An Integrative Perspective of Online Foraging ... - Wiley Online Library

An Integrative Perspective of Online Foraging Behavior with Search Engines ´ Raquel Gurrea, and Carlos Orus ´ Carlos Flavian, University of Zaragoza

ABSTRACT While technology evolves rapidly, humans have to adapt to the environment. Evolutionary theories are proposed as an overarching framework to explain the information systems phenomena. However, there is a need to develop these theories in more realistic contexts, and to integrate them with nonevolutionary theories, in order to gain a better understanding of human–computer interactions. Searching entails uncertainty, and the feelings that it arouses may affect search behavior. This research aims to fill this gap by examining the impact of situational factors on online foraging with search engines, taking into account users’ behavior and their emotional responses during the process. The results stress the importance of the initial emotional state and the temporal dimension C 2012 Wiley in shaping the online environment that determines the online search behavior. Periodicals, Inc.

Searching for information on the Internet is one of the most performed activities by online users (Jupiter Media Matrix, 2006; Pew Internet Research, 2009). The rise of the Internet and information technologies (ITs) have shaped a new environment where users have access to tons of information at a relatively low cost (Klein & Ford, 2003; Peterson & Merino, 2003). Consumers have enhanced opportunities to acquire, process, and use great amounts of information in order to make more efficient decisions. However, the human mind is limited to processing and analyzing every piece of information that would be optimal to solve any informational need ¨ (Haubl & Trifts, 2000). In this way, information retrieval systems such as search engines have spread out as powerful tools that assist consumers in their interaction with the online environment (Liaw & Huang, 2006). These tools help users to acquire better information in a more efficient way (Jansen, Both, & Spink, 2008). In fact, according to a Pew Internet report (2009), the 88% of Internet users use a search engine to get specific information; Google is the most visited Web site in the world (Alexa, 2011). However, little is known about the reasons behind the success of search engines and the relatively rapidity with which users are commonly using them. Evolutionary psychology stresses the potential of theories grounded in an evolutionary basis for understanding the complexity of human–IT interactions (Kock, 2009). Human behavior evolved hundreds of

thousands of years ago, and the ways ITs are used today could not be so different from those that were used to survive in the caves. Specifically, foraging theories have been extended to the study of users’ search behavior in online environments (Pirolli & Card, 1999; Rajala & Hantula, 2000). According to this perspective, online users forage on the Web trying to maximize the information value, while minimizing the amount of resources (e.g. energy, time) exerted to obtain it. In this way, the capacity of a piece of information to offer a good answer to a search query, or information scent (Pirolli & Card, 1999), is very likely to guide the search process (Nielsen, 2003; Sundar, Knobloch-Westerwick, & Hastall, 2007). However, there is a need for research that examines online foraging behavior in more realistic contexts (Rajala & Hantula, 2000), where factors that define the situation may exert a great influence on online search behavior (Foxall, 1992, 1997; Kock, 2009). Moreover, while several authors have acknowledged the potential of evolutionary theories to help explain the information systems phenomena, they also point to an integration of evolutionary and nonevolutionary theories in order to offer a more complete understanding of such behaviors (Kock, 2010; Kock et al., 2008). In this way, the uncertainty that any search entails creates feelings of confusion and anxiety that affect information search behavior, by means of changes on physical actions, cognitive processes, and affective outcomes of the search (e.g.

Psychology and Marketing, Vol. 29(11): 836–849 (November 2012) View this article online at wileyonlinelibrary.com/journal/mar C 2012 Wiley Periodicals, Inc. DOI: 10.1002/mar.20568 836

Kim, 2008; Nahl, 2004; Wang, Hawk, & Tenopir, 2000). Past research on decision making, marketing, and neuroscience reveal that emotional responses can guide the search process in a great extent (Bechara & Damasio, 2005; Bechara, Damasio, & Damasio, 2000; Shiv, Loewenstein, Bechara, Damasio, & Damasio, 2005). This research faces this challenge by offering an integrative approach of online foraging behavior. Specifically, this study combines evolutionary and nonevolutionary theories following behavioral and information processing perspectives. The purpose of this research is twofold: first, to explore the online search behavior in a realistic setting, where online users are very likely to use search engines at the beginning of the process; second, to examine the impact of environmental factors on such behavior and on the emotional response during its performance. Specifically, this research analyzes the online search patterns with search engines, and the emotions that may happen during the processes. Situational variables given by the type of task and the initial affective state are proposed to have an influence on search behavior. In addition, the direct impact of time pressure is examined. The temporal dimension has been considered in the literature on online foraging as an important factor that determines search behavior (Dennis & Taylor, 2006; Rajala & Hantula, 2000). Given the great impact of time pressure at the cognitive–psychological levels (e.g. Haynes, 2009; Nahl, 2004; Payne, Bettman, & Luce, 1996), the study of this variable could offer a better understanding about how consumers deal with time constraints.

THEORY Online Information Foraging Foraging may be seen as the general process for decision making in stochastic and uncertain environments (Hantula, 2010; Kock et al., 2008). Animals, as ancient and modern humans, are faced with uneven, patchy environments, in which decisions must be made regarding how to allocate resources so that the net energy intake is maximized (DiClemente & Hantula, 2003). From the evolutionary perspective, the ultimate goal is to reduce the time and efforts exerted on these tasks and use them with other survival purposes (Kock et al., 2008). As a result of natural selection and exaptations1 (Pirolli & Card, 1999), this behavior has prevailed and has been adapted to many others related to find, secure, handle, and use resources. In this way, information seeking and shopping have been studied from the evolutionary approach (Dennis & Taylor, 2006; Pirolli & Fu, 2003; Rajala & Hantula, 2000; Sundar, Knobloch-Westerwick, & Hastall, 2007), given that modern consumers deal with overwhelming amounts of information and promotions to efficiently gain knowledge and buy products. This 1

Adaptations that shift their original purpose and become adapted to another.

AN INTEGRATIVE PERSPECTIVE OF ONLINE FORAGING Psychology and Marketing DOI: 10.1002/mar

is particularly true during the last decades, where the development of ITs, and especially the Internet, have shaped a new environment to which consumers need to adapt (Kock, 2010). Online foraging theories are proposed to offer an overarching framework for the study of online search behavior (i.e. “information foraging,” Pirolli & Card, 1999; “behavioral ecology of consumption,” Rajala & Hantula, 2000). These theories are based on the assumptions of optimal foraging models (Lea, 1979; Stephens & Krebs, 1986). Specifically, there are three main assumptions that represent limiting factors for any search situation (Smith & Hantula, 2003). Firstly, decision assumptions specify the decision problem and are related to selective choices and time issues (DiClemente & Hantula, 2003). Secondly, currency assumptions stare at what is at stake during the search process. Information value, defined as the net rate of informational benefits (e.g. knowledge, formed decisions) and costs (e.g. time, energy), is considered as the currency in information foraging models (Pirolli & Card, 1999). Lastly, constraint assumptions limit and shape the links between decision and currency variables. These are generally established by the interactions between the forager and the environment (Hantula, 2010; Smith & Hantula, 2003). Grounded in these assumptions, information foraging behavior is viewed as rational “to the extent that it maximizes the value of the knowledge gained from the Web relative to the cost of interaction” (Pirolli, 2005; p. 344). In general terms, decisions in online foraging pertain to what links (patches) to follow, and when to leave a link and move (DiClemente & Hantula, 2003; Nielsen, 2003; Pirolli, 2005). Online foraging theories posit that individuals (online foragers henceforth) interact with a patchy information superhighway, where information is unevenly distributed in the environment. In this way, online foragers search for information within a Web site as long as the perceived marginal benefit of the information they gather exceeds the marginal cost of moving to another Web site (Dennis & Taylor, 2006; Pirolli & Card, 1999). Among the factors that may influence what Web links to choose for exploration, information scent is likely to play a major role (Pirolli & Fu, 2003; Spink & Cole, 2006; Sundar, Knobloch-Westerwick, & Hastall, 2007). That is, online foragers estimate the link’s capacity to match their informational needs. This estimation will be given by proximal environmental cues, such as words, images, or other links (Pirolli, 2005). The configuration of these cues constitutes the information scent that guides decisions throughout the navigation. In order to deal with the environment, foragers tend to sample and consume different patches depending on currencies, constructing information diets to optimize the net gain of information value (Hantula, 2010; Pirolli & Card, 1999). This behavior is likely to be governed by the matching law (Herrnstein, Rachlin, & Laibson, 1997), which ubiquitously demonstrated that resources are distributed between alternatives according to the

837

perceived match to their rates of return. The better the matching, the more straightforward the search process is; otherwise, search behavior could become random, and therefore costly (Pirolli, 2005). Recent empirical research on online information foraging shows that online search behavior fits fairly well into the evolutionary realm. Into the consumer domain, past studies have demonstrated that online search patterns can be explained in terms of information scent and matching in Web navigation and online shopping (e.g. Dennis & Taylor, 2006; DiClemente & Hantula, 2003; Foxall & James, 2003; Hantula & Bryant, 2005; Pirolli, 2005; Pirolli & Card, 1999; Pirolli & Fu, 2003; Spink & Cole, 2006). Most of this research is concerned with how online foragers deal with situational factors, which entail different costs to the search process. Specifically, the behavioral ecology of consumption revealed in a series of studies that time delays (i.e. cognitive cost) and prices (i.e. monetary cost) affect online search behavior and shopping to a great extent (DiClemente & Hantula, 2003; Hantula & Bryant, 2005; Hantula, DiClemente, & Smith, 2008; Rajala & Hantula, 2000; Smith & Hantula, 2003). Although users do not base their searches and purchases exclusively on the fastest and cheapest sites, they show a strong preference for the less consuming alternatives. Online foragers may search and buy more from Web sites with the least time delay and lowest prices, although they also spend some resources sampling other patches. Further, Dennis and Taylor (2006) adopted an information foraging perspective (Pirolli & Card, 1999) and found that, when faced with acceptable download delays, satisficing information foraging behavior was favored. Together, these studies provide new insights to better understand the online search behavior. However, in order to fully predict and explain this adaptive behavior, there is a growing need to integrate evolutionary and nonevolutionary theories (Kock, 2009), and to investigate its contentions in more natural and realistic environments. In this regard, although some attempts have been implemented (e.g. Card et al., 2001; Dennis & Taylor, 2006; Rajala & Hantula, 2000), online foraging research in realistic contexts is scant. Evolutionary theories draw several parallels with nonevolutionary disciplines that have been traditionally applied to the study of search behavior. Taking the concept of bounded rationality as a cornerstone (Simon, 1955; Stigler, 1961), a great amount of research on decision making, marketing, and cognitive psychology, has investigated online search behavior by placing individuals in a situation more related to their natural environment (Bettman, 1979; Bechwati & Xia, 2003; ¨ Haubl & Trifts, 2000; Hoque & Lohse, 1999; Klein & Ford, 2003; Kulviwat, Guo, & Engchnil, 2004; Payne, Bettman, & Luce, 1996; Stigler, 1961). As problem solvers, people usually search for information trying to maximize the accuracy of the search outcome while minimizing the effort exerted to acquire it (Bettman, 1979). As long as individuals have limited cognitive resources to acquire and process all the available informa-

838

tion, they frequently choose alternatives that are satisfactory, rather than optimal, expending only the effort ¨ necessary (Garbarino & Edell, 1997; Haubl & Trifts, 2000). In addition, research on marketing and cognitive psychology has dealt with the emotional currency of search behavior. At the psychological level, users seek to reduce uncertainty and risk during the search process (Peterson & Merino, 2003). In this way, the affective component has been revealed as a main dimension to explain search behavior (Kuhlthau, 1991; Nahl, 1998). Emotions may play different roles in the search process, specially motivating or inhibiting some behaviors and evaluating the course of action (Bagozzi, Gopinath, & Nyer, 1999). Moreover, these nonevolutionary approaches acknowledge the potential role of personal and situational variables to affect the search process, performance, and outcomes (Bilal & Kirby, 2002; Huang, Lurie, & Mitra, 2009; Nahl, 2004; Wang, Hawk, & Tenopir, 2000). According to the behavioral perspective (Foxall, 1992, 1993), it is claimed that consumer behavior results from the responses to the environmental stimuli, which depend heavily on the characteristics of the specific situation and the person. Therefore, given that information foraging is not always strategic and frequently tends to be quite open-ended (Sundar, Knobloch-Westerwick, & Hastall, 2007), this literature review points to the integration of evolutionary and nonevolutionary theories to obtain a better understanding of the online search behavior.

Foraging with Search Engines There is no doubt that the Internet has changed the ways in which people search for information, mainly due to the great convenience and the great amounts of information that it offers (Peterson & Merino, 2003). However, these vast amounts of information could cause information overload (Lurie, 2004). Consumers possess limited cognitive resources. They are not able to acquire and process all the relevant information that ¨ would be optimal to achieve the best alternative (Haubl & Trifts, 2000). Following this issue, the development of software tools that help users to acquire, filter, and process information, allows reducing the effort of search while improving the quality of decisions (Bechwati & ¨ Xia, 2003; Haubl & Trifts, 2000). Specifically, search engines represent information retrieval systems that assist users in their interaction with the online environment (Jansen, Both, & Spink, 2008). Search engines are widespread all over the world, and academic and field researchers acknowledge their importance into the online search realm (Jupiter Media Matrix, 2006; Lorigo et al., 2006; Pan et al., 2007; Pew Internet Research, 2009). In fact, a great amount of Internet episodes begin with a query in a search engine (Jansen, Both, & Spink, 2008). Moreover, the behavioral patterns in search engines appear to be quite homogeneous across cultures (Jansen & Spink, 2006), and research has

´ GURREA, AND ORUS ´ FLAVIAN, Psychology and Marketing DOI: 10.1002/mar

confirmed that online users firmly rely on the recommendations made by these tools to match their infor¨ mational queries (Haubl & Murray, 2003; Pan et al., 2007). Therefore, search engines could offer a more naturalistic setting for the study of online search behavior. Online search behavior with search engines can be interpreted in terms of online foraging models. Specifically, the search engine’s results page (SERP henceforth; Lorigo et al., 2006) may be seen as an information patch (Nielsen, 2003; Pirolli & Card, 1999) that leads to other patches where relevant information could be located. These systems try to match online foragers’ queries to the available information in the uncertain and stochastic environment of the World Wide Web. In other words, search engines serve the purpose of maximizing the information scent while minimizing the distal difference of other relevant patches. Following this idea, the reasons behind the success of search engines could be related to their capacity to enrich the environment (Pirolli & Card, 1999). One of the problems faced by patch models lies in the difficulty of foraging in unevenly settings (Dennis & Taylor, 2006). Search engines allow the online forager to mold the environment to minimize the between-patch time costs (Nielsen, 2003) and to offer better returns of information value (Pirolli & Card, 1999). Furthermore, search implies uncertainty, and feelings related to it may affect—even guide—the search process (Bechara & Damasio, 2005; Peterson & Merino, 2003; Shiv et al., 2005). Specifically, the emotions that occur during the search process may have informational value about the action progress (Bagozzi, Gopinath, & Nyer, 1999). Emotions often bias search behavior and determine all the subsequent interactions (Bechara & Damasio, 2005). This could be of particular interest in the study of online foraging, given the influence of users’ emotions on human–IT interaction (Charlton, 2009). Anxiety, a feeling of fear and nervousness, has been considered a common feeling of users when interacting with computers (Liaw & Huang, 2006). Download delay times, a common measure included in foraging models (e.g. Rajala & Hantula, 2000), have costs also at the psychological level, producing disutility, stress, and dissatisfaction (Dennis & Taylor, 2006). Contrarily, positive feelings related to pleasure, perceived self-efficacy, or enhanced control, have also been found in search processes and to influence human–computer interactions (Kim, 2008; Nahl, 2004). In a nutshell, it seems interesting to study the extent to which emotional responses happen during the online search process, particularly those related to the uncertainty dimension (Roseman, 1991).

Situational Influences of the Search Process The behavioral perspective of consumption (Foxall, 1993, 1997) in combination with the information processing account (Kuhlthau, 1991; Nahl, 2004) are fol-


lowed to examine the effects of defining factors of the search environment on online foraging with search engines. The behavioral perspective model was proposed by Foxall (1992, 1993) as a response to traditional cognitive consumer research, which mainly focused on internal, organocentric states and processes and largely overlooked the contexts in which purchase and consumption occur. Foxall (2010) suggests that the consumer brings a learning history that interacts with a current behavioral setting, creating a specific consumer situation. The consumer then behaves depending on the (expected) reinforcing and punishing consequences of his or her actions. Recent studies also acknowledge the role of the subjective experience and the motivating impact of some events on subsequent perceptions and behaviors (Fagerstrøm, 2010; Fagerstrøm, Foxall, & Arntzen, 2010; Foxall, 2010). Thus, one of the main concerns of the behavioral perspective, which is also the focus of the present study, accounts for the effects of situational factors on behavior. In addition, past research on decision making and information processing also analyzed the influence of situational factors on search behavior in online environments. Users’ motivations (Nahl, 2004; Quinn, 2003), the characteristics of the search task (Jansen, Both, & Spink, 2008; Lorigo et al., 2006), the search object (Pirolli, 2005), or the technol¨ ogy employed (Haubl & Trifts, 2000) can have notable influences on information search at the behavioral and psychological levels. Thus, this research examines the potential impact of three situational factors: the type of search task, the users’ initial affective state, and the existence of time pressure. People are said to search for information in order to satisfy an information need (Broder, 2002; Jansen, Both, & Spink, 2008). Depending on the nature of the information needed and the goals pursued, the search task could have a structure more or less well-defined (Browne, Pitts, & Wetherbe, 2007; Pirolli, 2005). In online environments, several authors from evolutionary and nonevolutionary perspectives have stressed that the type of search to be performed can determine the search strategies, and have developed different categories of searches serving different purposes (see Jansen, Both, & Spink, 2008, for a review). This research focuses on a taxonomy of search tasks that has been proposed and explored in online searching with search engines (Broder, 2002; Jansen, Both, & Spink, 2008; Lorigo et al., 2006; Matsuda, Uwano, Ohira, & Matsumoto, 2009): navigational tasks (i.e. the intent is to reach a particular Web site), informational tasks (i.e. the intent is to acquire some information assumed to be present on some Web pages), and transactional tasks (i.e. the intent is to perform some web-mediated activity). Past research has already shown that users search for information differently depending on the type of task. Moe (2002) found different search patterns for people who browsed the Web, searched for specific information, wanted to increase their knowledge, or make purchases, in terms of time spent on Web pages,

839

number of products seen, variety in product categories, and so on. Jansen, Both, and Spink (2008) examined the terms that people used in search engines and developed a classification of search queries that showed different behaviors depending on the type of search task. Regarding more specific search behavior on SERPs, Lorigo et al. (2006) examined search queries and scanpaths (i.e. sequences of eye fixations) on Google’s results pages for navigational and informational tasks. They found that the time spent in the results page and the number of abstracts viewed below the one that was eventually clicked (i.e. intrapatch foraging), was greater for navigational task than for informational tasks. In a similar vein, Matsuda et al. (2009) found that users tend to view search results longer in informational tasks than navigational tasks. Nevertheless, more research is needed in order to ascertain the real impact of the type of search on users’ behavior. Besides, past studies have overlooked the analysis of tasks related to mediated activities (i.e. transactional), which should be included in the analysis to obtain a complete picture of the online consumer’s search behavior on search engines’ results pages. Given that different searches require different degrees of effort, concentration, and challenge (Quinn, 2003), the type of search task could also influence the emotional responses during the search activity, leading to experiences more or less comfortable. Previous research has remarked the hedonic value of searching for information online, which is usually associated with navigational or browsing activities (Moe, 2002). On the other hand, it has been established that a search activity evokes uncertainty and confusion about the possible alternatives that could lead to the best solution (Kuhlthau, 1991). This uncertainty and confusion could make consumers feel anxiety during the search process (Nahl, 2004). In addition, depending on the degree of concreteness of the task structure, the chances of an optimal search outcome could be more diffuse, leading to different feelings during the search activity. For instance, browsing or navigational tasks could be more related to recreational activities, which could generate positive feelings of enjoyment and fun (Kulviwat, Guo, & Engchnil, 2004); however, searches more akin to problem-solving activities (i.e. informational tasks) tend to be ill-structured (Pirolli, 2005; Pirolli & Card, 1999; Sundar, Knobloch-Westerwick, & Hastall, 2007), so users may feel more uncertainty—and consequently more anxiety—about the possible best solution or alternative for their information need. Therefore, it is expected that the type of task, which varies in terms of goals, structure, and strategies, will affect the user’s online foraging behavior and emotions experienced during the search process: H1:

840

The type of task will have an impact on (a) search behavior with search engines, (b) the emotions experienced during the online search process.

The starting state with which the online forager faces an information search may have an impact on search behavior and on the emotional responses. When foraging for food, it is plausible to think that the predator would forage differently, whether it is starving or just hungry. It could also react differently when it fails to capture a prey. In this way, the online user’s initial emotional state could entail different degrees of motivation to perform a search activity. Whereas online foraging theories have not considered this issue yet, nonevolutionary research has stated that the individual’s emotional state could influence various aspects related to the acquisition and processing of the information (e.g. Bagozzi, Gopinath, & Nyer, 1999; Bilal & Kirby, 2002; Kuhlthau, 1991; Kulviwat, Guo, & Engchnil, 2004; Quinn, 2003). Kuhlthau (1991) developed a model of information searching in which the searcher’s affective states are a key driver of the search process. Moreover, Nahl (1998) proposed and demonstrated (2004) that several affective variables (e.g. task motivation, optimism) have a great influence on certain behaviors of searchers. More specifically, previous studies have demonstrated that different motivations can affect the ways by which people pay attention to the information they receive. Pieters and Warlop (1999) demonstrated that people highly motivated with a choice task presented different attention patterns than low motivated people. Also, individuals’ motivation to find a correct answer to a given search query affects their subsequent search behavior by means of different search strategies and mental rules (Bilal & Kirby, 2002; Nahl, 1998). Therefore, users’ initial emotional states, as an internal motivation that triggers behavior, is expected to influence their online information search behavior on a SERP. In addition, the initial emotional state could be viewed as a motivating operation of the search process, defined as an environmental event (i.e. external to the behavior per se) that determines the valuable impact of further events and the reactions toward those events (Fagerstrøm, Foxall, & Arntzen, 2010). Thus, the initial motivation is likely to affect the affective experience of the online forager. Previous research on cognitive psychology has demonstrated that the individual’s motivations may lead to different action readiness, which would determine the intensity of the emotions experienced (Frijda, 1987, 1993). Research on decision making has also considered the role of mood on decisionmaking processes. Different moods can influence subsequent emotions and behaviors (e.g. Louro, Pieters, & Zeelenberg, 2005; Raghunathan & Pham, 1999). In the specific context of online search behavior, evidence shows that motivations and affective states related to a search task correlate with the affective outcomes of the ´ Gurrea, & Orus, ´ 2011; Nahl, search activity (Flavian, 2004). If the affective states before performing a search task influence the emotional outcomes of the search, it would be logical to expect an impact on the emotions during the search activity as well.


Therefore, the second hypothesis proposes that the individuals’ intrinsic motivation, given by their initial emotional state, will affect the online search behavior and emotional responses during the search process. Formally: H2:

The user’s initial emotional state will have an impact on (a) search behavior with search engines, (b) the emotions experienced during the online search process.

Finally, the temporal dimension is considered as a key determinant of searching behaviors (Payne, Bettman, & Luce, 1996; Rajala & Hantula, 2000). Spending more time than necessary on foraging represents extra costs and a reduction of the available time to other activities. In this way, online foraging theories have widely studied and demonstrated the effects of delay times with computers on searching behavior, purchasing behavior, and satisfaction (Dennis & Taylor, 2006; DiClemente & Hantula, 2003; Hantula & Bryant, 2005; Hantula, DiClemente, & Smith, 2008; Rajala & Hantula, 2000). With the aims of broadening the analysis of the temporal factor on online search behavior, and of including a realistic situational variable into the study of online foraging with search engines, this research examines the effects of time pressure during the search process. Time pressure has been shown to diminish time-sensitivity (Ebert & Prelec, 2007) and to have similar psychological effects to those produced by waiting times, such as stress and dissatisfaction (Dennis & Taylor, 2006; Payne, Bettman, & Luce, 1996). Many decisions are made under time pressure, and this regulates the amount of information that can be processed. Time pressure has been studied in research related to decision making, information processing, and marketing (e.g. Dhar & Nowlis, 1999; Haynes, 2009; Nahl, 2004; Payne, Bettman, & Luce, 1996; Pieters & Warlop, 1999). In this way, people under time pressure change the search patterns and behaviors. Specifically, people attend to information about products differently depending on the existence of time pressure (Dhar & Nowlis, 1999); they tend to accelerate the examination of information by accelerating the visual scanning sequence and attention (Kozup & Creyer, 2006; Payne, Bettman, & Luce, 1996; Pieters & Warlop, 1999). Given the less time available, users could be expected to attend the results of a SERP as quick as possible, trying to scan at least partially all the possible alternatives that would have been scanned without time constraints. This could lead to more skips between results and less time examining them. In addition, people with limited time resources could alter their decision strategies by shifting from compensatory to noncompensatory rules, or using heuristics to determine the most important pieces of information (Dhar & Nowlis, 1999; Haynes, 2009; Kozup & Creyer, 2006; Payne, Bettman, & Luce, 1996).


Finally, consumers may find a decision-making process more stressful and difficult under time pressure, because they do not have enough time to process the information about each alternative and compare them with others (Haynes, 2009). As noted previously, the uncertainty that initiates an information search process provokes feelings of confusion, doubt, and anxiety. Time pressure may cause heightened feelings of stress and anxiety (Kozup & Creyer, 2006; Nahl, 2004). Therefore, the existence of time pressure may create a greater effect on the perceived difficulty and stress of the search process, leading to an increase of the (negative) emotional responses during the search process. Altogether, it is proposed that time pressure will affect the online search behavior and emotions during the search process: H3:

The existence of time pressure will have an impact on (a) search behavior with search engines, (b) the emotions experienced during the online search process.

METHOD Participants A total sample of 113 participants (62 men and 51 women; ages ranging from 18 to 45) enrolled in the experiment in exchange for participation in a draw. The study was based on eye-tracking methodology, which allowed recording the participants’ eye movements during the search activity, and paper-and-pencil questionnaires to gather information about their initial affective states and the emotions experienced during the search activity.

Apparatus The Tobii Studio T60 eye tracker device (www.tobii.com), which is integrated into a 17inch TFT monitor, was used to collect participants’ gaze data. The Web browser Internet Explorer 7.0 was employed. The study took place at the School of Business and Economics in a south-western European university. Since only one participant could carry out the search activity at each time, the sessions were scheduled and subjects were summoned to the lab room every 20 minutes. The eye tracker failed to adequately register the gaze movements of some participants. Thus, after deleting these incorrect recordings, the final sample consisted of 97 subjects.

Design and Procedure Participants were randomly assigned to one of 3 (type of task: navigational, informational, transactional) × 2

841

Figure 1. Experimental procedure.

(time pressure: yes or no) conditions in a betweensubjects factorial design. The initial emotional state of participants was measured within subjects. The procedure of the experiment was as follows (see Figure 1). Before entering the lab room, the participant read a brief introduction about the eye-tracking methodology and was told that the objective of the study was to explore online search behavior. No additional information was provided. At that moment, the participant reported his or her initial affective state. After that, the participant came into the lab. Google was selected as the target search engine, since it represents the most used search engine in the world and in the population of study (Alexa, 2011; ComsCore, 2009). The search task was related to the entertainment company “Le Cirque du Soleil.” Specifically in the navigational condition, participants were told that they had to use Google to go for “Le Cirque du Soleil” Web site and navigate for a while trying to find the place and date for a specific performance. For the informational condition, participants were asked to find the cheapest price for an adult ticket for a specific performance (the date and the city were fixed). In the transactional condition, participants were requested to simulate the purchase of a ticket for a specific performance and to stop before the information regarding payment was requested. For the time pressure conditions, participants were told that they had only 20 seconds in the navigational condition, 40 seconds for the informational condition, and 60 seconds for the transactional task.2 Just after the task was completed, participants filled out a questionnaire that contained, among other measures, the emotions experienced during the search 2

842

A manipulation check with a smaller (n = 10), similar, but separate sample confirmed that the tasks could be performed within the stipulated time periods.

activity. Finally, participants were thanked and debriefed.

Measurement Regarding the participants’ initial affective states, the PANAS scale was adopted (Watson, Clark, & Tellegen, 1988). The PANAS scale has been widely validated and used in the psychology and marketing literature (e.g. Dewitte, Bruyneel, & Goukens, 2009; Mano & Oliver, 1993) and is especially useful to measure individual’s emotions at the present moment of time, as their creators suggest (Watson, Clark, & Tellegen, 1988). Thus, participants reported on a 7-point Likert scale (1 = “not at all”; 7 = “extremely”) the extent to which they felt each one of the 20 items of the PANAS scale just before performing the search task. The principal component analysis with varimax rotation revealed four factors with eigenvalues higher than 1, which explained a 62.283% of variance (all factor loadings higher than 0.5; Hair, Anderson, Tatham, & Black, 1998). The four factors were labeled as follows: initial encouraged state (enthusiastic, alert, inspired, determined, attentive, and active), initial depressed state (upset, guilty, scared, ashamed, and afraid), initial anxious state (distressed, nervous, and jittery), and initial averse state (hostile, proud, and irritable). Although the PANAS scale was originally created to gather general positive and negative affective states, the results of this analysis confirm the notion that specific negative emotions with the same valence could have different meanings (Raghunathan & Pham, 1999). Moreover, somehow similar affective states have been already identified in human–computer interaction research (Rozell & Gardner, 2000). Google’s result pages were aggregated to gather information about participants’ interaction with the


search engine. Online search behavior was measured in terms of depth and breadth of search within the Google’s results page (i.e. intrapatch behavior). Following previous conceptualizations (Huang, Lurie, & Mitra, 2009), depth of search was defined as the total time that the consumer spent evaluating the results of the SERP, while breadth of search was referred to the number of abstracts that the consumers scrutinized during the time that they were viewing the results page. A result was defined to be scrutinized if it received at least two fixations or one fixation longer than 500 ms. Furthermore, the quotient between depth and breadth of search was calculated in order to know, on average, how long the participants viewed each abstract. The number of mouse clicks within the Google’s results page was also considered, which offers a guideline about how many Web sites (patches) the participant accessed from Google. Finally, the Roseman’s framework of emotions (Roseman, 1991; Roseman, Antoniou, & Jose, 1996) was used for measuring the emotions felt during the search task. This framework applies to the purposes of the study of analyzing the cognitive appraisals that individuals experience while performing the search activity. A pretest (n = 37) was conducted in order to test how many of the 17 emotions in Roseman’s framework were experienced during online information search episodes. Emotions rated by less than 10% of participants were deleted.3 The results of the pretest yielded five emotions (i.e. hope, joy, liking, disliking, and distress), which were used to measure participants’ emotions during the search activity in the main study (from 1 = “not at all” to 7 = “extremely”). The principal component analysis with varimax rotation revealed two main factors that explained a 74.856% of variance (all factor loadings higher than 0.5), corresponding to the conventional classification between positive and negative emotions (positive: hope, joy, liking; negative: distress, dislike). These global measures were used as dependent variables in the analyses. Nonetheless, given the special interest in looking at the specific emotions related to the uncertainty that any search entails, hope and distress were analyzed also separately (cf. Roseman, Antoniou, & Jose, 1996).

Table 1. Mean and Standard Deviations for the Total Sample.

Initial Emotional State Encouraged Negative Anxious Averse SERP’s search behavior Depth Breadth Depth/breadth ratio Mouse clicks Emotions during the Search Process Positive emotions Hope Negative emotions Distress

M

SD

4.50 1.58 2.66 2.36

1.02 0.77 1.15 0.93

9.03 4.45 2.03 1.46

8.33 3.55 0.84 0.95

3.66 3.46 2.59 2.82

1.27 1.45 1.22 1.49

RESULTS

The Google’s results pages that resulted from the search episodes were aggregated. This allowed examining the general patterns of intrapatch search. The most interesting results are that participants, on average, explored only four or five abstracts, which mostly matched with those that could be viewed without scrolling down the SERP. Participants spent less than 10 seconds on the results page, and employed around two seconds in scrutinizing each result. The data for the number of mouse clicks indicate that participants navigated through more than one Web site from Google’s results page. In addition, only 15.5% of participants scrolled down the results page, and just a limited 13.4% viewed the Adwords section allocated at the right side of the screen. If the first results displayed on the screen did not match the participants’ requirements, most of them (89.6%) preferred to change the search query rather than look into the same results page. Finally, positive emotions were generally more intensely felt than negative emotions. A repeatedmeasures analysis of variance (ANOVA) confirmed this result (F(1, 96) = 27.03; ω2 = 0.27). Specifically, participants felt more hope to find the correct answer than distress provoked by the situation (F(1, 96) = 8.97; ω2 = 0.08). However, participants reported feeling more distress than disliking the search experience (F(1, 96) = 7.48; ω2 = 0.07).

Descriptive Analysis

Hypotheses Testing

Table 1 displays the descriptive data of the dependent variables and the initial affective states for the total sample. On average, participants felt encouraged before performing the search activity, compared to the rest of states. Participants barely were in a depressed state, and slightly felt anxiety and aversion.

Separate ANOVAs were carried out in order to analyze the impact of the type of search task on the behavior with the search engine and on the emotions felt during the process. These analyses yielded two significant main effects. First, a significant effect was found for the depth/breadth ratio (F(2, 96) = 6.56; ω2 = 0.10). Post-hoc Bonferroni’s tests revealed that participants spent more time per result in the navigational task than in the informational task (p < 0.05) and the transactional task (p < 0.05), whereas this time was almost

3

The results of a nonparametric binomial test confirmed that these emotions were not especially felt when searching for information online (all ps < 0.001).


843

search behavior, significant differences emerged when participants were in a highly averse state before performing the search task. Specifically, high averse participants spent less time on the SERP than low averse participants (F(1, 49) = 10.80; ω2 = 0.17), viewed fewer abstracts (F(1, 49) = 4.03; ω2 = 0.06), and scrutinized each result during less time (F(1, 49) = 4.008; ω2 = 0.060). Removing the effect on breadth of search, the differences remained the same when comparing high averse participants to high encouraged participants (depth: F(2, 43) = 3.29; ω2 = 0.09; depth/breadth ratio: F(2, 43) = 3.130; ω2 = 0.09), high depressed participants (depth: F(2, 30) = 7.45; ω2 = 0.29; depth/breadth ratio: F(2, 30) = 6.23; ω2 = 0.26), and high anxious participants (depth: F(2, 37) = 4.12; ω2 = 0.14; depth/breadth ratio: F(2, 37) = 3.92; ω2 = 0.13). However, an initial state of aversion did not affect the emotions experienced during the search process. In this way, high encouraged participants felt more positive emotions than low encouraged participants (F(1, 65) = 18.22, ω2 = 0.22; hope: F(1, 65) = 8.68, ω2 = 0.10); high depressed participants felt more distress than low depressed participants (distress: F(1, 57) = 5.23, ω2 = 0.06). The most interesting findings are related to high anxious participants, who experienced more hope (F(1, 63) = 9.36, ω2 = 0.06) and negative emotions (F(1, 63) = 7.88, ω2 = 0.99; distress: F(1, 63) = 10.56, ω2 = 0.12) than low anxious participants (see Table 3). Therefore, Hypothesis 2, which proposed a main effect of the initial emotional state on search behavior in search engines and on the emotions felt during the search process, is generally supported. Finally, in support of Hypothesis 3a, the existence of time pressure affected the participants’ search behavior (see Table 4). Under time pressure, the depth of search decreased significantly (F(1, 96) = 24.22; ω2 = 0.21) as did the number of results viewed (F(1, 96) = 12.49; ω2 = 0.12); the Depth/Breadth ratio was also significantly shorter (F(1, 96) = 16.58; ω2 = 0.14). However, the number of Web links accessed from the SERP engine’s results page was not affected by the time pressure treatment (F < 1). Time pressure also affected significantly the negative emotions experienced during the search process (F(1, 96) = 6.58; ω2 = 0.06; distress: F(1, 96) = 9.63; ω2 = 0.08), supporting Hypothesis 3b.

Table 2. Mean and Standard Deviations for the Type of Search Task.

Dependent variable Depth Breadth Depth/ breadth ratio Mouse clicks Positive emotions Hope Negative emotions Distress

Navigational

Informational

Transactional

M

SD

M

SD

M

SD

9.02 3.46 2.43

11.30 3.69 0.89

9.44 5.03 1.91

7.03 3.59 0.78

8.53 4.57 1.85

7.15 3.34 0.79

1.10

0.92

1.72

0.97

1.52

0.85

3.86

1.33

3.50

1.27

3.67

1.22

3.60 2.35

1.45 1.28

3.42 2.68

1.52 1.48

3.39 2.74

1.31 1.24

2.50

1.53

2.89

1.45

3.06

1.48

the same for informational and transactional tasks (p > 0.1; see Table 2). Second, the number of Web links that the participant accessed from Google’s results page was also significantly affected by the type of search task (F(2, 96) = 4.35; ω2 = 0.06). Participants in the navigational task clicked on fewer Web links than participants in the informational task (p < 0.05) and in the transactional task (p < 0.05; see Table 2). Overall, little, although significant support for Hypothesis 1a was found. Regarding the emotions experienced during the search activity, participants in the navigational task had more positive and less negative emotions than in the other tasks. Although this is directionally consistent with the propositions, no significant effects were found. Thus, Hypothesis 1b should be rejected. Participants were split up according to standardized procedures taking the average of each initial emotional state plus or minus half the standard deviation (Bentler, 1995; Jaccard & Wan, 1996). Thus, participants were classified as high or low encouraged, high or low depressed, high or low anxious, and high or low averse (see Table 3). ANOVAs were performed in order to identify significant differences within each type of initial affective state and between different states. Only significant effect sizes are reported. Regarding the

Table 3. Mean and Standard Deviations for the Initial Affective State. Encouraged High

Depressed

Low

High

Anxious

Low

High

Averse Low

High

Low

Dependent variable

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

Depth Breadth Depth/breadth ratio Mouse clicks Positive emotions Hope Negative emotions Distress

7.92 4.06 2.09 1.42 4.31 3.91 2.51 2.74

6.88 3.26 0.91 0.96 1.22 1.54 1.17 1.52

11.06 5.28 1.96 1.40 3.21 2.97 2.27 2.42

9.69 3.90 0.93 0.88 0.91 1.19 1.53 1.36

9.82 4.79 2.25 1.50 4.01 3.45 3.15 3.40

7.93 4.25 0.91 1.10 0.94 1.14 1.21 1.39

10.01 4.60 2.19 1.45 3.74 3.50 2.37 2.71

9.72 3.93 0.89 0.92 1.41 1.69 1.26 1.64

9.62 4.73 2.10 1.54 3.88 3.86 3.14 3.61

8.67 3.66 0.91 1.03 1.13 1.21 1.18 1.49

7.97 4.28 1.84 1.55 3.25 2.94 2.39 2.58

6.89 3.42 0.62 1.02 1.29 1.49 1.26 1.46

5.07 3.45 1.66 1.37 3.67 3.46 2.85 3.13

4.32 3.10 0.59 0.97 1.25 1.35 1.29 1.59

10.78 5.52 2.05 1.58 3.26 3.08 2.61 2.88

7.06 3.85 0.74 0.99 1.43 1.69 1.39 1.58

Note: Significant differences at p < 0.05 are bold-typed.

844


Table 4. Mean and Standard Deviations for Time Pressure. Free Navigation

Time Pressure

Dependent variable

M

SD

M

SD

Depth Breadth Depth/breadth ratio Mouse clicks Positive emotions Hope Negative emotions Distress

12.85 5.80 2.31 1.49 3.69 3.51 2.33 2.41

9.65 4.11 0.92 1.01 1.33 1.42 1.14 1.31

5.11 3.07 1.74 1.44 3.64 3.42 2.87 3.25

3.97 2.17 0.65 0.90 1.22 1.45 1.24 1.55

DISCUSSION Overall, descriptive data lend support to several notions posited by online foraging theories that could be applied to search behavior with search engines. Participants presented very similar foraging patterns and strategies with the search engine. They paid attention to the first results of the page and overlooked other results at a deeper level as well as sponsored results; instead, they preferred to change the search query when the first results did not answer their information need. Thus, participants showed some level of intrapatch sampling within the results page, given that they did not choose exclusively the first result retrieved by the system. This finding is consistent with previous research on online foraging showing that consumers do not search for information and purchase exclusively in the most proximal cue in the environment (Hantula, DiClemente, & Smith, 2008; Rajala & Hantula, 2000). In addition, participants did perform strategies that allowed them to improve the information scent of the results page. Following the information foraging theory, if the forager has the opportunity, he or she will modify the environment in order to eliminate unproductive paths and to avoid incorrect answers (Pirolli, 2005; Pirolli & Card, 1999). Together, people seem to have adapted to the online search environment and have acquired a search technique that allows them to clear out the information superhighway of the World Wide Web, and this technique rarely changes. The Internet is a stochastic patchy environment where all preys do not flight or just wait to be gathered, but they want to be captured (Nielsen, 2003). Given that online users firmly rely on the search engine’s capacity to offer the results ¨ that better match their information needs (Haubl & Murray, 2003; Pan et al., 2007), they use these tools with the aim of enriching the environment, reducing search costs, and obtaining information that they consider valuable. Moreover, online searchers appear to be quite at ease with this type of search activities, given that participants reported significantly higher positive emotions than negative emotions during the process.


Regarding the predicted influence of situational factors, different effects raised for the type of search task, the initial emotional state, and the existence of time pressure. First, the type of search task had apparently no impact on the search process with the search engine. The only significant differences were between navigational tasks and the other tasks on the time spent viewing each abstract and the number of Web pages clicked; the total time, number of abstracts, and the emotional responses were not significantly affected. These results could be explained in terms of the different structure and goals pursued in the different search tasks. Navigational tasks may have well-defined purposes in the search engine: to detect the specific URL of the company and verify that is the official one. Therefore, the correct answer could be only one step beyond the SERP. On the contrary, informational and transactional tasks could be less concrete and defined (Pirolli, 2005), involve more subdecisions, and the SERP could represent just the first step of a long chain. Although the results show little evidence for differences on behavior and emotional response depending on the type of search task, future research should investigate whether other types of search tasks could have more prominent effects. The types of search tasks used in the study may be seen as similar in terms of the reinforcements that participants pursue (i.e. Foxall, 2010). Qualitative differences in the potential rewards of the search, or different degrees of personal relevance for the search topic, could offer further insights into the analysis of whether and how the type of search task affects online foraging with search engines. Before performing the search process, participants were generally in an emotional state that encouraged them to perform a goal-directed search (Pirolli, 2005). Among the negative-valenced initial affective states, anxiety appeared to be more salient than either depressed or averse states, which supports the notion that any search episode entails uncertainty that creates feelings of stress (Nahl, 2004; Peterson & Merino, 2003). In addition, participants’ initial emotional state influenced significantly their online search behavior with the search engine and their emotional responses during the process, which is consistent with previous research stressing the important role of emotions in motivating subsequent behaviors (Bagozzi, Gopinath, & Nyer, 1999). On the one hand, participants in a high averse state (i.e. hostile, proud, irritable) devoted fewer resources to the search activity than the rest of participants, as reflected by the less time they spent on the SERP, the less number of abstracts they viewed, and the less time they viewed each abstract. However, highly averse participants did not report feeling differently than other participants during the search process. Thus, it could be argued that when individuals feel some kind of aversion before the search task, they tend to show a certain degree of passiveness and lack of engagement. This finding is consistent with past conceptualizations of search behavior that state that when searchers are

845

under-aroused, they may fail to pay attention and conduct a superficial search (Quinn, 2003). On the other hand, the initial affective state affected significantly the emotions experienced during the search process. Interestingly, participants with a highly anxious initial emotional state felt significantly more hope and distress than low anxious participants. While the result for distress could be expected, the one for hope is surprising to a certain degree. Hope has been related to an appraisal of uncertainty (MacInnis & de Mello, 2005; Roseman, Antoniou, & Jose, 1996). To the extent that uncertainty leads to anxiety, it is speculated here that a possible connection between hope and stress may reside in the uncertainty that the search implies. This finding deserves further investigation, given that hope is an anticipatory feeling about the consequences of an uncertain decision making, which can alter such process (Bechara, Damasio, & Damasio, 2000; Loewenstein, Weber, Hsee, & Welch, 2001) and guide behavior (Baumgartner, Pieters, & Bagozzi, 2008). Lastly, time pressure strongly affected online foraging with the search engine, both at the behavioral and psychological levels. Time pressure forced participants to spend less time in the SERP, to view less abstracts, and to spend less time scrutinizing each abstract. This finding extends previous research regarding the effects of time pressure on decision making, which state that time pressure may switch the attention and search strategies, speeding up the examination of information by accelerating the visual scanning sequence and attention (Payne, Bettman, & Luce, 1996). However, participants under free navigation did not click on more Web links than those with time constraints. This result may indicate that online searchers exploit the same patches (Web sites) regardless of time issues, using the search engine to enrich the starting point of the search process. Nevertheless, the way they exploit the patches does vary depending on temporal constraints. Furthermore, participants under time pressure felt significantly more distress than participants without time constraints. This finding is consistent with appraisal theories (Roseman, 1991; Roseman, Antoniou, & Jose, 1996). In this sense, an external cause (i.e. time pressure) produces a threat to individuals’ goal (i.e. succeed in the search), and they have a low control of that situation (they could not broaden the time to successfully perform the task). At the psychological level, time pressure can operate in the same way as delay times (Dennis & Taylor, 2006), which can affect the search process and outcomes. Definitely, more research is needed in order to disentangle the psychological costs involved in the temporal dimension of online foraging.

CONCLUSIONS Recently, evolutionary theories have been considered as a cornerstone that may help researchers to better understand the information systems phenomena. However, these theories should be integrated with other

846

nonevolutionary theories in order to provide a more accurate and testable picture of human–IT interactions (Kock, 2009; Kock et al., 2008). This study is one of the first attempts to develop such integration in an online information search environment. Following the behavioral and information processing perspectives, this research examined the online foraging behavior and the emotional responses that occur during the process, given that emotions are likely to guide the search process and affect search outcomes. Taking the widespread use of search engines as a realistic context of analysis, the findings reveal that situational factors, especially those related to the person and the temporal dimension, affect in a great extent the search patterns and the emotions experienced. It should be noted that this research only focused on the search process per se, regardless of its outcomes. In this way, it would be noteworthy to analyze the relationships between the online search behavior, the emotions during the search activity, and the levels of perceived success. Perceived success in the search task may influence the affective consequences of the process, such as the emotional outcomes, the satisfaction with the search process, or the perceived effort and difficulty in the search task (Bechwati & Xia, 2003; Kozup & Creyer, 2006). In this way, a measure about the net information gain of the search process would consider both perceived benefits and costs. In addition, it should be noted that the emotions during the search experience were self-reported once the search task was completed. It would be desirable to gather information about the emotions at the moment they are occurring. This would be useful to better identify when they occur and how they are able to impact search behavior. Future research could use other tools to identify these conscious and unconscious emotions by means of facial expressions or protocol analyses (De Lera & Garreta, 2006; Groepper, Oliver, Phillip, & Anja, 2009), or more sophisticated techniques, such as functional magnetic resonance imaging (Bechara & Damasio, 2005).

REFERENCES Alexa. (2011). Top 500 global sites. Retrieved January 16, 2011, from http://www.alexa.com/topsites. Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206. Baumgartner, H., Pieters, R., & Bagozzi, R. P. (2008). Futureoriented emotions: Conceptualization and behavioral effects. European Journal of Social Psychology, 38(4), 685– 696. Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software, Inc. Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52(2), 336–372. Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex, 10(3), 295–307.


Bechwati, N. N., & Xia, L. (2003). Do computers sweat? The impact of perceived effort of online decision aids on consumers’ satisfaction with the decision process. Journal of Consumer Psychology, 13(1–2), 139–148. Bettman, J. R. (1979). An information processing theory of consumer choice. Reading, MA: Addison-Wesley Pub. Co. Bilal, D., & Kirby, J. (2002). Differences and similarities in information seeking: Children and adults as web users. Information Processing and Management, 38(5), 649–670. Broder, A. (2002). A taxonomy of web search. SIGIR Forum, 36(2), 3–10. Browne, G. J., Pitts, M. G., & Wetherbe, J. (2007). Cognitive stopping rules for terminating information search in online tasks. MIS Quarterly, 31(1), 89–104. Card, S. K., Pirolli, P., van der Wege, M., Morrison, J. B., Reeder, P. K., & Boshart, J. (2001). Information scent as a driver of web behavior graphs: Results of a protocol analysis method for web usability. In M. Beaudouin-Lafon & R. J. K. Jacob (Eds.), Proceedings of the ACM CHI 2001 Human Factors in Computing Systems Conference (pp. 498–505). Seattle, WA: ACM press. Charlton, J. P. (2009). The determinants and expression of computer-related anger. Computers in Human Behavior, 25(6), 1213–1221. ComsCore. (2009). ComScore’s qSearch 2.0 Retrieved October 10, 2010, from http://www.marketingcharts.com/ categories/searchengine-share-of-visits/. Dhar, R., & Nowlis, S. M. (1999). The effect of time pressure on consumer choice deferral. Journal of Consumer Research, 25(4), 369–384. Dennis, A. R., & Taylor, N. J. (2006). Information foraging on the web: The effects of “acceptable” Internet delays on multi-page information search behaviour. Decision Support Systems, 42, 810–824. De Lera, E., & Garreta, M. (2006). Ten emotion heuristics – Evaluating the affective dimension. Retrieved October 24, 2010, from http://www.uxmatters.com/mt/ archives/2007/09/images/ten-emotion-heuristics.pdf. Dewitte, S., Bruyneel, S., & Goukens, K. (2009). Selfregulating enhances self-regulation in subsequent consumer decisions involving similar response conflicts. Journal of Consumer Research, 36(3), 394–405. DiClemente, D. F., & Hantula, D. A. (2003). Optimal foraging online: Increasing sensitivity to delay. Psychology and Marketing, 20(9), 785–809. Ebert, J. E. J., & Prelec, D. (2007). The fragility of time: Timeinsensitivity and valuation of the near and far future. Management Science, 53(9), 1423–1438. Fagerstrøm, A. (2010). The motivating effect of antecedent stimuli on the web shop: A conjoint analysis of the impact of antecedent stimuli at the point of online purchase. Journal of Organizational Behavior Management, 30, 199–220. Fagerstrøm, A., Foxall, G. R., & Arntzen, E. (2010). Implications of motivating operations for the functional analysis of consumer choice. Journal of Organizational Behavior Management, 30, 110–126. ´ C., Gurrea, R., & Orus, ´ C. (2011). Analyzing the emoFlavian, tional outcomes of the online search behavior with search engines. Computers in Human Behavior, 27(1), 540–541. Foxall, G. R. (1992). The behavioral perspective model of purchase and consumption: From consumer theory to marketing practice. Journal of the Academy of Marketing Science, 20(2), 189–198. Foxall, G. R. (1993). A behaviourist perspective on purchase and consumption. European Advances in Consumer Research, 1, 501–506.


Foxall, G. R. (1997). The emotional texture of consumer environments: A systematic approach to atmospherics. Journal of Economic Psychology, 18, 505–523. Foxall, G. R. (2010). Theoretical and conceptual advances in consumer behaviour analysis. Journal of Organizational Behavior Management, 30, 92–109. Foxall, G. R., & James, V. K. (2003). The behavioral ecology of brand choice: How and what do Consumer maximize? Psychology and Marketing, 20(9), 811–836. Frijda, N. H. (1987). Emotion, cognitive structure, and action tendency. Cognition and Emotion, 1(2), 115–143. Frijda, N. H. (1993). The place of appraisal in emotion. Cognition and Emotion, 7(3/4), 357–387. Garbarino, E. C., & Edell, J. A. (1997). Cognitive effort, affect, and choice. Journal of Consumer Research, 24(2), 147– 158. Groepper, A., Oliver, H., Phillip, B., & Anja, D. (2009). Measurement of emotions. In Proceedings of the 38th EMAC Conference, Nantes, France. Hair, J. F. Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall. Hantula, D. A. (2010). The behavioral ecology of human foraging in an online environment: Of omnivores, informavores, and hunter-gatherers. In Kock, N. (Ed.), Evolutionary psychology and information systems research (pp. 85–99). New York: Springer. Hantula, D. A., & Bryant, K. (2005). Delay discounting determines delivery fees in an e-commerce simulation: A behavioral economic perspective. Psychology and Marketing, 22(2), 153–161. Hantula, D. A., DiClemente, D., & Smith, C. L. (2008). Online shopping as foraging: The effects of increasing delays on purchasing and patch residence. IEEE Transactions and Professional Communication, 51(2), 147–154. ¨ Haubl, G., & Murray, K. B. (2003). Preference construction and persistence in digital marketplaces: The role of electronic recommendation agents. Journal of Consumer Psychology, 13(1–2), 75–91. ¨ Haubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19(1), 4–21. Haynes, G. A. (2009). Testing the boundaries of the choice overload phenomenon: The effect of number of options and time pressure on decision difficulty and satisfaction. Psychology and Marketing, 26(3), 204–212. Herrnstein, R., Rachlin, H., & Laibson, D. (1997). The matching law: Papers in psychology and economics. Cambridge: Russell Sage Foundation. Hoque, A. Y., & Lohse, G. L. (1999). An information search cost perspective for designing interfaces for electronic commerce. Journal of Marketing Research, 36(3), 387– 394. Huang, P., Lurie, N. H., & Mitra, S. (2009). Searching for experience on the web: An empirical examination of consumer behavior for search and experience goods. Journal of Marketing, 73(2), 55–69. Jaccard, J., & Wan, C. (1996). LISREL approaches to interaction effects in multiple regressions. Thousand Oaks, CA: Sage Publications. Jansen, B. J., & Spink, A. (2006). How are we searching the World Wide Web? A comparison of nine search engine transaction logs. Information Processing and Management, 42, 248–263. Jansen, B. J., Booth, D. L., & Spink, A. (2008). Determining the informational, navigational, and transactional intent

847

of web queries. Information Processing and Management, 44(3), 1251–1266. JupiterMedia Matrix. (2006). How consumers search for products online. Retrieved July 25, 2009, from http://www. zeromillion.com/webmarketing/search-engine-marketing. html. Kim K. S. (2008). Effects of emotion control and task on web searching behavior. Information Processing and Management, 44(1), 373–385. Klein, L. R., & Ford, G. T. (2003). Consumer search for information in the digital age: An empirical study of prepurchase search for automobiles. Journal of Interactive Marketing, 17(3), 29–49. Kock, N. (2009). Information systems theorizing based on evolutionary psychology: An interdisciplinary review and theory integration framework. MIS Quarterly, 33(2), 395–418. Kock, N. (2010). Evolutionary psychology and information systems research: A new approach to studying the effects of modern technologies on human behaviour. New York, NY: Springer. Kock, N., Hantula, D. A., Hayne, S. C., Saad, G., Todd, P. M., & Watson, R. T. (2008). Introduction to Darwinian perspectives on electronic communication. IEEE Transactions and Professional Communication, 51(2), 133–146. Kozup, J. C., & Creyer, E. H. (2006). Boundary conditions of the impact of a hypervigilant coping style on the subjective decision-making experience. Psychology and Marketing, 23(11), 905–925. Kuhlthau, C. C. (1991). Inside the search process: Information seeking from the user’s perspective. Journal of the American Society for Information Science, 42(5), 361–371. Kulviwat, S., Guo, C., & Engchnil, N. (2004). Determinants of online information search: A critical review and assessment. Internet Research, 14(3), 245–253. Lea, S. E. G. (1979). Foraging and reinforcement schedules in the pigeon: Optimal and non-optimal aspects of choice. Animal Behaviour, 27(3), 875–886. Liaw, S. S., & Huang, H. M. (2006). Information retrieval from the World Wide Web: A user-focused approach based on individual experience with search engines. Computers in Human Behavior, 22(3), 501–517. Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267–286. Lorigo, L., Pan, B., Hembrooke, H., Joachims, T., Granka, L., & Gay, G. (2006). The influence of task and gender on search and evaluation behaviour using Google. Information Processing and Management, 42(4), 1123–1131. Louro, M. J., Pieters, R., & Zeelenberg, M. (2005). Negative returns on positive emotions: The influence of pride and self-regulatory goals on repurchase decisions. Journal of Consumer Research, 31(3), 833–840. Lurie, N. H. (2004). Decision making in information-rich environments: The role of information structure. Journal of Consumer Research, 30(1), 473–486. MacInnis, D. J., & de Mello, G. E. (2005). The concept of hope and its relevance to product evaluation and choice. Journal of Marketing, 69(1), 1–14. Mano, H., & Oliver, R. L. (1993). Assessing the dimensionality and structure of the consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20(3), 451–466. Matsuda, Y., Uwano, H., Ohira, M., & Matsumoto, K. (2009). An analysis of eye movements during browsing multiple search results pages. In J. A. Jacko (Ed.), Proceedings of the 13th International Conference on Human-Computer Inter-

848

action, part I: New trends (pp. 121–130). San Diego, CA: Springer-Verlag Berlin, Heidelberg. Moe, W. W. (2002). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1–2), 29– 39. Nahl, D. (1998). Learning the Internet and the structure of information behavior. Journal of the American Society for Information Science, 49(11), 1017–1023. Nahl, D. (2004). Measuring the affective information environment of web searchers. Proceedings of the American Society for Information Science and Technology, 41(1), 191–197. Nielsen, J. (2003). Information foraging: Why Google makes people leave your site faster. Retrieved January 26, 2011, from http://www.useit.com/alertbox/20030630.html. Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007). In Google we trust: Users’ decisions on rank, position, and relevance. Journal of ComputerMediated Communication, 12(3), article 3. Retrieved December 15, 2009, from http://jcmc.indiana.edu/vol12/issue3/ pan.html. Payne, J. W., Bettman, J. R., & Luce, M. F. (1996). When time is Money: Decision behavior under opportunity-cost time pressure. Organizational Behavior and Human Decision Processes, 66(2), 131–152. Peterson, R. A. & Merino M. C. (2003). Consumer information search behavior and the Internet. Psychology and Marketing, 20(2), 99–121. Pew Internet Research. (2009). Daily Internet activities, 2000–2009. Retrieved January 18, 2009, from http://www. pewinternet.org/Trend-Data/Daily-Internet-Activities-2000 2009.aspx. Pieters, R., & Warlop, L. (1999). Visual attention during brand choice: The impact of time pressure and task motivation. International Journal of Research in Marketing, 16(1), 1– 16. Pirolli, P. (2005). Rational analyses of information foraging on the web. Cognitive Science, 29, 343–373. Pirolli, P., & Card, S. K. (1999). Information foraging. Psychological Review, 106, 643–675. Pirolli, P., & Fu, W. T. (2003). SNIF-ACT: A model of information foraging on the World Wide Web. In P. Brusilovsky, A. Corbett, & de Rosis, F. (Eds.), Proceedings of the 9th International Conference on User Modeling (pp. 45–54). Johnstown, PA; Berlin: Springer Verlag. Quinn, B. (2003). Overcoming psychological obstacles to optimal online search performance. The Electronic Library, 21(2), 142–153. Raghunathan, R., & Pham, M. T. (1999). All negative moods are not equal: Motivational influences of anxiety and sadness on decision making. Organizational Behavior and Human Decision Processes, 79(1), 56–77. Rajala, A. K., & Hantula, D. A. (2000). Towards a behavioral ecology of consumption: Delay-reduction effects on foraging in a simulated Internet mall. Managerial and Decision Economics, 21, 145–158. Roseman, I. J. (1991). Appraisal determinants of discrete emotions. Cognition and Emotion, 5(3), 161–200. Roseman, I. J., Antoniou, A. A., & Jose P. E. (1996). Appraisal determinants of emotions: Constructing a more accurate and comprehensive theory. Cognition and Emotion, 10(3), 241–277. Rozell, E. J., & Gardner, W. L. (2000). Cognitive, motivation, and affective processes associated with computer-related performance: A path analysis. Computers in Human Behavior, 16(2), 199–222.


Shiv, B., Loewenstein, G., Bechara, A., Damasio, H., & Damasio, A. R. (2005). Investment behavior and the negative side of emotion. Psychological Science, 16(6), 435– 439. Simon, H. A. (1955). A behavioral model or rational choice. The Quarterly Journal of Economics, 69(1), 99–118. Smith, C. L., & Hantula, D. A. (2003). Pricing effects on foraging in a simulated Internet shopping mall. Journal of Economic Psychology, 24, 653–674. Spink, A., & Cole, C. (2006). Human information behaviour: Integrating diverse approaches and information use. Journal of the American Society for Information Science and Technology, 57(1), 25–35. Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton, NJ: Princeton University Press. Stigler, G. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225. Sundar, S. S., Knobloch-Westerwick, S., & Hastall, M. R. (2007). News cues: Information scent and cognitive heuristics. Journal of the American Society for Information Science and Technology, 58(3), 366–378.


Wang, P., Hawk, W. B., & Tenopir, C. (2000). Users’ interaction with World Wide Web resources: An exploratory study using a holistic approach. Information Processing and Management, 36(2), 229–251. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. The authors are grateful for the financial support received from the European Union (7 Framework Program) (FP7-ICT2007–1). The authors are deeply grateful to the anonymous reviewers and to the Executive Editor of Psychology & Marketing, Professor Rajan Nataraajan, for providing very helpful comments and suggestions for improvement. Correspondence regarding this article should be sent to: ´ Department of Economy and Business AdCarlos Flavian, ministration, Faculty of Economics and Business Studies, University of Zaragoza, Gran Via, 50005 Zaragoza, Spain ([email protected]).

849