Blending Time:
Scale Response Grouping Biases Wait Time
Differences
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Blending Time:
Scale Response Grouping Biases Wait Time
Differences
University of Groningen
Faculty of Economics and Business MSc Marketing Management Master Thesis
15-6-2020
Name: Manon Klein
Address: Westerveen 35, 7711 DA Nieuwleusen. The Netherlands Phone number: +31640917008
E-mail: m.a.klein.2@student.rug.nl Student number: S3799670 Supervisors:
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Abstract
Consumers have become more impatient over the years because they are not used to waiting anymore. Drawing on the psychology of scale response, this research examines how presenting times in a blended (vs. continuous) scale format influence consumers’ wait time and purchase intentions accordingly. The results showed that the format by which future times were presented significantly influenced perceived time. Different waiting times are considered similar when they occur within a group (i.e., blended) and are considered different when they are presented on a continuous scale. Accordingly, when two future delivery times are presented on a continuous scale, a longer waiting time leads to a longer perception of wait time, which reduces the purchase likelihood. However, when time presentations are blended in one bucket, a longer waiting time does not differ from that of the shorter waiting time and therefore the purchasing likelihood does not change.
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Preface
The paper you are currently reading is my master thesis for the degree of Master of Science in Marketing, with a specialization in Marketing Management at the University of Groningen. This was the moment of my studies where I put all my learnings about marketing and research into practice, even though I still learned a lot of new things during this thesis project.
I conducted two studies about perceptions of wait time, more specifically about the influence of score blending on time perception. I examined how presenting different appointments on
different scale formats (blended vs. continuous) would influence perceptions of waiting time and if this would mediate purchase likelihood.
I would like to thank some people who have helped me with this thesis. First, special thanks go to my supervisor Mehrad Moeini Jazani. He provided me with an unlimited number of insights and ideas and gave helpful directions and feedback during the process of my thesis. Second, I would like to thank Norbert Schwarz, who took the time to go over the study design and share his thoughts about grouping biases. Finally, I would like to thank Bob Fennis, for taking the time to evaluate this thesis.
Manon Klein
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Table of content
Preface 4 Introduction 6 Theoretical framework 7 Psychological time 8Scale information processing 9
Research design 11 Data collection 11 General procedure 11 Results 13 Study 1A 13 Study 1B 15 Discussion 24 Limitations 24 Managerial Implications 25
Future research implications 25
Conclusion 26
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Introduction
Not so long ago, next day delivery was a unique selling point. Right now, next day delivery is the norm, and there are even companies that can deliver their products and services on the same day. Also, taxi’s don’t need to be ordered in advance anymore. We simply open our Uber app, and the taxi can be there in only a couple of minutes. Similarly, reaching out to a company was only possible during business hours, while right now, we can reach a lot of them any time we want. Meaning that we are not used to waiting anymore. Therefore most technologies and products became more time-efficient (Levine, 1997), like 3-in-1 skincare solutions, four-slice toasters, and fast food chains (Zhong & DeVoe, 2010). Because of all these developments, we became even more impatient, and the moments we have to wait, are experienced as more unpleasant. Waiting can lead to anger, switching to a competitor, and impatience, which are all very undesirable. What can companies do about these negative responses of consumers on their waiting time? Being faster or more time-efficient is not possible in many cases without throwing out much money. For webshops, same-day delivery is interrupting the e-fulfillment process, and it is often harmful for the environment. On top of that, it does not give companies enough time to solve problems. In other cases, being faster is also not an option. For example, the waiting time for a visa interview cannot be reduced due to the incoming workload and staffing. It would be interesting to find a solution for the drop in the purchase intentions because of these long wait times. Therefore the problem statement of this research is: How can businesses prevent a low likelihood of buying due to long waiting times?
These insights can be rather helpful for companies or other service providers that have to
communicate a certain wait time. The outcomes of this research can result in a piece of advice on how to present future waiting time most favorably.
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Theoretical framework
As already stated briefly in the introduction, the waiting time is something we have to deal with quite often. Because the way we perceive time influences various decisions including those related to purchasing and ordering products and services, it is critical to examine factors that may influence time perception.
This research builds on the psychology of scale response and examines how two scales formats (blended vs. continuous) in presenting times might influence perceived wait time and purchase intention. More specifically, the focus lies in grouping time. By grouping or blending times (see Figure 1), we mean an instance where two delivery times occur in one bucket on a scale in contrast to continuous scale (see Figure 2). This leads to the following research question: What effect does blending time have on the perception of future waiting time, and how does time perception then influence consumers' purchase intention?
Figure 1: Grouping delivery time
Figure 2: Continuous delivery time
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This is not only a contribution to research but also very interesting for service providers, shipping companies, and marketers. This can give insights on how to present their waiting time most favorable for the services they provide. With this research, we want to find out when consumers have the perception of the shortest waiting time, and if this can lead to higher buying intentions.
Psychological time
Earlier research from Zauberman, Kim, Malkoc, & Bettman (2009) showed that the perception of time is subjective, meaning that people can perceive the same time as longer or shorter depending on their psychological state or the context they are in. When the wait time is perceived to be long, we discount outcomes over time, which is better known as delay discounting. Meaning, we prefer smaller, sooner rewards over bigger, later rewards. However, according to Zauberman et al. (2009), this effect gets smaller for times further away in the future. This is better known as hyperbolic discounting. An example of hyperbolic discounting found by Thaler (1981) is as follows: “when evaluating a lottery, people required $30 rather than $15 to wait for 3 months (a discount rate of 277%); however, the same people required only $60 to wait for 1 year (a discount rate of 139%) and $100 to wait for 3 years (a discount rate of 63%).” Meaning that the discount rates drop sharply as the length of time increases. This shows that the perceived value of a reward is influenced by how fast people can get it. How long or short people perceive future time does have an impact on their patience, which influences how much they will discount outcomes over time. If they perceive 3 months from now as really far away in the future, they will discount more (because it looks further).
Several factors can influence how far or near, we see something in the future. These factors can be stimuli, a specific manipulation, or someone’s psychological state. One of them is (sexual) arousal (Kim & Zauberman, 2013). Arousal makes the length of future time seem longer, therefore people get more impatient. The visceral states directly change the value of the targeted reward, and it decreases the perceived value of delayed rewards by influencing future time perception. This study showed that people who were confronted with Victoria Secret models wearing lingerie perceived future durations as farther away and became more impatient about the gift card they would receive afterwards. They requested a greater amount in delayed money than those in the control condition. Research from Zhong & DeVoe (2010) showed the same effect, but then for fast food. Peoples’ visceral states directly changed the value of the targeted reward. Exposure to fast food did make people more impatient, also outside of the food domain. Even to the extent that it reduces peoples’ willingness to save since they preferred immediate gain over greater future return.
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Other research looked into the possibility to present the same time in different ways: 1 week versus 7 days or 2 days versus 48 hours, for example. This can also affect the perceived time. Zhang and Schwarz (2012) showed that the higher the numbers (i.e., the larger the units) used, the longer time is perceived. This is better known as numerosity (Pelham, Sumarta, and Myaskovsky 1994), which means that the same time presented in minutes is perceived as longer than when it is presented in hours. In this case, 60 minutes is perceived as longer than 1 hour. However, research from Bagchi and Monga (2012) found that this is not always the case; sometimes the opposite happens, the phenomenon known as unitosity. A delay expressed in large units was considered bigger than when expressed in small units because a change in weeks (big unit) seems bigger than a change in days (small unit). In this case, a delay of 1 week is perceived as longer than a delay of 7 days. According to Bagchi and Monga (2012), it depends on whether you are affected by unitosity or numerosity on perceptual salience and the construal level. When you focus on numbers due to a concrete mindset; it yields numerosity. But, when you focus on units due to an abstract mindset, it yields unitosity.
Looking into this earlier research above, we know that the perception of future waiting time can be influenced. The same waiting time can be perceived short or long based on your mindset, feelings, stimuli, and the presentation of time. We think that the perception of waiting time can also be influenced by grouping time, that is the way times are presented on the scale, and that this can have consequences for consumers’ decision-making.
Scale information processing
The way we interpret information on scales has been researched extensively. Schwarz, Hippler, Deutsch & Strack (1985) showed that scales used in surveys could influence how people perceive their standing. Participants saw the middle options as “average behavior” and the endpoints as extremes. Meaning that if their answer is below the middle option, they consider it as below average and vice versa. The scale used can also influence people who are just observing the responses of others and the inferences they draw from them. People that estimated their television consumption to be 2 to 2.5 hours a day were more satisfied with their leisure time when they answered this question on a low‐frequency scale (ranging from not at all to more than 2 hours) than on a high‐ frequency scale (ranging from up to 2 hours to more than 4.5 hours) (Schwarz, Bless, Bohner, Harlacher, & Kellenbenz, 1991). This can also lead to different answers. Medical professionals estimated the same symptom frequency as more problematic when people checked this on a low‐ frequency scale than on a high‐frequency scale. This shows that also observers use scales as an indication for the relative position.
More recently, research about score blending (Hauser and Schwarz, 2018) showed that whether the same is presented in the grouped vs. continuous scale has implications for how that score is evaluated. Grouping is simply putting – in this case- scores in buckets. In their research, Hauser and Schwarz (2018) showed that the same low score seemed to be worse when it was presented on the blended scales than on a continuous scale. This is like receiving the grade 3.5 on a 0 to 10 points continuous scale or being blended with the category F on an (A, B, C, D, E, and F groups). The blended format made the score feel worse. Conversely, for a good grade, the blended format (e.g., receiving an A) made it feel even better compared to a continuous format (e.g., receiving 8 on 0 to 10 scale).
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distribution. The groups themselves convey meaning; people may rely on the group as a whole as a measure of waiting time, regardless of where in the group their waiting time falls. Therefore 3 days delivery time could seem very long for a T-shirt on a continuous scale, but when 3 days is considered to fall under the fast delivery group, the same time may not seem that bad.
We apply the same psychological principles governing the score blending research and extend that research by focusing on how “two” scores are perceived relative to each other when they are presented in the blended vs. continuous scale. Importantly, due to the principle of assimilation1, we expect that scores blended in the same group to be seen as more similar than when those scores are presented on a continuous scale. We apply this principle to how such representation of scores influences psychological time and its downstream consequences.
1We tend to ignore differences when the original and the alternative both belong to the same group because
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Research design
I will investigate how the grouping of values on a scale affects one's perception of future time and if this change in perception can impact the purchase intention. Reviewing previous studies, the expected results are illustrated in figure 3, but only for continuous presentation formats and not for blended presentation formats, since they will most probably be perceived as similar.
Figure 3: Conceptual Model
Data collection
For this research, an online survey was conducted to gather data. The participants were gathered through Amazon Mechanical Turk (MTurk), meaning that the sampling strategy which is used is non-probability sampling, since not all members of the population (e.g., the US residents) got an equal chance of being selected. The specific sampling method is convenience sampling because participants who are the easiest to access are being selected. The downside is that the sample can be biased because people who are using MTurk do not represent the whole population. They could be using this because they need some extra money or because they feel bored. This makes the research, not representative of the entire population. This sampling method was still chosen because of its cost-time efficiency, which is an important factor since this master thesis should be finished within five months. On top of that, this is the most representable pool of participants where the researcher could get access to procure a large sample. The benefits of online surveys are that they are fast and the stimuli could be displayed easily. It is user-friendly; there is no interviewer bias, meaning that answers cannot be influenced by the way the interviewer asks the questions. On top of that, participants usually answer more honestly in online surveys. The biggest downsides of an online survey are that the environment is not as controlled as one would have in a lab study. But, as already mentioned, this is as best as one could get.
General procedure
At the beginning of the survey, participants were faced with a control question and a Captcha before they could proceed with the survey. After that, participants were briefed about the survey. This briefing included the instructions and indicated that it would take around 10 minutes to fill in the questionnaire. Next to that, the subject ‘internet’ was introduced, but it did not include the true meaning of this research. In the next part, participants needed to fill in questions about the internet. These questions were included to remind people of how important internet is in their everyday lives. Afterwards, they read a scenario in which they have moved to a new apartment and have ordered Wi-Fi from an Internet Service Provider (ISP) in that neighborhood. In Study
1A, half of the participants learned that the earliest installation date is in 5 days from now. The
other half of the participants learned that the earliest date is in 8 days (i.e., between subjects).
Presentation
format Perceived wait time
-+
Wait length Purchase intention
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Participants saw the waiting time presented either on a grouped (blended) or a continuous scale (i.e., between subjects). They were asked how likely they are to subscribe to this provider and how long they perceived the waiting time. Expectations are that there will be no difference
between the answers on these questions between people in different appointment conditions when they were confronted with the blended scale since both appointment dates would be considered similar due to grouping. But, a difference was expected when people were confronted with a continuous scale since the different appointments would then be considered as individually different.
These questions were followed by questions about their willingness to pay and attitude. The next part consisted of control questions related to internet usage. The survey continued with several questions about their current feelings. Participants had to indicate on each of 22 states how they felt on a 5-point Likert scale (for example, interested, scared, and inspired). This was followed by demographics about age, gender, household size, their last educational degree, employment status, ethnicity, personal gross annual income, and household gross annual income. The last part consisted of questions about their attentiveness during the survey. Questions were for instance about whether they left their device or listened to music, the quietness of the environment, how often they checked their phone and what kind of device they used. But also about their level of English proficiency, their attentiveness and whether they have already seen similar questions before. The survey ended with a multi-item 7 point-Likert scale with concerns about the current Covid-19 pandemic and some last attention checks.
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Results
Study 1A
Data inspection
During the data cleaning process, cases were identified, which failed the control questions, did not follow instructions, or failed in one or more of the attention checks. These cases were excluded from the analysis. Our attention check questions were several subtle ones. The most important was the question at the end of the survey that asked participants to recall their wait time they read about in the scenario. On top of that, participants were asked to indicate “what day was yesterday” and to briefly write what they did on that day. Participants who failed to provide a correct answer or write a meaningful text were excluded from the analysis. Finally, participants who took less than 25 seconds to read the scenario were excluded.
Overall, 335 (out of approximately 400) participants (Mage = 37.82, SD = 13.009; 177 males) remained in study 1A. 164 of the participants were in the blended condition and 171 participants in the continuous condition. 56% of them are full-time employed, and 51% own a college or university degree.
Results Perceived wait time
For this study, a 2 (presentation format: continuous vs. grouped) × 2 (wait length: 5 vs. 8 days) between-subjects ANOVA on perceived wait time was conducted. The results of a two-way ANOVA revealed a significant main effect of presentation format (F(1,331) = 7.828, p=0.005), a significant main effect of wait length (F(1,331) = 10.372, p=0.001), and the expected, two-way interaction effect between these variables (F(1,331) = 4.147, p=0.043) which can be seen in figure 4.
Figure 4: Moderation of presentation format on the relationship between wait length and perceived wait time Study 1A
Consistent with our reasoning, the contrast analysis revealed that people who were in the continuous presentation format condition, those who had to wait 8 days (M=7.252, SD=0.196), perceived the waiting time to be longer than those who had to wait 5 days (M=6.367, SD=0.192; F(1,331): = 14.115, p<0.001). However, for people in the blended presentation format, there was no significant difference in the perceived waiting time, whether they had to wait 5 days
(M=6.262, SD=0.274) or 8 days (M=6.587, SD=0.281; F(1,331) = 0.687, p=0.408). This is illustrated in figure 5. Presentation format 7.828** P=.005 4.147** P=.043
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We also report the other contrasts here though we did not have any specific hypotheses here. Among those who were exposed with an 8 day waiting time (M=7.252, SE=0.196), participants who were exposed with a continuous scale (M=7.917, SE=2.74), perceived the wait time as longer compared to those who were exposed with a blended scale (M=6.587, SE=0.281; F(1,331) = 11.444, p=0.001). However, for people in the 5-day waiting time condition (M=6.367,
SE=0.192), there was no significant difference in perceived waiting time between those who saw a continuous scale (M=6.471, SD=0.270) compared to a blended scale (M=6.262, SD=0.274; F(1,331) = 0.296, p=0.587)
Figure 5: Differences of perceived wait time among actual wait times and presentation formats Study 1A.
Results Purchase intention
Again, a 2 (presentation format: continuous vs. grouped) by 2 (wait length: 5 vs. 8 days)
between-subjects ANOVA was conducted, this time on participants' purchase intention. Results revealed a significant main effect of presentation format (F(1,331) = 3.923, p=0.048), a
significant main effect of wait length (F(1,331) = 5.261, p=0.022), and importantly the expected two-way interaction effect between these two variables (F(1,331) = 3.998, p=0.046). These results are illustrated in figure 6.
Figure 6: Moderation of presentation format on the relationship between wait length and purchase intention Study 1A
Presentation format
3.923** P=.048 3.998**
P=.046
Wait length Purchase intention
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As expected, for people who were in the continuous presentation format condition, those who had to wait 8 days (M=4.643.203, SD=0.281), were less likely to subscribe to the internet provider than those who had to wait 5 days (M=5.851, SD=0.276; F(1,331): = 9.414, p=0.002). However, for people in the blended presentation format, there was no significant difference in the purchase intention whether they had to wait 5 days (M=5.845, SD=0.281) or 8 days (M=5.762, SD=0.288; F(1,331) = 0.042, p=0.837). This can be seen in figure 7.
The other sets of contrasts were examined too, despite any particular hypotheses. Specifically, among those who were exposed with an 8 day waiting time (M=5.203, SE=0.201), participants who were exposed with a continuous scale (M=4.643, SE=0.281), were less likely to subscribe to the internet provider compared to those who were exposed with a blended scale (M=5.762, SE=0.288; F(1,331) = 0.402, p=0.006). However, for people in the 5-day waiting time condition (M=5.848, SE=0.197), there was no significant difference in purchase intention between those who saw a continuous scale (M=5.851, SD=0.276) compared to a blended scale (M=5.845, SD=0.281; F(1,331) = 0.394, p=0.989)
Figure 7: Differences in purchase intention among actual wait times and presentation formats Study 1A.
Study 1B
Data inspection
For data inspection of study 1B, the same criteria were used as in Study 1A. In Study 1B, 355 participants were used for analysis (Mage = 37.75, SD = 13.246; 197 males), with 170
participants in the blended condition, and 185 participants in the continuous condition. 54% of them are full-time employed, and 47% had a college or university degree.
Results Perceived wait time
Again, a 2 (presentation format: continuous vs. grouped) × 2 (wait length: 13 vs. 16 days)
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Figure 8: Moderation of presentation format on the relationship between wait length and perceived wait time Study 1B
Results of the contrast analysis revealed that for people who were in the continuous presentation format condition, those who had to wait 16 days (M=8.835, SD=0.220), perceived the waiting time to be longer than those who had to wait 13 days (M=7.940, SD=0.203; F(1,351): = 8.966, p=0.003). However, for people in the blended presentation format, there was no significant difference in the perceived waiting time, whether they had to wait 16 days (M=8.165, SD=0.220) or 13 days (M=8.059, SD=0.220; F(1,351) = 0.116, p=0.734). This can be seen in figure 9. Again, despite having no specific hypothesis, we proceed to report the other contrasts.
Specifically, among those in the 16-day waiting time condition (M=8.500, SE=0.155), those who were exposed with a continuous scale (M=8.835, SE=0.220), perceived the wait time as longer compared to those who were exposed with a blended scale (M=8.165, SE=0.220; F(1,351) = 4.653, p=0.0.032). However, for people in the 13-day waiting time condition (M=7.999,
SE=0.149), there was no significant difference in perceived waiting time between those who saw a continuous scale (M=67.940, SD=0.203) compared to a blended scale (M=8.059, SD=0.220; F(1,351) = 0.158, p=0.691)
Figure 9: Differences of perceived wait time among actual wait times and presentation formats Study 1B.
Presentation format
1.636 P=.202 3.350*
P=.068
Wait length 5.388** Perceived wait time
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Also, for the purchase intention, a 2 (presentation format: continuous vs. grouped) by 2 (wait length: 13 vs. 16 days) between-subjects ANOVA was conducted. Results of a two-way ANOVA revealed a significant difference between the groups. There was again no main effect of
presentation format (F(1,351) = 0.114, p=0.736), but only a significant main effect of wait length (F(1,351) = 8.951, p=0.003), and there was a marginally significant two-way interaction effect between these two variables (F(1,351) = 3.291, p=0.071). This can be seen in figure 10.
Figure 10: Moderation of presentation format on the relationship between wait length and purchase intention Study 1B
Results of the contrast analysis revealed that, for people who were in the continuous presentation format condition, those who had to wait 16 days (M=2.918, SD=0.295), were less likely to subscribe to the internet provider than those who had to wait 13 days (M=4.310, SD=0.272; F(1,351): = 12.017, p=0.001). However, for people in the blended presentation format, there was no significant difference in the purchase intention whether they had to wait 16 days (M=3.541, SD=0.295) or 13 days (M=3.882, SD=0.295; F(1,351) = 0.667, p=0.415). This is presented in figure 11.
The other sets of contrasts were examined too, despite any particular hypotheses. Among those who were exposed with a 13 day waiting time (M=4.096, SE=0.201), there was no difference in the purchase intention between participants who were exposed with a continuous scale (M=4.310, SE=2.956) and those who were exposed with a blended scale (M=3.882, SE=2.732; F(1,351) = 1.134, p=0.288). We found the same results for participants in the 16-day condition (M=3.229, SE=0.209), there was no significant difference in purchase intention between those who saw a continuous scale (M=2.918, SD=2.475) compared to a blended scale (M=3.541, SD=2.662; F(1,351) = 2.229, p=0.136) Presentation format .114 P=.736 3.291* P=.071
Wait length Purchase intention
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Figure 11: Differences of purchase intention among actual wait times and presentation formats Study 1A.
Control Check
Several ANCOVA analyses were conducted to test whether the significant levels of the main effects and the interaction between presentation formats and the waiting length would still hold after controlling for covariates. This way, precision was increased by removing sources of variance in the purchase intention and the perceived time. This analysis accounts for systematic differences across treatment groups that were not controlled for in the experimental design. Table 1 shows the results of our analysis for the perceived wait time, while table 2 shows the results of the analysis for the purchase intention. Specifically, in both tables, Model 1 shows the results of our original effect without any covariates included for Study 1A. Model 2 included the participant's attitude towards the provider and the degree of dependency on Wi-Fi at home as covariates. The same was done for Study 1B; Model 3 represents the ANOVA, and model 4 includes the covariates attitude and dependency again.
Based on the results (see Table 1) for perceived waiting time, the interaction effects of
presentation format and wait length were still significant after controlling for the covariates in models 2 and 4. The analysis showed that internet dependency was not a significant predictor of the perceived waiting time in study 1A (F(1,329): = 1,021, p=0.313), but it was significant in study 1B. The more dependent a participant was on the internet, the longer they perceived their waiting time (F(1,349): = 5,113, p=0.024). Meaning that this effect was stronger for waiting times on the last half of the scale. Attitude towards service provider was a significant predictor in study 1A (F(1,329): = 61,432, p<0.001) and study 1B (F(1,349): = 78,620, p<0.001). If
participants had a positive attitude towards the service provider, the waiting time was perceived as shorter.
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internet, the less likely they were to purchase (F(1,349): = 2,772, p=0.097). This effect was stronger for waiting times on the last half of the scale. Attitude was a significant predictor in study 1A (F(1,329): = 192,667, p<0.001) and study 1B (F(1,349): = 332,824, p<0.001). If
The mediating role of perceived waiting time
Based on the results above, we can state that the interaction of time presentation (presentation format x wait length) is indeed predicting perceived waiting time and purchase intention. However, there is no proof of the mediation yet. To examine a possible mediation, PROCESS model 8 from Hayes (2013) was employed with 5000 bootstraps resamples, testing a moderated mediation for participants in Study 1A. In this model, we test whether the perceived waiting time mediates the interaction between presentation format of time and wait length on purchase
intention. Model 8 allows the direct and indirect effects of an independent variable (X = wait length) on a dependent variable (Y = purchase intention) through a mediator (M = perceived waiting time) to be moderated (W = presentation format).
The analysis showed that the moderated mediation index is significant (index= -.5433 95% CI [-1.1188; -0.0204]) for the moderated mediation with presentation format as moderator of the effect of wait length on purchase intention via perceived waiting time. The indirect effect of wait length on purchase intention via perceived waiting time differs significantly between different presentation formats (continuous versus blended scale). When checking the IV’s together with perceived waiting time as predictors for the purchase intention, the effects of the wait length (b=.0752, t(330)=.2120, p=.830), presentation format (b=.1069, t(330)=.3079, p=.7584), and more importantly, the interaction on the purchase intention (b=-.5817, t(330)=.-1.1652, p=.2448) became insignificant. This means that all effects are going through the mediator: perceived waiting time (b=.-,4852, t(330)=.-9.7839, p<0.01). This can be seen in figure 12.
Figure 12: Moderated Mediation Analysis Study 1A
The indirect effect analysis explained that participants in the condition with the continuous scale were less likely to buy when they had to wait 8 days compared to participants that had to wait 5 days (b= -.7013 95% CI [-1.0765; -.3531]). The analysis showed that when waiting time became longer, the purchase intention went down. This is in line with what was expected. In the blended condition, there was no significant difference in the likelihood of buying between participants that had to wait 5 days or 8 days (b= -.1580 95% CI [-.5462;.2537]). This means that the mediation only holds in the continuous scale condition. This is illustrated in figure 13.
Presentation format
Perceived wait time 1.1198**
P=0.0425
-.5817 P=.2448
Wait length .0752 Purchase intention
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Figure 13: Difference in purchase intention between different wait times and presentation format through perceived wait time Study 1A
For Study 1B we performed the same moderated mediation analysis as described above. This analysis showed a nonsignificant moderated mediation index (index= -.6019 95% CI [-.2850; 0.0299]). The confidence interval includes zero, therefore, we could not definitively claim that the indirect effect was related to the moderator (Hayes, 2013). Meaning we do not have proof that the indirect effect of wait length on the purchase intention via perceived waiting time differs significantly between different presentation formats. When checking the IV’s together with perceived waiting time as predictors for the purchase intention, the effects of the wait length (b= -.2604, t(350)=.7563, p=.4500), presentation format (b=.3370, t(350)=.1.0175, p=.3096), and more importantly, the interaction on the purchase intention (b=-.4492, t(350)=.-.9360, p=.3499) were insignificant. This means that all effects are going through the mediator: perceived waiting time (b= -.7625, t(350)=.-12.8974, p<0.01). This can be seen in figure 14.
Figure 14: Moderated Mediation Analysis Study 1B
The indirect effect analysis explained that participants in the condition with the continuous scale were less likely to buy when they had to wait 16 days compared to participants that had to wait 13 days (b= -.6825 95% CI [-1.1175; -.2680]). The analysis showed that when waiting time became longer, the less likely people were to buy. This is in line with what was expected. In the blended condition, there was no significant difference in the purchase intention between
participants that had to wait 13 days or 16 days (b= -.0807 95% CI [-.5435; .4161]). An illustration of these results can be found in figure 15.
Presentation format
Perceived wait time
-.7625*** P<0.01
.1059
P=.7336 -.4493P=.3499
Wait length Purchase intention
-.2604 P=.4500 1.8302*
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Discussion
The results suggest that longer wait times can lead to lower purchase intentions because consumers’ perception of the wait time is then longer. This effect is moderated by the
presentation format of the wait time, which means that this mediation is only true when the wait time is presented on a continuous scale. As soon as different wait times are lumped together in one group, the effect is gone. It shows that people perceive the duration of two appointments similarly when those times are lumped together in one group, relative to when they are presented on a continuous scale. Waiting times which are at the end of a group are perceived as shorter compared to the same waiting time shown on a continuous scale. This is the reason the purchase intention only differs for waiting time communicated on a continuous scale. Consumers perceive both wait times as similar in the blended condition because they tend to ignore differences when things belong to the same group (Biernat, 2005; Bless & Schwarz, 2010).
We can conclude that this research contributes to the findings of Hauser and Schwarz in 2018 about the effect of score blending. They found that when values are grouped, individuals merge the values within each group. This research showed that this blending effect also holds for time, and it affects the purchase intension through this perception of wait time. However, there is a certain boundary to this effect. For waiting times which are relatively far (as in Study 1B), even though the interaction of time presentation (presentation format x wait length) can predict
perceived waiting time and purchase intension, the moderated mediation model did not reach the conventional significance level. Thus, in Study 1B, we cannot certainly establish that the effect of our IVs on purchase likelihood is mediated by perceived time. However, the pattern of results are consistent with such an interpretation.
The insignificance of the moderated mediation model in the Study 1B could have several explanations. It could be because the waiting times are on the second half of the scale since Schwarz, Hippler, Deutsch & Strack (1985) showed that scales could influence how people perceive their standing. Options above the middle are perceived as “longer than average” waiting times. Therefore it could be that participants simply do not tolerate this. However, this is
unlikely, given the interaction we already found between our IVs on perceived time. Another explanation is hyperbolic discounting. Which is the phenomenon that future durations are not discounted linearly but rather logarithmically and lack a constant discount rate (e.g., Zauberman et al., 2009). According to Zauberman et al. (2009), subjective estimates of future time horizon change less than the same change in objective time. This means that for example, the time horizon for 3 months feels like 3, and the time horizon for 12 months feels like 3,7. While the growth in objective time from 3 to 12 months is quadruple, the growth in subjective time
perception is less than twofold. At some point, all future time perceptions become constant. This hyperbolic discounting could mean that the difference between 13 and 16 (study 1B) days is perceived as smaller than the difference between 5 and 8 (study 1A) days because 13 and 16 days are perceived as really far in the future when it comes to waiting for a Wi-Fi appointment.
Limitations
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participants had to indicate how likely they were to subscribe to the internet services of the company solely based on wait time. In real life, consumers usually need more information to indicate if they want to subscribe to an internet provider.
As already introduced in the discussion part, the waiting days for a Wi-Fi appointment which are used in study 1B are relatively long. The expectation is that this is the reason the results were not significant because participants indicated how many days it would take before the situation becomes unbearable for them when their Wi-Fi gets disconnected. They indicated that this would be the case around almost 5 days (M=4.7145, SD=5.822). This number is way below the waiting time, which was used in study 1B. Meaning that the suggested wait times (13-16 days) would not be tolerated are too far in the future and therefore did not lead to significant differences between the conditions.
Managerial Implications
The findings of this research have important implications for businesses or service providers that have to communicate waiting times. It shows that a difference in wait time can predict the purchase intention via perceived wait time if it is presented on a continuous scale. Meaning, that it is possible to increase or decrease someone’s intention to buy by the way the wait time is presented. An unfavorable wait time from a company does not always have to mean that the purchase intentions of customers significantly drop. In the middle of this pandemic, due to Covid-19, which is now happening while writing, a lot of companies are struggling with their delivery time. Customers have to wait longer than usual. Especially in a situation like this, it would be advisable to consider the presentation of the delivery time. This research showed that long waiting times could better be communicated in groups combined with rather short waiting times instead of communicating it on a continuous scale. This way, the waiting time is perceived similarly to the shorter waiting times they are grouped with. This makes customers more likely to purchase. However, there are boundaries. If the delivery date is too far away in the future, it does not work anymore. The same holds for expediting options. People do not differentiate between durations when they are blended into one group. Therefore consumers will most certainly be less affected by changes in waiting time when it contains a within-group change. Therefore, it is expected that people should be less willing to pay for expediting the delivery of a product or service when the expedited delivery time also falls within the same category as the original delivery time. I will elaborate on this in ‘future research implications’.
Future research implications
The findings of this research have important implications for expediting and delaying decisions. Specifically, if people do not differentiate between durations when they are blended in one group, it could follow that people should be less willing to pay for expediting the delivery of a product or service when the expedited delivery time also falls within the same category as the original delivery time. Similarly, when an opportunity to refund is available by delaying the delivery of a product, people should ask for fewer refunds when the delayed time is blended with the original delivery time. This was not tested due to the current scope of the research, but they should be in future research.
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underweight the differences between wait times when they are lumped under one group.
However, it remains to be examined as to how people judge and perceive two wait times that fall into different groups. Based on the familiar principles of assimilation and contrast, one should expect that in such situations, the contrast between the two groups captures the mind leading to overweighting of the difference between two times. The expectation is that people think a 3-day delay is perceived as worse when this leads to a shift between groups of waiting time compared to within-group changes since Hauser and Schwarz (2018) found in their research about score blending that participants undervalued a shift within the same group and overvalued a shift between different groups. This means that an equally big change in scores is perceived as more impactful when it leads to a change between groups than when it is a within-group change. Besides, it would be good to replicate the current research with different waiting times and scenarios and testing its boundary conditions.
Conclusion
27 Literature
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