Customers’ valuation of time and convenience in e-fulfillment

Tobias Gawor (Department of Logistics, Kühne Logistics University, Hamburg, Germany)
Kai Hoberg (Department of Logistics, Kühne Logistics University, Hamburg, Germany)

ISSN: 0960-0035

Article publication date: 10 August 2018

Issue publication date: 8 February 2019

Abstract

Purpose

The purpose of this paper is to derive monetary benchmarks and managerial implications for omni-channel retailers’ B2C e-fulfillment strategies by investigating the trade-offs between lead time, delivery convenience and total price including shipment in the context of online electronics retailing.

Design/methodology/approach

Based on a choice-based conjoint analysis among 550 US online shoppers, the monetary values of lead time and convenience were calculated in a log-log regression model. In addition, latent class segmentation was applied to identify consumer segments according to their differing e-fulfillment preferences.

Findings

From a consumer perspective, the analysis suggests that price is the most important criteria in omni-channel retailer selection, followed by lead time and convenience. The value of time is, on average, $3.61 per day. Regarding convenience, the results indicate that delivery to the home is highly preferred over pick-up options. The value of the consumer’s travel time was estimated at$10.62 per hour. The latent class segmentation identified four segment groups with different preferences.

Research limitations/implications

To validate the findings, future research could analyze real data from omni-channel retailers’ customers’ buying behavior. It should also be interesting to extend the research to other price ranges, market segments and e-fulfillment factors, such as return options, shop ratings and membership programs aiming for further generalization.

Practical implications

The findings guide omni-channel retailers to focus on efficient B2C e-fulfillment strategies. Considerable competitive advantages may be gained by reducing lead times and offering convenient delivery in line with the lead time valuation of the identified customer segment.

Originality/value

This study fills gaps in the academic research of consumer behavior in retailer selection, which has primarily concentrated on the choice between “brick-and-mortar” and online sales channels. It paves the way for a more service-oriented perspective in omni-channel retailing research.

Citation

Gawor, T. and Hoberg, K. (2019), "Customers’ valuation of time and convenience in e-fulfillment", International Journal of Physical Distribution & Logistics Management, Vol. 49 No. 1, pp. 75-98. https://doi.org/10.1108/IJPDLM-09-2017-0275

Publisher

:

Emerald Publishing Limited

Empirical results

The analysis follows the standard conjoint experimental design (Hair et al., 2009). First, we report the descriptive statistics on the sample’s characteristics. Next, we estimate the attributes’ pat-worths and the aggregate attribute importance using the choice simulation. Finally, we test for the heterogeneity of consumer groups in a latent class segmentation.

Sample description

As previously noted, the total usable sample size was 550 (296 females, 254 males). Table II provides the sample’s descriptive characteristics. The results indicate that the majority of participants (86.4 percent) regularly shops online at least once a month, and no participant never shops online. Adequate for this study, electronics was the product that most people had previously purchased online. Demographics show that 71.3 percent of the participants are between 18 and 39 years, and 53.8 percent of the participants have completed a college degree. Participants’ locations are well mixed among urban, suburban and rural areas, while 85.3 percent of the participants own a car. Accordingly, it takes the average participant 17.1 min to travel to an electronics store. Overall, the sample was found appropriate to yield representative results for the total population of 208 million people who regularly shop online in the USA (comScore, 2015).

Aggregate results

To provide methodological consistency, the sample’s aggregate results were calculated using Sawtooth Software, Inc.’s Latent Class Segmentation Module. Accordingly, the number of groups was set to one, making the assessment on the aggregate level analogous to CBC’s logit approach (Sawtooth Software, Inc., 2004).

Importance of attributes

The results are presented showing the utilities for the different attribute levels and their importance. Note that the overall usage of the none-option was modest across the three products’ CBCs (digital camera: 7.1 percent; laptop: 13.1 percent; and smartphone: 13.4 percent), which is lower than the standard expectation of 15.0 percent. Table III contains the attributes’ part-worth utilities, which were rescaled for comparability using the zero-centered “diffs” method.

Interpreting the estimated part-worths in Table III provides a number of interesting observations. In accordance with expectation, we find that, across all three products, the lowest price incl. shipment was associated with the highest utility (e.g. 109.17 for the digital camera), while the highest total price is the least desired (e.g. −100.54 for the digital camera). However, the relative advantage of low prices in comparison to other attributes, such as “Today,” is smaller for laptops and smartphones than for the digital camera.

Next, regarding the estimated delivery, “Today” and “In 1–2 days” yield positive utilities in all three cases, while longer lead times are associated with lower preferences. The range between “Today” and “In 5–10 days” slightly increases from the digital camera over the smartphone to the laptop, which has the highest reference base price (RBP) of all three products. Furthermore, as the utilities closely center around zero, the impact of the delivery method is limited when consumers make their choice between different e-fulfillment offers. While the literature assumes time-window-based home delivery to be preferable to consumers (Boyer et al., 2009), i.e., generating higher utility than standard home delivery, the survey results indicate the opposite; standard home delivery is preferred over time-window-based home delivery, which contradicts expectations and may indicate insufficient familiarity with this concept among the sample. Pick-up in a local store is the least preferred method across all three product categories.

Despite relative differences of utilities between the products, the rankings of the attributes’ levels remain the same across all three conducted CBCs. These observations were confirmed when examining the importance of the investigated attributes, which are displayed in Table IV.

The results show that, when selecting an omni-channel retailer, the total price incl. shipment is the most important criterion across all three products, with an average attribute importance of 65.0 percent. The importance of the delivery method is modest at an average importance score of 10.8 percent but constant across all products. However, at the cost of the importance of total price incl. shipment, the importance of the estimated delivery increases with the RBP of a product; that is, the importance of lead time is higher when purchasing a laptop (28.8 percent) or a smartphone (26.0 percent) than for a digital camera (17.7 percent). While the difference between the laptop and the smartphone is negligible, there is a distinct increase in the importance of the estimated delivery between the digital camera (with an RBP of $129.99) and the smartphone (with an RBP of$479.99). This finding suggests a price threshold in e-fulfillment between these two price points, in which the lead time of the estimated delivery garners significantly more importance than the importance of total price incl. shipment when purchasing electronics online. On average, the attribute importance of the estimated delivery is 24.2 percent across all three experimented product categories.

An extensive assessment of interaction suggested minor interaction between the described attributes. However, these interaction effects were not sufficiently large to add substantial predictive validity to the conjoint model. Therefore, the selected aggregate additive model was confirmed as valid and appropriate for this study.

Next, we want to determine the monetary value of lead time in B2C e-fulfillment. Therefore, Sawtooth Software, Inc.’s SMRT choice simulator was used to simulate a number of competitive scenarios and then estimate how the participants react to each scenario. The simulation compares the sample’s share of preference for different lead times at differing price points against a standard e-fulfillment scenario. In accordance with Hair et al. (2009), we first specify the scenario, then simulate choices, and finally calculate the share of preference.

Beginning with the specification of the scenario, we assume a standard e-fulfillment profile, in which the estimated delivery is set to “In 3-4 days.” For the delivery method, “Time-Window-Based Home Delivery” was selected across all scenarios, as this level’s estimated part-worth utilities varied the least among the different CBCs and avoided the conjoint models’ prohibition to combine a delivery “Today” with the “Standard Home Delivery” option. The price point of the standard profile was specified at the second lowest price, i.e., the RBP plus $20.00 for all three products. To the best of the authors’ knowledge and thorough market research, this standard profile was found to best reflect common practice among the leading online electronics retailers. The standard profile was compared in multiple isolated scenarios against profiles with shorter and longer lead times at differing price points. In the next step, choices were simulated by using the individuals’ estimated part-worths to predict the choice between two profiles in each scenario. Finally, preferences for each individual were predicted and then used for calculating the proportion of preferences for each profile by aggregating the individual choices. This study used a logit rule-based preference probability model as it approximates certain elements of product similarity and is well-suited for repetitive purchase situations (Green and Krieger, 1988). In this regard, the calculated proportion of preference predictions reflects the relative indications of preference and should not be interpreted as market shares (Chakraborty et al., 2002). The initial simulation findings further revealed that consumers have a higher willingness-to-pay (WTP) for shorter lead times. However, it was not possible yet to precisely derive this finding’s monetary value. Therefore, in accordance with Orme’s (2014) proposal, this study applied log-log regression on the proportion of preference simulation results to accurately determine the scenarios’ demand curves. We apply the log-log regression model: ln y=α+β ln x, where x is the total price incl. shipment, y is the share of preference, β is the price elasticity of the demand curve, and α is the intercept’s coefficient. Next, the natural log of preference share was regressed on the natural log of price. The regression results proved that the applied model is a significant fit. Accordingly, the log-log regression model was applied for both the standard and the alternative profile in each scenario across all three products. For reasons of simplicity and comparability and its limited overall impact, the none-option was not included in the scenarios. A limitation in this context is that the log-log regression proportion of preference values do not always add to (or may exceed) 100.0 percent, as the functions for the alternative and standard profile were calculated separately. However, this method remains more accurate and realistic than other comparable approaches, such as the midpoints formula, because more than two price points were estimated along the preference curve (Orme, 2014). The exact point of indifference between the “Today” and the standard profile scenario for the laptop is$1,315.08. When subtracting the standard profile’s total price incl. shipment of $1,299.99, a value of$15.09 was obtained. This maximum value is the most consumers are willing to pay to receive the item today instead of a lead time of 3–4 days. The results for all scenarios are displayed in Table V.

On average, the sample’s simulation results suggested that consumers are willing to pay as much as $12.80 for a same day delivery when compared to the standard lead time of 3–4 days. This amount is nearly double the monetary value of lead time of 1–2 days. In contrast, consumers expect lower overall prices of as much as an average of −$9.20 when the estimated delivery is longer than the standard lead time.

Again, the results are similar for the smartphone and vary for the digital camera. Accordingly, it is suggested that the absolute monetary value of lead time increases with the product’s total price incl. shipment to a maximum certain threshold value of the product’s total price, which is in between the RBP of the digital camera ($129.99) and the smartphone’s RBP of$479.99, and then increases at a considerably lower rate. The detailed assessment of such a threshold value implies a need for further research.

After the calculation of the absolute monetary values of lead time in each scenario, a precise value per day of lead time can be derived. This finding was achieved by applying a further log-log regression, in which the natural log of lead time was regressed on the natural log of total price incl. shipment (RBP plus the coparticipant absolute monetary values of lead time from Table V). The first observation from these results can be shown as an indifference curve of the price surcharge on a product’s RBP along specified lead times compared to the standard profile’s lead time of 3–4 days, which is displayed in Figure 2.

Due to the applied log-log regression, these values slightly differ from Table IV. Nevertheless, these values realistically map the sample’s indifference of price surcharges on the RBP of a product along the specified lead times. For instance, on average, a price surcharge for a delivery “Today” of $14.09 on the RBP of the standard alternative yields the same preference among the sample as an e-fulfillment with a lead time of seven days at a price of −$7.01 below the RBP. Again, prices vary from product to product.

By dividing the monetary values from Figure 2 by their corresponding lead times we find that the monetary value per day of lead time for each scenario ranges from $6.28 to$1.54. The average monetary value of lead time per day is considerably higher for the laptop ($4.44) and the smartphone ($4.04) than for the digital camera ($2.33). Furthermore, shorter lead times than the standard lead time have a higher monetary value than lead times exceeding the standard. Therefore, the monetary value of lead time per day decreases with longer lead times. The average value of delivery lead time was found to be$3.61 per day.

Value of convenience

Next, we consider the value of convenience in B2C e-fulfillment. While certain differences between standard and time-window-based home deliveries exist, we are particularly interested in the comparison of home delivery and in-store pick up. Similar to the approach for lead time, a standard profile across all three products was designed using the “Pick-up in store” option in “1-2 days” at the RBP of “+$20.” Calculating the point of indifference to which consumers are willing to pay more to receive the ordered item at their home instead of needing to travel to the next electronics store is shown in Table VI. The results suggest that the sample is, on average, willing to pay a maximum of$8.94 more for a standard delivery to the home compared to the “Pick-up in store” option. We further consider the role of the distance the customer must travel. The experiment contained a question about the time needed to travel to the closest electronics store (one-way; in minutes). The sample’s mean was 17.1 min. Assuming that a consumer would need to travel back and forth from home to the electronics store and that the pick-up in the store requires 5 min, it would take, on average, 39.2 min to pick up an online ordered item from a local electronics store. A limitation of this assumption is that it does not consider the possibility that customers combine activities and shopping trips (see Bhat, 1996). This assumption also does not examine the impact of transport costs, such as costs for fuel or public transportation, as included by Hsiao (2009).

Conclusion

Our results indicate that on average, lead time is a critical factor for consumers when selecting an omni-channel retailer, while convenience is of minor importance. However, the empirical findings suggest that there are distinct types of consumers who place differing importance on the considered attributes. According to their attribute preferences, we identified these segments as budgeters, lead time shoppers, convenience shoppers, and balanced buyers. We next discuss the theoretical and managerial implications of our results and provide directions for further research.

Theoretical implications

This study contributes to the previous academic studies in the field by proffering the CBC analysis for modeling the decision process that consumers experience when selecting between omni-channel retailers with differing B2C e-fulfillment offerings in online buying. In particular, it renders new detailed insights to omni-channel strategic planning frameworks’ last mile distribution parameters, such as the delivery time and delivery mode (Hübner et al., 2016). From the empirical findings of our study, several theoretical implications were derived.

First, referring to the attribute importance assessment when purchasing online, the predominant role of price is smaller than previously thought. Comparing this study’s empirical results to a recent US American online market experiment, which was published by comScore (2015), the order of the attribute importance is identical, listing price at the top, followed by delivery time and delivery method. However, our study suggests considerably more weight for lead time (24.2 vs 7.0 percent) and moderately more preference for convenience (10.8 vs 4.0 percent) at the cost of price-related factors (65.0 vs 89.0 percent). These results may be driven by differences in the research design, whereas CBC is acknowledged to more accurately capture the true preference of customers than the direct questioning of factor preference (Green and Srinivasan, 1990). For academia, this calls for a gradual shift in the literature of omni-channel planning from a major focus on costs to a more service-oriented perspective based on actual consumers’ behavior.

Second, this study calculated customers’ monetary value of lead time at an aggregated average of $3.61 per day in the online electronics industry, ranging from$0.91 to $10.79 among the identified segments. These values are considerably higher than Dinlersoz and Li’s (2006) and Hsiao’s (2009) findings of$1.84 and $0.53 per day of delivery lead time, respectively, in the online book retailing market. This finding leads to the insight that the monetary value of lead time in online retailing may be higher than previously supposed. Moreover, as books are assumed to generally cost less than electronics, the absolute monetary value of lead time increases with a product’s RBP to a certain maximum threshold value. Third, this study estimated the value of convenience in online shopping at an aggregated average value of personal travel time of$10.62 per hour. However, comparable literature is scarce. Extending beyond the consumer segment, a relatively recent US American study found that the WTP for savings in travel time varies between $7.32 and$29.31 per hour (Hensher and Greene, 2011). Hence, this study’s empirical findings are generally in line with the existing body of literature and call for further research to validate our findings across other price and product segments. Nevertheless, it can be concluded that consumers generally associate lower value with convenience in their e-fulfillment experience, when ordering low-priced products.

Finally, this study identified four segment groups of consumers with different preferences. This amplifies the current literature on consumer behavior and is new to the context of omni-channel retailing. While the segments of price-sensitive budgeters, balanced buyers, and convenience-oriented consumers are known from prior research, i.e. Rohm and Swaminathan (2004), the large proportion of lead time shoppers that are, on average, 24.2 percent of the sample, represents new insight into the segmentation of customers. Overall, the derived typologies extend the current understanding of consumers’ channel choices. Accordingly, consumer behavior regarding channel choice can to a great extent be predicted with reference to their assigned segment group, i.e. convenience shoppers likely choose fulfillment options requiring home delivery, while others would consider pick-up options depending on price and timely availability as a possible channel choice. Not only do the findings state clear preferences for price sensibility as well as valuation of lead time and convenience, but also are the segmentation groups supported by demographic and endogenous characteristics, which indicates that these typologies can also be found in other segments of omni-channel retailing.

Managerial implications

Our empirical findings suggest that the understanding of consumer behavior is essential for omni-channel retailers to decide on their fulfilment strategy. The results provide practitioners with real dollar values for customers’ perception of time and convenience. Using the aforementioned omni-channel strategic planning frameworks and provided good knowledge of supply chain costs (e.g. related to warehouse footprints, inventory deployment, and transportation times), the derived benchmarks support managers when designing and configuring their omni-channel strategies. Managers can test their fulfillment costs against this study’s findings or use the presented methodology to validate the valuation of the attributes in their respective product segment. Therefore, key supply chain-related questions can be answered: Do customers’ preferences and WTP support the offering of BOPS or same day delivery? Is the reduction of one day in lead time profitable for the business given the higher costs of increased local inventories or LSPs express shipments? Is the offering of – logistically expensive – time-based delivery windows a true competitive advantage?

Another interesting conclusion can be drawn for managers from our behavioral-based approach: Letting the customer control the last mile according to his or her preferences of lead time and delivery method, automatically adjusts their expectations. Therefore, omni-channel retailers need to establish close collaborations with LSPs to offer customers a wide range of reliable services. However, this need may simply be the very beginning as, in contrast, omni-channel retailers’ strategic network design and higher number of upstream SCM processes, such as procurement and warehousing, have very significant effects on the total price incl. shipment, lead times, and the options offered to bridge the last mile. In particular, retailers with decentralized operations, high shares of own inventory and multiple channels should strive to further reduce lead times by leveraging the synergies of their upstream SCM processes and their distributed network. Assuming sophisticated inventory visibility, orders for lead time sensitive customers should be fulfilled from local stores, while others could be managed from a central warehouse. In contrast, it is suggested that pure online retailers should strive to yield a competitive advantage by pooling inventory wherever possible to achieve low prices with reasonable lead times. In a nutshell, there is not just one optimal omni-channel retail model for every company, business sector and customer (Laseter et al., 2006; Hübner et al., 2016). Still, the model and findings of this study may help managers from various omni-channel retail segments to validate and improve their supply chain activities based on their customers’ valuation of time and convenience. Ideally, each target group is addressed with a tailored and profitable omni-channel strategy.

Limitations and future research

Despite a thorough research design, this study may be subject to several limitations. First, there can be other attributes than the investigated omni-channel e-fulfillment factors, such as information quality, return options or online shop ratings, which can render consumers utility when making their retailer choice online and, thus, influence the selection of omni-channel retailers and associated importance with price, time, and convenience. It would be interesting to see more research on other factors in omni-channel retailer selection. Next, the empirical findings of this study were conducted in a single context, i.e., the online electronics retailing market. However, the results cannot necessarily be generalized to the omni-channel shopping of other product and price categories. This requires further research in other price categories, countries, and market segments, e.g., the online purchasing of clothes or groceries, aiming for further generalization in omni-channel retailing. Furthermore, the experimental design assumed total prices incl. shipment and did not consider the impacts of different shipping pricing approaches, such as cost shifting or dynamic pricing. In addition, the experimental design neglected CDPs and only considered BOPS as a delivery method option due to the limited number of options in the CBC design. As the number of such lockers is growing rapidly worldwide, this should be subject to future research. Finally, a major assumption of this study was that the online purchase is a single transaction of buying one specific item. However, consumers could also buy multiple products simultaneously in one purchase or combine their shopping in “brick-and-mortars” with other activities (e.g. travel to work), which could considerably change the CBCs’ derived utilities and obtained monetary values of lead time and convenience. In addition, the role of membership programs (e.g. Amazon Prime) can ultimately alter the consumer’s preferences.

Future studies can extend this research by examining the WTP for shorter lead times. In addition, the investigation of the role and opportunities of same day delivery requires more research, particularly with respect to the link to the product category. Finally, to validate this study’s findings, future research could analyze real data from omni-channel retailers regarding their customers’ e-fulfillment choices in the buying process and derive monetary values of lead time and delivery convenience from the service possibilities offered. This finding, in turn, could be matched against the associated supply chain costs of these services to obtain more precise managerial implications.

Figures

Figure 1

Screenshot of experiment

Figure 2

Price surcharge indifference curve along specified lead times

Figure 3

Segment size and attribute importance per segment

Table I

Attributes and levels (for smartphone)

Attributes Estimated delivery Delivery method Total price incl. shipment
Levels Delivery today
Delivery 1–2 days
Delivery in 3–4 days (standard)
Delivery in 5–10 days
Standard home delivery
LSPs deliver the order at any time during the day to the customer’s home. The customer needs to be home in order to receive the parcel
Time-window-based home delivery
When placing the order online, customers select a time-window of typically 3 hours on a specific date, in which LSPs deliver the order. The customer needs to be home in these three hours in order to receive the parcel
Pick-up in store
LSPs deliver the order to a predefined collection and deliver point (CDP), from which customers have to pick up their parcels themselves
US$479.99 US$499.99
US$519.99 US$539.99

Table II

Descriptive sample characteristics

Variable n %
Online shopping experience
About once a day 5 0.9
A few times a week 76 13.8
A few times a month 276 50.2
About once a month 118 21.5
Less than once a month 75 13.6
Never 0 0.0
What products do you shop online?a
Books 343 62.4
Clothes and shoes 385 70.0
Drugstore and convenience goods 158 28.7
Electronics 400 72.7
Groceries 85 15.5
Specialty goods (i.e. furniture, washing machines) 139 25.3
Toys, leisure articles, and/or household products 344 62.5
None of the above 0 0.0
Gender
Female 296 53.8
Male 254 46.2
Age
Between 18 and 29 years old 208 37.8
Between 30 and 39 years old 184 33.5
Between 40 and 49 years old 73 13.3
Between 50 and 59 years old 57 10.4
60 years or older 28 5.1
Area
Urban 175 31.8
Suburban 284 51.6
Rural 91 16.5
Car ownership
Yes 469 85.3
No 81 14.7
How many people live in your household?
1 115 20.9
2 183 33.3
3 114 20.7
4 84 15.3
5 or more 54 9.8
What is the highest degree or level of school you have completed?
Some high school 4 0.7
High School degree, GED or equivalent 57 10.4
Some college credit, no degree 193 35.1
Bachelor’s degree 220 40.0
Master’s degree or higher 76 13.8
Occupation
Student 49 8.9
Working full time 316 57.5
Working part time 89 16.2
Unemployed 74 13.5
Retired 22 4.0
Total household income before taxes
Less than or equal to 30,000 USD 169 30.7
Between 30,001 and 60,000 USD 183 33.3
Between 60,001 and 120,000 USD 153 27.8
More than 120,000 USD 39 7.1
Does same day delivery add a significant additional benefit to shopping online?
Yes 322 58.5
No 198 36.0
Don’t know 30 5.5
When shopping online, is “pick-up” in a store or at a CDP a valid option for you?
Yes 375 68.2
No 137 24.9
Do not know 38 6.9
Distance: How long does it take you to travel closest to an electronics store? (one-way, in minutes)
Mean 17.1
SD 17.4
Total 550 100.0

Note: aMultiple response possible

Table III

Aggregate choice-based conjoint utility estimates (rescaled for comparability)

Attributes and levels Digital camera (RBP: $129.99) Laptop (RBP:$1,279.99) Smartphone (RBP: $479.99) Estimated delivery Today 25.10 41.09 36.65 In 1–2 days 9.24 15.78 17.34 In 3–4 days −6.38 −11.68 −12.63 In 5–10 days −27.96 −45.19 −41.35 Delivery method Standard HD 15.95 16.64 12.88 Time-window-based HD 5.32 −0.96 2.22 Pick-up in store −21.27 −15.69 −15.10 Total price incl. shipment RBP 109.17 90.25 98.32 +$20 31.24 36.54 32.07
+$40 −39.86 −35.66 −34.69 +$60 −100.54 −91.14 −95.69
None-option
None −45.66 −20.49 −25.28

Note: HD, home delivery; RBP, reference base price

Table IV

Aggregate choice-based conjoint attribute importance

Attributes and levels Digital camera (RBP: $129.99) Laptop (RBP:$1,279.99) Smartphone (RBP: $479.99) Average Estimated delivery 17.7 28.8 26.0 24.2 Delivery method 12.4 10.8 9.3 10.8 Total price incl. shipment 69.9 60.5 64.7 65.0 Note: In percent; RBP is reference base price Table V Comparison of the monetary value of lead times for tested scenarios Digital camera (RBP:$129.99) Laptop (RBP: $1,279.99) Smartphone (RBP:$479.99) Average
Attributes and levels Absolute Relative Absolute Relative Absolute Relative Absolute Relative
Today vs standard 8.49 6.5% 15.09 1.2% 14.83 3.1% 12.80 3.6%
Today vs In 1–2 days 4.08 3.1% 6.17 0.5% 5.57 1.2% 5.28 1.6%
In 1–2 days vs Standard 4.02 3.1% 6.87 0.5% 8.84 1.8% 6.58 1.8%
Standard (in 3–4 days)
In 5–10 days vs standard −5.98 −4.6% −12.73 −1.0% −8.90 −1.9% −9.20 −2.5%

Note: Absolute Values in US$; RBP, reference base price Table VI Comparison of the monetary value of convenience for tested scenarios Digital Camera (RBP:$129.99) Laptop (RBP: $1,279.99) Smartphone (RBP:$479.99) Average
Scenario Absolute Relative Absolute Relative Absolute Relative Absolute Relative
Standard HD vs pick-up 10.15 7.8% 8.44 0.7% 8.23 1.7% 8.94 3.4%
Time-window-based HD vs pick-up 7.10 5.5% 2.75 0.2% 4.96 1.0% 4.94 2.2%
Pick-up in store

Note: Absolute Values in US$; HD, home delivery; RBP, reference base price Table VII Segment sizes and attribute importance of identified segments for all products Digital Camera (RBP: US$129.99) Laptop (RBP: US$1279.99) Smartphone (RBP: US$479.99)
Attributes Budgeter LT shopper Conv. shopper Balanced buyer Budgeter LT shopper Conv. shopper Balanced buyer Budgeter LT shopper Conv. shopper Balanced buyer Total
Segment size (n) 327 78 51 94 237 166 98 49 235 155 106 54 550
Percent of total 59.5 14.2 9.3 17.1 43.1 30.2 17.8 8.9 42.7 28.2 19.3 9.8 100
Estim. delivery time 15.6 42.8 11.3 12.6 17.7 49.4 13.9 22.9 15.9 51.6 7.9 27.6 24.1
Delivery method 5.8 12.3 39.8 15.1 8.1 7.5 39.4 4.2 3.8 4.6 34.7 12.5 10.8
Total price 78.6 44.9 48.9 72.3 74.1 43.1 46.7 72.9 80.2 43.8 57.4 59.9 65.0

Note: In Percent; RBP, reference base price; LT, lead time

Table VIII

Comparison of the monetary value of lead time per day for identified segment groups

Delivery today 4.70 2.33 10.79 2.98 3.09
Delivery tomorrow 4.23 2.00 9.58 2.45 2.59
Delivery in 2 days 2.89 1.00 6.37 0.91 1.14
Standard (delivery in 3–4 days)
Delivery in 5 days −4.37 −2.91 −9.94 −4.31 −4.13
Delivery in 6 days −2.90 −1.84 −6.55 −2.67 −2.59
Delivery in 7 days −2.55 −1.44 −5.26 −2.07 −2.02
Average of absolute values 3.61 1.92 8.08 2.57 2.59

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Corresponding author

Kai Hoberg can be contacted at: Kai.Hoberg@the-klu.org