Currency of Search: How Spending Time on Search is Not the Same as Spending Money

Journal of Retailing 85 (3, 2009) 245–257

Currency of Search: How Spending Time on Search is Not the Same as Spending Money Ashwani Monga a,∗,1 , Ritesh Saini b,1,2 a

Moore School of Business, University of South Carolina, 1705 College Street, Columbia, SC 29208, United States b College of Business, University of Texas at Arlington, 701 S. West Street, Arlington, TX 76019, United States

Abstract Search theories suggest that a decline in search costs increases search behavior. This relationship has been well supported by prior experimental research but not by studies conducted in retail settings. Our review of the literature suggests that this discrepancy might be driven by the fact that prior experiments typically involve money-based search whereas actual search in retail settings is usually time-based. We argue that the currency of search plays a moderating role. We find that when participants spend money on search, a decrease in search costs has a significant effect on search decisions but, when they spend time on search, a decrease in search costs either has a relatively weak effect (Experiment 1) or no effect at all (Experiment 2). Furthermore, this insensitivity in time also emerges for search payoffs (Experiment 3). We also offer evidence for the processes underlying these effects. Our results provide a new lens to examine inconsistencies in the search literature, and present a view of search that is more applicable to the retail context. © 2009 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Consumer search; Time versus money; Judgment and decision making

Search is a key component of the decision process in retail settings (Miller 1993), and involves seeking information to resolve purchase uncertainties (Moorthy, Ratchford, and Talukdar 1997). Consumers make search decisions in either sequential (Schotter and Braunstein 1981, Zwick et al. 2003) or non-sequential (Burdett and Judd 1983) settings. Sequential search is open-ended and the search decision pertains to when search will be terminated. For example, a consumer trying to buy a couch at the lowest price could keep on visiting furniture stores, and decide to stop searching as soon as she finds a satisfactory price. In non-sequential search, consumers make search decisions even before commencing search. For example, the consumer trying to buy a couch could make an a priori decision about the number of stores to visit in order to check prices. What is common to both these kinds of search, however, is a tradeoff between costs and payoffs. Search costs have to be incurred (e.g., spending time to visit stores) in order to achieve potential search payoffs (e.g., finding a lower price). The will-

∗ 1 2

Corresponding author. Tel.: +1 803 777 5918; fax: +1 803 777 6876. E-mail address: [email protected] (A. Monga). The authors contributed equally to this article. Tel.: +1 817 272 2876; fax: +1 817 272 2854.

ingness to search refers to the number of stores that one decides to visit. This paradigm follows from Stigler’s (1961) seminal paper in which he analyzed search as an optimization-underconstraints problem; greater search leads to a higher likelihood of success but involves greater costs as well. Although search entails costs, it can lead to a better payoff, such as a lower price for the good. Therefore, a decline in search costs or an increase in search payoffs should increase consumers’ willingness to search. We propose that these fundamental relationships are moderated by the currency of search: time versus money. We experimentally show that willingness to search is less sensitive to changes in costs and payoffs when search is conducted by spending time rather than money. Our findings have direct relevance for retail theorists and practitioners. As we discuss in the next section, the effect of search costs on search decisions has been well supported by prior experimental research but the evidence from retail settings is not supportive. Our review of the literature suggests that this discrepancy might be driven by the fact that prior experiments typically involve money-based search whereas search in retail settings is usually time-based. We argue that search is likely to occur differently in settings in which money is spent (e.g., paying a real-estate agent to search for home buyers) than in retail settings in which search involves spending time (e.g., visiting different

0022-4359/$ – see front matter © 2009 New York University. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jretai.2009.04.005

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retailers or e-tailers). This has consequences for retailers trying to be in the consideration set of consumers searching for products and services. One interesting implication relates to the Internet context, which has been widely studied in the retailing literature (Grewal and Levy 2007). Given that search costs are lower in the electronic world than in the physical world, retailers worry that the Internet leads to higher consumer search and, therefore, more intense competition (Lynch and Ariely 2000). While these worries are perfectly understandable, it seems that people might not be searching very extensively over the Internet (Brynjolfsson and Smith 2000). We argue that, because the Internet reduces search costs of time (not money), the increase in search activity will not happen as would be expected from search models that have been supported in monetary settings. Consequently, retailers need to be less fearful of lower search costs on the Internet, and more enthusiastic about the opportunities offered by online environments (Lynch and Ariely 2000), such as the potential to reduce product performance uncertainty by using various communication practices (Weathers, Sharma, and Wood 2007). Implications also arise for store location models (Achabal, Gorr, and Mahajan 1982). Our results imply that consumers will be relatively more sensitive to monetary aspects of stores (the lower price offered by an outlet mall relative to the neighborhood store) than to temporal aspects (the time required to visit the outlet mall). We do not suggest that time does not matter; the time of travel will indeed be a cost to consumers. What we suggest instead is that consumers will react more strongly to price differences than to time-of-travel differences. We next present the literature that motivates our inquiry into the currency of search. Then, we offer a prediction about its role in moderating the effect of search costs on search behavior and present supporting evidence from two laboratory experiments. We then extend our theorizing to search payoffs and find a similar moderating effect in a third experiment; people are less sensitive to changes in payoffs when the currency of search is time rather than money. Finally, we conclude with the implications of our results for the theory and practice of retailing. Time versus money as currency of search Search is frequently conducted by spending time. People spend time searching inside stores, in traveling from one store to another, and in searching over the Internet. The prevalence of time-based searching is evident from field research. When we examined the retail situations that are studied in this literature, we found that they overwhelmingly relate to expenditures of time. When consumers search for automobiles (Moorthy et al. 1997; Punj and Staelin 1983; Srinivasan and Ratchford 1991) or generally for products in the marketplace (Pratt, Wise, and Zeckhauser 1979), they usually spend their time. And when researchers study the effects of lower search costs on the Internet relative to conventional markets (Brynjolfsson and Smith 2000), the costs refer to the time that consumers spend. This consideration of the costs of time rather than money is also inherent in the measures that are used. In field research, researchers usually measure search costs via questions that directly assess

respondents’ own valuation of the time required to search (Srinivasan and Ratchford 1991) or that indirectly assess respondents’ opportunity costs of time from other indicators (Punj and Staelin 1983). That is, field studies seem to consider search as an activity that involves expenditure of time. In stark contrast to the field research, our review of search experiments revealed an overwhelming reliance on the currency of money. Barring rare exceptions (Smith, Venkatraman, and Dholakia 1999), search costs are operationalized in terms of money. This is true for experimental economics research (Cox and Oaxaca 1989; Kogut 1992; Schotter and Braunstein 1981) as well as experimental consumer research (Diehl 2005; Srivastava and Lurie 2001; Zwick et al. 2003). The use of money is appropriate because it enables easy quantification of search costs as researchers focus on the phenomena that they are studying. However, from the perspective of ecological validity, these experiments seem disconnected from the reality of consumers often spending their time rather than money in order to search in retail settings. This disconnect is especially consequential because, as we discovered from a comparison of results from several experiments (manipulating monetary search costs) and field studies (measuring temporal search costs), there is an inconsistency between the two. The theoretical prediction of lower search costs leading to higher search behavior (Stigler 1961) has been repeatedly demonstrated in experimental studies (Kogut 1992; Schotter and Braunstein 1981, see Davis and Holt 1993 for a review). In contrast, the support from field research is rare (Moorthy et al. 1997). Consider the findings of Putrevu and Ratchford (1997). Although search costs had a significant effect when they were measured in terms of opportunity costs from an economic perspective (e.g., wage rate), their effect was not significant when they were measured in terms of felt time pressure, which represented the psychological cost of time. Even in other field studies, the effect of search costs on search behavior has been found to be either only marginally significant (Punj and Staelin 1983), or completely non-significant (Srinivasan and Ratchford 1991). Our review of the literature on price dispersion further highlights the discrepancy in results. Theoretically, if search costs decrease, the increase in search behavior should deter sellers from offering discrepant prices and, therefore, price dispersion in the market should decrease. For example, because search costs are believed to be lower over the Internet, search models suggest that price dispersion on the Internet should be lower than that in comparable conventional markets (Bakos 1997). This effect on price dispersion is evident from experiments involving money (Cason and Friedman 2003) but, once again, not from field research (Brynjolfsson and Smith 2000; Pratt et al. 1979). For instance, the price dispersion in online markets is comparable to that in offline markets (Brynjolfsson and Smith 2000). We clearly recognize that this inconsistency between experimental and field results may be driven by the numerous differences between the two settings and not just by the currency of search (i.e., money in experiments and time in field studies). However, these results do underscore the importance

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of isolating the effect of currency and investigating whether and how this variable can influence the relationship between search costs and search behavior. Currency of search will moderate the effect of search costs Currency of search need not be an issue if one considers the perspective of time and money being economically equivalent. In his economic theory about the allocation of time, Becker (1965) equates the value of time to its opportunity cost, which is usually assumed to be one’s wage rate. Similarly, Graham (1981) discusses time as a straight line from the past into the future that can be separated into discrete units, and allocated just as money is. In other words, time is believed to be a resource that is akin to money. However, other researchers suggest a more complex view of time. From the perspective of economics, time can indeed be conceptualized as an objective resource; from the perspective of sociology, it refers to a social construction in a cultural context in which it is valued; and from the perspective of psychology, it is a subjective perception (McDonald 1994). For instance, Marmorstein, Grewal, and Fishe (1992) found that consumers’ subjective value of their time is influenced by not just wage rates, but also the perceived enjoyment of shopping. These insights have spawned further research on how consumers view the notion of time, especially in comparison to money. Research on time–money differences has not studied the effect of search costs and payoffs, as we do in the current research. However, in our prior work (Saini and Monga 2008), we have employed the context of search to study how heuristics – rules of thumb that simplify decision making – are used differently in time than in money. Because our experiments were focused on examining heuristics rather than the search process, we had held constant the magnitude of relevant search determinants (i.e., search costs), but manipulated unrelated information that could be used as a heuristic (e.g., value of an arbitrary anchor). We demonstrated that when decisions related to time rather than money, people displayed a higher use of the anchoring heuristic; they were more prone to considering the value of the irrelevant anchor. So, if time makes people more sensitive to irrelevant information (e.g., anchors), does it suggest that it will make people more sensitive to even relevant information (e.g., changes in search costs)? Or, does it suggest that time will make people less sensitive to relevant information? A host of research suggests that the latter possibility is more likely; people are less likely to respond to changes in information that relates to time (vs. money). Although the disutility from spending money is usually a direct function of the magnitude of the money spent, it has been found that the disutility from spending time is less sensitive to the magnitude of the time spent (Elster and Loewenstein 1992; Fredrickson and Kahneman 1993). In general, when experiences extend over time, people are not adept at judging them based on the duration of those experiences. Apart from rare situations, such as when individuals can easily compare magnitudes of time (Ariely and Loewenstein 2000), people exhibit a duration neglect. They judge an experience based on aspects such as the final intensity of an experience, rather than its duration (Varey

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and Kahneman 1992). Ebert and Prelec (2007) extend the idea of duration neglect to time intervals that extend from now into the future, and find that choices are not sufficiently sensitive to time. In other research by Okada and Hoch (2004), time has been labeled as being more ambiguous than money. For example, although the value of an hour might seem low on a leisurely Sunday afternoon but high on a hectic Monday morning, the value of a dollar does not change as easily across situations. Because of such ambiguity, Okada and Hoch (2004) find that it is much easier for people to rationalize their expenditures of time, but not of money. Finally, research by Soman (2001) suggests that most people are not good at accounting for different magnitudes of time because they do not routinely keep account of time the way they keep account of money. He demonstrates that people are not very sensitive to the amount of time they have spent in the past, even though they carefully consider past expenditures of money. It is important to note that the above stream of research does not suggest that expenditures of time are completely ignored. When people spend time, they do notice the presence of that expenditure. What they ignore, however, is the magnitude of that expenditure, leading to a lack of sensitivity toward changes in the magnitude. So while people respond to small versus big amounts of money in a relatively precise fashion, their response to changes in time is not as precise. The above arguments have important consequences for the premise that a decline in search costs increases search, a premise that has been long established in the theoretical and experimental literature on search (Davis and Holt 1993; Stigler 1961). As discussed earlier, established search theories do not consider any role for the currency in which search is conducted, and search experiments supporting these theories have overwhelmingly relied on the currency of money, implicitly assuming that money can be a surrogate for all expenditures. However, the literature overviewed in the previous paragraphs suggests that even though people consider monetary information carefully, they disregard the magnitude of temporal information. Therefore, if search involves spending time instead of money, people are likely to be less sensitive to changes in the magnitude of search costs. That is, the extent to which they are willing to search (e.g., go to different stores to find a lower price) will not change much with the time that it takes to search. This leads to our first hypothesis: H1. Currency of search will moderate the effect of magnitude of search costs on people’s willingness to search. Specifically, when the currency is money, lower (vs. higher) search costs will result in higher willingness to search. When the currency is time, this effect of search costs on willingness to search will be relatively weaker. Following our earlier discussion, H1 is predicated on the idea that people are relatively indifferent to the magnitude of search costs, when search involves spending time rather than money. This proposed process is consistent with research on prospective duration judgments, in which it has been demonstrated that people show a lack of attentional focus toward temporal information (Block and Zakay 1997; Zakay 1998). Therefore, our

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next hypothesis offers a direct test of the process that we believe underlies lack of sensitivity to changes in search costs. H2. Currency of search will determine the extent to which people rely on search costs as a basis for their willingness-tosearch decisions. Specifically, when the currency is time (vs. money), people will be less likely to report search costs as a basis for their willingness-to-search decisions. We test the above hypotheses in two experiments. The first experiment tests the focal effect (H1) in a type of set-up that is used in experimental economics research. In the second experiment, we test the focal effect (H1) as well as the underlying process (H2) in a service context; participants are provided with the situation of choosing a moving company and asked to make decisions. Later on, we extend our theorizing to search payoffs and test it in a third experiment in a product context; participants are provided with the situation of buying a digital camera and asked to make decisions.

indicate the minimum dollar amount that they would be willing to accept in return for one hour of simple data-entry work. By relying on the notion of a wage rate, we ignore other factors that might influence the subjective value of time (Marmorstein et al. 1992). But, we do maintain economic parity between conditions, and remain consistent with prior time–money research that has relied on wage-rate equivalence (e.g., Okada and Hoch 2004). This equivalence is, however, not critical to our experiment because our prediction is not about a main effect between time and money, but about an interaction effect: how sensitive people are to changes in time versus changes in money. Design A between-subjects design was used in which both currency of search (time vs. money) and magnitude of search cost (low vs. high) were manipulated. The dependent variable was the willingness to search—the number of rounds played before terminating search.

Experiment 1 Procedure Overview In this experiment, we test our core prediction (H1) that states that the currency of search will moderate the effect of search costs on willingness to search. We consider a sequential search setting in which search is open-ended and the decision to terminate it is contingent on results from prior search effort. Participants keep on searching for the best payoff but can terminate search at any stage in a Bingo-based game (Cox and Oaxaca 1989). The experimental apparatus was a Bingo cage that contained balls numbered from 1 to 50. In each round, a number would be randomly drawn. The payoff would be the highest number drawn before the subject decides to stop (i.e., if x were the highest number drawn, $x would be the earnings). The costs would be a multiple of the number of rounds played (i.e., if y were the cost of one round, n × y would be the cost of n rounds). This setup represented the relationship between search costs and search behavior very well. If one chose to search more (i.e., play more rounds), the likelihood of a higher payoff (i.e., higher number) increased but so did the search costs (i.e., cost of playing the rounds). The cost of playing each round was either monetary or temporal. In the case of money, search costs referred to a dollar amount that one would need to pay ($1 or $4 in the two search-cost conditions) whereas, in the case of time, search costs referred to the amount of time that one would have to spend on data-entry work (5 min or 20 min). Therefore, the implicit hourly wage rate that we used in our manipulations was $12 (5 min ∼ $1 and 20 min ∼ $4). This wage rate was very similar to the rate of $12.50 used by Okada and Hoch (2004) to maintain equivalence between the time and money conditions (e.g., in their Experiment 1, 4 h of data-entry work was the time condition, and $50 was the money condition). Our pretest also revealed a similar wage rate (M = $11.8; SD = 5.6); this involved asking 27 participants, who were not part of the main experiment, to

Sixty-three undergraduate students participated in this experiment in exchange for partial course credit and a chance to win some money. The study was conducted in multiple sessions on the same day. Each session began with the experimenter explaining how the modified Bingo game would be played. The concepts of payoff and cost were explained via a PowerPoint presentation. In order to make participants have a real stake in the search process, it was announced that at the end, one randomly chosen participant would actually pay the costs of playing rounds and receive the payoff from those rounds. Participants were told that if they chose not to play any rounds, there would be no payoffs and no costs. If they chose to play one or more rounds, their payoff would be the dollar amount of the highest number that is drawn in those rounds, and their cost would be the cost of each round multiplied by the number of rounds they chose to play. Each participant was told that if s/he were randomly chosen in the end, s/he would receive the payoff but would also have to incur the cost. After the verbal explanation, the experimenter demonstrated how balls would be drawn and how earnings might change with each round. Apart from making the workings of the game clear, the purpose was also to communicate that no artifice was involved. The experimenter then handed out the questionnaires. The written instructions repeated what the experimenter had already explained and provided information specific to the experimental condition that the participant was randomly assigned to. Before the first round, the experimenter gave participants the option to terminate search (i.e., with zero costs and zero payoffs). Those who decided to do so returned their questionnaires. Then one number was drawn from the bingo cage and participants wrote, in two separate columns, their earnings and costs after the first round. After the first round, some more participants terminated search and the experimenter continued drawing a Bingo ball in successive rounds till all participants had terminated search and returned their questionnaires. While

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receiving the questionnaires, the experimenter checked them to ensure that earnings and search costs accumulated till that round were honestly reported. Finally, one randomly chosen participant actually received the payoffs and incurred the costs. In addition, all participants received partial course credit for participation. Results Our experimental manipulations ensured a 75% decline in search costs such that the money and time costs in the low condition ($1 or 5 min) were one-fourth of those in the high condition ($4 or 20 min). However, we expected this decline in search costs to have a greater impact in the money conditions than the time conditions. To test this prediction, we employed a between-subjects analysis in which willingness to search was the dependent variable, and currency of search and magnitude of search cost were the independent variables. Fig. 1 depicts the results of the Analysis of Variance. There was a main effect of magnitude of search cost (F(1, 59) = 91.8, p < .001) such that those in the low search cost condition (M = 5.9) were willing to search more (i.e., play more rounds) compared to those in the high search cost condition (M = 2.6). There was also a main effect of currency (F(1, 59) = 5.1, p < .05) such that those in the money condition (M = 4.7) were willing to search more compared to those in the time condition (M = 3.9). However, as is clear from Fig. 1, these main effects are qualified by the second-order interaction that is pertinent to our prediction. The magnitude × currency interaction (F(1, 59) = 4.7, p < .05) confirmed that the willingness-to-search difference between the low and the high search cost conditions was significantly greater for money (M = 4.1) than for time (M = 2.6). Planned contrasts revealed a significant increase in the money condition such that willingness to search was higher (F(1, 59) = 65.8, p < .001) when search cost was low (M = 6.7) rather than high (M = 2.6). An increase also emerged in the time condition such that willingness to search was higher (F(1, 59) = 28.9, p < .001) when search cost was low (M = 5.2) rather than high (M = 2.6). However, as indicated by the significant magnitude × currency interaction, the increase in the money

Fig. 1. Willingness to search (for high-value bingo chip) as a function of search currency and search cost.

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condition was significantly larger than the increase in the time condition. Discussion In this experiment, we employed a sequential search setting in which search was open-ended and participants terminated search at the point beyond which they did not want to search any longer. The results support the prediction that currency of search moderates the effect of search costs on search decisions. When we experimentally induced a 75% decline in search costs, the increase in willingness to search was much weaker in time than in money. It is important to note that, in the time condition, even though participants showed insensitivity to different magnitudes of search costs, they did treat time as a cost rather than as something that they can freely spend. If participants had thought of time as simply being less valuable than money, they should have chosen to search more when they were spending time than when they were spending money, but they did not. In sum, our results show that people do treat time as a cost just as they treat money as a cost. However, the magnitude of those costs matters less in the case of time than in the case of money. In the next experiment, we examine this idea once again, as well as the underlying process, in a service context: searching for a moving company. Experiment 2 Overview In Experiment 1, we employed a sequential search setting and found support for our core prediction (H1). In this experiment, we examine whether this effect replicates in a non-sequential search setting in which people make search decisions in advance, before commencing search. In addition, this experiment also aims to examine the process (H2). Specifically, we examine whether people are less likely to consider search costs of time (vs. money) when they decide on the extent of their search. To test this, we asked participants to explain the reasons for their decision. If time (vs. money) participants attend less to search costs, they are less likely to mention the use of search costs in arriving at their decision. The situation that we used was a modified version of a moving-company scenario that we have used in earlier research (Saini and Monga 2008). There, we had manipulated a menu of different choice options because our focus was on the compromise heuristic rather than on search costs. Given our focus in the current research, we manipulated search costs instead. In the scenario, one needed to spend either time or money in order to receive estimates from different moving companies. The situation clearly represented the relationship between search costs and search behavior. If one chose to search more (i.e., invite more moving companies for estimates), the likelihood of a desirable outcome (i.e., finding a cheaper moving company) increased but so did the search costs (i.e., cost of inviting the moving companies).

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Design A between-subjects design was used in which both currency of search (time vs. money) and magnitude of search cost (low vs. high) were manipulated. The dependent variable was the willingness to search—the number of moving companies that the participants wanted to invite. Procedure Ninety-seven undergraduate students participated in this experiment in exchange for partial course credit. In the following paragraphs, underlining highlights the four conditions to which the participants were randomly assigned. Imagine that you are moving to a different location and have decided to hire a moving company. Because you have never tried movers before, you ask a knowledgeable friend for help. He picks up the Yellow Pages and tells you the names of 50 moving companies that provide a good level of service. He adds that the prices could vary a lot—anywhere from $500 to $1000 for the amount of stuff you have. He therefore suggests that you randomly choose some of these 50 moving companies, get them to your apartment so that they can give you their price estimates, and then pick the one that is the cheapest. The problem that you now face is to decide how many you should get home for an estimate. To get the cheapest rate, the best thing to do would be to let each of 50 companies visit your apartment and give you an estimate so that you may pick the cheapest one. However, there is a cost involved in terms of the amount of money (time) you spend on this activity. Each moving company will charge $5/$40 (take 30 min/4 h) to inspect your stuff and provide an estimate. Out of the 50 moving companies that you are considering, how many are you going to get over for price estimates? (Please provide one number, not a range.) I will ask moving companies to come over and provide their price estimates. For these manipulations, a wage rate of $10 per hour was used to achieve round numbers. Therefore, the time and money conditions are equivalent (i.e., 30 min = $5; 4 h = $40) and we study how participants respond to an identical decrease in search costs (i.e., 30 min is one-eighth of 4 h; $5 is one-eighth of $40). After participants had indicated the number of moving companies, they were asked to turn to the next page and then given two minutes to explain the thought process that led to the answer they wrote. Finally, they were debriefed and given partial course credit for participation. Results Results for H1 As is clear from the search costs we imposed, there was an 87.5% reduction in costs from the high condition to the low

Fig. 2. Willingness to search (for cheap moving company) as a function of search currency and search cost.

condition. However, we expected this decline in search costs to have a higher impact in the money condition than the time condition. To test this prediction, we employed a between-subjects analysis in which willingness to search was the dependent variable, and currency of search and magnitude of search cost were the between-subjects independent variables. Fig. 2 depicts the results of the Analysis of Variance. There was no effect of currency (F(1, 93) = .02, p > .80) such that, on average, the willingness to search (i.e., number of moving companies) was comparable in the money (M = 4.6) and the time (M = 4.5) conditions. There was a main effect of magnitude of search cost (F(1, 93) = 17.9, p < .001) such that those in the low search cost condition (M = 5.8) had a higher willingness to search compared to those in the high search cost condition (M = 3.4). This main effect was qualified by the second-order interaction that is pertinent to our prediction. The magnitude × currency interaction (F(1, 93) = 6.7, p = .01) confirmed that the willingness-to-search difference between the low and the high search cost conditions was greater for money (M = 3.8) than for time (M = .9). That is, even though the decline in search costs was exactly 87.5% for both currencies, participants in the money condition were more sensitive to this decline than those in the time condition. Planned contrasts revealed that willingness to search significantly increased with a decline in monetary search costs, but was unaffected by a decline in temporal search costs. Specifically, the willingness to search in the money condition was higher (F(1, 93) = 23.1, p < .001) when search cost was low (M = 6.5) rather than high (M = 2.7) but the willingness to search in the time condition was not statistically different (F(1, 93) = 1.4, p > .24) between the low search cost (M = 5.0) and the high search cost (M = 4.1) conditions. Results for H2 To gain additional insights into the process leading to the time–money differences we observed, we analyzed the cognitive responses—the comments that the respondents wrote about their thought process. Our prediction about lower search-cost sensitivity in time (vs. money) is based on the underlying process that people disregard relevant information when the currency of search is time rather than money. If this is true, respondents’

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comments should reveal a greater tendency to ignore information about search costs when they are making decisions related to spending time rather than money. Therefore, we asked two independent judges who were blind to the experimental conditions to code participants’ responses. It was explained to the judges that search-cost processing is reflected if participants rely on the search costs that are mentioned in the situation. They coded some responses as “0,” indicating that the participant had not taken search costs into consideration (e.g., “Find 2 and pit them against each other,” or “Didn’t feel it necessary to ask a lot of companies to give an estimate.”), and some as “1,” indicating that the participant had taken search costs into consideration, even if only to a limited degree (e.g., “30 min × 5 companies = more than enough” or “I chose 10 because that would be $50 for estimates—I wouldn’t want to spend more.”). Out of the 97 responses, the two judges agreed on 93 (r = .91, Cohen’s κ = .91). They then discussed the four dissimilar responses and arrived at a consensus. We used the mutually agreed list of responses to analyze 49 participants in the time condition and 48 participants in the money condition. Consistent with our theorizing, the proportion of participants who had considered search costs was only 34.7 % (17 out of 49) in the time condition but 58.3% (28 out of 48) in the money condition. This difference was statistically significant (z = −2.33, p < .01), indicating that participants were indeed more prone to disregarding search-cost information if search involves spending time rather than money. Discussion The current experiment employed a different setting from the one used in Experiment 1 but yielded the same result: currency of search moderates the effect of search costs on search decisions. When we lowered the search costs by 87.5%, the increase in willingness to search was much weaker in time than in money. In fact, the increase was significant only for money, not for time. One discrepancy from the earlier experiment was that Experiment 1 revealed a main effect for time but Experiment 2 did not. Although we cannot be sure, this might be due to the different settings that we employed in the two experiments. As a conjecture that future research could examine, maybe people are more sensitive to time in sequential-search settings (Experiment 1) than in non-sequential ones (Experiment 2). But, given the focus of the current research, the absence or presence of a main effect of time does not deny the key interaction that we found in both experiments—that sensitivity to search costs is lower in time than in money. As is true for Experiment 1, our results cannot be explained in terms of people treating time as less valuable than money. If this were true, participants ought to have searched, on average, more in time than in money, which they did not. What participants did differ on was the extent to which they responded to changes in the costs of time and of money; they expressed insensitivity in the case of time, but not money. We also demonstrated support for the hypothesized process. When the currency was time, participants did not report the use of search costs in decision making

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as much as they did when the currency was money. This explains why participants in time (vs. money) were less influenced by the magnitude of search costs. We conducted a follow-up experiment to gain further insights into the underlying process. Specifically, if the process for insensitivity to temporal search costs is just as we propose, can we alter that process to increase the sensitivity and make time seem more like money? We earlier argued that responses to time are less precise because time is ambiguous (Okada and Hoch 2004) and people are unable to do accounting for time the way they do for money (Soman 2001). If this is true, then making the value of time more concrete would make it easier for people to account for changes in its magnitude. For instance, Soman (2001) showed that although people do not usually consider past expenditures of time, they do account for them when they are first informed about the value of time in terms of a wage rate. Consistent with this, our follow-up experiment primed participants with a wage rate to see whether that makes people more sensitive to changes in search costs of time. We used the same low and high search-cost conditions of time as used in the main moving-company experiment. We also added two more conditions of time that were identical except that participants responded to a prime before responding to the scenario. Specifically, participants were told that, according to one estimate, students in the United States get paid around $10 per hour of work. Then, on a 7-point scale, they responded what they themselves thought (1 = students earn a lot lower than $10; 4 = students earn about $10 per hour; 7 = students earn a lot higher than $10). After indicating their responses to this question (M = 4.5), they proceeded to the moving-company scenario. The analysis involved a between-subjects design with two levels of magnitude of search cost (low vs. high) and two levels of wage prime (absent vs. present), which we tested with 110 student participants. An ANOVA revealed that the main effect of prime was not significant (F(1, 106) = .08, p > .77). There was a main effect of magnitude of search cost (F(1, 106) = 13.5, p < .001) that was qualified by the key second-order interaction. The magnitude × prime interaction (F(1, 106) = 4.5, p < .05) confirmed that the willingness-to-search difference between the low and the high cost conditions was greater when prime was present (M = 2.4) rather than absent (M = .6). Specifically, the willingness to search in the prime-present condition was higher (F(1, 106) = 16.2, p < .001) when search cost was low (M = 5.6) rather than high (M = 3.2), but the willingness to search in the prime-absent condition was not statistically different (F(1, 106) = 1.2, p > .25) between the low search cost (M = 4.8) and the high search cost (M = 4.2) conditions. Therefore, time participants behaved more like the money participants of the main experiment, when they were first primed with the value of their time. That is, when the value of time was primed to be more concrete rather than ambiguous (Okada and Hoch 2004), participants did engage in accounting of time (Soman 2001); they responded differently to different search costs. This follow-up experiment, coupled with the results of the main experiment, provides robust evidence for the process that underlies the moderating influence of currency of search on the

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effect of search costs on search decisions. We now examine the moderating influence that currency can have on the effect of search payoffs on search decisions.

to-search decisions. Specifically, when the currency is time (vs. money), people will be less likely to report search payoffs as a basis for their willingness-to-search decisions.

Currency of search will moderate the effect of search payoffs

Experiment 3

In Experiments 1 and 2, we kept the payoffs constant (i.e., potential benefits from search were the same in all conditions) and demonstrated that people are relatively insensitive to a reduction in search costs, when the currency is time rather than money. By analyzing participants’ written responses, we further revealed a lack of focus on search costs. It is possible that, rather than attending to this relevant information about costs, participants attended to more unrelated information in order to arrive at their decisions. For instance, a decision to invite three moving companies might have been based on a heuristic of always considering three options (Saini and Monga 2008). Irrespective of the type of other information that participants were relying on, what is clear is that they were ignoring information about a relevant determinant of search decisions: search costs. However, search costs are not the only relevant determinants of search decisions. Search theory suggests that people treat a search situation as an optimization-under-constraints problem in which they try to maximize the potential for search payoffs while minimizing the search costs (Stigler 1961). Therefore, when our experimental participants show disregard for a change in the magnitude of search costs, they are also displaying a disregard for the tradeoff between search costs and payoffs. Given that this cost-payoff tradeoff can be altered not only by varying costs but also payoffs, people are also likely to demonstrate insensitivity to changes in payoffs when the currency of search is time. That is, if search involves spending time instead of money, the extent to which people are willing to search (and incur search costs) will not change much with the size of the search payoff (e.g., whether the payoff is getting a product that is only slightly better, or one that is a lot better). This leads to our third hypothesis: H3. Currency of search will moderate the effect of magnitude of search payoffs on people’s willingness to search. Specifically, when the currency is money, higher (vs. lower) search payoffs will result in higher willingness to search. When the currency is time, this effect of search payoffs on willingness to search will be relatively weaker. As detailed in our discussion about search costs, and as evidenced by the results from our earlier experiments, people show a lower consideration of relevant information (e.g., search costs), when the currency of search is time rather than money. By the same token, we predict that people will not adequately consider the relevant information of search payoffs when they make decisions regarding willingness to search in time. Our next hypothesis offers a direct test of the process that we believe underlies lack of sensitivity to changes in search payoffs. H4. Currency of search will determine the extent to which people rely on search payoffs as a basis for their willingness-

Overview In Experiments 1 and 2, we observed that the currency of search moderates the impact of search costs on willingness to search. In the current experiment, we examine whether this moderating impact of currency extends to search payoffs as well (H3 and H4). Examining changes in payoffs requires a different format from the one we employed in our first two experiments. In those experiments, the potential for payoffs was probabilistic. For instance, inviting more moving companies only increased the chance of getting a cheaper moving company; it did not ensure it. To manipulate payoffs in a more definitive manner, we make them deterministic in that greater search necessarily leads to a better payoff, and then test whether a fixed increase in payoff changes the willingness to search. We borrow this approach of deterministic payoffs from decision-making research that studies consumer search in the context of relative savings. Thaler (1980) suggests that people are more willing to extend search for a $5 saving on a $25 radio than on a $500 TV because, as a proportion of the product price, the saving is higher on the former than the latter. Kahneman and Tversky (1984) found empirical support for this relativesavings idea using the classic “jacket and calculator” problem; they showed that, given a potential saving of a specific dollar amount, people are more eager to save it on a low-priced (vs. high-priced) product. Our next experiment is set in this tradition. However, rather than focusing on the effect of relative savings, we focus on the effect of absolute payoffs in the search process. The situation used was that of a customer visiting a store to purchase a 1.3 MP (Mega pixel) camera. Upon reaching the store the customer learns that there is a possibility of receiving a better camera of the same brand. Participants are asked to state their maximum willingness to search for the better camera in terms of either money or time. Our prediction was that a higher increase in search payoffs – from 1.3 MP to 5.1 MP rather than from 1.3 MP to 2.4 MP – would lead to a weaker change in the willingness to search when the search was being conducted in the currency of time rather than money. Design To test H3 and H4, a between-subjects design was used in which participants were randomly assigned to one of four conditions. The two manipulated factors were currency of search (time vs. money) and magnitude of search payoff (low vs. high). The dependent variable was the willingness to search which was operationalized as the amount of time or money that the participants were willing to spend in order to receive the payoff of a camera upgrade. Although our design was a between-subjects one, we also added a within-subjects element to test H3. We asked those in

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the low-payoff condition to later indicate their willingness to search for the high-payoff condition, and those in the high-payoff condition to later indicate their willingness to search for the low-payoff condition. Although the between-subjects analysis would have been sufficient to test our predictions, the additional within-subjects analysis was useful because of two reasons. The first reason was the kind of dependent variable that we used. In the time conditions, willingness to search referred to the amount of time that participants were willing to spend whereas, in the money conditions, the same measure referred to the money that participants were willing to spend. As we explain later, we did create standardized scores for time and money, in line with prior time–money research (Okada and Hoch 2004). However, the within-subjects analysis helped us employ a measure that is already standardized: percentages. Specifically, given two responses from the same participant, we could look at the percentage change in willingness to search brought about by a change in search payoffs. Another reason for having the within-subjects design was that, in spite of randomization across cells, the between-subjects results might have been influenced by individual differences in how time is subjectively valued (Marmorstein et al. 1992). The within-subjects analysis would tell us whether, even for the same individual, sensitivity to changes is lower in time than in money. Procedure Eighty-four undergraduates participated in this experiment in exchange for partial course credit. The scenario for the two conditions of time was as follows: You have decided to buy a Digital Camera and you know the specific brand you want. You go to your neighborhood electronics store and consider buying the brand’s 1.3 MP (1.3 Mega Pixel) camera. Just when you are about to pick up the 1.3 MP camera for purchase, the salesperson tells you that you might want to consider the 2.4 MP (5.1 MP) camera of the same brand. This better-quality camera is priced the same but you would need to wait in the store while the salesperson goes to the warehouse to get it. As he heads toward the warehouse to get the 2.4 MP (5.1 MP) camera, you are wondering how much more you would be willing to wait, over and above the time you have already spent on shopping for the 1.3 MP camera.

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priced higher. As he checks into his computer to find out the price of the 2.4 MP (5.1 MP) camera, you are wondering how much more you would be willing to pay, over and above the money that you would be paying for the 1.3 MP camera. The maximum amount of additional money that I am willing to pay in order to receive the 2.4 MP (5.1 MP) camera is $ . After participants had indicated their willingness to search in either time or money, they were asked to turn to the next page where they were given 2 min to explain the thought process that led to the answer they wrote. They were then asked to turn to the next page where they indicated their willingness to search for the other payoff condition. Finally, they were debriefed about the real purpose of the study and given partial course credit for participating in the experiment. Results Results for H3 (between-subjects) Given the baseline of 1.3 MP, search could lead to a payoff of either 2.4 MP or 5.1 MP. Given our theorizing, we predicted that the increase in payoff would have a lower impact when the currency is time rather than money. To test this prediction, we employed a between-subjects analysis in which willingness to search was the dependent variable and currency of search and magnitude of search payoff were the between-subjects independent variables. As can be seen from Fig. 3, the pattern of means seemed consistent with H3. Specifically, for money, willingness to search seemed higher when search payoff was high rather than low, but, for time, willingness to search seemed similar across the two conditions. An Analysis of Variance cannot however be conducted with the raw means presented in Fig. 3 because the money measure (i.e., dollars) has different scale properties of mean and variance than the time measure (i.e., minutes). Therefore, consistent with prior research (Okada and Hoch 2004), we transformed dollars into z-scores by subtracting the mean of the entire money data from each data point and then dividing by the standard deviation of the entire money data. In a similar fashion, the minutes were

The maximum amount of additional time that I am willing to wait in the store in order to receive the 2.4 MP (5.1 MP) camera is Min. The scenario for the two conditions of money was as follows: You have decided to buy a Digital Camera and you know the specific brand you want. You go to your neighborhood electronics store and consider buying the brand’s 1.3 MP (1.3 Mega Pixel) camera. Just when you are about to pick up the 1.3 MP camera for purchase, the salesperson tells you that you might want to consider the 2.4 MP (5.1 MP) camera of the same brand. This better-quality camera is, of course,

Fig. 3. Willingness to search (for camera upgrade) as a function of search currency and search payoff.

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transformed separately into z-scores. This process yields data that does not violate any of the assumptions of the Analysis of Variance. Analysis using the z-scores revealed that there was no main effect of either magnitude of search payoff (F(1, 80) = .50, p > .4) or of the currency of search (F(1, 80) = .001, p > .90). What was most pertinent to our prediction was the second-order interaction. The magnitude × currency interaction (F(1, 80) = 4.43, p < .05) confirmed that the willingness-to-pay (z-score) difference between the low and the high search payoff conditions was greater for money (M = .61) than for time (M = −.30). That is, even though the low (2.4 MP) and high (5.1 MP) search payoffs were exactly the same for both currencies, participants in the money condition were more sensitive to this increase than those in the time condition. Planned contrasts revealed that willingness to search significantly increased with an increase in search payoffs when the currency of search was money, but was unaffected by search payoffs when the currency was time. Specifically, the willingness to search (z-score) in the money condition was higher (F(1, 80) = 3.95, p < .05) when search payoff was high (M = .32) rather than low (M = −.29), but the willingness to search in the time condition was not statistically different (F(1, 80) = .98, p > .32) between the high search payoff (M = -.14) and the low search payoff (M = .16) conditions. These results are consistent with the prediction that we made in H3. Results for H3 (within-subjects) We then proceeded to examine how participants changed their responses when they were asked to respond to one payoff condition after having already responded to another payoff condition. In other words, we wanted to examine if our results also hold within subjects. For this analysis, a percentage-increase measure was created for each participant by first subtracting the willingness to pay of the low payoff condition from that of high payoff condition, then dividing by the willingness to pay of the low payoff condition, and finally multiplying by 100. This measure therefore indicated the degree to which a respondent was willing to search more for an upgrade to the 5.1 MP camera rather than the 2.4 MP camera. The total sample size for the within-subjects analysis was 79 rather than 84 that we used for the between-subjects analysis because the percentage change could not be calculated for five participants (e.g., because they had failed to respond to the second willingness-to-pay measure). Given our prediction, we expected the percentage increase in willingness to search to be higher in the money condition than the time condition. In line with this, the change in the willingness to search was higher for the money condition (M = 125.9 %) than the time condition (M = 50.4 %). This difference was significant (F(1, 75) = 7.69, p < .01). Additionally, there was no significant interaction because of order effects (F(1, 75) = .16, p > .6). That is, the percentage change was not influenced by whether the first question was about the low payoff condition or the high payoff condition. The results are depicted in Fig. 4.

Fig. 4. Percentage increase in willingness to search (for higher vs. lower camera upgrade) as a function of search currency.

Results for H4 As in Experiment 2, to gain some additional insights into the process leading to the observed time–money differences, we analyzed the cognitive responses. Our prediction about lower search-payoff sensitivity in time (vs. money) is based on the underlying process that people disregard relevant information when the currency of search is time rather than money. If this is true, respondents’ comments should reveal a greater tendency to ignore information about search payoffs when they are making decisions related to spending time rather than money. Two independent judges coded some participants’ responses as “0”, indicating that the participant had not taken search payoffs into consideration (e.g., “I am a busy person,” or “I’m not sure, it just seemed like a good amount to pay.”), and some as “1,” indicating that the participant had taken search payoffs into consideration, even if only to a limited degree (e.g., “The 5.1 MP camera does seem like a much better deal, so I’d be willing to wait a fair amount of time for it,” or “5.1 MP and 1.3 MP are very different numbers.”). Out of the 84 responses, the two judges agreed on 81 (r = .92, Cohen’s κ = .92). They then discussed the three dissimilar responses and arrived at a consensus. We used the mutually agreed list of responses to analyze 42 participants in the time condition and 42 participants in the money condition. Consistent with our theorizing, the proportion of participants who had considered search payoffs was only 14.3 % (6 out of 42) in the time condition but 42.9 % (18 out of 42) in the money condition. This difference was statistically significant (z = 2.90, p < .01) indicating that participants were indeed more prone to disregarding search-payoff information when search involved spending time rather than money. Discussion These results showing insensitivity to one kind of search incentive (increase in search payoffs) complement those of the first two studies that showed insensitivity to another kind of search incentive (decrease in search costs), when the currency is time rather than money. Using both a between-subjects and a within-subjects design, we found that currency of search moderates the effect of search payoffs on search decisions. Fur-

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thermore, in the currency of time (vs. money), consideration of search payoffs was found to be lower in participants’ verbal responses, just as the consideration of search costs was found to be lower in Experiment 2. General discussion

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be noted that we observe differences between time and money even when we kept their values equivalent. We find that participants are not simply more liberal in spending time; they are less sensitive to changes in temporal costs than to changes in monetary costs. These results afford interesting implications for the theory and practice of retailing.

Summary

Implications for theories of retailing

In this research, we commence our inquiry with the observation that the effect of search costs on search behavior is well supported by prior experimental research but not by field studies conducted in retail settings. We argue that this discrepancy might be driven by the reliance on money in the former case and the reliance on time in the latter case and, consequently, try to understand the moderating effect of currency. We experimentally demonstrate that in the currency of money, a decrease in search costs has a consistent and significant effect on the willingness to search but that, in the currency of time, a decrease in search costs has a significantly weaker effect in the context of sequential (Experiment 1) as well as non-sequential search (Experiment 2). Furthermore, we find that this relative insensitivity in time emerges for not only search costs, but also search payoffs (Experiment 3). Participants’ verbal responses in Experiments 2 and 3 provide direct evidence for why this happens: People are more likely to ignore information about costs and payoffs when the currency is time rather than money. Finally, the follow-up study detailed after Experiment 2 revealed that even time participants can be made to behave like money participants, if they are first made aware of the value of their time. Overall, we show why and how search occurs differently in time than in money. We find that the willingness to search is less influenced by search incentives (lower search costs or higher search payoffs) when people search by spending time rather than money. One limitation of the current research is that it is focused on situations in which participants make decisions based on the search costs they expect to incur, rather than actually experience. Could the neglect in the currency of time disappear if costs are experienced, the way they are in real life? Although this is a possibility, it seems unlikely given that related phenomena such as duration neglect seem to occur irrespective of whether experiences are hypothetical (Varey and Kahneman 1992) or real (Fredrickson and Kahneman 1993). Moreover, even if actual experience increases sensitivity to search costs, it is likely to do so for both time and money. If that happens, the time–money differences that we demonstrate will persist. A limitation related to the above is that we examine costs that people expect to incur sometime in the future rather than the present (especially in studies 2 and 3). Could our results change if people incur costs in the present? Extending prior work by Soman (1998), Zauberman and Lynch (2005) show that time costs are discounted more than money costs because slack (i.e., perceived surplus) for time is perceived to be higher in the future than the present. This could suggest that the timeinsensitivity that we demonstrate might reduce when people are making decisions about spending time and money in the present. This possibility is worth exploring further. However, it needs to

A fundamental premise of search theories (Stigler 1961) is that a decrease in search costs increases consumers’ willingness to search. We offer a refinement of this premise; it holds well only for money, not for time. This finding provides a plausible explanation for prior inconsistencies between experimental results and field results from retail settings. Given the numerous differences between the two styles of research, it cannot be argued that the currency of search is the sole reason for the inconsistency in results. However, our results do suggest that currency might be one of the culprits. Therefore, search theorists in retailing could consider including the currency of search to their models. We also add to prior research that has examined search behavior in the context of savings that occur if one goes from one store to another. In the classic “jacket and calculator” study (Kahneman and Tversky 1984), people are influenced by relative savings—they are more willing to spend time when the saving is on a low-priced product than when it is on a relatively high-priced product. That is, consumers use a psychophysics-of-price heuristic (Grewal and Marmorstein 1994). Given the results from Experiment 3, we suggest that, when people spend time rather than money to search, they are less sensitive to absolute differences in potential benefits, such as the payoffs in a search process. Implications also arise for search-related emotions. Reynolds, Folse, and Jones (2006) argue that retailers ought to reduce search regret, which is a post-search dissonance that results from an unsuccessful prepurchase search. If people are insensitive to the amount of time they spend on search, as our results suggest, their regret in retail settings might only be a function of success or failure in search; it might depend less on the amount of time invested in the process. Implications for practice of retailing Because increased search by consumers heightens competition and forces marketers to lower prices, Kuksov (2004) urges marketers to defend themselves from a decline in search costs by differentiating their products. Given our results, this prescription is valid if consumers search by spending money but, if they search by spending their time (as they do in most retail situations), retailers need not fear that a decline in search costs will increase search behavior; they need not invest precious resources on product differentiation. The lowering of search costs is also a matter of concern for e-retailers. They worry that because it is easier to search on the Internet, competition will intensify and margins will be lower (Lynch and Ariely 2000). The underlying assumption, of course,

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is that lower search costs always lead to higher search behavior. Given that search across stores and over the Internet encompasses expenditures of time rather than money, the effect of search costs on search behavior is likely to be minimal. For instance, in the pre-Internet era, a consumer might have visited two brick-and-mortar bookstores to check prices of a book before buying it from the cheaper bookstore. In this Internet era, even though temporal search costs are lower, a consumer might still visit only two websites – amazon.com and barnesandnoble.com – even though one can potentially visit dozens of other similar websites. Consequently, retailers need to be less fearful of decreasing search costs, and more enthusiastic about the opportunities offered by online environments (Lynch and Ariely 2000; Weathers et al.+ 2007). That said, this suggestion regarding insensitivity to search costs on the Internet needs to be considered tentative. For example, consumers do not search endlessly on the Internet, suggesting that search costs of time do matter to them. So, retailers still need to watch out for the challenges in the world of Internet commerce. Perhaps further research using field studies can examine this aspect more directly. Finally, implications also arise for store location models (Achabal et al. 1982). How much of a deterring effect does the distance to a store have on its success? Our results suggest that people will be less sensitive to changes in the time of travel than to changes in price. For instance, the willingness of consumers to drive to far-flung outlet malls might be due to the fact that they are much more sensitive to the lower price that it offers, than to the additional time that it takes to visit the outlet mall rather than the neighborhood store. Once again, this suggestion needs to be considered tentative. If people were completely insensitive to time, they would go to outlet malls for every shopping trip, but they do not. So even though people are less sensitive to costs of time than of money, one should not infer that consumers do not care about temporal costs. In conclusion, search is an integral part of the retail experience. The current research establishes that one’s willingness to search is not simply dictated by costs and payoffs, but also by the currency in which search is conducted. In fact, changes in costs and payoffs seem to have little influence on search decisions when people spend time rather than money to search. Acknowledgement The authors would like to thank seminar participants at the University of Texas at San Antonio and at George Mason University for valuable feedback on a previous version of this article. References Achabal, Dale D., Wilpen L. Gorr and Vijay Mahajan (1982), “MULTILOC: A Multiple Store Location Decision Model,” Journal of Retailing, 58 (2), 5–25. Ariely, Dan and George Loewenstein (2000), “When Does Duration Matter in Judgment and Decision Making?,” Journal of Experimental Psychology: General, 129 (4), 508–23.

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