Under Pressure: An Integrative Perspective of Time Pressure Impact on Consumer Decision-Making

ABSTRACT Time pressure (TP) constrains consumers’ decisions: stores have fixed opening hours, promotions have deadlines, and a house for rent may not be available tomorrow. Evidence about the impact of TP on decision-making suggests that when facing complex decisions, consumers do not process all the information, ground decisions upon a restricted set of attributes, and are less likely to defer choices, but still accomplish utility choices. However, these effects of TP have been typically observed in experimental paradigms that manipulate specific deadlines for task completion. In two experiments involving consumer goods and service choices we have introduced two additional TP manipulations (time limited price discount and stock-out threat), building an integrative approach where information processing strategies, choice deferral, and final choice utility were measured. Our results emphasize the differences between TP manipulations. When applied to real buying contexts, price discounts may not be so effective anymore, whereas stock-out threats have surprising effects, decreasing deferral and final choice utility. These findings contribute to a better understanding of the differences between decision-making upon consumer goods and services, discriminating the effects of TP in real scenarios.

Imagine that you are planning a trip. It seems like a pretty simple task to choose accommodation. However, when the online search begins, you face over 1000 accommodation options. To make it harder, besides considering room rates, distance to city center, or even other travelers' reports, you are constantly reminded that if you do not choose fast enough, your "perfect room" may be taken or that you may fail an amazing promotion. This example illustrates how in real-life situations consumers experience decision complexity and varying degrees of time pressure (TP).
Consumer literature has shown that the process of searching information and choosing among different options changed significantly over the past decades (e.g., Kahn and Wansink 2004). With the increase in assortments' size and variety, or even the virtually endless number of options available online, choosing a consumer good or a service has become a demanding task for consumers. Assortment variety has been an extensively researched topic in the consumer behavior domain. However, whether more varieties constitute a benefit for consumers and increase their satisfaction (Anderson 2006) or make it harder to decide by increasing choice difficulty (Park and Jang 2012) still remains an unsolved paradox (for an interesting meta-analysis about moderators on choice overload, see Chernev, B€ ockenholt, and Goodman 2015). Furthermore, since most of the daily choices that consumers face seem to imply a certain degree of urgency (e.g., Reutskaja et al. 2011;Samson and Voyer 2014), decision processes can themselves induce negative feelings (Schwartz 2000). In fact, the decision-making process in this context is almost always dependent on time constraints, such as opening hours of stores, promotion deadlines, or even the possibility of product stock-out.
But does TP change the way consumers decide? Empirical evidence has shown that when consumers feel pressured to decide, the amount of information they are able to process decreases (Iyer 1989). In these situations, they tend to focus on characteristics that can be quickly and easily evaluated (Lenton and Francesconi 2010) and defer choice more often (Tversky and Shafir 1992). However, when consumers perceive that despite the time limitations there is still enough time to choose, deferral may decrease (Lin and Wiu 2005).
Theoretically, TP is defined as the perceived cost of time scarcity. Indeed, it has been suggested that "it is only when the available time to complete a task is perceived as insufficient or limited that TP begins to manifest itself and induce feelings of stress" (Thomas, Esper, and Stank 2010, 288). This subjective definition of TP, dependent on individuals' appraisal, has important implications. First, the exact amount of time given to individuals to decide may significantly influence their decisions. Second, since TP relies on subjective perceptions, the way in which TP is induced may also alter these perceptions and, consequently, decision-making behavior.
Previous research has been using creative TP manipulations such as testing several time intervals, using different environmental cues as, for example, ticking metronomes as background noise (Inbar, Botti, and Hanko 2011) or even making the monetary compensation of participants dependent on the time spent on task completion (Dambacher and H€ ubner 2015). Nevertheless, TP has been typically equated to the objective time given to participants for task completion (e.g., Dhar and Nowlis 1999;Haynes 2009;Young et al. 2012). To the best of our knowledge, despite some theorization about individual orientations and attitudes toward TP types (e.g., time orientation models-Bergada a 2007; Usunier and Valette-Florence 2007), the experimental designs that have been used to manipulate TP have not yet provided a solid comparison between the effects of different TP types (e.g., Lallement 2010). On the other hand, it is plausible to suggest that TP may also benefit the decision-making process. For instance, in competitive contexts, TP was found to increase individuals' enthusiasm and to shape perceptions of a given game as a more defying task (Freedman and Edwards 1988). Possibly, similar effects can be extended to consumer decisions. We argue that different TP manipulations may have distinct effects on consumer decision-making. For example, the TP experienced when the milk "two-for-one" promotion is about to end can be completely different from the TP felt when the grocery shop closes and one needs milk for next day's breakfast.
Research on this subject has been following two distinct methodological paths: forced choice scenarios (where participants are compelled to make a decision and such decision is recorded) and deferral choice scenarios (where participants have the option not to choose any of the available options and it is recorded whether a decision is made). Forced choice scenarios have been examining the decision process and measuring the cognitive processes underlying the decisionmaking as well as final choice utility (e.g., Pachur et al. 2013;Payne, Bettman, and Johnson 1988). However, since participants are forced to make a decision, there has been some controversy about its ecological validity. Indeed, real consumers may also decide not to choose. This possibility is taken into account by deferral studies that measure whether consumers decide or not (Dhar and Simonson 2003). The research on deferral does not traditionally examine the chosen option, thus it may also present limitations. Finally, most of the evidence reviewed on choice deferral and final choice utility under TP predominantly examines choice upon products such as consumer goods (e.g., Dhar and Nowlis 1999;Lallement 2010;Lin and Wu 2005;Reutskaja et al. 2011), whereas service-related decisions remain relatively unexamined (for an exception, see Rieskamp and Hoffrage 2008).
Consumer decision-making under TP seems, therefore, a far more complex cognitive task than isolated studies may capture. The present research examines whether using TP manipulations resembling real-life consumer experiences-such as giving participants a time frame to benefit from a promotion or by presenting a stock-out threat-will yield different results from those previously reported in the decision-making literature. Specifically, we have conducted two studies aiming to (1) compare different manipulations of TP on consumers' decision-making; (2) examine, within the same experimental design, several dependent variables such as choice deferral, cognitive processing strategies, and final choice utility; (3) test two assortment size manipulations as a form of task complexity; and (4) provide a comparison between decision-making about goods and services.

Time pressure impact on consumer decisionmaking
Decision-making rules, that is, the cognitive models adopted in the decision-making process, are one of the most well-researched topics in consumer behavior literature (e.g., Dieckmann, Dippold, and Dietrich 2009;Payne, Bettman, and Luce 1996;Rieskamp and Hoffrage 2008). Research has repeatedly shown that participants indeed adopt (and adapt) different decision models and strategies depending on the situation. Each of these models affects the outcome of the decision-making process, since they rely on different levels of difficulty of the decision process or the motivation to decide (e.g., Dhar 1997).
When facing an assortment, consumers initiate a cognitive process that leads them to a final decision. Such process may be guided by compensatory or noncompensatory strategies and be based upon option or attribute level comparisons (e.g., Bettman, Luce, and Payne 1998). While compensatory strategies rely upon an extensive examination of available information, non-compensatory strategies refer to task simplification (Mowen and Minor 2003;Schiffman and Kanuk 2000;Solomon 2002).
In order to decide whether the product under evaluation meets the desired standard, to simplify the task, consumers may focus on a restricted set of attributes or even on a single attribute (Lye et al. 2005). Evidence suggests that the urgency to decide limits the amount of information analyzed (Iyer 1989) and increases the tendency to decide based on habits (Wood and Neal 2009) and to use non-compensatory strategies to process information (Lallement 2010). Therefore, judgments under TP are characterized by the application of cognitive shortcuts and affected by different peripheral cues (Madan, Spetch, and Ludvig 2015;Pachur and Hertwig 2006;Rieskamp and Hoffrage 2008;Verplanken 1993) as well as by an increase in choice deferral (Dhar 1997;Lin and Wu 2005;Luce 1998). However, in specific conditions (e.g., assortments with highly attractive options) TP produces the reverse effect on deferral (Dhar and Nowlis 1999). Such counterintuitive phenomenon has been explained by faster cognitive processing (Edland and Svenson 1993) or by increased motivation to analyze the available information (Suri and Monroe 2003).
Contradictory findings were also reported when the quality of the final decision is considered. While some authors reported the negative impact of TP on final decision quality (e.g., Payne, Bettman, and Johnson 1993), others reported that even under severe TP conditions (e.g., 3 seconds do decide) the final choice utility remains high (Reutskaja et al. 2011). To explain this inconsistent pattern of data, Maule, Hockey, and Bdzola (2000) emphasized the idiosyncratic and subjective nature of TP, concluding that the TP effect on decision-making may be dependent upon the relevance of each decision to the individual.
Building on such assumption, several questions emerge regarding current evidence on TP research in the context of consumer decision-making. In particular, whether it is appropriate to make direct inferences about the effect of TP on decision-making from experiments that merely ask participants to make decisions within a given time period without further explanation or clue about the motive of such TP.

Time pressure: Alternative manipulations
Marketing evidence namely about the impact of stockout threats and promotions in consumer behavior may provide important insights in this matter. When facing real or experimental stock-out scenarios, consumers feel the urgency to decide, either by being forced to replace the previously chosen items that are out of stock or by postponing or cancelling purchases (e.g., Sloot, Verhoef, and Franses 2005). Overall, decision-making is quicker when there are stock-out products in the assortment or even when there is only a warning that stock-out may occur (Ge, Messinger, and Li 2009).
The similarities between stock-out literature and TP evidence in consumer decision-making contexts also occur at the deferral level. For instance, Sloot (2006) reviewed several studies where stock-out increased deferral because the options available were perceived as inferior to the sold-out items. Likewise, since promotions also influence consumer behavior (Herrington and Capella 1995), it is frequent for stores and e-commerce websites to set deadlines for sales or promotions. This forced urgency seems to compel consumers to decide and several authors have been thriving to find the optimal timing for promotions to have the desired impact on sales (e.g., Chiang, Lin, and Chin 2011). Research has also revealed that consumers are sensitive to the promotion and its deadline even when the reference price is exaggerated (Aggarwal and Vaidyanathan 2003), and that these conditions increase their buying intentions (Krishnan, Dutta, and Jha 2013). Therefore, in addition to understanding how experimental TP (deadline to task completion) affects decision-making, it is important to investigate how other TP types, namely stock-out threat and price promotions, affect consumer decisions.

Assortment size: Do larger assortments increase decision complexity?
Research about decision-making in both psychological and marketing literatures has also been focusing on the characteristics of the available assortments. Indeed, the number of options available (Rolfe and Bennett 2009) as well as the way in which they are described or even presented (Pizzi and Scarpi 2016) are known to affect consumer behavior. For example, research has shown that visually highlighting some attributes and making them more salient can increase their consideration in decision-making (Lynch and Ariely 2000). Presenting options simultaneously or sequentially also alters consumer preferences (Hsee 1996). Assortment variety depends upon individual perception, which, in turn, is known to be affected by the total number of stock keeping units (SKUs), and the total space used by the category and by the presence of the favorite product among the options available (Broniarczyk, Hoyer, and McAlister 1998).
Despite some interesting analysis about other variables affecting assortment evaluation (e.g., the moderating role of the perceived quality of products, Kwak, Duvvuri, and Russell 2015), size can be considered the most relevant feature for perceiving an assortment as varied (SKUs), being, therefore, the feature most frequently manipulated in consumer research. Variety effect has been tested from 2-option to 300-option assortments (Diehl and Poynor 2010). Such studies yielded somehow contradictory results. On the one hand, larger assortments seem to be preferred by consumers (Broniarczyk 2008), boosting their satisfaction (Anderson 2006). From the merchants' point of view, larger assortments attract new customers (Lohse and Spiller 1999) and increase loyalty (Srinivasan, Anderson, and Ponnavolu 2002) and market share (Bown, Read, and Summers 2003). On the other hand, a growing body of literature has been reporting the "paradox of choice" (Schwartz 2004). Large assortments make it harder to decide, since cognitive overload increases (Park and Jang 2012), satisfaction with decision-making process decreases (Broniarczyk and Hoyer 2010) and the likelihood of choice deferral increases (e.g., Gourville and Soman 2005;Sela, Berger, and Liu 2009). For example, when exposed to a limited assortment of six jams, consumers were more likely to purchase the product than when 24 different jams were available (Iyengar and Lepper 2000). The larger assortment was, in this study, evaluated as more attractive and more people actually stopped to look at the jam display stand. Although consumers were initially attracted towards the extensive assortment, the number of purchases did not increase. Thus, larger assortments (instead of increasing) may actually decrease sales (Borle et al. 2005;Kuksov and Villas-Boas 2010;Reinartz and Kumar 1999).
Assortment structure is another variable contributing to decision complexity. The number of attributes in each assortment option and the level in which each attribute varies also determine the amount of information that individuals have to consider before deciding (Jacoby, Speller, and Kohn 1974;Malhotra 1982). For instance, when buying a laptop, consumers may focus on attributes such as price, brand, speed, or even the software installed. Moreover, each of those attributes may vary in a wide range of levels (e.g., memory may vary from 2GB up to more than 2TB). In contrast, choosing a jam is expected to be less complex since consumers have to compare fewer attributes (e.g., price, brand, flavor, and nutritional information). Also, the levels in each attribute are less diverse (e.g., the price of a jar of jam obviously varies less than the price of a laptop). Therefore, variability between attributes increases decision complexity and consumers tend to defer choice less when the difference in attractiveness among the assortment options is small (used as a measure of choice difficulty by Dhar 1996).
In sum, consumer decisions are influenced by several factors such as the way in which options are described (Read and Loewenstein 1995), the distinctiveness between such options (Van Herpen and Pieters 2007), the quantity of information under evaluation, and the overall complexity of the decisionmaking process (Fasolo et al. 2009). Therefore, recent experimental approaches to assortment size (e.g., Chernev 2003) seek to control not only the total number of options available, but also the composition of each option under consideration.

Overview
The first goal of this research is to provide comparative evidence about the effect of different TP manipulations on consumers' decision-making. The insights provided by marketing literature on the impact of stock-out threats and price promotions on consumer behavior, as specific pressuring factors in the decisionmaking process, will be compared with the "classical" manipulation of TP (i.e., requesting participants to complete a task in a given time period) used in psychological research.
The second goal is to bring together two distinct experimental approaches, typically used in consumer decision-making research. Forced choice scenarios, in which participants are required to choose one of the given options (Haynes 2009) and deferral studies, where the no-choice or the deferral options are available (Dhar and Nowlis 1999). While forced choice scenarios have been used to measure the final choice utility and cognitive processing strategies, deferral studies have been focusing only on whether consumers chose or not. If both experimental paradigms can provide evidence about TP impact on decision-making (for a review, see Lallement 2010), bringing them together in the same research design may provide more comprehensive insights on the way TP affects the consumer decision-making process.
Studies using forced choice scenarios have provided important insights about the information processing models used. Specifically, under TP, consumers tend to (a) process information faster (e.g., Ben-Zur and Breznitz 1981), (b) filter information (Miller 1960) and choose products using heuristics (Hamlin 2010;Pachur and Hertwig 2006;Scheibehenne, Miesler, and Todd 2007;Suri and Monroe 2003), but (c) accomplish high utility choices (Kocher and Sutter 2006). Moreover, high utility choices may be achieved by focusing in some of the attributes (e.g., Wright 1974), while ignoring some options in the assortment (Beach 1993) or by using lexicographic strategies (Rieskamp and Hoffrage 2008), such as deciding upon more visual than textual information (Pieters and Warlop 1999). Overall, time pressured consumers clearly engage in non-compensatory processing models (e.g., Payne, Bettman, and Johnson 1988;Svenson, Edland, and Slovic 1990).
On the other hand, and despite their focus on whether individuals decide or not, typical deferral studies may also provide interesting insights about information processing strategies. In line with the hypothesis that decision difficulty is dependent of the processing model used, evidence reveals that participants defer choice more when they are asked to use a compensatory model (Lye et al. 2005). Therefore, if decisionmaking is an adaptive process, when facing a significant amount of information, consumers' deferral behavior may suggest that individuals fail to adapt their decision model to the context (Scheibehenne, Greifeneder, and Todd 2010).
In the present experiments, the deferral or nochoice option was included, but data concerning the final choice made and the processing strategy used (when a choice was in fact made) was also recorded. As argued by Dhar and Nowlis (1999), including the option to deferral does not necessarily facilitate decision-making, given that individuals face two decisions: (1) whether they are willing to choose or not, and if so (2) which option to select. Therefore, the inclusion of a no-choice option was made with the full awareness that the task may become harder for participants. The advantages in doing so are the increased realism of the experimental scenario, which is a good proxy to reallife decisions as well as the possibility of comparing processing strategies, deferral and final choice utility. Assortment size was also manipulated to better understand the effect of different complexity degrees in the decision-making process under different TP conditions. Finally, we have also addressed product type, including consumer goods (digital camera, Experiment 1) and services (accommodation, Experiment 2).
Study 1: Influence of time pressure and assortment size in consumer good choices Participants were given the opportunity to choose a digital camera in an e-commerce store. The experimental scenario was a reproduction of an actual e-commerce platform, framing this research as an ecological representation of a real consumer decisionmaking process.
TP was introduced in three experimental conditions through the information presented in the website header. Specifically, participants were either informed about (1) a promotion that only lasted for the next three minutes; (2) the need to accomplish the task in three minutes; and (3) the danger of stock-out due to other online visitants. In the control condition, time constraints were not mentioned. Assortment size was manipulated by presenting either 8 or 32 digital cameras.
In line with the reviewed literature, when exposed to TP and when facing complex decisions (similar attractiveness levels) participants are expected to defer less (Dhar and Nowlis 1999), use non-compensatory information processing strategies (Payne, Bettman, and Johnson 1988;Payne, Bettman, and Luce 1996), and ground their decisions on a limited set of product attributes (Wallsten and Barton 1982) but still accomplish good final choices (Kocher and Sutter 2006). Hence, we predict TP to not only (a) induce non-compensatory information processing strategies and (b) decrease decision deferral for larger assortments, but also (c) allow high utility choices across conditions.

Participants and design
Participants were 322 individuals (97 male; M age D 33.28, SD D 11.18 and 225 female; M age D 30.01, SD D 10.76) who voluntarily participated in an online survey and were randomly assigned to one condition of a: 4 (TP: control group-no mention of time constraints; classic experimental TP-3 minutes to complete the task; price promotion-3 minutes to benefit from the promotion; and stockout threat-without a specific time deadline) £ 2 (assortment options: 8 vs. 32) between-participants design.

Procedure
Participants were recruited from universities and associations of young professionals, who volunteered to answer a Qualtrics online survey about "online stores and e-commerce processes." Initial instructions made clear that all the data collected would be treated anonymously and participants could terminate their collaboration at any point just by closing the browser. After giving informed consent, we asked participants to imagine they had won a 7-day cruise. The scenario also emphasized that participants did not own a digital camera to take along and should visit an online store to buy one. To ensure that all camera options presented would be considered highly attractive (Chernev and Hamilton 2009), the online store was presented as a leader in e-commerce with excellent assortments and competitive prices. Instructions clearly stated that participants were free to defer their decision, or even to search more information by visiting another website. It was also highlighted that their decisions should be as similar as possible to a real-life situation.

Independent variables
Time pressure TP for both classic experimental and price promotion conditions was manipulated by giving participants a total of 3 minutes to make their decision 1 and by presenting a counter for countdown on the survey page. Variable operationalization and instructions given to participants are presented in Table 1.

Assortment
Assortment size was manipulated by testing two assortment sizes, namely 8 and 32 options (Diehl and Poynor 2010). Furthermore, since cognitive complexity of the decision-making process depends also from the set of attributes and levels used to describe each option (Chernev 2003), each option presented to the participants, in both types of assortment, was described with the same five attributes. Summing the 5 attributes and their respective levels, there were a total of 11 levels under evaluation (see Table 2).

Deferral
Deferral was measured by registering participants' clicks on a button at the end of each assortment presentation stating "Search for other alternatives now or Search later."

Cognitive processing strategies
To identify the cognitive processing strategies, the relative importance given by participants to each attribute, while deciding, was measured using a regression model that quantified the weight of each factor (attribute level) on the final choices made. To allow the statistical test of several factors without testing every possible combination of factor levels, an orthogonal design was generated. The orthogonal plan successfully generated eight product descriptions, the minimum number of digital camera profiles needed to assess the relative importance of each factor. To generate the larger assortment (32), the same orthogonal plan was used but with 24 holdout cases (the procedure allows the experimenter to request extra product descriptions without compromising the orthogonal design). Compensatory strategies would then be inferred from participants who took all the attributes under consideration and non-compensatory strategies from those who did not consider some of the attributes as relevant to the decision.

Final choice utility
To measure final choice utility the real value of each option had to be quantified. In a previous pilot survey (N D 42), 63 digital camera brands (logo and image of the digital camera) were evaluated in a 9-point Likerttype scale (1 D I would never buy it to 9 D I would buy it for sure). Subsequently, the 16 brands with the highest and the 16 with the lowest evaluation were selected to compose the final assortment (32), and were classified either as highly attractive brands (valued with 2 points for choice utility) or as non-attractive brands (valued with 1 point). All levels in the remaining attributes-price, zoom, delivery availability, and costumers' referral rates-were also classified according to their predicted desirability. For example, items with lower price were rated higher (e.g., 2 points) than items with higher prices (1 point), the highest optical zoom option was rated higher (3 points), followed by the average optical zoom (2 points) and the lowest optical zoom option (1 point). Immediate availability obtained higher scores (2 points) than delivery in 7 days (1 point), and the highest referral rate obtained 2 points and the lowest 1 point. After computing and summing all the scores for each item in the assortments (generated randomly through the orthogonal plan), the final utility of each choice was obtained (ranging from a minimum of 6 and maximum of 11 points).

Control questions
To check the effectiveness of the experimental manipulations, at the end of the study, participants were asked a set of questions (in 9-point Likert-type scale): Perceived time pressure Perceptions of time available for decision-making questions were "Do you believe you had enough time to make a good choice?" (1 D Not at all to 9 D Very much); "Do you believe you had enough time to carefully evaluate each item available?" (1 D Not at all to 9 D Very much), and the extent to which they felt time pressured to decide "How pressured did you feel while making your decision?" (1 D Not pressured to 9 D Highly pressured; see Dhar and Nowlis 1999;Inbar, Botti, and Hanko 2011;Pieters and Warlop 1999).

Assortment size
To control assortment size manipulation, participants were asked "How do you evaluate the options available in the present website?" (1 D Few chances of finding the best option to 9 D A lot of chances of finding the best option; Iyengar and Lepper 2000). Additionally, to examine if cognitive overload was experienced in larger assortments we asked participants "How do you evaluate the buying process in the present website?"  (1 D Not Confusing at all to 9 D Very Confusing; 1 D Not hard at all to 9 D Very hard; and 1 D Not exhausting at all to 9 D Very exhausting; Diehl and Poynor 2010).

Realism of the experimental scenario
A final question was used to tap whether participants felt as if they were making a real decision "How likely would you visit such a website in a real life scenario?" (1 D Not probable to 9 D Highly probable; Thomas, Esper, and Stank 2010).

Manipulation checks
A set of analysis performed on the manipulation check variables confirmed that all the manipulations were successful.

Perceived time pressure
The items "Do you believe you had enough time to a) make a good choice? and b) to carefully evaluate each item available?" were significantly correlated (r D .859, p < .001) and therefore averaged into a single item renamed as "time for evaluation." As expected, there were significant differences between the TP conditions both for the time evaluation index x 2 (3) D 20.163, p < .001 and the item assessing perceived TP (i.e., "How pressured did you feel while making your decision?"), x 2 (3) D 43.887, p < .001. Results indicated that participants in the control and stock-out threat conditions (without reference to a specific time limit) were the ones who reported having more time for decision-making and feeling less time pressured. Participants in TP conditions with specific time limits reported less time for decision-making and feeling more time pressured (see Table 3).

Assortment size
Results indicated that the manipulation used was successful: participants facing the small assortment (M D 4.30, SD D 1.97) reported lower evaluations of the options available (i.e., perceived fewer chances of finding the best option) when compared with those presented with the large assortment size (M D 5.55, SD D 2.11), Z D ¡4.457, p < .001.

Cognitive overload
To measure overload, the three items evaluating whether the choice task was perceived as difficult, confusing, or tiring (a D .848) were averaged into a single item and compared across the different TP manipulations (see Figure 1). Results confirmed that being exposed to larger assortments generated higher cognitive overload (Z D 3.187, p < .001). Nevertheless, when comparing the level of overload reported for different assortment sizes and for each TP condition, significant differences were only observed in the experimental TP condition.

Main dependent variables
All participants who choose to quit the survey after being exposed to the assortment (for more than 1 second) were included in the final sample and their choices were coded as deferral. Deferring a choice online exactly reflects our participants' behavior: switching off the browser and moving to another window or tab. The inclusion of such participants obviously has some foils, because data has some missing values, namely regarding manipulations check data. Nevertheless, the aim to conduct an experiment with higher ecological validity, capturing real-life behavior as much as possible, prevailed.

Information processing strategies
As an alternative to full-profile conjoint models, we choose to run a logit model also designated as choicebased conjoint analysis (e.g., Loebnitz, Loose, and Grunert 2015;Louviere and Woodworth 1983;Manski and McFadden 1982). Since full-profile conjoint analysis requires that participants rate every option and combination possible to quantify the utility of each attribute (Green and Srinivasan 1978), several authors have been warning that such unrealistic cognitive effort may skew results (e.g., Orme, Alpert, and Christensen 1997). Evaluating and rating every single option hardly reflects real choice behavior because most consumers do not look through every single item to choose. Therefore, to examine the information processing strategies a logistic regression (Gourville and Soman 2005;Gunasti and Ross 2009;Novemsky et al. 2007) was projected for each TP condition, with the dependent variable being the log ratio of choosing (or not) a given option in the assortment. Each attribute and its respective levels were the factors under analysis (see Table 4), functioning as predictors of the final choice. Such method reveals the factors used to ground individual decision and the comparison across conditions depicts TP effect on such processing strategies. All the models projected with small assortments failed to reach significant levels, making it impossible to identify information processing models when the amount of information under evaluation was low. Models built upon large assortment conditions, however, were all significant and the results are presented in Table 5.
As expected, results indicated that when choosing a product, the "default" processing strategy (control condition) can be classified as a compensatory one. Participants decided upon all available attributes (except for referral), with brand and price being the most powerful final choice predictors. However, some of these attributes seemed to have a negative predictive power. For instance, cameras with 25x optical zoom and 6x digital zoom and cameras with higher optical zoom 30x and even smaller digital zoom 5x decreased the logged odds of the camera being chosen (B D ¡1.936 and B D ¡1.202, respectively).
These results indicated that customers prefer the dummy category that presented a smaller optical zoom 15x, but a comparatively higher digital zoom 10x. Please note that the total zoom, multiplying optical per digital zoom, is equivalent in all options. Nevertheless, from a technical point of view, optical zoom produces higher-quality images than digital zoom, thus being classified as superior. In the same way, immediate availability seemed to have a negative effect on the final choice. Such results are in line with previous studies showing that consumers tend to prefer products that are not available (e.g., Ge, Messinger, and Li 2009).
Importantly for our first hypothesis regarding the effects of the different TP conditions, the predicted engagement in non-compensatory strategies was observed. Classic experimental TP induced participants to engage more in non-compensatory processing strategies. When asked to complete a task in a specific time period, participants made their decision based on the brand only, B(1) D 1.894, p D .003. Both in price promotion and stock-out threat conditions, participants appeared to have also used non-compensatory strategies. Nevertheless, they did so in a less extreme way, that is, grounding their choices in more than one single attribute (brand), namely zoom features in the promotion TP and in price and availability in stock-out threat.

Choice deferral
In line with previous research (e.g., Dhar and Nowlis 1999), a logit model was developed to test the hypothesis that TP and higher decision complexity (larger assortments) lead to deferral decrease. The dependent variable was the logs ratio of choosing or not choosing. The model was developed ascribing the control group in TP manipulation and the small assortment group in the assortment size manipulation to the baseline condition. Both main effects as well as interaction effects of TP and assortment size were assessed (see Table 6).
The results presented in Figure 2 reflect the main effect of classic experimental TP on deferral decrease, B(1) D 2.142, p D .045. In both assortment conditions, participants significantly choose more, not deferring their decisions, when facing experimental TP than in any other condition (see Figure 2).
As in previous studies (Dhar and Nowlis 1999), experimental TP decreased deferral and induced participants to make prompt decisions. There were no further significant differences between assortment sizes or among other TP conditions. Results confirmed previous evidence of TP impact on deferral, but failed to confirm our second hypothesis that all TP conditions would decrease deferral when choosing from large assortments. First, the effect of TP on choice deferral did not interact with assortment size, B(1) D .532, p D .254, occurring also in small assortments. Second, stock-out threats and price promotions failed to reduce deferral in consumer decision-making. Since only experimental TP manipulation replicates previous evidence on choice deferral while both of the more ecologically valid TP manipulations failed to do so, initial concerns about the ecological validity of classic TP manipulation seem to be corroborated. Such assumption will be developed further in the final discussion.

Final choice utility
To test our prediction that final choice utility would remain high across all TP and assortment size conditions, we calculated the final utility of each option and compared the results across conditions. No significant differences between TP manipulations, x 2 (3) D .934, p D .817, as well as between assortment sizes (Z D ¡1.376, p D .169) were observed. As expected, despite TP or increasing decision complexity due to assortment size, participants were still able to make good utility choices: In short, results from experiment 1 indicated that (a) non-compensatory strategies to process information emerge when any kind of TP is imposed and when individuals face large amounts of information (large assortment); (b) decision deferral only decreases when individuals are specifically told to complete the task in a given time interval (experimental TP  condition); and (c) choice utility remains high, independently of the TP conditions. To acknowledge that different TP manipulations have distinct effects on deferral may have considerable practical implications. Given that only experimental TP reduced decision deferral (while the TP manipulations that are more akin to real-world situations did not), the generalization of the impact of TP to real-life consumer decisions, namely in compelling consumers to decide promptly, needs to be addressed with caution and certainly requires further examination. In a second study we examined the effects of the same TP and assortment size manipulations in the final decisions about a service.

Study 2: Influence of time pressure on service choices
Most of the evidence on the effects of TP in consumer decision-making has been focusing on goods, while evidence about services remains sparse. From a marketing point of view services have different characteristics. Services lack tangible components such as weight, size, and even color which have an evident impact on how consumers and merchants see them (Bebko 2000). One cannot store services such as toys, papers, or pencils because services perish (for a review on service marketing "P"s like Perishability, see Swartz and Iacobucci 2000), which constitutes a major challenge for management. On the other hand, such an intangible nature makes it harder for consumers to decide between options. For example, one can evaluate two lipsticks and try them out before choosing, but when it comes down to a haircut there is no way of weighting, measuring, or previously testing the final outcome. Furthermore, this intangible nature of services has severe implications in a stock-out scenario. When we are searching for services there is a lower probability of finding the exact same offer in any other provider: one flight seat on a particular day to a specific destination, a dinner table in the fanciest restaurant on Valentine's day, or a specific house to rent are hardly replaceable. Mowen and Minor (2003) reported that the strategy used by consumers to process information depends on the type of product in question, which, in turn, determines how involved consumers are in the decision-making process. Non-compensatory information processing occurs more often when consumers decide upon low-involvement products (i.e., those that imply simpler buying decisions-like commoditiesor that are more familiar to the consumer, Solomon 2002). Extending such considerations to a service level, we may argue that despite the consequences of service intangibility and perishability, the fact that, in general, they imply a single occurrence (e.g., dinner service, flight, or accommodation) may render them to be classified as low involvement. Notably, similar to low-involvement consumer goods such as commodities (e.g., toilet paper or napkins) and high involvement consumer goods (e.g., cars or laptops), the involvement level of services may also vary. The  choice of an accommodation for a honeymoon will surely be made more carefully than a single night in a hotel for a business trip. Therefore, we can expect that when consumers decide upon a new laptop, TV, car, or restaurant to announce their engagement, the experience resulting from their decision will last longer. Such assumption is corroborated by evidence suggesting that purchasing more durable or expensive products or making purchases for which consumers expect to be held accountable by others lead to more careful information processing (Ben-Zur and Breznitz 1981), smaller impact of peripheral cues (Petty, Cacioppo, and Schumann 1983), and more reflective decisions (Strack and Deutsch 2006). In line with the assumption that some services may be considered equivalent to low-involvement products, we asked participants to visit a website to choose accommodation for a single night. As in the first experiment, the website header was similar to a wellknown online accommodation booking provider and each assortment was presented in an attractive and standardized way, combining an image stimulus with text describing each option. As described in the following sections, all the manipulations were similar to those of experiment 1.
Our hypotheses were the following: (a) as default, participants should use non-compensatory strategies to process service options information, and, under TP conditions, the examination of the information should be even more superficial; (b) deferral decision should decrease for larger assortments across all TP conditions, such decrease being more relevant in the stockout threat since it emphasizes service perishability; and (c) choice utility should remain high across all TP and assortment size conditions.

Participants and design
Participants were 268 individuals (104 male; M age D 32.36, SD D 12.22 and 163 female; M age D 28.37, SD D 10.71) who voluntarily participated in an online survey and were randomly assigned to one condition of 4 (TP: control group-no mention of time constraints; classic experimental TP-3 minutes to complete the task; price promotion-3 minutes to benefit from the promotion; and stock-out threat-without a specific time deadline) £ 2 (assortment options: 8 vs. 32). Both factors were manipulated between participants.

Procedure
The experimental scenario included the same cover story about the imaginary prize of 7-day cruise. However, instead of buying a digital camera, participants were asked to choose accommodation for the night prior to boarding. Again, to ensure that participants would consider all options presented as highly attractive, the online store was presented as having great customer referrals because of its high quality. The deferral option was made available and, as in experiment 1, a button for such option was added to the assortment.
To ensure that task complexity was comparable to experiment 1, the number of options in each assortment (i.e., 8 and 32), number of attributes (i.e., 5), and number of total levels under evaluation (i.e., 11) were kept constant. Nevertheless, the attributes chosen for this scenario were changed to be consistent with the required decision (Table 7).
TP manipulation was similar to the one used in experiment 1: in the experimental TP condition participants were given 3 minutes for task completion and the web page displayed a countdown clock. In the price promotion TP condition, the website header adverted for the 50% off promotion for shopping made within the next three minutes (the same countdown clock was shown). In the stock-out threat TP condition, the following website header text was adapted: "there are only 5 beds left in this accommodation and 15 visitors online. Book right away!" 10. 92% recommends 11. 97% recommends (no clock was shown). In the control condition, no TP information or countdown was introduced.

Dependent variables
All the dependent variables were similar to the ones used in Experiment 1, namely:

Deferral
Deferral was measured by adding a button stating "Search for other alternatives now or Search later."

Cognitive processing strategies
The relative importance given by participants to each attribute while deciding was measured using a regression model that quantified the weight of each factor on the final choices made. The presentation of each item in the assortment was developed with the same orthogonal design used in experiment 1.

Final choice utility
Again, to measure final choice utility, the real value of each option was quantified. In a previous pilot survey (N D 40), 90 room pictures were evaluated in a Likerttype scale ranging from 1 (Not attractive) to 9 (Very Attractive). The 16 pictures, with the highest and the lowest evaluation, were selected to compose the final assortment (32) and were classified either as highly attractive rooms (valued with 2 points for choice utility) or less attractive rooms (valued with 1 point). All levels in the remaining attributes (price, room capacity, extra services, and costumers' referral rates) were also classified according to their predicted desirability. For example, items with lower price were rated higher (e.g., 2 points) than items with higher prices (1 point)-the highest room capacity was rated lower (1 points). As for extra services, the option with breakfast and dinner scored a higher score (2 points) while the breakfast only option scored 1 point. The highest referral rate scored 2 points and the lowest scored 1 point. After computing and summing all the scores for each item in the assortments (generated randomly through the orthogonal plan), the final utility of each choice was obtained (ranging from a minimum of 6 to a maximum of 11 points).

Control questions
Control questions regarding effectiveness of the TP manipulation, assortment size, and the realism of the scenario were adapted from the first experiment.

Manipulation check
As in the first experiment, the results indicated that all manipulations were successfully manipulated.

Perceived time pressure
Results replicated the ones obtained in experiment 1. Since time-related questions were significantly and positively correlated (r D .842, p < .001), we averaged the scores computing the "time evaluation" variable (see Table 8). Expected differences between TP conditions were found both in time for evaluation variable, x 2 (3) D 10.303, p D .016, and TP experienced, x 2 (3) D 21.520, p < .001. Participants in the control and stock-out threat conditions, both without reference to a specific time limit, reported having more time for decision-making and feeling less time pressured. Participants in the other TP conditions reported having less time for decision-making and feeling more time pressured.

Assortment size
This manipulation was also successful: participants facing the small assortment evaluated the options available as being worse (M D 4.40, SD D 2.29) than those facing the large assortment (M D 5.14, SD D 2.34, Z D ¡2.222, p D .026).

Cognitive overload
As in experiment 1, evaluations about whether the choice task was difficult, confusing, or tiring (a D .807) were averaged and compared across the different TP manipulations (see Figure 3). Final scores confirmed that when choosing a service, being exposed to larger assortments implies higher cognitive overload than the exposure to small assortments (Z D 3.596, p < .000). Such differences were significant in experimental (Z D ¡2.020, p D .043) and stock-out threat TP conditions (Z D ¡2.956, p D .003).

Realism of the experimental scenario
The experimental scenario was evaluated as realistic by participants, who reported a high probability of visiting a similar website in a real context in all condi-

Main dependent variables
As in the first experiment, participants who were exposed to the assortment for more than 1 second were retained and abandoning the survey was coded as deferral.

Information processing strategies
As in experiment 1, a logistic regression was projected for each TP condition, with log ratio of choosing a particular option or not as the dependent variable. Each attribute and levels within them were the factors under analysis (see Table 9). Again, all models built upon small assortment conditions failed to be significant, being only presented the models with data from the large assortment conditions.
As expected, when asked to decide upon a lowinvolvement service like the accommodation for a single night, participants' decision without TP (control group) was based on a limited amount of attributes like the room picture, B(1) D 1.839, p D .004; including more extra services, B(1) D 1.149, p D .036; and other clients' referral, B(1) D 1.336, p D .034, suggesting that a non-compensatory strategy was used. Furthermore, both price promotion and stock-out threat appeared to imply similar processing strategies. In price promotion condition the factors used to decide were room picture, B(1) D 1.903, p D .020, and room price, B(1) D 2.902, p D .008; when facing a stock-out threat, participants selected their room based only on the room picture, B(1) D 2.436, p < .001. Such results confirmed the hypothesis that non-compensatory strategies were used to decide upon low involvement services, even in the absence of TP, and became more extreme when the TP manipulations were introduced. Nevertheless, experimental TP condition produced unexpected effects. Despite the nature of the choice (service/low involvement) participants seem to have changed their information processing strategy and engaged in compensatory models. In this condition, all factors were carefully examined (excluding room capability) and had a significant impact on the final decision.
It seems that, when asked to accomplish a (experimental) task, participants altered their "typical" behavior, abandoning the non-compensatory processing strategy. Such evidence seems to explain previous findings about the consequences for decision-making of focusing participants in the task completion. When participants are warned, previously to decision-making, that they will have to justify their decisions, they engage in easier to explain and more rational decisions (Sela, Berger, and Liu 2009) and report that it is harder to decide (Scheibehenne, Greifeneder, and Todd 2009). Considering this pattern, we hypothesized that when consumers focus on task completion (a) a shift toward compensatory information processing strategies occurs, and (b) that the usage of such demanding information processing strategies raises decision-making difficulty, affecting the final choices made. Importantly for the implications of the present study, the behavior shift observed in participants in the experimental TP condition did not reproduce the behavior of participants in other TP conditions, suggesting that the outcomes observed in experimental TP conditions may not reflect the behavior of real-life pressured consumers.

Choice deferral
In line with experiment 1, a logit model was developed, with logs ratio of choosing or not choosing as dependent variable. The model measured the main effects of TP condition and assortment size as well as interaction effects (see Table 10).
Overall, deferral did not decrease in any TP condition when compared with the control group (see Figure 4). Therefore, although the results from experiment 1 replicated previous evidence of TP effects on deferral, that is not the case in the present experiment.
Nevertheless, the interaction effect derived from the perishable nature of services emerged. When exposed to large assortments in a stock-out threat condition, participants choose more, decreasing deferral, B(1) D 1.596, p D .024. Therefore, when facing a complex decision and the possibility of option unavailability participants were compelled to decide.

Final choice utility
Based on participants' ratings of all the attributes, final choice utility was calculated for every assortment option (ranging from 6 to 11).
As depicted in Figure 5, when choosing a service, stock-out threat lead to significantly worse decisions (M D 8.42, SD D 1.50) than price promotions (M D 10.00, SD D 0.82), Z D ¡3.596, p < .001. Here the perishable nature of the service seemed to be the underlying motive for such results. When a nonreplaceable option is about to become out of stock, Table 9. Attribute weight in a service decision-making process-logistic regression models.  consumers become eager to decide, deferring less and seemingly making worse decisions.

General discussion
In two experiments we tested the combined effect of assortment size and TP, manipulated in three conditions-experimental, price promotion, and stock-out threat. Results were analyzed upon decision deferral, information processing strategies, and final choice utility for a consumer good (experiment 1) or for a service (experiment 2). In a high-involvement decision scenario (consumer good), the effect of experimental TP was replicated across the three levels under evaluation-participants deferred their choices less, they used non-compensatory strategies, and the final utility of the decision was high. Yet such findings could not be reproduced in the other two TP manipulations (i.e., price promotion and stock-out threat). This highlights that the conclusions deriving from evidence obtained with classic experimental TP manipulations may not be directly generalizable to real consumer decision-making scenarios.
As expected, in a low-involvement decision scenario (service), consumers' "default" (control condition) information-processing strategy was noncompensatory. However, experimental TP produced an interesting unexpected effect. There was no decrease in deferral rates, and the participants shifted toward compensatory processing strategies (the final choice utility remained high). Furthermore, stock-out threat affected consumer decision-making by decreasing deferral (in the large assortment condition), leading to non-compensatory strategies and significantly decreased final choice utility. Again, these results raise important questions regarding experimental TP manipulation and its effects. The shift in the information processing strategy observed in the classical experimental TP manipulation may suggest that participants felt compelled to successfully fulfill the experimental task, not reacting to TP as they would do in a real scenario (price promotion or stock-out threat).
Overall, the hypothesis regarding the adoption of non-compensatory decision strategies when deciding under TP conditions was supported in both experiments across all TP manipulations (except for the classic experimental TP manipulation in the service decision). Results also indicated, in line with the imagetic theory advanced by Beach (1993), that TP increases the usage of picture stimulus (like room picture or logo and digital camera picture), rather than taking all of the attributes into consideration to support the decision.
Instead of manipulating the presence of equally attractive options (Dhar and Nowlis 1999), we chose to vary assortment size to introduce another variable-cognitive complexity. Experimental manipulations of assortment size were successful, since participants in the large assortment condition reported finding the best option as more likely.  Nevertheless, the assortment size variable only had a clear effect on the stock-out threat condition in a lowinvolvement scenario (service). Such finding corroborates the final choice utility literature, confirming that consumers' shift in processing strategies allows them to cope efficiently with more complex decision-making scenarios. Thus, despite the TP felt, the assortment size did not affect their ability to decide or the utility of their final choices. The exception was, as mentioned, the stock-out threat in a service scenario. It may be argued that since services are perishable, and irreplaceable to a certain extent, choosing (or not) may imply more severe consequences. Therefore, in such a stark scenario, having more options to look upon may fuel the decision-making process.
Since deferral effects were not constant across TP manipulations in both low-and high-involvement (consumer good or service) decision-making situations, we can only conclude that TP types have different consequences. If so, we can argue whether participants in previous research defer less because they felt time pressured or because they were attempting to successfully accomplish the experimental task proposed. Indeed, the results obtained in the service decision-making task seem to suggest that in the classical TP experimental condition, participants felt the decision-making process was more important (adopting compensatory strategy rules). What if the smaller deferral rate observed in the product scenario is only another indication of the same phenomenon-compliance or greater effort to accomplish the task? Indeed, deferral decreased under experimental TP (experiment 1), promoting a more extreme engagement in non-compensatory strategies in a scenario where the "default" decision strategy is compensatory (consumer good scenario). TP is expected to enhance non-compensatory decision models, based on sensorial attributes such as design (Cohen, Pham, and Andrade 2008;Shiv and Fedorikhin 1999). However, we have observed that in a service (low-involvement) scenario, experimental TP had the opposite effect (while other TP manipulations still induced non-compensatory strategies). Again, this result questions what is really being measured with experimental TP manipulations and to what extent can we use such evidence to make assumptions about real-world consumer decision-making behavior.
Despite the adoption of different processing strategies, final decisions maintained their quality. Our data supported such hypothesis, although with one exception-stock-out threat in the service scenario. Findings suggest that stock-out warnings (extensively used by online service dealers such as flight companies) have a high impact on consumers. Consumers defer their choices less, they adopt non-compensatory strategies to process information and the utility of their final choices decreases significantly.
Our main conclusion is that high-involvement products such as the consumer good examined (digital camera) are chosen after a more careful, cognitive examination. Participants looked through information in a compensatory way, achieving good results from the utility point of view. However, all TP conditions lead the participants choosing consumer goods to engage in non-compensatory strategies. Thus, if merchants are using TP techniques to simply increase sales, this goal may not always be accomplished: costumers do not defer less when choosing high-involvement products because of any type of TP. Our data suggests, however, that by inducing non-compensatory decision strategies, TP highlights some attributes of the options in the assortment, shadowing others. Indeed, image stimuli became more relevant to decision-making in TP scenarios, suggesting that merchants would benefit if they included attractive images of their products during time-limited promotions or other campaigns. On the other hand, if a strong and robust (high involvement) product is to be sold, TP may be counterproductive, restraining costumer attention from relevant attributes.
Low-involvement services like accommodation tend to be evaluated by "default" in a more perceptual manner (Langner and Krengel 2013) and experimental TP seems to reverse this information processing strategy since it creates task awareness. Imagining a scenario where a service that is above the average is being put to the market, the best option would be to highlight the importance of making a good decision (creating task awareness) and promoting a consumer decision process that is more conscious (compensatory). Consumers must be aware, though, of the extreme effect of stock-out threats in this kind of decision-making. Such marketing strategy appears to be extremely effective in urging consumers to decide (when the decision is complex as with large assortments), making their decision rules weaker (extreme non-compensatory processing) and significantly reducing the final choice utility.
Framing our results for theoretical and methodological considerations, it seems that there is a lot to be gained from introducing new TP manipulations in experimental scenarios, thickening a more ecological body of research about TP effects (e.g., De Paola and Gioia 2016). Future studies should also consider product type and the relevance that each particular item may have to the consumer. In fact, those distinctions may also be useful to re-examine previous findings given the diversity of decision targets included-from binoculars or shaving machines (Dhar 1997) to CDs, dating services, or flavored water (Chernev and Hamilton 2009). Indeed, if TP induces stress and diminishes our capability to process information but the level of stress felt is dependent on the meaning of the decision (Janis and Mann 1977), then different TP types may impact differently on consumer behavior. Across experimental scenarios, cognitive complexity of the decision was always higher in large assortments. However, higher complexity only affected consumer choices (i.e., deferral, processing strategy, and final choice utility) when participants had to choose a service facing a stock-out threat.

Limitations and avenues for further research
The role of assortment size manipulation as an inducer of cognitive complexity must be addressed in future studies. Despite the effectiveness of the assortment size manipulation (i.e., when facing large assortment sizes, participants were better at evaluating their options), larger assortments did not produce consistent effects on cognitive overload across TP conditions. Therefore, other complexity levels must be tested, using a varying number of assortment options and attributes (ideally, an inverted U distribution; Chernev and Hamilton 2009).
Furthermore, other price promotion or promotion manipulations should be tested. Several marketing authors have been arguing that certain promotions (e.g., "x% off") have lost their strength, with consumers nowadays being suspicious and negative about price promotions (e.g., Garbarino and Lee 2003;Grewal, Hardesty, and Iyer 2004) and more sensitive to other type of promotions (e.g., coupons; Suri, Swaminathan, and Monroe 2004).
More evidence is needed about the differential effects on low-and high-involvement products and services. Do TP manipulations have equivalent effects in low-involvement products and low-involvement services? Do consumers also process high-involvement services in a non-compensatory way, due to its perishable nature? Or, is the processing of these services similar to the one observed for high-involvement products?
On the other hand, fundamental differences between the ways consumers decide about low-and high-involvement products may request another caveat: consumers seem to defer important decisions more than unimportant decisions (Krijnen, Zeelenberg, and Breugelmans 2015). Current evidence does not support for such assumption, highlighting the need for further research.
Our experiments manipulated assortment as a whole, and not upon one option (we did not try to induce participants toward a particular option, like retailers and brand managers often do). In this domain, Sugden, Wang, and Zizzo (2015) found with a somehow complex lottery game that consumers are more likely to choose offers that are time-limited. Therefore, in order to achieve an even more ecological experiment these effects upon a particular option of the assortment should be tested. For instance, measuring if discounting one product or associating such product to TP (e.g., "Brand A is about to stock-out" or "Buy Brand A today and get X% off") would raise the odds of that product being selected.
Finally, new research integrating the role of cultural characteristics such as time orientation (Legoh erel et al. 2009; for a very recent international analysis, Wang, Rieger, and Hens 2016) and time perception (e.g., Lakens, Semin, and Garrido 2011; see for reviews Semin, Garrido, and Palma 2013;Semin, Farias, and Garrido 2014;Semin and Garrido 2015) is suggested. A better understanding of the way consumers subjectively experience TP may help discriminate incongruent results and fully explain consumer behavior within diverse contexts.

Implications
Our goal of contributing to TP research in consumer decision-making behavior was accomplished with the introduction of new TP manipulations and also by the simultaneous examination of different dependent variables, which have been separately investigated in previous research. The funneled approach used-first measuring deferral, next examining cognitive processing strategies, and then, in the same sample, assessing the final decision utility-is likely to contribute to the current understanding of consumer decision-making processes and outcomes. The present experimental design may be considered as the first integrative attempt to bridge several findings regarding the impact of TP in consumer decision-making. Analyzing the effect of TP on deferral in a first stage of decision-making and subsequently on the information processing models adopted and final choice utility accomplished may provide a deeper and more holistic look into consumer decision-making.
As we noted, in several daily choices consumers face different types of TP that may cause direct changes in their decision-making. Therefore, if consumers may benefit from acknowledging retailers' strategies to induce sales, becoming aware of the impact that attractive images may have when deciding on a rush, managers, on the other hand, may profit from understanding the behavioral consequences of different cognitive processing strategies that consumers engage in. If TP increases the relevance of visual attributes (Lynch and Ariely 2000), managers should be fully aware about how and when this happens. Managers ought to know that a bad cover picture may be an immediate sales killer, whereas a good picture combined with TP may even beat deals with better value. Such knowledge may help coordinate promotional allocations and overall pricing strategies for products, allowing for a better optimization of marketing investments.
Marketing standard models are being challenged in this new era of global and instant competition. Since the present marketplace is clearly overcrowded and consumers are increasingly savvy, professionals must ponder more carefully on which products to develop and how to do it in a compelling way, which are the most suitable promotional strategies to boost sales without any pitfalls in the long run, and how to manage stocks avoiding a backlog of excessive inventory or an overcomplex supply chain. Research so far has provided interesting insights to support daily decisions, but more straightforward evidence is needed. Applied research projects such as the present one may provide interesting leads to improve the current marketing practices.
Overall, the present results and the future research avenues they may encourage are likely to have important implications. Managers can improve their strategies to really boost the sales of their products and services. Researchers may reflect upon this new methodological framework and conceptual insights. Finally, consumers can acknowledge the persuasive mechanisms that are used to induce and shape their own choices, becoming aware of the potential impact of marketing actions and how contextual variables such as TP or assortment size may affect their daily decisions.