Low Involvement Product Marketing Using Fine Granular Real

Space Information

Tatsuya Inaba 1

and Yusuke Ito 1

1 Department of Information and Computer Sciences, Kanagawa Institute of Technology, Japan

Abstract. This study proposes novel marketing methods for low involvement products by using fine

granular real space information and evaluates the methods with an experimental digital signage system.

Usually low involvement products are selected as a result of consumers’ internal information search because

consumers do not want to spend cost to collect external information about these products. As a result,

marketing methods for these products are limited. However, our proposal makes it possible to use external

information source such as digital signage effectively. Two methods are proposed in this study: a method to

lower the cost for external information search and a method to enable external information search adaptively

based on consumer’s prior knowledge level, both of which utilize real space fine granular information by

information technology. We develop an experimental digital signage system as an external information

source and identify that people can remember contents of the signage more when they adaptively view the

signage pages than they view pages that change automatically. This result shows that marketing for low

involvement products could become more effective by using real space fine granular information.

Keywords: Low involvement product marketing, Internet of Things, Fine granular real space information

1. Introduction

The progress of information technology (IT) has changed marketing. It becomes common to see

recommended products when you purchase products from online shopping site or just to search products on

the web. Not only shopping online but also shopping in the real space stores, people start selecting products

referring to the prices of the same product at other stores or word of mouth (WoM) information posted on

social networking services. However, this kind of shopping behavior is not applied to all the products but

only products with high involvement. Involvement is a marketing term to describe a state of interest or

motivation in selecting products [1]. If involvement is high, people spend time to collect information by like

collecting catalogs, asking for evaluation of the product to their friends, going to stores to ask details of the

product to shop representatives, and so forth. If the involvement is low, however, people do not spend time

to collect information; they select a product brand based on their prior knowledge, or just follow the opinion

leaders of the product category. Price also affects involvement level of the product [2]. Examples of high

involvement products are car and fashion products, whereas those of low involvement products are detergent

and coffee. From the marketing theory, it is important to advertise product with mass media and make

consumers recognize and recall the product brand in order to sell low involvement product efficiently [3].

In addition to using mass media, companies start using push media such as digital signage and point of

purchase (POP) display with motion picture in real space stores. This push commercial media is effective

because consumer will get information about low involvement products with less cost. However, since the

contents are designed for consumers with certain prior knowledge about the product or the product category,

consumers who have more information about the product get bored to see all the contents, and consumers

who have less information get lost while watching the contents. Effective information delivery with these

push media is not easy.

Corresponding author. Tel.: + 81 46 291 3242.

E-mail address: tinaba@ic.kanagawa-it.ac.jp.

International Proceedings of Management and Economy

IPEDR vol. 84 (2015) © (2015) IACSIT Press, Singapore

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IT, which is originally developed in the cyber space, also gives impact on the real space, since IT also

makes it possible to deal with information in the real space. The concept in which people can deal with

things in the real space through the cyber space using IT is called Internet of Things (IoT). Automated data

capturing (ADC) technologies such as radio frequency identification (RFID), sensor and actuator

technologies are thought to be enabler of IoT. Since ADC makes it possible to capture information in the real

space with less cost, they are used in areas where manual intervention is required such as warehouse and

retail stores.

IT technologies are not only used to capture real space information to manage things. They are also used

to give feedbacks to real space. For example, when a consumer picks up a product in a super market, this

pick can be captured by a system with ADC technology and the system may show the detail product

information to the person who picks the product, which may help her to make a purchase decision [4]. Since

IoT technologies deal with things in fine granular level, there are lots of application areas and marketing is a

promising area of IoT as shown in this example.

In this study, we propose marketing methods for low involvement products using IoT technologies. The

goal of this study is to show a way with which IoT technologies help companies to give information about

low involvement products to their consumers. Moreover, the way is not required a costly process like getting

WoM information through a smart phone but a less costly process such as just watching a product, picking

up a product or hand waiving.

The rest of the study is as follows: the related marketing theories and related studies are explained in the

second section, possible new marketing methods by IoT are proposed in the third section, the proposal is

evaluated in the fourth section, and the fifth section concludes this study.

2. Related studies

2.1. Marketing for low involvement products

It is known that people make a purchase decision in several steps. One study suggests that the steps go

with “Need recognition Information search Evaluation of alternatives Purchase Post purchase

behavior [5].” The information search also goes in two steps; one is internal information search and the other

is external information search. The internal information search is a process in which a consumer evaluates a

brand based on her memory, whereas external information search is a process in which a consumer evaluates

a brand by collecting information from outside sources [5].

It is also known that the involvement level affects a consumer in evaluating the product brand. When

involvement is low, a consumer is likely to take an internal information search only because it is less costly

and she is not so interested in shopping the product in the first place, and when involvement is high, a

consumer is likely to take both internal and external information searches [6]. One theory that tries to explain

this consumer behavior is the elaboration likelihood model (ELM) [7]. In the ELM model, two routes are

proposed; one is a central route and the other is a peripheral route. If a consumer is not so interested in (or

involved in) the purchase, she takes a peripheral route. Even though she is interested in the product, but if

she is not capable of processing the information, she also would take a peripheral route. That is, if she is

interested in and capable of understanding the information, she would take a central route and elaborate the

products that she want to buy.

In this theory, consumers do not tend to look for the information about low involvement products and

may decide a product brand based on her memory that has been formed about the product. Usually the

memory of the less interested products would be formed through passive information exposure since

consumers basically do not want to spend cost to collect information. On this point, push media would play

an important role for the sales of low involvement products.

Delivering messages using mass media like TV commercials could be the most popular push media, but

advertisement such as POP display and digital signage also is push media in real space stores. These media

are useful to provide information, but the problem of these media is that the efficiency of the information

delivery is low when the recipient (consumer) is not ready for the information. Even if she is seeing a shoes

advertisement on a digital signage, if she is not interested in buying shoes, the advertisement is mere a noise

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for her. So a key to success for the push media is to adapt delivering contents according to the consumer

interests in such a way that less effort is required to the consumers. The behavior information of the

consumer could be used for this purpose because consumers behave anyway in front of products and the

technologies can capture the behavior with less cost.

2.2. Application of fine granular real space information

ADC technologies such as RFID, sensor, and image recognition make it easy to process information in

the real space as we do in the cyber space. The concept that we utilize information in both real and cyber

space seamlessly is called IoT, and this IoT affects not only our daily life but also business scenes. IoT

enables companies to capture information of things; what it is, where it is/was and when, and its status. We

call this IoT enabled information as fine granular real space information and management enabled by the

information as fine granularity management in this paper.

Fine granular real space information and management using the information is commonly used in supply

chain management (SCM). In SCM, RFID applications are developed to streamline shipping and receiving

process. Companies start applying RF tags to cases of their products and remove manual checking with an

RFID application system [8]. To improve inventory management process is another popular RFID

application in SCM [9]. To apply tags to individual products, companies can check number of products

without human intervention. Moreover, this real time product counting can help optimize inventory level of

the product.

In addition to the processes to improve management of things, real space fine granular information is

also used in marketing. Applications are proposed to give people incentives to buy a specific product.

Offering discount is often used to remove excess inventory from stores, but by identifying customer profile

using real space fine granular information, companies can offer good prices only to specific customers. The

offering could be based on their loyalty status [10]. There is a beverage vending machine system that uses

real space fine granular information. The vending machine has a camera and recognizes a person who is

about to buy a drink. Then the system infers gender and age of the person and shows the recommended

product to him [11]. With the development of IoT technologies, applications using fine granular real space

information become real.

3. Effects of real space fine granular information on product selection

This section proposes possible application areas of real space information in marketing. Existing

literatures reveal that consumers use both internal information and external information in evaluating the

product. Whether or not a consumer uses both internal and external information searches depends on the

involvement or attitude toward the product. The ELM theory does not specify when consumer’s attitude is

formed, but it is implied that attitude is formed through external information search since the authors

mention that watching advertisement helps form people’s attitude toward a product. To companies that try to

sell non-popular products, it is important to make consumers search information from external sources

otherwise their products are not included in consumers’ alternatives. If their product is a typical high

involvement product, this is not that difficult. But if their product is a low involvement product, it is a touch

task. For companies, to lower the hurdle between internal and external information search is a key to success.

Another important point is people’s capability of information processing since it affects the elaboration

route. Basically if a company is selling an unpopular product, the company needs to make efforts to let

consumers take a central route otherwise its product is never selected. However, taking into account of

information processing capability, this is not enough. Even if a company successfully make a consumer refer

to an external information source in selecting a low involvement product, but if the information is too

difficult to understand or too easy to keep watching, the consumer will quit accessing the information. That

is, to deliver information to meet consumer capability is another key to success to let consumer to take a

central route.

Based on this observation, we propose two marketing methods for low involvement products using fine

granular real space information.

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(i) Lowering a hurdle to move internal search to external search

By lowering a hurdle to move internal information search to external information search, it is expected

that consumers evaluate the product not only with internal information but also external information. As a

result, a new low involvement product may be listed in consumers’ alternatives and possibility that the

product is selected becomes high. The IoT technologies can be used to lower the hurdle. Natural behavior of

a consumer, such as watching a shelf that sells products she is interested in or picking up an product that has

an attractive package, may trigger the information push. An application system with an RFID reader and a

display may be used for this purpose.

(ii) Enabling an adaptive information search

By enabling an adaptive information search, it is expected that a consumer gets more information about a

product even when she is not so interested in selecting the product. As a result, the possibility that she selects

the new low involvement product becomes high. The IoT technologies can be used to enable this adaptive

information search. Natural behavior of a consumer, such as waving a hand while watching a display to

change contents or nodding her head while watching a digital signage, may trigger the information change.

An application system with a camera and a display may be used for this purpose.

These are the two possible marketing methods using fine granular real space information. Evaluation of

the second method is explained in the next section.

4. Experiment

4.1. Hypotheses and experiment design

To show the effectiveness of our proposal, we develop hypotheses regarding proposed marketing

methods and validate the hypotheses with an experiment. This section introduces hypotheses and experiment

design. As explained in the previous section, we assume that consumer will get more information about

product through push media if he can control an information delivery speed. Based on this assumption, we

develop the first hypothesis.

H1: Consumer can remember information through push media more accurate if he can control speed of the

information delivery.

We also assume that consumer controls delivery speed based on his prior knowledge about the product

so that he can process the information from the push media. Based on this second assumption, we develop

the second hypothesis.

H2: Consumer will spend more time to understand unfamiliar information than familiar information when he

can control speed of the information delivery.

In order to validate these hypotheses, we develop an experimental digital signage system with two modes.

In one mode, research participants can move digital signage pages forward and backward with a hand

motion(control mode), and, in the other mode, research participants only view digital signage pages that

change automatically with a fixed time interval (non-control mode). To validate hypotheses, we conduct an

experiment with two groups (Group A: control group and Group B: experiment group), ask several questions

about the contents of the signage pages, and compare the number of correct answers of each group. The

number of research participants is five for each group, and all of them are undergraduate students of the

authors’ university.

In this experiment, we design two types of questions for each product; one is from conspicuous product

information on the signage page with large and/or high-lighted characters (easy question) and the other is

from less conspicuous information with plain characters (difficult question). It is expected that the difference

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of the correct answers is significant in the difficult questions since time is limited in the non-control mode to

read non high-lighted characters.

Regarding the digital signage contents, we choose advertisement of soft drinks, which is considered to be

a low involvement product. In order to validate H2, we select six product brands, three of which are national

brand soft drinks with TV commercials and the rest are local brands without mass media commercials. All

the participants are requested to view advertisements of all six product brands and to answer 12 questions (6

products x 2 types of questions). In the non-control mode, each participant has 15 seconds to watch signage

pages for each product (3 pages per product, 5 seconds per page) and 90 seconds (15 seconds x 6 products)

in total. However, in the control mode, research participants are given the same total time (90 seconds) and

they are free to use their time to view each page. If the H2 is valid, research participants are expected to use

more time to view unfamiliar product advertisement than familiar ones. Since total duration of experiment in

each mode is the same and research participants are instructed to answer questions about the signage pages,

it is expected that participants would use their given time effectively. Table 1 shows conditions of each

group.

Table 1: Two groups for experiment

Group Page change mode No. of

participants

No. of

products

No. of pages

per product

Duration

per page

Total

duration

A Non-control mode 5 6 3 pages 5 sec. 90 sec.

B Control mode 5 6 3 pages N/A 90 sec.

4.2. Application system for experiment

Fig. 1 shows the abstract of the experimental application system. The system uses Kinect, which is a

device developed by Microsoft [12]. Kinect originally is an interface device for Microsoft game machine, but

Microsoft released a software development tool kit and now it is used by PCs as well. The interface device

has a color sensor (camera), an infrared depth sensor, four-microphone array and a tilt motor. Kinect system

recognizes skeleton joints in twenty points like head, right hand, left hand, right foot, left foot, and so on.

Using this function, Kinect system can recognize person’s behavior like raising a hand and moving right

hand from left to right. With this function, Kinect is used to evaluate natural user interface [13].

Three main units are implemented in this system; control unit, display unit, and analysis unit. The control

unit controls Kinect and processes data from the device. The display unit receives control signals from the

control unit and controls digital signage pages. In this system we customize Microsoft PowerPoint for this

display unit. The analysis unit collects data from the control unit and display how each page is viewed. We

chose hand motions to change digital signature pages. To move a page forward in control mode, the viewer

moves his right hand from left to right, and to move backward, he needs to move his left hand up and down.

We chose these actions considering the accuracy of the motion recognition and naturalness of the behavior.

Kinect

Main display

Control unit

Sub display

Display unit

Analyze unit

Control PC

Fig. 1: Abstract of application system for experiment

The control unit also records number of times the viewer watches each page and the total view duration.

The data is visualized by the analysis unit with a bar chart and a line graph. The bar chart shows times of

each page is viewed and the line graph shows each page’s total view duration. Fig.2 shows the console

window which appears on the sub-display. The figure on the left is the visualized image which is captured by

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Kinect and processed to reduce recognition error, the table in the middle shows the order of the pages and

duration of the viewing and the graph on the right shows the analysis result. The result also downloaded as a

CSV file.

Fig. 2: Console window

4.3. Experiment result

Table 2 and Table 3 show the result of the experiment. Firstly, the degree of difficulty of the questions is

checked. Correct answer rates of easy questions are higher than that of difficult questions in almost all the

participants. Therefore, the degree of difficulty is confirmed that the level is set as designed. Next, correct

answer rates between Group A and Group B are compared. The average correct rates of Group B (53%) are

higher than that of Group A’s (38%). From this result, we assume that participants of Group B can use the

viewing time efficiently and remember more information about the products. Since the number of

participants is limited and the difference is not tested, we cannot conclude that H1 is valid. But we still see

that controlling information delivery would give a positive impact on information delivery.

Table 2: Result of Group A

Participant A1 A2 A3 A4 A5 Total

Easy questions 50% 33% 67% 50% 33% 46%

Difficult questions 33% 33% 33% 0% 67% 30%

All questions 42% 33% 50% 25% 42% 38%

Table 3: Result of Group B

Participant B1 B2 B3 B4 B5 Total

Easy questions 83% 67% 50% 50% 67% 63%

Difficult questions 83% 33% 33% 33% 33% 43%

All questions 83% 50% 42% 42% 50% 53%

Secondly, the viewing time of each page in Group B is analyzed. Table 4 shows the result.

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Table 4: Viewing time analysis of Group B

Question type Correct rate Answer Ave.[sec] Std. [sec]

Products with TV

advertisement

Easy 60% Correct 13.11 6.45

Wrong 13.75 4.13

Difficult 60% Correct 13.36 7.02

Wrong 13.39 2.39

Products without

TV advertisement

Easy 65% Correct 16.36 6.21

Wrong 15.24 3.49

Difficult 35% Correct 16.34 4.04

Wrong 15.76 6.06

Average viewing time of the pages of products with TV commercial is about 13 seconds, whereas

average viewing time of products without TV commercial is about 16 seconds. Standard deviation of the

viewing time ranges from 3 to 7 seconds. From this result, it is inferred that participants use their given time

for viewing based on their prior knowledge and/or interests. The same as the previous analysis, we could not

conclude that H2 is valid, but we still see the effectiveness of the push media control from this result.

In addition to the questions to test hypotheses, we also include questions of user interface of the digital

signage system in the questionnaire. Since the number of participants of Group B is only five, the answer is

too few to judge whether the user interface is good or bad. But some favorable opinions are expressed, such

as controlling digital signage is convenient and this kind of digital signage should become popular. Fig 3 is a

photo of our experiment.

Fig. 3: Experiment

5. Discussion

The progress of IT impacts our life in many aspects. One area, the impact of the IT on marketing, is

studied in this paper. This study specifically deals with marketing of low involvement products. Taking into

account of the low involvement product characteristics in the ELM theory, we propose novel marketing

methods using fine granular real space information. This fine granular real space information becomes

available with the development of IoT technology, such as RFID and sensor/actuator technologies.

The ELM theory proposes that consumer takes two routes in selecting product brands: a central route and

a peripheral route. Consumers elaborate product information in the central route, whereas they decide

product based on their prior knowledge or easy to access information such as WoM in the peripheral route.

Low involvement products are considered as peripheral route products. Therefore, it is considered that

advertisements to which consumers access easily, such as TV commercials and WoM using SNS, are

effective to sell these low involvement products. This study, however, proposes an interactive marketing

method that requires consumers less cost in collecting information about the products that catch their interest

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just a little. To lower the search cost, we propose the use of fine granular real space information, which

becomes available with the development of the IoT technologies.

We suggest two possible marketing methods. One is the marketing in which information of the low

involvement products can be delivered to consumers utilizing their natural behavior during shopping such as

picking up a product or staring at a shelf. The other the marketing in which information of the product can be

delivered to consumers adaptively using the same natural behavior.

We also evaluate the effect of the second method using an experimental digital signage application

system. From this experiment, we see that consumers can remember more information about the information

of products when they are free to control the signage than they just view pages changing automatically. The

experiment also shows that time to view each page of the signage differs by research participants. This result

indicates that consumers use their time to collect information based on their prior knowledge and interests

and that low cost interactive media system enabled by real space fine granular information might help this

consumer instinct.

There are several limitations in this study. First limitation is the evaluation. In this study, we conduct a

laboratory experiment with 10 participants. The number is enough to understand the possible impact of the

real space fine granular information, but a larger size experiment will be required to show the effect with

confident. Another limitation is also evaluation of the first proposal. This time, we only conduct an

experiment to the second proposal, but evaluation for the first proposal is also necessary.

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