Dynamic Prices in Retail and Its Impacts on Logistics

Onur Guvenc

, Aiman Kazybayeva

and Kuanysh Abeshev

School of Management, Almaty Management University, 227 Rozibakiyeva, Almaty, Kazakhstan

School of Engineering Management, Almaty Management University, 227 Rozibakiyeva, Almaty, Kazakhstan

Keywords: Gamification, Retail, Inventory, Logistics, Dynamic Price.

Abstract: Various factors have contributed to the immense growth of dynamic pricing: demand data, technology, and

decision support tools. A sample survey was conducted to get the perspectives of small business owners in

retail and consumers to understand their perspective on dynamic consumer pricing and its effects on logistics.

The survey questions were structured in a way to provide perspectives on consumer experience and buying

behaviour concerning dynamic pricing and gamification. The study realized retail companies are not well

prepared for the logistical changes due to dynamic pricing. Traditionally, retail stores have focused on

ensuring that the supply chain is responsive to client demands. For instance, leftover inventory was seen as a

problem arising from poor decisions on dynamic pricing. After a promotional selling season, many of the

retail respondents indicated that they face problems of when and how much to mark down leftover inventory.

1 INRODUCTION

The research paper will be focused on dynamic prices

in retail and its impacts on logistics. Also,

gamification will be studied as a part of dynamic

pricing. The study is based on the perspective of

consumers and retailers about the dynamic pricing

and the logistics issues, especially for the retailers. To

better understand the situation, two surveys were

conducted, and the findings were discussed in the

study. The discussion facilitates managerial and

theoretical insights on the study in a business context.

The existence of different pricing strategies and,

modern technologies and tools have subjected

companies to change their logistic operations to

remain competitive and optimize the profits.

2 LITERATURE REVIEW

2.1 Big Data

Various factors have contributed to the immense

growth of dynamic pricing: demand data, technology,

and decision support tools (Chen et al., 2020; Chen,

https://orcid.org/0000-0002-1550-3810

https://orcid.org/0000-0001-6474-4189

https://orcid.org/0000-0003-1140-7431

2016). Big data analytics is proving to be the gold in

the 21st century- allowing companies to easily track

customer purchase metrics and other indicators that

could drive sales. Indeed, determining the appropriate

price to charge a customer for a product is often a

complex task – requiring the company to have

knowledge of its operating costs and supply as well

as current consumer valuation of the product and

changes in future demand (Cope, 2007; de Boer

2015). Charging the customer, the right price,

therefore, requires that a store has a wealth of

information about consumer habits and be able to set

and adjust prices at minimal costs.

2.2 Price

Prices are also formed based on costs. And, research

studies provide that up to 50-70% of all costs in every

product consists of logistic costs (Abbasi, 2011).

Equally, Abbasi (2011) finds that warehousing,

deterioration, loss, insurance, package, and

administration make stocks comparatively expensive.

Abbasi (2011) indicates that inventories can absorb

up to 30% of logistics costs and represent a significant

proportion of the total assets of an organization

Guvenc, O., Kazybayeva, A. and Abeshev, K.

Dynamic Prices in Retail and Its Impacts on Logistics.

DOI: 10.5220/0009838706590666

In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020), pages 659-666

ISBN: 978-989-758-419-0

659

Inventory carrying costs can be considerably high –

amounting to one-fifth of the total costs. Abbasi

(2011) likens dynamic pricing to contingencies –

from which there is a need to hedge the supply chain.

For retailers, there is a need to keep additional

inventory for various situations such as unexpected

price changes, which often comes as a result of

dynamic pricing and gamification. Effective

management of inventory reduces carrying costs and

increases customer satisfaction.

2.3 Gamification

Gamification refers to the application of elements of

game playing to other areas of activity, such as

marketing, pricing, and the enhancement of other

non-game contexts (Huotari & Hamari, 2017). The

method of gamification is less important than the

presence of gamification (Rodrigues, Oliveira, &

Rodrigues, 2019). Indeed, the method of gamification

matters only within the context of the amount of

interest and engagement maintained by consumers

and the identification of how those gamification

methods can be changed in order to increase overall

interest and participation, and whether that interest

and participation leads to the purchase of goods or

services from the goods or services provider using

gamification methods (Rodrigues, Oliveira, &

Rodrigues, 2019).

Koivisto and Hamari (2019) have noted an

increased shift within today’s society toward making

reality “increasingly game-like” (p. 191).

Researchers have noted that the use of gamification

serves as a motivator, fulfilling the psychological

need satisfaction of participants, causing individuals

to continue to utilise programs and services that

integrate gamification in an effort to continue to

achieve feelings of satisfaction (Sailer, Hense, Mayr,

& Mandl, 2017).

Given the use of gamification in applications

(apps) accessible via computer, smart phone and

tablet, and the pre-existing integration of dynamic

pricing strategies by online retailers such as Amazon,

the argument can be made that the potential

integration of gamification in dynamic pricing is not

a large leap, as the technology necessary to integrate

the two components already exists (Chen, Mislove, &

Wilson, 2016; Kessels, Kraan, Karg, Maggiore, &

Valkering et al., 2016). The researchers believe that

the integration of an app could be used to allow

customers to track changes in pricing, with the prices

of different items moving based on the demand for

those items.

2.4 Supply Chain Management

Abrate and Viglia (2016) also believe that

advancement in information and technology has

provided remarkable opportunities for both

marketing and supply chain management. In the

marketing domains, stores have increased the ability

to understand individual consumer preferences and to

adjust prices – improving the ability to optimize

revenues dynamically. However, not much research

has been done on the influence of dynamic pricing

and gamification on the supply chain and logistics.

Abrate, Fraquelli, and Viglia (2012) suggest that

firms can use technology to improve their visibility

costs and lead times – internally throughout the

supply chain continuum.

The authors believe that the next major

development for competitive advantage is for firms to

link innovations in marketing with those in the supply

chain management – allowing them to refine pricing,

capacity, production, and inventory decisions. Such

smooth coordination could offer managers visibility

to true costs and responsiveness as they make pricing

and promotion decision – and equally provide supply

chain managers a perfect understanding of pricing

structures when they decide to expand capacity and

strategic location of inventories (Elmaghraby &

Keskinocak, 2003; Faraquiy, 2012). The results will

be an optimized revue structure and optimized profits

across the entire supply chain.

2.5 Targeting Audience

Other recent studies have further expressed concerns

about the failure to link logistics with dynamic

pricing (Liu, Guan, & Wang, 2019; Pupavac, 2016;

Sen, 2013). Various industry experts have concluded

that opportunities exist for linking the supply chain to

dynamic pricing and gamification; an opportunity

that will increase the ability of stores to serve their

customers in a highly targeted manner – the key to

profit optimization (Zhou, Li, & Tang, 2009; Zhang

& Weatherford, 2017). Digital technology has

provided the capability of sharing information

promptly – however, organization cultures have been

relaxed in keeping with the pace of technology. For

instance, in several instances, retails stores have run

out of inventory during offers. For instance, the Black

Friday is a perfect example where retail stores

provide insane offers to their customers; however,

several have missed on their items even after making

purchases (Levin, McGill, & Nediak, 2010). This is

an inconsistency in the supply chain system and

logistics – which fails to augment dynamic pricing

iMLTrans 2020 - Special Session on Intelligent Mobility, Logistics and Transport

660

and gamification. It is also not unlikely to miss Ubers

during promotional pricing. And, the hotel and airline

industries are some of the most affected – they have

inconsistently matched dynamic pricing to capacity

(Petruzzi & Dada, 2002; Pupavac, 2016).

3 METHODOLOGY

A sample survey was conducted to get the

perspectives of small business owners in retail and

consumers to understand their perspective on

dynamic consumer pricing and its effects on logistics.

A total of 100 people was interviewed (50 male and

50 female) to reduce bias in response. The survey

questions were structured in a way to provide

perspectives on consumer experience and buying

behavior concerning dynamic pricing and

gamification. All the respondents were emailed the

survey questions. An online link was further sent to

them for easy response. For retail stores, the

researcher conducted manual surveys to understand

how dynamic pricing and gamification affects their

logistics. The survey was applied to 100 people with

the 85% (Table 1) of confidence level by using the

formula below:

𝑛=

𝑁⋅𝑡



⋅𝑝⋅𝑞

𝑁⋅Δ



+𝑡



⋅𝑝⋅𝑞

where:

𝑁 – the amount of population in the city which is

1.002.000 (Nur-Sultan);

𝑡 – the function of confidence coefficient that can be

determined according to the Table 1 and 𝑡=1,5

with the confidence of 85%;

𝑝 and 𝑞 – sampling ratios where both events have the

same probability, 𝑝=𝑞=0,5;

Δ – maximum-permissible non sampling error and as

the organizational-technical system is large, Δ is

considered as 0,075.

Hence; 𝑛= 99.9901.

Table 1: Dependence of the 𝑡 from the confidence needed.

Confidence, % 85 95 99 99,9

Function 𝑡

1,5

2,6 3,3

3.1 Participants

The study participants were recruited from various

sources. The inclusion criteria were that a person

must have purchased an item from online stores for

the past three months during ‘rush’ hours, peaks, and

other promotional periods. Income level was not an

indicator of concern as the objective of the study was

to understand inconsistencies in logistics due to

dynamic pricing and gamification. A person was also

eligible if they have used ridesharing services for the

past three months. A simple deterministic analysis

was conducted to analyse consumer responses. A

total of 70 retail stores were considered. For a store to

be included in the survey, it should have at least 30

employees, an annual turnover of $500,000, be in the

consumer goods retail segment, and at least 5

departmental stores. Traditionally, understanding if

revenue management type dynamic pricing works for

business requires that they have a relatively fixed

capacity, a predictable demand, fixed or sunk costs

substantially comparable to variable costs, and have

varying demands – a reason for using the criteria

above.

3.2 Survey Questions

There were two surveys – one conducted to

understand the perspectives of consumers about the

influence of dynamic pricing models, especially the

supply chain network and another to get the views of

small retails stores about dynamic pricing and its

effects on their profit margins and supply chain

frameworks. Emphasis was given to the logistics –

integration of information flow, production,

packaging, inventory, transportation, and

warehousing.

Hypothesis

1. All else equal, returns are positively associated

with post-purchase price drops

2. Increased demand during dynamic pricing and

offers constrain the supply chain network

4 RESULTS

In this section, the findings of the study are given. The

questions of the survey are presented in the appendix

section.

4.1 Consumers

The table below shows consumer perspectives on

dynamic pricing.

Dynamic Prices in Retail and Its Impacts on Logistics

661

Figure 1: Consumer Perspectives on Dynamic Pricing.

As shown, millennials have the highest approvals of

dynamic pricing and gamification among the various

categories and groups. The highest proportion of the

age segment (50) approves of dynamic pricing.

Millennials are tech-savvy and confident in their

ability to game various retailers on dynamic pricing

practices. They are often computer knowledgeable

and tend to spend substantial amounts of time

scouring the internet for best prices – making them

approach dynamic pricing from an informed

perspective and more of sense of whether their

behavior or actions of other retailers could trigger a

price drop at another retailer.

Have you had a delay in the delivery of an online

product you bought during promotions and peak

sales?

Figure 2: Delays.

What was the reason for your delay?

Figure 3: Reasons for delay.

What was the reason, if you did, for returning a

product after purchase?

Figure 4: Reasons for returns.

Age category and number of products returned.

Figure 5: Number of returns.

4.2 Retailers

The graph below shows the number of 30-day returns

for various retailers. The respondents indicated their

average 30-day rate of returns during dynamic pricing

offers, and when such offers are not available (Figure

6).

18-29 30-34 49-59 60+

No. of Respondents

Age Group

Consumer Pespectives on

Dynamic Pricing

I love it Its okay

I don’t like it

I hate it

0 10203040

18-29

30-34

49-59

60+

Total respondents

Age group

Delays

No Yes

0 204060

Wrong Adress

Surge in delivery…

traffic

Unexplained

Number of respondents

Reasons

Reasons for Delay

02040

Faulty

Didn’t like it

New cheaper offers

delays

Number of respondents

Reasons

Reason for Returns

0 5 10 15 20 25

18-29

30-34

49-59

60+

Number of returns

Age gproup

Number of returns

iMLTrans 2020 - Special Session on Intelligent Mobility, Logistics and Transport

662

Figure 6: 30 day returns.

The data indicates that many retail outlets

experience a lot of returns during dynamic ricing

offers as compared to normal sales. This raises

questions about the supply chain systems of such

establishments and their ability to respond to

increased consumer demands. More analysis of the

same is provided in the discussion section.

Do you experience delivery delays with dynamic

pricing offers?

From the data (Figure 7), 70% of retail stores

indicated that they experience a lot of delays from

their suppliers in goods delivery during promotional

offers.

Figure 7: Delivery delays.

The increasing surge constrains the supply chain

as suppliers lack the empirical estimates to

understand customer volumes and needs. This

indicates a need to revamp the supply model to be

real-time or guided by metrics that show consumer

preferences during this time. It is evident that during

dynamic pricing offers, the supply chain is

overwhelmed.

5 DISCUSSIONS

5.1 Leftover

The study realized retail companies are not well

prepared for the logistical changes due to dynamic

pricing. Traditionally, retail stores have focused on

ensuring that the supply chain is responsive to client

demands. For instance, leftover inventory was seen as

a problem arising from poor decisions on dynamic

pricing. After a promotional selling season, many of

the retail respondents indicated that they face

problems of when and how much to mark down

leftover inventory. Some firms, however, have

understood the role of smart pricing of products to

ensure a seamless supply chain. Running regular

promotions that increase sales to a specific customer

segment increases their inventory response by

concentrating on a specific domain.

5.2 Opportunistic Returns

Additionally, the analysis indicates that opportunistic

returns as a result of dynamic pricing affects logistics.

Opportunistic returns were mostly observed in the

millennial category – they can take time monitoring

product prices over the internet to opportunistically

seek benefits from price changes. Secondly, as widely

observed in the millennials, when retails provide for

more than one payment method, customers

anticipating future price drops after purchase consider

payment methods with lower return costs – known as

strategic choice of payment method. Opportunistic

returns provide critical information on customer

satisfaction and greatly influences the supply chain

management for online retailers (Faruqui, & Sergici,

2010; Garcia 2010). Such returns can hurt profit

margins by posing substantial costs in shipping,

handling, and liquidation. Reducing such returns is an

immediate concern for major retailers, especially for

online stores. Banjo (2013) indicates that managing

returns are highly crucial for online retailers as up to

1/3 of the online transaction are returned by

customers.

5.3 Cash on Delivery

Consistent with our findings, Bandi et al. (2018)

investigated to understand how returns and strategic

choice of payment during dynamic pricing affect

retail logistics. Some customers insist on cash on

delivery (COD) as their model of payment. The COD

method has been used in the traditional retail segment

in other countries, including China, Russia, and India,

1000

2000

3000

4000

5000

4666812

Number of returns

Number of retailers

30 day returns

Normal sales Promotional offers/peaks

0 10203040

Yes

Number of retail stores

Answer

Delivery delays

Dynamic Prices in Retail and Its Impacts on Logistics

663

where significant populations lack credit cards. In

emerging markets, COD accounts for up to 60% of

online transactions. From the survey, customers

expressed that they could decline deliveries without

paying for anything. Bandi et al. (2018) found out that

customers who expect a higher probability of

returning products often use COD more frequently.

Retailers are then affected by higher return rates,

which compromises logistics. Such a segment of

customers constantly feels that dynamic pricing will

change their favor – or more return such items when

other dynamic pricing options are offering lower rates

elsewhere. COD further induces longer collection

cycles, which are costly to firms.

5.4 Types of Consumer

Liu, Guan, and Wang (2019) further takes issues with

strategic consumers and how they affect the supply

chain, especially within the confines of dynamic

pricing and gamification. The authors believe that

there exist two types of consumers – a myopic

consumer whose purchase decisions are based on the

fact that the price tag is lower than his valuation of

the product irrespective of the potential markdown on

the price in the future. The other type of consumer is

a strategic consumer – deciding when to buy

depending on the present valuation and price, but also

timing purchase decisions to maximize consumer

surplus. For Aziz, Saleh, Rasmy, and ElShishiny

(2011), the market is always a mixture of these two

types of consumers – compelling retailers to take into

account the ratio of such consumers in a market

segment when implementing dynamic pricing models

– delaying the purchase of product in anticipation for

price reduction sacrifices present usage and current

value.

5.5 Effects on Demand

For our analysis, it is evident that for buyers,

especially for the strategic buyers, cost reduction

informs waiting in anticipation for higher price cuts

in the later period. And for sellers, dynamic pricing

can delay sales, as there exists a higher profit margin

in the later period. We find that, from the demand

side, an increased number of strategic consumers

delay the purchase, manifested through demand

decrease during the first stages, and increases in the

second period. On the supply chain aspect, when

considering the delay, the seller can adjust the pricing

strategy to remedy the trend – resulting in decreasing

the profit. Myopic demand, therefore, is an issue of

concern in dynamic pricing affecting supply chain

and logistics. Myopic demands increase the costs of

inventory as well as the proportion of dead inventory

(Levin, McGill, & Nediak, 2010; Herbon &

Khmelnitsky, 2017). While gamification can be used

to beat myopic demands, it can fail in other industries

such as brick and mortar retail stores, but work in

airline bookings, ridesharing, and hotel books –

enterprises that require no inventories.

5.6 Limits

Our study is limited by several factors – it is based on

simple models. The sample size is satisfactory with

the confidence level of 85%. But as the survey was

conducted in Nur-Sultan; in the capital, it is still

needed to be conducted in other representative cities

and/ or regions of Kazakhstan.

Purchasing behaviors of the consumers can be

different from country to country according to some

other variables such as cultural issues. The costs, can

vary in terms of logistics, mainly delivery and

warehouse cost due to geographic situation of a

country. That’s why, enlarging the geography by

looking in other countries can be the further steps of

the study.

6 CONCLUSIONS

The analysis provides critical perspectives on the

readiness of supply chain to respond to intricacies of

dynamic pricing. Our evidence finds that dynamic

pricing reduces the supply chain efficiency. Various

retail stores are just not prepared to handle

voluminous deliveries at some specific times.

Equally, strategic buying is another factor that

compromises the supply chain in dynamic buying

behavior. Strategic buying increases the inventory

costs – affecting the logistics and supply chain. To

improve logistics, there is need to adopt direct to

consumer models – which reduces inventory costs

and returns. However, our study is limited by several

factors – it is based on simple models. The sample

size is satisfactory with the confidence level of 85%.

But as the survey was conducted in Nur-Sultan; in the

capital, it is still needed to be conducted in other

representative cities and / or regions of Kazakhstan.

This paper serves as a foundation for the

remainder of the research study, providing both a

basis that the reader can use to understand the

findings and serving as the means through which the

findings of the current study will be situated within

the context of the extant body of literature. At this

time, enlarging the data set, recommendations for

iMLTrans 2020 - Special Session on Intelligent Mobility, Logistics and Transport

664

practice, recommendations for areas of future study

and the final conclusion to the study remain a work in

progress.

ACKNOWLEDGEMENTS

During our research, the assistance provided by Mr

Yevgeniy Shin; marketing student at Almaty

Management University was greatly appreciated.

REFERENCES

Abbasi, M. (2011). Storage, Warehousing, and Inventory

Management. Logistics Operations and Management,

181–197. doi:10.1016/b978-0-12-385202-1.00010-4

Abrate, G., & Viglia, G. (2016). Strategic and tactical price

decisions in hotel revenue management. Tourism

Management, 55, 123-132.

Abrate, G., Fraquelli, G., & Viglia, G. (2012). Dynamic

pricing strategies: Evidence from European hotels.

International Journal of Hospitality Management,

31(1), 160-168.

Aziz, H. A., Saleh, M., Rasmy, M. H., & ElShishiny, H.

(2011). Dynamic room pricing model for hotel revenue

management systems. Egyptian Informatics Journal,

12(3), 177-183.

Bayoumi, A. E. M., Saleh, M., Atiya, A. F., & Aziz, H. A.

(2013). Dynamic pricing for hotel revenue management

using price multipliers. Journal of Revenue and Pricing

Management, 12(3), 271-285.

Bichler, M., Kalagnanam, J., Katircioglu, K., King, A. J.,

Lawrence, R. D., Lee, H. S., ... & Lu, Y. (2002).

Applications of flexible pricing in business-to-business

electronic commerce. IBM Systems Journal, 41(2),

287-302.

Chen, J. M., & Chang, C. I. (2013). Dynamic pricing for

new and remanufactured products in a closed-loop

supply chain. International Journal of Production

Economics, 146(1), 153-160.

Chen, K., Zha, Y., Alwan, L. C., & Zhang, L. (2020).

Dynamic pricing in the presence of reference price

effect and consumer strategic behaviour. International

Journal of Production Research, 58(2), 546-561.

Chen, M. K. (2016, July). Dynamic pricing in a labor

market: Surge pricing and flexible work on the uber

platform. In Proceedings of the 2016 ACM Conference

on Economics and Computation (pp. 455-455).

Chen, L., Mislove, A., & Wilson, C. (2016). An empirical

analysis of algorithmic pricing on Amazon

Marketplace. Proceedings of the 25th International

Conference on World Wide Web - WWW '16. doi:

10.1145/2872427.2883089

Cope, E. (2007). Bayesian strategies for dynamic pricing in

e‐commerce. Naval Research Logistics (NRL), 54(3),

265-281.

den Boer, A. V. (2015). Dynamic pricing and learning:

historical origins, current research, and new directions.

Surveys in operations research and management

science, 20(1), 1-18.

Elmaghraby, W., & Keskinocak, P. (2003). Dynamic

pricing in the presence of inventory considerations:

Research overview, current practices, and future

directions. Management science, 49(10), 1287-1309.

Faruqui, A. (2012). The ethics of dynamic pricing. In Smart

Grid (pp. 61-83). Academic Press.

Faruqui, A., & Sergici, S. (2010). Household response to

dynamic pricing of electricity: a survey of 15

experiments. Journal of regulatory Economics, 38(2),

193-225.

Fleischmann, M., Hall, J. M., & Pyke, D. F. (2003). Smart

pricing: linking pricing decisions with operational

insights.

García, J. P. (2010). Dynamic pricing in the Spanish

gasoline market: A tacit collusion equilibrium. Energy

Policy, 38(4), 1931-1937.

Herbon, A., & Khmelnitsky, E. (2017). Optimal dynamic

pricing and ordering of a perishable product under

additive effects of price and time on demand. European

Journal of Operational Research, 260(2), 546-556.

Huotari, K., & Hamari, J. (2016). A definition for

gamification: Anchoring gamification in the service

marketing literature. Electronic Markets, 27(1), 21-31.

doi: 10.1007/s12525-015-0212-z

Kessels, K., Kraan, C., Karg, L., Maggiore, S., Valkering,

P., & Laes, E. (2016). Fostering residential demand

response through dynamic pricing schemes: A

behavioural review of smart grid pilots in Europe.

Sustainability, 8(9), 929. doi: 10.3390/su8090929

Koivisto, J., & Hamari, J. (2019). The rise of motivational

information systems: A review of gamification

research. International Journal of Information

Management, 45, 191-210.

Levin, Y., McGill, J., & Nediak, M. (2010). Optimal

dynamic pricing of perishable items by a monopolist

facing strategic consumers. Production and Operations

Management, 19(1), 40-60.

Liu, G., Guan, Z., & Wang, H. (2019). Dynamic Pricing

under Cost Reduction in the Presence of Myopic and

Strategic Consumers. Discrete Dynamics in Nature and

Society, 2019.

Liu, X., Tang, O., & Huang, P. (2008). Dynamic pricing

and ordering decision for the perishable food of the

supermarket using RFID technology. Asia Pacific

Journal of Marketing and Logistics.

Netessine, S. (2006). Dynamic pricing of

inventory/capacity with infrequent price changes.

European Journal of Operational Research, 174(1),

553-580.

Petruzzi, N. C., & Dada, M. (2002). Dynamic pricing and

inventory control with learning. Naval Research

Logistics (NRL), 49(3), 303-325.

Pupavac, D. (2016). Dynamic Pricing: The Future of Retail

Business. Business Logistics in Modern Management.

Dynamic Prices in Retail and Its Impacts on Logistics

665

Rodrigues, L., Oliveira, A., & Rodrigues, H. (2019). Main

gamification concepts: A systematic mapping study.

Heliyon, 5, e01993.

Sailer, M., Hense, J., Mayr, S., & Mandl, H. (2017). How

gamification motivates: An experimental study of the

effects of specific game design elements on

psychological need satisfaction. Computers in Human

Behavior, 69, 371-380. doi: 10.1016/j.chb.2016.12.033

Şen, A. (2013). A comparison of fixed and dynamic pricing

policies in revenue management. Omega, 41(3), 586-

597.

Transchel, S., & Minner, S`, 196(3), 940-948.

APPENDIX

Research Questionnaire

Kindly note that the data or information collected

from this survey will be used solely for academic

purposes and will not be shared with any other third

party, whatsoever. The research collects information

on dynamic pricing and gamification.

(A). Consumers

1. Kindly, indicate your age group by ticking against

an option

1. 18-24

2. 25-35

3. 36-48

4. 49+

2. Dynamic pricing is the practice of varying the price

for a product or service to reflect changing market

conditions, in particular the charging of a higher price

at a time of greater demand. Examples include hotels

and airlines charging high during peak seasons. How

do you feel about it?

1. Just okay

2. I don’t like it

3. Neutral

3. Have you recently purchased an item online?

1. Yes

2. No

4. If you answered yes to question 2 above, was the

item bought during peak hours, offers, and

promotions

1. Yes

2. No

5. How often do you shop when offers are provided

such as reduced prices and promotions?

1. Often

2. Not often

3. I shop regularly despite offers

6. If you shopped during an offer, how long did it take

for delivery?

1. The item was delivered on time

2. There were delays in delivery

7. How often, if any, do you experience delays for

deliveries of item (s) purchased during offers?

1. Very often

2. Often

3. Never experienced delay for promotional items

8. Which is your preferred payment method for items

purchased online?

1. Pay on deliveries (cash)

2. Card and online payments

3. Any, applicable

9. How often do you return products purchased on

offers for defects, and if you do, please provide

reasons?

1. Often

2. I don’t

Part B: Retailers

Kindly note that the data or information collected

from this survey will be used solely for academic

purposes and will not be shared with any other third

party, whatsoever. Dynamic pricing, the basis of this

study, is the practice of varying the price for a product

or service to reflect changing market conditions, in

particular the charging of a higher price at a time of

greater demand.

1. Is your capacity relatively fixed?

1. Yes 2. No

2. Is your demand predictable at all?

1. Yes 2. No

3. Is your inventory perishable? (For example, a seat

on an airline or at a live concert)

1. Yes 2. No

4. Are your fixed or sunk costs relatively significant

compared to your variable costs?

1. Yes 2. No

5. Does demand vary by time? (For example, is there

more demand on weekends?)

1. Yes 2. No

6. Do you experience delay in delivery during offers

and promotions?

1. Yes 2. No

7. What are some of the causes of delays you

experience when you use dynamic pricing?

8. Do you feel prepared to handle extra deliveries and

inquiries during with dynamic pricing?

1. Yes

2. No

3. Somewhat

9. Do you experience logistical challenges, including

high costs, for dynamic pricing or offers?

iMLTrans 2020 - Special Session on Intelligent Mobility, Logistics and Transport

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