets between Washington and Paris. We want to make the round-trip in less than 24 hours total traveling time, and as cheap as possible. There are 689 available flights/routes. Sorting by Duration, we find that there are 237 flights that that has a total travel time of less then 24 hours (round-trip).
Introduction to Information Foraging We found that ticket prices range from $719 to $4,384 for our flight. The order which the 237 flight prices are listed is illustrated in this graph. Charnov's Marginal capture Theorem (Pirolli, 2007)
Agenda • Introduction • Approach • Literature review • Optimal Foraging Theory • Human Cognitive Factors • The Economic Perspective • Developing a Behavioral Foraging Model • Theoretical Model and Hypotheses • Theoretical Components • Methods • The Experiment • Instrument • Sample Population • Questions and Answers
Overall Approach The proposed research consists of 1. Building a theoretical model 2. Test the proposed relationships 3. Build a simulation model 4. Simulate changes in characteristics and explore asymptotes and economic aspects of the foraging model
Agenda • Introduction • Approach • Literature review • Optimal Foraging Theory • Human Cognitive Factors • The Economic Perspective • Developing a Behavioral Foraging Model • Theoretical Model and Hypotheses • Theoretical Components • Methods • The Experiment • Instrument • Sample Population • Questions and Answers
Optimal Foraging Theory In 1966 the field known as Optimal Foraging Theory (OFT) was established through the publication of Emlen’s article on foraging behavior of birds and by MacArthur and Pianka’s work, published the same year, on optimization models. In general, the models established over the next ten years focused on four core areas that became known as elements of a micro-ecological theory. Micro-ecology is defined by Merriam-Webster’s dictionary (2003) as “a branch of science concerned with the interrelationship of organisms and their environments. For foraging theory it refers to the study of: 1) What to eat (optimal diet) 2) Where to eat (optimal patch choice) 3) Optimal allocation to each patch (time) 4) Optimal patterns and speed of movements.
Optimal Foraging Theory Combined as a whole, the micro-ecological theory forms the platform for macro-ecological theory, which has far reaching implications. Presumably, over time through natural selection, parents that are successful foragers will pass some inheritable traits to their offspring and, thereby, evolve better foragers adapted to the environment. This also implies that in a stable environment, such an evolution has already occurred in the past and that the average observed foraging behavior of the current foragers is already close to the optimal foraging behavior. If the environment changes, as it always does, the unsuccessful tails of the distribution will become the new “norm”; in theory. This is known as saturation of the behaviors.
Agenda • Introduction • Approach • Literature review • Optimal Foraging Theory • Human Cognitive Factors • The Economic Perspective • Developing a Behavioral Foraging Model • Theoretical Model and Hypotheses • Theoretical Components • Methods • The Experiment • Instrument • Sample Population • Questions and Answers
Human Cognitive Factors Classification of HCI research (Zhang and Li, 2005). Based on our literature review of Cognitive traits and Demographics we look at a set of HCI factors that relate to foraging at the individual level. (Pirolli, 2007; Newell, 1990)
Demographical factors - Education Education is a significant factor in technology system interaction. A study by Argarwal and Prasad (1999) found education to be closely related to the beliefs about both technology and actual usage. Others have found that a low level education not only acts as an access barrier to technology, but also impacts what services are being used and how they are used (Larsen and Rainie, 2002). Specifically, higher education has been found to be linked with more technology usage (duration), more exploration (search), and earlier adoption of new technology products and services. These findings have held very consistent over time. For example, in an experimental study of 100 individuals, the education level was found to be a significant factor measuring user competence (Munro et al., 1997). A correlation between a user’s general educational level and technology usage has been confirmed in longitudinal observations. In a study of Internet usage over a five year period, Losh (2003) found that while Internet usage increased for all education levels in the period, higher educated individuals tend to use technology for more diverse tasks and for longer periods. These research findings infer that higher educated users tend to exhibit increased technology awareness, increased computer skills, and is a factor that should be included in our research effort.
Demographical factors - Gender Gender is one of the validated measures of the UTAUT (Venkatesh et al., 2003), and has been consistently validated in HCI in areas such as TPB. In a study on specific gender differences in system usage, using TPB as their framework, the authors conclude their findings by stating “Clearly, gender shapes the initial decision process that drives new technology adoption and usage behavior in the short-term, which in turn influences sustained usage, thus establishing that early intentions formed by women and men will have a lasting influence on their usage of the said new technology—it is critical to recognize that the underlying drivers of these stable early intentions are different for women and men. Gender differences were observed even when key potential confounding variables (i.e., income, organization level, education, and computer self-efficacy) were taken into account” (Venkatesh et al., 2000, p. 50). Studies have also found gender differences in use of technology. Colley and Matlby (2008) found that females are more likely to buy travel on-line (6% for females vs. only 1.5% for men), and to conduct general information research on-line (36% for females vs. only 25.5% for men). Due to these consistent findings in the literature, we will propose to examine gender as one factor in our study.
Demographical factors - Age Age is an important factor in many research papers published. Age has consistently been found to have an impact on behavior in online settings (Zaphiris and Sarwar, 2005). The reasons for differences in computer behavior of older users were found to be: a) Vision (decline in static acuity, dynamic acuity, contrast sensitivity, color sensitivity, sensitivity to glare, decrease in visual field, and decrease in ability to process visual information). b) Psychomotor abilities (mouse movements and typing) c) Attention (declines in selective and divided attention), d) Memory and learning (decline in the ability to process items from working memory into short term memory; decline in episodic memory (memory for specific events) and decline in procedural memory (memory for how we carry out tasks). e) Intelligence and expertise (decline due to memory loss)
Demographical factors - Age In a follow-up to these research findings on age, Zaphiris et al. (2007) also noted that cognitive impairments were often subtle in nature, gradual and often not visible (i.e., gradual memory loss and gradual processing capability loss). Furthermore, “aging is also sometimes accompanied by significant changes in personality, independently of changes in cognitive abilities, and these may represent barriers to the productive use of information technology” (Cuttler and Graf, 2007). The personality changes manifest themself by a propensity to have smaller social networks online and in person (Pfeil et al., 2008), access fewer on-line sites and have more hierarchical organized on-line communication with few central sites and few people when engaged in communication (Zaphiris and Sawar, 2006). In short, a smaller breadth of experiences and contacts. Research has also revealed that there are differences not only among teenagers and very old individuals but also within the middle age-range group as well. When examining thirty young adults (age 20-29) and thirty middle-aged adults (46-59) and their interaction with portable multimedia players (radio, audio, video), it was found that the middle aged group had much more rigid exploration of new features, made more mistakes, demonstrated less exploration, and reported a higher workload for the same tasks as those completed by the younger group (Kang and Yoon, 2008). Since the age findings in the HCI literature are consistent, we include age as a factor in our study.
Agenda • Introduction • Approach • Literature review • Optimal Foraging Theory • Human Cognitive Factors • The Economic Perspective • Developing a Behavioral Foraging Model • Theoretical Model and Hypotheses • Theoretical Components • Methods • The Experiment • Instrument • Sample Population • Questions and Answers
Do people have rational expectations? Foragers make purchasing decisions without perfect knowledge of prices and price distributions and continuously estimate what the probabilities are of : a) Finding a similar items (that meets the need) b) Finding it in a reasonable time (foraging costs) c) Finding the item at a lower cost that offsets the foraging costs.
Do people have rational expectations? In general, the Rational Expectations Hypothesis (REH) assumes that the average expectation of foragers provides the best point estimate of price, uses all available information and is non-biased (Muth 1961). Specifically, REH suggests that the expectations are identical to the best guess of the future (Sargent, 1993), and that people’s rational expectations do not differ systematically. That means that any errors in prediction of prices and foraging costs are random. The work on REH gained recognition when the future Nobel laureate Robin Lucas published his paper on the effects of unexpected changes to monetary policies (1972). Specifically, he found that it was not the change that mattered is was the change relative to the expectations of participants that had the greatest influence on markets (expectations drive behaviors). For our research, all else held constant, the prevailing price of an item in a market should be equal to the local sales price plus the foraging cost of acquiring the item at another location. Foragers should have rational expectations that leads them to make rational foraging decisions (any errors are due to mistakes and occurs randomly).
Why Forage more than once? The core benefit to continued foraging after an item that satisfies a need has been identified, is the ability to find better items, or items at a lower cost. It is this variability of costs and benefits that drives the forager’s behavior. If the variability of prices was zero, the forager would not forage at all and word rely on the preciously gathered information for all events.
Price distributions can be calculated Predicted price distribution using Pirolli's lognormal probability density function (graphed 3,666 prices, in increments of one from $719 to $4,319) Actual observed prices in example (graphed the 237 ticket prices, from our example, from $719 to $4,319) - [note: that makes the graph non-continuous] (Pirolli, 2007)
Do people have rational expectations? Using Piroelli's equations, we can find the expected value for the plane ticket we started this session with. Based on our data the predicted mean price of this plane ticket is $1,302.44 Notice that the predicted price is extremely close to the actual average price of the 237 plane tickets provided in our search $1,322 (98.5% accurate). The simulation part of our experiment will examine how good people are at estimate prices and price distributions (needed to optimize foraging and decide when to buy and when to abandon a web site).
Agenda • Introduction • Approach • Literature review • Optimal Foraging Theory • Human Cognitive Factors • The Economic Perspective • Developing a Behavioral Foraging Model • Theoretical Model and Hypotheses
Cognitive Absorption and Exploration Behavior From the HCI literature we define Cognitive Absorption (CA) as “a state of deep involvement with software” (Agarwal and Karahanna, 2000; p. 673), while “exploration behavior” is defined as an individual’s motivation to investigate his surroundings (Houghton Mifflin, 2008). Since cognitive absorption can manifest itself as exploration within a site (many page visits) or exploration in larger contexts (more sites), we measure the exploration behavior as both the number of sites an individual visits (exploration breath) as well as the number of pages the individual is accessing (exploration depth). Curiosity (cu), a key component of CA is also a key element of exploration. We therefore expect that a high CA score is positively related with the exploration behavior construct. Other CA components also suggest this relationship. For example, Kao et al. (2008) found that individuals engaged in a concrete task (instead of abstract issues) exhibited a high degree of focused immersion (fi). This is important since Huang (2003) found that the immersed attention of an individual is positively linked with utilitarian web performance and task performance such as buying (as opposed to “hedonic performance” which refers to other usage such as entertainment). Huang also found close links between CA components such as control (cd), curiosity (cu) and utilitarian/task performance (i.e., searching for an item). In addition, Liaw and Huang (2006) found that another CA component, heightened enjoyment (he), as reported by 116 individuals, was closely linked with their perception and use of search engines (exploration behavior). These consistent cognitive findings lead us to the following hypotheses:
Cognitive Playfulness and Exploration Behavior Cognitive playfulness (CPS), describes an individual’s tendency to interact spontaneously, inventively, and imaginatively with microcomputers (Webster & Martocchio, 1992). These researchers argued that “when left to their own devices, individuals high in cognitive playfulness are more likely to explore the features of computers than individuals low in playfulness” (p. 761). In a follow-up study by the same authors (1995), the researchers found that involvement in a playful, exploratory experience is self-motivating because it encourages repetition (more exploration). These findings are supported by Agarwal and Karahanna (2000) who found that CPS has a positive effect on usage of information technology since individuals with high CPS scores also had a high degree of self-motivation. Specifically, the researchers found that the more playful you are, the more likely you are to enter into a state of deep involvement with software (exploration). This leads us to hypothesis number two:
Personal Innovativeness and Exploration Behaviors Innovativeness is a key cognitive characteristic of individuals. Agarwal and Karahanna (2000) found that a person with a high Personal Innovativeness with Information Technology (PIIT) score, is more likely to enter into deep involvement with software and be more likely to explore. Others found that such individuals were faster to adapt to technology environments and were more likely to experiment with technology (Agarwal and Prasad, 1998). One area of uncertainty is the propensity to explore different patches (web sites). One could argue that a person with a high PITT score (above average) would be likely to explore a higher number of patches, while an equally strong case could be made that a person with a high innovativeness would explore a single site in more innovative ways (visit more pages), resulting in fewer patches being visited. For example, the relationship between innovativeness and number of patches could manifest itself as innovation within sites, (resulting in fewer web sites being visited), or as innovativeness with the Internet (resulting in more sites being visited). The relationships between PITT and exploration behavior therefore lead us to hypothesis number three:
Computer Self-Efficacy and Exploration Behaviors Computer Self-Efficacy (CSE) is defined as a judgment of one’s capability to use a computer (Compeau and Higgings, 1995). CSE is basically an extension from Bandura's (1971) Social Cognitive Theory (SCT) from psychology that emphasizes the social origins of human behavior, rather than environmental influences. One key element of CSE is that beliefs and self-reflections about one’s own capabilities drive behavior. A person who believes that they have good computer skills is more likely to engage in targeted computerized foraging efforts, while individuals who believe that they have weak computer capabilities are likely to extensively engage in exploration by visiting more web sites and more web pages before making a purchase The link between CSE and exploration has been validated many times in computer training (Compeau and Higgings, 1995; Compeau et al., 1999; Bolt et al., 2001; Hasan and Ali, 2004), in Internet bank usage (Chan and Lu, 2004), in cultural studies (Pearson, 2004), and in technology adoption (Agarwal and Karahanna, 2000). Others who examined web searching behavior also found that the propensity to search was significantly related with an individual’s CSE score (Kuo et al., 2004). It is likely that an individual with a high CSE has strong beliefs in his own exploration skills with computers. Therefore we expect an individual with a high CSE score to visit fewer sites and also to examine fewer pages (since the individual believes that he already ‘knows’ how to find the best items), than individuals with low CSE scores. This leads us to hypothesis number four:
Age and Exploration Behaviors Li and Chatterjee (2005) found that younger individuals were more likely to explore more sites when buying than older individuals. The differences in exploration behavior are sometimes due to cognitive impairments that are often subtle in nature, gradual and often not visible (i.e., gradual memory loss and gradual processing capability loss), and, thereby, an unwillingness to learn new sites and explore more web pages. “Aging is also sometimes accompanied by significant changes in personality, independently of changes in cognitive abilities, and these may represent barriers to the productive use of information technology” (Cuttler and Graf, 2007). The personality changes manifest themselves by a propensity to have smaller social networks online (Pfeil et al., 2008), access fewer on-line sites (less exploration) and have more hierarchically organized on-line interaction with few central sites (Zaphiris and Sawar, 2006). In short, a smaller breadth of exploration and experiences. Research has also revealed that there are differences not only among teenagers and very old individuals but also with middle age users. Kang and Yoon (2008) found that middle aged people were much more rigid in their exploration of new technology and demonstrated less propensity to explore, than younger individuals. Research has consistently shown that age-related differences exist in exploration, and that increased technology experience is unable to offset the effects of aging (Czaja and Sharit, 1997). We therefore expect exploration behaviors to be related to the participant’s age, leading us to a set of hypotheses relating to age:
Gender and Exploration Behaviors Purchasing behavior is also different across genders. Imhof et al. (2007) found that males tend to search the Internet more intensely than females, visit more sites and also use the Internet substantially more for shopping than females. This research is supported by Nielsen’s Marketing Research (2002) which found that males visited 801 web pages each week, while females visited only 573 web pages in an average week. These findings appear to be relatively consistent in the literature. For example, Ono and Zavodny (2003) found that females spent about the same amount of time on-line as males, but accessed fewer web sites. In general, females appear to be more task oriented when on-line and use the Internet to buy (not shop), while males are more likely to use the Internet for exploration and entertainment (Shaw and Gant, 2002). This implies that females view shopping as a social activity, a need that is not met by the Internet, while males view shopping as an exploration activity resulting in more web sites, more web pages being accessed and more shopping overall (Kennedy et al., 2003). This leads us to hypothesis number six:
Education and Exploration Behaviors Education is also a factor in exploration behaviors. For example, Larsen and Rainie (2002) found that a low level education not only acts as an access barrier to technology, but also impacts what services are being used and how they are used. Specifically, higher education has been found to be linked with more usage and more exploration. This link has held true over time. Munro et al. (1997) found that the general education level was significantly correlated with computer usage (breath of experiences and willingness to explore new technology) and Losh (2003) found that, while Internet usage has increased for all education levels over time, higher educated individuals tend to use technology for more diverse tasks than users with lower education levels, indicating that higher educated users are more likely to exhibit different exploration behaviors than those with less education. The reason for the lower propensity to explore among those less educated may be grounded in their belief of the usefulness of the Internet overall. For example, Zhang (2005) found that users with bachelor degrees expressed a belief that the Internet was much more useful than those with only high-school diplomas. This link between levels of education and exploration behaviors leads to a set of additional HCI hypotheses:
Exploration Behavior and Likelihood of Surrender/Acquisition Naturally, as the number of web sites accessed increases, the likelihood of buying from any given site decreases at a rate of 1/N, we therefore do not believe this is an interesting relationship despite being part of our model. However, there is an interesting relationship between the number of web pages accessed and the likelihood to buy from a given site. Li et al. (2002) explored 132,000 web sessions of 300 people and found that the average number of pages viewed during a retail web session was 12.09, but the variance was very large. A quarter of the users viewed two pages or less, while the median was 5 pages and the standard deviation was 28.23. This indicates that without more information, we can only conclude (95% confidence) that the average person accessing retail sites will look at between one and 68.5 web sites, hardly a good predictor. However, when analyzing only the lowest 75 percentile of the users, we find that this group will access only 13 or less web pages in a retail session. In short, there is a large variance in web page access, but this variance is mostly due to a few individuals who do extensive shopping. We suggest that there is a non-linear relationship between site surrenders and the number of pages accessed. For example, if a person accesses only a few sites, it may be an indication that he is not a committed buyer, while a person that accesses a very high number of web pages may simply be gathering information and is not planning to make a purchase either (does not have to buy).
Exploration Behavior and Likelihood of Surrender/Acquisition For example, Suh et al. (2004) explored over 1 million web sessions of 166,794 users accessing an eCommerce site and found that while 14 different web pages could be accessed in 175 different patterns before purchases were made, the best predictor for a buyer was only a viewing of 7 web pages (and seven access patterns). Web users that accessed fewer, or more web pages, were less likely to buy anything. These findings are highly consistent with Li and Chatterjee’s research (2005) that examined 11,139 web user sessions at Barnes and Noble’s web site and found that the “committed buyer” views a significantly higher number of pages than non-committed buyers (8.75 pages for committed buyers). However, they also found that the higher the number of pages a person had to visit before a purchase could be executed, the more likely the shopper was to abandon the site, indicating a non-linear relationship between web pages visited and the likelihood of surrender. This leads us to hypothesis number eight:
Perceived Usefulness based on Previous Experience and Foraging Time King and He (2006) did an article review and found 140 relevant technology adaptation and assessment articles published in 22 journals between 1989 and 2005 and found that it appears to be a substantiated relationship between PU and actual usage. Therefore, we expect to find that the PU based on prior experiences is related to the time a person is willing to devote to reviewing items offered at a web site. For example, if a person perceives the web site to be very useful based on prior experiences, we expect that the user will spend more time reviewing items at the site, than on sites that are perceived to be less useful. It may be important to separate the time that a person has direct control over from the time that is controlled by the site. For example, a person has little control over the time it takes to access (load) a web page, nor the time that is required to orient, enter a search, execute the search and to finalize a purchase (some control can be exerted if the user choses to abandon the site). However, a person does have substantial control over the time he is willing to spend reviewing items presented by a search. We propose that the key to understanding how much time a person is willing to spend on the review of search results and overall foraging is determined by the perceived usefulness of the site based on prior experiences, leading us to hypotheses:
Patch Exhaustion and Foraging Time Patch exhaustion is a process where individuals use their previous experience to solve a foraging problem. For example, patch exhaustion predicts that a person buying an airline ticket will first access a website that they are most familiar with. If unsuccessful at this site, they will proceed to the site they know second best and try there (Smith and Dawkins, 1971). The sites (patches) are accessed in order of familiarity and only when known sites are fully explored (exhausted) will the person try new sites. It is important to note that it is not the experience that matters but the Perceived Usefulness (PU) based on the previous experience.
Patch Exhaustion and Foraging Time Previous experience and pre-existing knowledge has been shown to restrict foraging behaviors of individuals. Experiments in birds discovered that they were good at selecting optimal patches based on controlled food availability. When the best patch was swapped with the second best (food was moved), birds quickly identified the new best patch. However, when the worst patch was made the best, birds settled for the second best patch as determined by their previous foraging experience and took a long time to identify the new best patch (Smith and Dawkins, 1971). This demonstrates a learning behavior that can work against an individual relying on past experiences (Smith and Sweatman, 1974). While creating distortion in the foraging surplus (s) in the short run, the behavior is actually a foraging optimization method. The birds simply assume that a previously poor patch would be unlikely to become the optimal patch in a short time period. By using this probabilistic model, the birds simply moved to the second best patch once food had been removed from the best patch. Due to the relocation costs (ta and to), the search costs (ts), and the low probability of fundamental changes in the environment, birds do not conduct a full survey of all patches, thereby optimizing their patch selection through the reduction of foraging costs (Smith and Dawkins, 1971).
Patch Exhaustion and Foraging Time In a foraging environment where information technology is used, this is analogous to web site selections. Once a person is unable to find the item he is searching for (at a reasonable price), the person may migrate to another web page that he is already familiar with. Only when the familiar sites are exhausted, or substantially reduced, will the person start a new search of web sites that may meet his requirements. This may occur even if a new site is now the optimal site. The person may simply perceive that the search costs (ts) or the orientation costs (to) are too high, or unlikely to exist outside previously explored patches (p). While this behavior is predicted by Optimal Foraging Theory, it has not been tested in an information technology setting. This is a very important, since the theory predicts that increased knowledge and experience may work against a person in an environment where rapid changes occur. We propose that e-commerce participants leverage the same foraging optimization as animals and use similar implied probabilities and, therefore, are willing to devote more time to the sites that are accessed first than subsequent sites. Again, we propose that the time a person is willing to spend reviewing the items at a site may be different from the overall time a person is willing to spend at a site, since a person has a significant control over the first aspect, but little control over the latter. As a result, we examine these time groupings separately and expect that time spent at a site will be related to site exhaustion (the order a site is accessed is related to the time a person is willing to spend at the site), leading us to the following hypotheses:
