Table of Contents
Chapter 1 Introduction
Chapter 2 Literature Review
Defining Online Consumer Information Search Behavior
Understanding Online Consumer Information Search Behavior
The Decision to Use the Web as a Consumer Information Resource
Web Navigation and the Process of Online Information Searches
Consumer Search Behavior and Interdisciplinary Cognitive Foundations
Categorizing and Measuring Online Consumer Information Search Behavior: the SST Web Behavior Measurement Model
Chapter 3 Hypotheses
The Web as an Information Environment and Consumer Search Behavior
Demographics and Consumer Web Searches
Individual Web Characteristics and Consumer Web Searches
Sources of Involvement and Consumer Web Searches
Chapter 4 Experimental Method
Human Subjects Review Committees
Chapter 5 Analytical Methods and Results
Data Collection Procedure and Overview
Coding and Transcribing Consumer Web Behavior
Preliminary Data Analyses
Hypothesis Testing Methods
Chapter 6 Conclusion and Discussion
Individual Web Attributes
Internal and External Sources of Involvement
Implications for Consumers and Society
Implications for Managers
Study Limitations and Drawbacks
Despite the prevalent use of the Web for consumer information searches, very little is known about this behavior or the influences that individual and contextual factors may have on it. Moreover, no methodology exists for comprehensively measuring the complex array of behaviors that occur during a consumer Web session. Accordingly, a lab experiment, a contrived online product search-and-purchase task, was used to determine how these factors influence search behavior and purchase outcomes. Purchase contexts were manipulated by variations in task instructions. A survey was used to measure individual traits. A newly proposed measurement schema, the Source Site Target codification model, was used to quantify session-wide Web behaviors—leading to a variety of original findings. Contrary to past research, education was a non-factor and women outperformed men across online search behaviors. Age was negatively associated with consumer Web searches. Contrastingly, Web experience and search skill were positively associated with consumer Web searches, whereas purchase experience was negatively associated with consumer Web searches. Individual and contextual derivations of involvement (motivation) influenced not only the extent of a given Web search, but the nature of the search as well. Surprisingly, although individual and situational factors significantly and sometimes dramatically impacted consumer Web behaviors, changes in behavior were not associated with purchase performance. While the Web is adaptable to a variety of users, it is not a “perfectly efficient” medium. Individuals were susceptible to making sub-optimal purchase decisions regardless of individual traits or contexts.
Key Words: Consumer Web Behavior, Web Research Methods, Online Consumer Searches, Online Purchases, Demographics, Involvement, and the Need for Cognition.
Chapter 1 Introduction
As an information and communication medium, the Web and underlying Internet have been adopted at a faster rate than any other means have since the beginning of known history. Radio, the previous record holder, took more than thirty years to reach fifty million users, which the Internet achieved in less than 5 years (Bush & Gilbert, 2002; Kerschbaumer, 2000). With regard to utilization, over one hundred Web usage motives have been noted though four are prevalent (Rodgers & Sheldon, 2002): the acquisition of information, communication, exploration, and e-commerce. The two predominate uses are email and information searches.
External consumer information searches, or how people collect, analyze, and utilize information (beyond the use of memory) to meet some consumer need (Guo, 2001), is an enduring research area within the field of consumer behavior. The importance of this area of study stems from the fact that consumer information searches provide a central link that connects individual consumers with businesses, manufacturers, retailers, and markets in general. As individual consumers, our ability to find and use consumer information impacts the selection of goods and services that we purchase, the resulting quality or value derived from these purchases, and the subsequent satisficing of individual needs.
On a larger scale, consumer search behavior acts to shape both markets and consumer well-being. For example, by understanding how individuals go about collecting and using consumer information, marketers gain knowledge about not only how and what information consumers search for, but also about the products that consumers want. How well we as individuals search for and use consumer information and how well businesses use this knowledge to better communicate and meet consumer needs directly influences the quality and efficiency of markets for both consumers and providers.
A critical component of any given open market is the communication media through which information is conveyed. In addition to the behaviors of consumers and marketers, each medium or mode of communication itself will impose specific information boundary conditions on any given market and accordingly, also impact the nature of buyer-seller relationships. Established media such as print-catalogs, radio, and television each provide unique examples of this influence. The printed catalog, which arrived in mass at the turn of the twentieth century, is widely acknowledged as the first modern day marketing communication channel (Malhotra, 2007). This channel gave individuals their first real opportunity to collect consumer information from a variety of manufacturers and traders. When radio outpaced catalogs a few decades later, consumers were not only privy to more information, but this information could also be regularly changed or updated by marketers. As consumers adopted television, they were suddenly exposed to information that was delivered through both sight and sound; consumers could finally see a product in action.
While each of these noted modes of communication represents increasingly more stimulating information presentation formats, business and behavioral scientists learned over time that each medium has its own unique information-related benefits and limitations as well. In turn, these mode-specific advantages and disadvantages determine the boundary conditions of a given communication channel. Although readily at hand for consumers to use, information is limited within catalogs via the finite amount of available print-space and simple presentation format. Information is readily accessible, but is limited in scope and depth. Radio and television broadcasts respectively provided information in increasingly richer formats, but this information is in the form of a fleeting message that is further restricted via limited available airtime.
While these noted media each have unique attributes, they all share common fundamental communication-debilitating flaws. In sum, information content is highly limited, almost solely controlled by marketers, and travels in only one direction. Of course these information restrictions suddenly changed when consumers gained a new source of theoretically unlimited amounts of information readily available on demand—the World Wide Web. Without question and with few exceptions, the Web can gratify a much wider spread of information needs as compared to traditional media (Kink and Hess, 2008).
Just as individuals had adopted, integrated, and were shaped by radio and television, so too have they accepted the Web and have begun to be shaped by it. In fact, the acceptance rate of the Internet has exceeded the acceptance rate of every other form of communication technology by several magnitudes (Hannemyr, 2003). For the majority of Americans, the Web has been integrated into everyday life and has become indispensable (Jansen, Booth, and Spink, 2008; Plummer, 2007; Hoffman et al., 2004). In support of these findings, large scale commercial research studies have consistently found that three quarters of the adult U.S. population uses the Web regularly, of which roughly ninety percent have used it to look for information in general and almost fifty percent engage in Web search activities on any given day (PEW Internet and American Life Project, 2008a, 2007, 2006, 2005a, 2005b, 2005c, and 2004). In astounding testament to the prevalence of online information search behavior, four of the five most visited Websites are search engine providers (NielsenNetRatings, 2008a, 2008b, 2005, 2004a, and 2004b).
Likewise, academicians from information systems, communications research, and Internet research to marketing, broadcasting, and sociology have established that the acquisition of information is a primary online activity (Choudhury and Karahanna, 2008; Jansen, Booth, and Spink; 2008; Keane, O’Brien, and Smyth, 2008; Allred et al., 2006; Marchionini, 2006; Ylikoski, 2005; Jansen and Spink, 2004; Rodgers and Sheldon, 2002; Papacharissi and Rubin, 2000). The need and ability to find information online is becoming a normal part of life (Plummer, 2007; Jansen and Spink, 2006; Kulviwat, Guo, and Engchanil, 2004; Lucas and Topi, 2004; Jansen and Pooch, 2001). Unfortunately, within the area of online information search behavior, consumer information search behavior represents a highly important but understudied area of research (Ylikoski, 2005).
Although this research area is nearly two decades old, approximately sixty percent of the consumer and marketing Web research that has been published has appeared in the literature within only the past three years (Schibrowsky, Peltier, and Nill, 2007). Although much has been learned about Web topics ranging from communication and business effectiveness to political, legal, and cross-cultural issues, the aforementioned researchers determined that consumer search research, as well as rigorous and robust consumer Web research methods, tops the list of important current and future Web research topics. Even if the drive for purely scientific understanding is ignored, the impacts of online searches on consumers, marketing, and commerce in general make this subject matter too important to ignore.
While the Web affords an individual access to large amounts of information in comparison to traditional media, the Internet also imposes considerable challenges for consumers. Management, cultural, and Web researchers have demonstrated that Web users face an information medium that is constantly growing in size (Baye et al., 2007; Keen, 2007; Gulli and Signorini, 2005). Marketing, education, and decision science researchers have demonstrated that the Web is difficult to search through (Bellman et al., 2006; Liaw and Huang, 2006; Wu et al., 2006; Amir, 2003). Studies ranging from legal to information systems research topics have helped to establish that online information is regularly restricted by retailers (Matin, 2007; Weber and Zheng, 2007) and manufacturers (Markopoulos, 2004) and in many cases, is limited to a small number of true third-party consumer information providers (Ylikoski, 2005).
Given that science long ago established that humans are limited in their ability to collect and process information (i.e., Miller, 1956) and given the size of the Web and the specific difficulties associated with finding consumer information online (Amir, 2003), consumer users may face a variety of nontrivial information search barriers. While one might argue that information search sources such as search engines (i.e., Google) can be used to negate the size problem, the fact of the matter is that these search agents are limited in capability as well. No one search source can reference the entire Web (He et al., 2007) and, because different search engines are heterogeneous with regard to both finding individual Websites (i.e., Web reach) and retrieving information (i.e., communication protocols, file formats, and media types), substantial capability and search result variations exist between different search sources (Giles, 2007; He et al., 2007; Lewandowski et al., 2006; Nicholson et al., 2006; Beg, 2005; Crudge and Johnson, 2004; Vaughan and Thelwall, 2004; U.S. Department of Commerce, 2003).
In short, search engines are an inadequate search tool (Gan, 2008; Asutay, 2006; Beitzel, 2006). More specifically, search engines regularly provide a variety of results that are in some way incoherent (Martin, 2008), irrelevant (Asutay, 2006; Tella, 2006), or otherwise deceitful (Gan, 2008; Gyongyi, 2007; Wu, 2007). Taken together, search engines have capability issues with regard to both Web reach and consistently providing pertinent or well matched search-results. As indemnified by the aforementioned list of studies, these search source inadequacies are not the purview of any single research field but represent a recurrent set of findings that appear across psychology, marketing, computer and information science, and business/commerce publications.
Furthermore, the search for online consumer information is predicated upon the individual’s Web skills and ability to make an assortment of information search-related decisions (Chiou and Wan, 2007). To find information, consumers must successfully complete an assortment of sub-tasks that range from finding a search engine to discerning appropriate search terms to analyzing search engine results (Weber and Zhang, 2007; Asutay, 2006; Hodkinson et al., 2000: Rowley, 2000). During this search process individuals must also make a multitude of decisions about which Websites are relevant, accurate, trustworthy, or otherwise appropriate (Benedicktus, 2008; Meziane and Kasiran, 2008; Arnold, Landry, and Reynolds, 2007; Hsu, Lai, and Chen, 2007; Lwin and Williams, 2006).
In addition, these noted decisions and assessments all take place while individuals attempt to achieve their primary consumer information goals. In other words, they are examining, analyzing, or utilizing targeted information content to meet specific consumer knowledge and/or purchase (transactional) objectives. Not surprisingly, academic researchers have established that the Web already presents a considerable challenge to those individuals who attempt to search through it for information (Baye et al., 2007; Ylikoski, 2005; Page and Uncles, 2004; Kim and Allen, 2002; Jansen and Pooch, 2001; Huang, 2000; Rowley, 2000).
Although on the surface online consumer searches might appear to be a simplistic act of search and selection, the knowledge established from previous research from across disciplines plainly conveys a high degree of complexity in terms of cognitive demands, required search effort, and Web-related communication and information artifacts and boundary conditions (Gan, 2008; Mosteller, 2007; Wu, 2007; Markopoulos, 2004; Amir, 2003; Chiang, 2002). The implication for consumer Web researchers is that an intricate array of variables, relationships, information, and mode-specific parameters must be considered in the pursuit of better understanding this particular form of human-Web behavior. In sum, consumer Web searches represent a substantial iterative blend of thinking and navigation in an impressive but imperfect virtual environment.
While the evidence thus far overwhelmingly establishes society’s acceptance and consumer usage of the Web, very little knowledge has been established about online consumer information search behavior (Ylikoski, 2005). As a scientific quest, these research objectives can be divided into two groups: (1) the larger goals of understanding consumer Web behavior and its influences and; (2) the specific research methodologies that are employed to gain this knowledge. As will be extrapolated upon throughout the following sections, there are deficits in both of these research fronts which in turn served as the primary motivators for this study.
Although online information search research has grown sizably across disciplines during the past few years, it is still fragmented (Cho and Khang, 2006) and lacks clear indications of how demographic attributes, individual online traits and experience, and cognitive dispositions impact online consumer information search behavior (Chiou and Wan, 2007; Jin and Villegas, 2007; Williams, 2007). Accordingly, variables were selected for study from each of these aforementioned areas of potential behavioral influence. With regard to demographics, age, education, and gender were examined. With reference to individual online traits or “Webographics”, Web experience, search skill, and past online purchase experience were studied.
To discern impacts from cognitive predispositions, each individual’s Need for Cognition and Need for Closure was assessed. Additionally, given that this study pertains to consumer behavior and in an effort to detangle potential effects from a particular consumer product versus other individual search factors, product knowledge was also studied. Finally, as will be discussed in detail later, because involvement (motivation) is generally considered to be an important contributor to information search behavior, internal and external forms of involvement were tested as well, including product (internal) and purchase context (external) involvement, which is also otherwise known as situational involvement.
More specifically, as will be presented and cited in detail in the hypothesis section, each studied factor was chosen for one or more of three primary reasons: it is an understudied or un-established variable within the context of consumer Web searches, its current findings were mixed or otherwise conflicting, or there were anticipated changes in its influence as compared to past studies. For example, with regard to demographics, gender was studied due to anticipated changes in how this variable will influence consumer searches as compared to the past two decades of Web/computer research. This work challenges negative stereotypes about women and Web usage. Education was studied because it was not expected to play the same role in Web search behavior as compared to other media and because current research has demonstrated conflicting findings (past studies show both positive and null relationships between education and various consumer Web behaviors). Involvement, cognition, and “Webographic” traits were examined because there is little to no research about if or how these factors specifically influence consumer Web behavior and purchase outcomes.
Furthermore, beyond understanding the impact of these aforementioned individual factors, there are no commonly accepted methods for comprehensively categorizing or measuring online information search behavior (Schibrowsky, Peltier, and Nill, 2007; Cho and Khang, 2006). For example, Huang et al. (2007) looked at simple visit frequency types but ignored time, context, and individual attributes. Lin and Tsai (2007) conducted Webpage, view time, and search query counts, but ignored interactive, individual attribute, and context measures. Moreover, many researchers have only studied behavior at individual Websites (Huntington, Nicholas, and Jamali, 2008; Jansen, Booth, and Spink, 2008; Browne, Pitts, and Wetherbe, 2007; Sohn, Ci, and Lee, 2007; Whittle et al., 2007), ignoring the larger navigational cycles or Web session within which these individual Webpage visits occur.
While simple pragmatic individual Web metrics are widely used, such as the number of Website “hits” or banner ad “clicks”, no standardized method exists for codifying and quantifying all or most all of the individual’s activities that occur throughout a typical online consumer information search session (i.e., navigational actions, Website types, visit durations, and Website-interaction quantifications). Finding a better way to structure and measure an individual’s online consumer information search behavior as a comprehensive collection of meaningful dependent variables is of course critical to the future of empirical consumer-Web theory testing. Accordingly, an original means of coding and measuring consumer Web behavior is presented in this study in addition to the hypothesis tests. Ranging from a variety of Website and Webpage visit classifications to specific acts of Web navigation, site interaction, and purchase outcomes, it is believed that the measurement purview of this research study represents one of the most comprehensive assessments of overt consumer Web behavior that is currently present in the literature.
To summarize, in response to these aforementioned gaps in academic knowledge and Web-specific research methodologies, this study fills multiple research niches by examining several consumer-Web information search research questions and also by proposing a means to quantify observations of individual online consumer search behavior, transforming observable Web behaviors into testable information. While the research questions are founded on established academic research and share commonalities with other studies in some instances, the research questions were answered through empirically testing author-originated hypotheses with the use of an author-originated and created computer laboratory experiment.
Specifically, the computer lab experiment, presented to subjects as an online product shopping and purchase activity, was used to examine consumer information search and purchase behavior. From a preselected assortment of digital cameras, subjects were asked to search for, select, and purchase one model from any online retailer of their choice via a provided computer and Internet connection. Through codifications and measurements of their online search, navigation, and interactive behaviors, tested hypotheses addressed how specific individual Internet attributes, demographic traits, apriori consumer knowledge, cognitive attributes, and contextual factors impacted specific online consumer search and purchase behaviors.
Finally, as is critical to this area of research, although rarely done (Shneiderman, 2007), the underlying academic theory and support was based on a multidisciplinary versus a singular academic perspective. While not an exhaustive list, this support ranges from Marketing, Consumer Behavior, Economics, Psychology, and Sociology to E-commerce, Information Science, End-user Computing, and Internet/Web Research. While the explicit focus of this research is on consumer Web behavior, a cursory review of the included knowledge and citations as well as the condensed listing of publications in the bibliography clearly conveys that this research study would have been difficult if not impossible without the support from a wide variety of academic disciplines and investigations from a plethora of specific research fields.
Chapter 2 Literature Review
Defining Online Consumer Information Search Behavior
As a starting point to understanding online consumer information search behavior, it can be conceptually positioned by established marketing principles and information search research from across traditional marketing mediums, or in other words, from earlier behavioral research in catalog, broadcast media, and retail formats. Online consumer information search behavior, like other forms of mode-specific consumer information search behavior, can be considered to be a part of the well-recognized and widely-known Consumer Decision Process. The Consumer Decision Process, established by multiple marketing researchers in the late sixties and early seventies (Howard and Sheth, 1969; Engel et al., 1973), provides a general template for understanding the iterative process that consumers go through as decision-makers and buyers. Within this process an individual’s consumption of any good or service can be characterized by five general steps: (1) needs recognition, (2) information search, (3) alternative comparisons, (4) purchase and consumption, and (5) post purchase evaluation.
Needs recognition refers to the event(s) that trigger one’s awareness of a consumer need or desire (i.e., being hungry, remembering an upcoming birthday, or needing to buy a new car). Upon identifying a need, consumers will then tend to search for information, compiling knowledge about their potential options. Next, based on the information collected, each alternative (product or service) is evaluated and compared until a final selection is made. Once the individual makes this determination, the appropriate product is then typically purchased and consumed or utilized. Finally, either overtly stated or latently considered during or after consumption, many consumers will proceed to evaluate the product, the consumption or buying experience, and/or the providing retailer.
In short, the Consumer Decision Process provides a useful backdrop for understanding online consumer information search behavior in that a consumer’s search for information online does not happen spontaneously. It is started or spawned by some preceding consumer motivation to collect information. Furthermore, the information gathered during the search process is not collected without motive. It is accumulated to meet some ultimate consumer goal. In other words, and as noted by other researchers (Peterson and Merino, 2003), this well-established decision process framework provides an excellent foundational backdrop for understanding where the online information search process is positioned or fits within a larger process of satisfying consumer needs.
Although online consumer information searches can generally be understood as being a part of the Consumer Decision Process, this distinct type of search behavior can also be precisely defined by comparatively more recent Web and Marketing literature. Online consumer information search behavior is considered to be a common but distinct type of online search behavior; hyper-mediated goal-directed (versus experiential) search behavior (Novak et al., 2003; Moe, 2003; Peterson and Merino, 2003; Janiszewski, 1998; Hammond et al., 1998; Hoffman and Novak, 1996). Hyper-mediated merely refers to the consumer’s usage of the Web during the information search process versus the use other information sources (i.e., retail stores, word-of-mouth, etc.). More importantly, the contrast between goal-based and experiential behavior is a critical distinction because both types of behavior represent uniquely different forms of search behavior (Bridges and Florsheim, 2008; To, Liao, and Lin, 2007). Consequently, they demand a definitive distinction with regard to theoretically positioning and defining this research study.
Hyper-mediated goal-directed consumer information search behavior represents online search behavior that is typified by rational behavior as well as focused Web navigation and search activities. Its counterpart, experiential navigational behavior, is best characterized by open-ended unfocused Web “surfing”. Goal-based behavior tends to be extrinsically (versus intrinsically) motivated, or in other words, behavior is motivated by overtly recognized consumer needs and the achievement of some target goal(s). Accordingly, goal-driven consumer information seeking behavior is functional in nature and distinct from other non-functional motivations that may underlie open-ended “shopping” behavior (Harold, 2007; To, Liao, and Lin, 2007; Parsons, 2002), where the individual is more likely to be less rational and more driven by the entertainment provided by the Web navigation experience or surfing itself (Moe, 2003; Hoffman and Novak, 1996; Bloch, Sherrell, and Ridgway, 1986).
In succinct terms, online consumer information search behavior is best understood as being utilitarian (versus hedonic) driven behavior (Harold, 2007; Moe, 2003; Hoffman and Novak, 1996). Utilitarian behavior, the purview of this research study, is predominately driven by pragmatic information goals (i.e., which product performs the best, has the lowest price, is the most reliable, and so on). This supposition does not imply that consumers do or do not enjoy searching online, but that within the boundaries of goal-directed consumer information searches, individuals are primarily focused on achieving pragmatic information objectives which aid in the attainment of consumer knowledge, the selection, or consumption of some consumer good (To, Liao, and Lin, 2007).
Understanding Online Consumer Information Search Behavior
To understand this form of online behavior, the combined potential impact from a variety of relevant behavioral and cognitive principles and the unique composition of benefits and boundary conditions of the Web as a consumer information resource must be considered. Furthermore, the act of online navigation, as well as the individual decisions and effort that underlie the search for consumer information, must also be acknowledged. The simple act of Web navigation is a complex blend of input, output, and decision activities that becomes increasingly more complex as participants attempt to engage in relatively more advanced goal-directed consumer information search activities as compared to simple “everyday information” searches or knowledge goals (Amir, 2003).
Armed with an understanding of various behavioral and cognitive aspects of the information search process and the benefits and limitations of the Web as an information search tool, the established knowledge presented throughout the following sections is used to provide a framework and support for the later discussed hypotheses. In short, several general aspects of consumer Web searches should be considered in any academic discussion, including the antecedent conditions that compel individuals to utilize the Web as a resource, the process of Web navigation and other search-related activities, and the decision-making and behavioral factors that drive goal-directed consumer information searches. More specifically, this review roughly categorizes the online consumer information search process into three main areas. These major areas include the consumer’s decision to use the Web, online navigation and the process of consumer information searches, and the latent decision-making and information processing behaviors that underlie goal-directed consumer information searches.
Finally, the aforementioned sub-sections are followed by the author’s original propositions of how observations of overt traces of online behavior (search behaviors, Website visits, observed navigation, site interactions, etc.) can be categorized and measured as standardized indicators, enumerating individual online consumer information search and purchase behavior (referred to as the Source Site Target or SST measurement model). As noted previously, this method provides a means for transforming observable individual online behavior into a collection of testable dependent variables. In turn, the ability to measure and thus detect potential differences in individual Web behaviors provides the empirical foundation for testing for the presence and strength of potential affects that individual and contextual factors may have on online consumer behavior.
The Decision to Use the Web as a Consumer Information Resource
The first step or prerequisite to the online consumer information search process is the individual’s initial decision to use the Web as a consumer information resource. This decision, or first step, is predicated upon the individual having reached a sufficient level of motivation to use the Web (Flores, 2008; Ramos, 2003) and typically having engaged in some form of pre-online information search behavior (i.e., memory recall or word-of-mouth). As discussed previously as a part of the Consumer Decision Process, this motivating condition is met when an individual first reaches a state of needs recognition or in other words, has identified a consumer need. Assuming that a consumer need is identified, the economics, communication, marketing, and psychology literature all point to search behavior starting with the individual’s review of internal information which is then expanded to include external information sources (Flores, 2008; McMahan, 2005; Schmidt and Spreng, 1996).
Researchers such as DeSarbo and Choi (1999) and Bettman (1979) point out that the individual’s typical first step is to review relevant internal information (i.e., review memories). If this source of internal information is insufficient, the individual’s next step is to then seek out external information sources. The process between these sources is connected and iterative. As new external consumer information is processed, it is framed with and judged by its interplay with past and continually updated internal knowledge.
Some of the most common sources of internal information (Peterson and Merino, 2003; Beatty and Smith, 1987) include memories concerning previously gained information and past consumer experiences (past consumption). Beyond the Internet, common external information sources include other individuals (word-of-mouth), print and broadcast media, and retailers. Together, the motivating consumer need(s) along with this early considered knowledge, which is established through the individual’s initial or preliminary reviews of internal and external information, may next lead the consumer to consider using the Web as an additional information source. In other words, online consumer information searches do not typically occur spontaneously, but are the result of a primary consumer motivation and the individual’s desire to gain additional knowledge.
While motivation and the desire to find information are necessary prerequisites, one’s ability to search online must be considered as well. In this regard, conventional consumer information search research provides important theoretical foundations that can be used to understand online search behavior (Peterson and Merino, 2003). Traditional consumer search researchers (Beatty and Smith, 1987; Bettman, 1979) identified that an individual must have both the ability and motivation to process information before that individual will engage in effortful cognitive processing. Not surprisingly, this research premise has been expanded into the online world as well or more specifically, in the cases of everyday Web usage (Liaw, 2002), premised online search costs (Bellman at al., 2006), online shopping (Elliott and Speck, 2005; Jaillet, 2003), educational-related information searches (Slone, 2002), and price-related information search behavior (Fiang, 2002). These studies, which come from human-Web, marketing, information science, consumer behavior, and Internet research venues (respectively), repeatedly demonstrate a common finding. In brief, as Web users and consumers, we must be able and motivated to use the Web before we will use it for information searches.
These same ideas are echoed and added to by economic, sociology, and psychology research as well. Still assuming that an individual has achieved a state of needs recognition, we can use economic information theory to posit that an individual will use a particular information resource only when that individual perceives a potentially favorable cost-benefit result, prior to selecting that specific information resource (Williams, 2007; Klein and Ford, 2003; Stigler, 1961). Likewise, researchers using applications of the Theory of Planned Behavior, ranging from psychology to information science, marketing, decision support systems, and communications research (Kink and Hess, 2008; Lim and Dubinsky, 2005; Hsu and Chiu, 2004; Kim, 2004, respectively), have found ties between the individual’s positive perceptions of achieving goals online and the use of the Internet for e-commerce purposes (Rao et al., 2007; Kim, 2004). In summation, prior to using the Web as an information resource, we must have the perception that valuable, findable, and relevant information exists online, in addition to having both the primary motivation and ability to use the Web.
Web Navigation and the Process of Online Information Searches
With regard to early conceptions of Web navigation, marketing researchers Muylle, Moenaert, and Despontin (1999) first proposed a general theory of Web search behavior in which human-Web interactions are layered as three distinct levels of virtual or hyper-space navigation; macro-, meso-, and micro-sessions. The macro-session involves connecting to and terminating the connection with the Internet. The meso-session or inter-site movement (Hodkinson and Kiel, 2003; Hodkinson et al., 2000) involves the activities that are a part of moving between or getting to a particular Website (i.e., typing an address or clicking a navigational link with a mouse). Micro-sessions or intra-site movement encompasses navigation within a specific Website (i.e., clicking Website menu options or otherwise moving to another Webpage within a particular Website).
Of course these noted acts of simple navigation occur within the combined framework of a general information search process, the boundary conditions of the Web, individual behavior and related online decision-making, and problem-solving. Assuming that the Web is selected as an information resource, the next step for users is to determine how to get to the targeted information or transactional content that is sought. In simple terms, how do we find the Websites, specific Webpages, and the embedded information content that we are looking for?
The research thus far, from areas that range from marketing, information science, and human-computer behavior to library science, computer assisted learning, and consumer behavior, collectively indicate that when an individual lacks sufficient internal sources of information and subsequently decides to use the Web as an external information source, search engines appear to play an overwhelmingly dominant role (Keane, O’Brien, and Smyth, 2008; Jansen and Spink, 2006; Liaw and Huang, 2006; Griffiths and Brophy, 2005; Liaw, 2004; Brandt and Uden, 2003; Joines et al. 2003). In academic support of this common search practice, two critically relevant process-based components of goal-directed searches stand out: (1) the search process paradigm, which comes from information science research and, (2) the search activity decision process, which appears in human-computer research literature.
An online search process paradigm, such as that presented by Dennis, Bruza, and McArthur (2002), models the interactive search process that users go through with various search engine layouts. At a general level, this model includes search query input, results assessment, and search results refinement processes as well as the ultimate document [Webpage] selections (Gan, 2008; Wu, 2007; Asutay, 2006). Related work regarding search-associated decision activities, such as Hodkinson et al. (2000), was used to model a detailed process of online searching that identified many of a user’s pertinent decision activities. These activities include the users’ choice to go to a known Web address, to interact with search engines, to find and select relevant links, and to determine if individual information needs have been satisfied.
Together, these aforementioned works connect meso- or between-Website navigation with the human interaction decisions associated with search engine usage. In addition to search engine interactions, these works also tie navigation with many of the other detailed multi-faceted decision-making search acts that occur throughout the overall online information search process. While a consumer may also utilize other means to find online information, such as homepage links, peer-based online information sources (i.e., Craig’s list), or even guess the address name of a Website, as noted previously, search engines represent the most complex, comprehensive, and widely used sourcing resource from the currently existing assortment of online search agents/sources.
Give the dominance of search engines as a consumer tool, search engine usage decisions and process activities should be a significant if not large part of any given consumer Web search behavioral model. Of course, search engine usage may also be supplemented by individual attempts at sourcing for added Websites through other means. Put differently, users may also try to find additional Websites on their own accord.
Finally, in addition to search engine utilization decisions, psychology, marketing, management, consumer behavior, and information system researchers all posit that individuals must also make judgments about individual Websites throughout the information search process as well (Benedicktus, 2008; Harold, 2007; Mosteller, 2007; Gabriel, 2007; Beitzel, 2006; Bucklin and Sismeiro, 2003; Chen, 2003). For example, one of the first to delve into this research area, pioneering marketing researchers Bucklin and Sismeiro (2003), observed Website browsing behavior which was estimated from “clickstream” data, or in other words, user Web-navigation information that was collected via electronic logs of individual navigational artifacts such as linked-to Website addresses.
Together, these aforementioned researchers were able to add to our understanding of online behavior by establishing the presence of individual decision-making needs concerning accessing or continuing to view each Website that is encountered during any given online session. More specifically, such as in the case with Bucklin and Sismeiro, this research supports the presence of individual decision-making activities that range from selecting a Webpage link from a search-results page listing to determining what constitutes a pertinent Website selection during the consumer information search process. In simple terms, consumers determine whether each particular Website and its content will aid her or him in achieving target knowledge or purchase goals.
Consumer Search Behavior and Interdisciplinary Cognitive Foundations
Given an understanding of online information search processes and the associated decision-making, how does individual cognition impact consumer behavior in this information-intense environment? The use of search sources as well as information-specific resources (i.e., specialized Web-portals or review Websites) necessitates a variety of skills and cognitive demands from each individual user. Related studies from psychology, consumer behavior, management, and information science range from the exploration of cognitive and task influences on Web searches (Chiou and Wan, 2007; Mosteller, 2007; Chen and Macredie, 2002; Chiang, 2002; Kim and Allen, 2002) to the analyses of search engine queries (Jansen, Booth, and Spink, 2008; Whittle, et al., 2007; Asutay, 2006; Jansen et al., 2000). For example, Kim and Allen (2002) point out that online consumer search behavior requires at least three different cognitive processes: information seeking, knowledge acquisition, and problem-solving.
With regard to these cognitive processes, economic theory provides support for online information search behavior on two related fronts of rational decision-making: (1) the underlying motivation to maximize benefits and minimize costs and; (2) the individual’s derived search cost-benefits analysis (Stigler, 1961). Given that the focus of this study is on goal-directed and extrinsically driven consumer information searches that center on utilitarian benefits, the underlying information search process and associated hypotheses reflect these rational decision-making assumptions. For example, ceteris paribus, a rational consumer engaging in a consumer information search should be motivated by paying less versus paying more money for a given product and focus on finding the “best” product. These are critical points because non-directed or intrinsically driven online search behavior stems from different forms of utility and would subsequently lead to different patterns of Web behavior as compared to extrinsically motivated behavior (Harold, 2007; Ko, 2002). An example of this type of behavior would be that of an online user who enjoys shopping and subsequently searches the Web for a dramatically long amount of time that goes well beyond adding any further functional benefits. As noted previously, the drive from these comparatively more hedonic motivations shift the focus from the utility of the outcome to the utility (enjoyment) of the shopping experience itself (Cotte et al., 2006; Bloch, Sherrell, and Ridgway, 1986).
Cognitive effort is related to one’s cost-benefit analysis as well. From Traditional Information Economics (TIE) theory as originally established by Stigler (1961), we know that individuals will continue to engage in a search activity as long as the search benefits (i.e., reduced risks, cost savings, increased knowledge) outweigh the search costs (i.e., time, cognitive loads, or frustration). The interplay between benefits and costs should drive the specifics of search behavior (Flores, 2008; Bellman et al., 2006; Sanchez-Franco and Rey, 2004) including the duration of the search, the types of utilized sources, the number of referenced sources, and the depth of the search within each particular source. With regard to online search behavior, this theory may imply that individuals will start an online search only if they perceive that they can achieve a positive net gain from doing so (Choudhury and Karahanna, 2008; Bellman et al., 2006; Kumar, Lang, and Peng, 2005; Lim and Dubinsky, 2005; Hsu and Chiu, 2004; Teo, 2001) and that in general, individuals will regularly use the Web for information searching if they are overall reasonably successful at doing so.
Likewise, once individuals start a given search, they will constantly update their assessment of their search costs and benefits and terminate the search process once it appears that the potential benefits from continuing the search no longer outweigh the costs of searching (Flores, 2008). Finally, past search experiences should aid the individual in assessing the likelihood of successful future searches (Fidali, 2006). In sum, looking at the Web as a collection of benefits and costs provides a backdrop for understanding how different factors may act to drive individuals to use the Web for consumer information searches. In other words, a cost-benefits perspective may provide a foundation for predicting how individually (internal) and contextually (external) driven differences might influence search behavior (Bellman et al., 2006).
In opposition to this theoretical foundation one might argue from the alternative belief that the Internet has so dramatically lowered search costs as compared to other media that all people will effectively search the Web and search it often (Choi et al., 2006; Johnson et al., 2004) because it is so easy and nearly costless to do so. This belief would subsequently make information economics (and other theories of cognitive behavior) immaterial or at least dramatically less important and theoretically position the Web as a “perfectly efficient” information medium. The majority of academic research thus far seems to conflict with this belief.
From the standpoint of search interactions and Web-navigation, large numbers of individuals have been observed as engaging in comparatively limited search behaviors, typically using inadequate and often irrelevant search terms and not using advanced search engine capabilities (Jansen and Spink, 2006; Griffiths and Brophy, 2005; Lucas and Topi, 2004; Murray and Haubl, 2002; Spink et al., 2001). Additionally, most users view only a few Websites in any given search session (Keane, O’Brien, and Smyth, 2008; Jansen and Spink, 2006; Johnson et al., 2004; Klein and Ford, 2003; Adamic and Huberman, 2001). If search costs were indeed so globally low, we would not expect to consistently see such highly limited and simple search behaviors from the majority of Web users as indemnified by the aforementioned information science, library science, consumer behavior, management science, and marketing researchers.
Furthermore, psychology, marketing, and information science researchers have found wide variations in the time spent by various subjects to complete comparable online search tasks (Chiou and Wan, 2007; Klein and Ford, 2003; Hargittai, 2002), which clearly demonstrates both that time is still an important search cost and that individual differences lead to variations in these time-based search costs. One could further argue that these various time costs would lead to differences in or create other costs such as frustration and confusion as well (Pew Internet and American Family Life Project, 2008c). Finally, from a consumer perspective the most damaging blow to the perception of a “perfect information” Web universe is the observation of widely divergent price fluctuations (Baye et al., 2007; Venkatesan, Mehta, and Bapna, 2007; Xing et al., 2006; Du, 2005; Kumar, Lang, and Peng, 2005; Johnson et al., 2004; Pan et al. 2004; Suri et al., 2003a; Suri et al., 2003b; Pan et al., 2002; Brynjolfsson and Smith, 2000; Bailey, 1998).
In support of these long-observed price variations, Kim and Xu (2007) not only note a variety of contributing factors, but also demonstrate how Web retailers can utilize various tactics to successfully charge higher prices online. Even in the case of commodity products, such as books and CDs, which should facilitate the greatest of all possible price efficient markets as compared to more experiential or unique product categories (Byramjee, 2007), price differences of thirty-three and twenty-five percent (respectively) have been found in these markets (Du, 2005; Pan et al., 2004; Clay et al., 2001). Repeatedly observed price variation exists within service (versus product) categories as well, even when retailer quality has been taken into account (Clemons et al., 2002).
Additionally, as is well established across information channels research, search costs can be both tangible and intangible, but Web-based search costs tend to be intangible (Hodkinson and Kiel, 2003). The main tangible costs include financial expenditures as they relate to computer ownership and Internet access, but, because these are fixed costs that are relatively inconsequential to any specific individual search task, they are considered to be an irrelevant expense (Hodkinson and Kiel, 2003). Therefore, intangible costs and the individual’s ability to overcome these costs will tend to dominate the cost-benefit aspects of online searches. In short, one’s relative advantages from Web searching should dictate one’s consumer Web usage (Choudhury and Karahanna, 2008).
Several specific derivations of intangible online search costs stand out. As pointed out by Hodkinson and Kiel, some of the comparatively more dominate costs relate to time, cognition, physical, and psychological factors. Although time costs can be considered to be tangible (the financial cost or lost wages) and intangible (as an opportunity cost), time is treated as an intangible cost. As is well known by economic, consumer behavior, and psychology researchers, an individual consumer is more likely to judge allotments of time in relation to her or his own personal temporal resources (i.e., time-rich and time-poor) versus calculating the dollar-cost of time for a particular search effort (Flores, 2008).
Cognitive costs relate to the specific cognitive loads incurred during the search process (Mosteller, 2007; Chiang, 2002), such as the load that builds up from the latent decision-making requirements that underlie one’s use of a search engine (i.e., search term selection, assessment, refinement, etc.). These costs also come from the mental demands of sorting out and managing information (i.e., finding and selecting Websites and information content). Additionally, the costs may be physical in nature as well (being physically fatigued or tired from a search effort).
Finally, a variety of psychological costs can arise as a result of online information intensity. Given that the Web is an information-intense environment, commercial, cultural, education, Internet, as well as decision science researchers have found that information searchers can easily find themselves in a state of information overload (Pew Internet and American Family Life Project, 2008c; Keen, 07; Liaw and Huang, 2006; Wu et al., 2006; Kulviwat, Guo, and Engchanil, 2004) and, subsequently, become irritated, frustrated, confused, or even angry. Even the best trained U.S. intelligence agents suffer from search-related online information overload (Wu et al., 2006).
As a combined force, these aforementioned costs act as a general barrier that the individual user must face as part of the online search process and thereby theoretically, should at least in part determine if, how, and how well individuals search the Web (Rao et al., 2007; Williams, 2007). From a search cost-benefit perspective, one could reason that individuals who decide to use the Web for information search purposes do so based on a perceived net gain from at least one of two main perceptual contrasts. For searches to occur, individuals must perceive that the benefits are great enough to outweigh the costs and that their search skills or other related knowledge are sufficiently great enough to overcome, reduce, or otherwise minimize the costs of online searching.
Conversely, information economics can also be used to posit that if either the benefits of a Web search are perceived to be minimal or if the user believes that they have insufficient search skills, an online search would be unlikely because the perceptions of minor benefits or unacceptably high search costs (respectively) would discourage the user from searching online. For example, if a consumer has poor Web search skills, the perceived psychological costs (i.e., confusion or frustration) of the search are likely to increase (Hodkinson and Kiel, 2003). Even if that consumer is in a high state of needs recognition, he or she may completely forgo an online information search because the challenge of searching the Web appears to be insurmountable. Indeed, Web searching can require nontrivial complex skills (Nachmias and Gilad, 2002) and significant cognitive effort (Mosteller, 2007).
Although this research investigation primarily uses Consumer Information Search (CIS) and Traditional Information Economics (TIE) from the disciplines of marketing and economics (respectively) as a basis for theoretical support, note that other disciplines, such as sociology and psychology, also offer generally accepted representations of human behavior and cognition that provide similar and added support. In particular, two behavioral models stand out, the Theory of Planned Behavior (TPB), from psychology, and the Uses and Gratifications model (UGM), from sociology. The TPB is a specific derivation of the Theory of Reasoned Action (Ajzen 1991, 1988, 1985; Fishbein and Ajzen, 1975) that has already been successfully used to support online behavioral research or more specifically, the usage of search engines (Kink and Hess, 2008; Liaw, 2004) and e-commerce (Pavlou and Fygenson, 2006; Kim, 2004).
In relation to this research study, two important aspects of behavior can be taken from the TPB model. Perceptual attitudes about the outcome of a behavior and perceived behavioral control both work to influence behavior. If a behavior is perceptually deemed to be both possible and key to achieving a valued outcome, an individual is likely to both intend to and consequently, carry out that behavior.
Likewise, the Uses and Gratifications model (McGuire, 1974) has also been previously applied to Web research with topics that include heavy versus light Web usage (Stafford, 2008), search engine usage (Fidali, 2006; Armstrong, 1999), online advertising (McMahan, 2006), and the study of online human behavior (Lin, 2007; Joines et al., 2003; Eighmey and McCord, 1998, Korgaonkar and Wolin, 1999). With regard to this research, the UGM posits that the individual’s continued use of a media is based upon the underlying motives that compel the individuals to repeatedly use it. If individuals were not receiving rewards or gratifications from using the Web, they would stop using it (Fidali, 2006; Joines et al., 2003). In sum, Web users must perceive some potential gratification(s) prior to using the Web for consumer purposes and, they will continue to search as long as they feel that they are being rewarded for doing so.
Together, the Theory of Planned Behavior and the Uses and Gratification Model provide a sense of inter-theory reliability that supports TIE and CIS theory in potentially explaining consumer Web behavior. Analogous to TIE and CIS theory, TPB supports the notion that a consumer will use the Web for online searches only if that individual believes that she or he is capable of using the Web and that valuable and findable consumer information exists as well. Furthermore, based on the UGM, consumers will continue to use the Web for information searches only if they have previously been successful or otherwise perceive a successful outcome. In summation, whether it is ability and motivation, costs and benefits, what is possible and valued, or what has resulted in failures and gratification in the past, these theories illustrate that the interplay between the individual’s perception of their abilities (and knowledge) and valued Web content should at least partially influence online consumer information search behavior.
Categorizing and Measuring Online Consumer Information Search Behavior: the SST Web Behavior Measurement Model
The interplay between cognition, decision-making, search engine use, and virtual movement between Websites intermingles to create a hybrid information search-navigation process (Hodkinson et al., 2000). In practice, researchers have tended to only focus on a specific part(s) of this search-navigation process. Unfortunately, without a method to properly account for and classify the variety of activities that comprise consumer Web behavior, a consumer’s online session is nothing more than a chaotic mixture of hyper-mediated events and artifacts. In terms of scientific inquiry, observing only a few of these behaviors or pieces of the larger consumer search process as dependent variables may obviously not only limit the scope and extent of a Web study’s findings, but lead to erroneous findings as well. Although a sizable amount of micro-level Web research exists, there is a complete lack of global [session-wide] user activity research (Huang et al., 2007). Given this measurement deficit, the need for a means to comprehensively qualify and quantify observable consumer Web behavior becomes critically apparent.
Examples of limited research perspectives are evidenced by information science, marketing and advertising, Internet, and human-computer researchers (Huang et al., 2007; Cho and Khang, 2006; Sanchez-Franco and Rey, 2004; Hargittai, 2002; Jansen and Pooch, 2001; Hodkinson et al., 2000), who in chorus note that most Web-based information search and retrieval research is too divergent, does not focus on the larger research picture, and/or does not employ rigorous research methods. For example, several studies, from areas such as information science, marketing, library science, human-computer research, and decision science, have focused only on the use of search engines (Jansen, Booth, and Spink, 2008; Lorigo et al., 2008; He et al., 2007; Weber and Zheng, 2007; Whittle et al., 2007; Griffiths and Brophy, 2005; Liaw, 2004; Brandt and Uden, 2003; Ford et al., 2003; Eastman, 2002) and/or search results (Keane, O’Brien, and Smyth, 2008; Jansen, Brown and Resnick, 2007; Weber and Zheng, 2007; Beg, 2005; Crudge and Johnson, 2004; Vaughan and Thelwall, 2004).
Other researchers in psychology, marketing, and e-commerce have looked at users’ behaviors, but at only one or a few Websites (Chung and Ahn, 2007; Sohn, Ci, and Lee, 2007; Jarvenpaa and Todd, 1996). Furthermore, coming from sources that range from management, information systems, and e-commerce to consumer behavior and psychology, many studies have viewed online searches only as they relate to purchases (Zhang, Fang, and Sheng, 2006; Lee, 2002; Jaillet, 2003; Rowley, 2000; Jarvenpaa and Todd, 1996) versus multifaceted information goals (Moe, 2003). Human-computer researchers who have looked at browser navigation (Cockburn et al., 2002; Toms, 2000) focused on the impact of pull-down menus and browser buttons versus the overall search process.
Additionally, a number of information systems, information management, and consumer research studies have used online navigation or site log data (Jansen, Booth, and Spink, 2008; Huang et al., 2007; Whittle et al., 2007; Huntington, Nicholas, and Jamali, 2007; Spink and Jansen, 2004; Cothey, 2002; Jansen et al., 2000; Huberman et al., 1998) to collect exceedingly large samples of natural individual Web behavior. Unfortunately, most of these investigations lack information about the individuals being observed and consequently lack the ability to link navigational behavior with individual attributes, capabilities, or motives. Moreover, in most of these research cases, absolutely no form of experimental control was used. Finally, like all log data-based Web research, these studies tend to collect only very basic Website information (i.e., site addresses and search terms), with little or no temporal measures of detailed Web behavior. Subsequently, these types of studies do not include the rich array of user search behaviors and actions (i.e., visit times, site interactions, or Website type classifications) that are also a part of consumer Web behavior.
In sum, as a collection of dependent variables, researchers have not considered or measured the wide variety of specific behaviors that occur throughout a consumer’s Web session. Accordingly, to address these unmet research needs, this study employs an author-originated comprehensive array of measurements to codify and scale these overt hyper-mediated behaviors. This proposed method is referred to as the Source Site Target (SST) codification model. The remainder of this section provides a brief overview of how the SST method was used to conceptualize and measure consumer Web behavior to lay the foundation for empirical testing. In short, this method quantifies Web behavior into a collection of dependent variables that are used for testing the later proposed hypotheses. The measurement properties of each specific dependent measure (Web behaviors) are presented in detail in the later following Measurement section. While not exhaustive, the objective for this section is to provide the reader with an overview and understanding of how consumer Web behavior was conceptualized prior to the discussion of the hypothesized relationships between individual factors and specifically observable consumer Web behaviors.
The main purpose of the proposed Sourcing Site Target (SST) navigational and activity codification model is to comprehensively categorize and measure Web behavior as it pertains to goal-directed online consumer information searches. In other words, as a collection of dependent variables, this set of proposed measures combines overt observations of Web behavior within consumer behavior and online search process perspectives. The SST codification schema employs a variety of nominal, ordinal, and interval scale-based measures to comprehensively quantify consumer Web behavior. In general terms, consumer Web behavior as conceptualized via these measures can be broken down into three groupings: (1) Website type and attribute classifications; (2) Web navigation and; (3) Website interactions. For example, Website type and attributes reflect where a user goes—the type of Websites visited (i.e., search engine versus a retail Website) and the relevance of the informational or transactional content presented in any given Website.
An important but simple premise underlying this measurement method is that regardless of the end goal, decision complexity, or information requirements, users are engaging in one of two primary activities when conducting online consumer searches; they are either looking for Websites to visit (sourcing for Websites) or visiting Websites (goal-mediating Website visits). As noted earlier, in the absence of past experience or knowledge individuals use search and meta-search engines to source for (find) Websites that contain goal-mediating information and then search within these selected goal-mediating Websites to find the specific Webpages that will help them achieve targeted information goals (i.e., viewing a product review, retailer’s price, or a manufacturer’s Website).
Accordingly, at the simplest level of measurement categorization in the SST model, every Website visited is unidimensionally considered to be either a sourcing or goal-mediating site visit. For example, using Google.com as a Website search engine (versus Google “product search”) is considered to be a “sourcing” visit (variable name; So). In contrast, visiting a consumer review site, such as Cnet.com, a blog site, or a retailer site, such as Target.com, is considered to be a normal or goal-mediating site visit (variable name: Sn) that is pertinent to a particular search. Furthermore, for each Website type, users either will or will not find targeted information content (Target Pages: Tp) in each Website’s collection of Webpages. In the case of sourcing site visits (So), targeted content refers to generating a results-listing page that contains search-pertinent Websites to link to (SETp). In the case of normal site visits (Sn), targeting refers to the relevance of site content (to the consumer) that is contained in a particular goal-mediating Website’s Webpages (SnTp). For example, does a particular review site provide information about a desired product or product category or does a retailer Website carry a specifically desired product line or model?
Considering both process and relevance provides a means to not only measure how much effort a user expends on searching for Websites (So) versus visiting them (Sn), but also how fruitful the user’s efforts are in both cases (SETp and SnTp, respectively). For example, the number of page visits that have targeted information or transaction content divided by the total number of page visits (i.e., (SETp+SnTp)/Pg) conveys the efficiency of a given search. All together, this process notion of sourcing for sites, site visits, and finding targeted information content also provided the basis for naming this codification process the Source, Site, Target or SST model.
From this viewpoint, as a process, a consumer’s Web session is an iterative progression of Website sourcing, selecting and evaluating individual sites, and gathering targeted information content. For example, if an individual does not find what he or she needs after looking through a number of Websites during an initial search engine interaction, he or she might type in new search terms or go to a new search engine, starting the sourcing process all over again. Furthermore, the individual may also act as a mental miser, taking short cuts when possible to avoid using a search engine, going straight to a goal-mediating Website or specific Webpage when possible (i.e., bookmarks/favorites, word of mouth, guess a site name, etc.) to crash the search process. Crashing, or shortening the search process, is likely related to context of the search, past knowledge and experience, and other individual differences. For a search that is not considered to be important (i.e., buying a CD), a user might stop at the first good Website that is found. Comparatively, if a user were spending thousands of dollars on a video camera, she or he would be much more likely to conduct multiple searches, visit multiple information providers and retailers, and compare product information, consumer reviews, and retailer prices.