User Requirements of Audio and Video Products with

Pan-Knowledge Based on Kano Model

Ye Wang and Fei Wang

School of Management, Guangzhou Xinhua University, Tianhe District, Guangzhou, China

Keywords: Pan-Knowledge, Knowledge Payment, Product Research, User Requirements Analysis, The Kano Model.

Abstract: With the in-depth development of Internet information technology, the knowledge payment market has been

booming since 2016 in China. The pan-knowledge audio and video products with a wider range than

traditional online education products are popular among users. Pan-knowledge learning has also become a

new learning mode with the rise of the Internet. Based on the pan-knowledge audio and video products as the

research object, by using the Kano model to analyze the user requirements of this kind of products, we find

the audio and video products of pan-knowledge with the following features can be more competitive: unusual

theme, summary of the products, providing graphic pages and annotations. The products with editing

professionally improve let the user satisfaction.

1 INTRODUCTION

According to the "China Sharing Economy

Development Report (2022)", the market transaction

scale of China's sharing economy reached 3 trillion

yuan in 2021. The sharing economy in the knowledge

and skill sector developed rapidly, with the

transaction scale growing by 13.2% year on year.

With the slowdown in the growth of the scale of

Internet users, high-quality content resources such as

knowledge and skills have become a new focus of

commercial competition, as it can stimulate the

degree of activity and conversion rate of existing

users. Internet digital content in the field of

knowledge and skills is called pan-knowledge

content, which is different from traditional online

education because it has a broader scope. In addition

to knowledge products such as online education, it

also includes knowledge sharing products to improve

personal literacy, such as career experience, life

skills, philosophy and literature. Pan-knowledge

learning has also become a new learning model with

the rise of online knowledge sharing. Pan-knowledge

audio and video products have become an important

part of digital content products. Audio and video

platform enterprises hope to have high-quality

original content products. Therefore, it is of practical

significance to study the user requirements of pan-

knowledge audio and video products.

2 LITERATURE REVIEW

2.1 Pan-Knowledge

Pan-knowledge learning is usually mediated by

platforms such as TikTok, Iget and Himalaya. Users

select the audio or video products that they are

interested in on these platforms. Knowledge bloggers

produce pan-knowledge-themed audio and video

content on Internet platforms, and gain attention or

profits by sharing and disseminating these digital

content products. The high-quality original digital

content attracts users who have knowledge needs to

make payment for knowledge, which is called

knowledge payment. After comparing and analyzing

the three existing business models of knowledge

payment, Jinzhuo Ma put forward that richer content

services and cost-effective pan-knowledge payment

will be the new trend (Ma 2018).

2.2 Kano Model

Under the influence of Herzberg's two-factor theory,

Professor Noriaki Kano, a Japanese scholar, proposed

the Kano model in the 1980s, which introduced the

criteria of satisfaction and dissatisfaction into the

field of product quality management for the first time.

It is an analytical method to distinguish and rank user

requirements. As shown in Table 1, the model divides

Wang, Y. and Wang, F.

User Requirements of Audio and Video Products with Pan-Knowledge Based on Kano Model.

DOI: 10.5220/0011934800003613

In Proceedings of the 2nd International Conference on New Media Development and Modernized Education (NMDME 2022), pages 603-608

ISBN: 978-989-758-630-9

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

603

the elements of product or service into five features,

namely, the essential feature M, the desired feature O,

the attractive feature A, the indifference feature I and

the reverse feature R. For different features, the

research aims to improve their satisfaction, so as to

provide reference and suggestions for product

research.

Table 1: Product features of Kano model

Features Code Meanin

Attractive feature A

If such features are not provided, user satisfaction will not be reduced, but when

such features are provided, satisfaction will be greatly improved, sometimes as a

guarantee that the product is competitive.

Expected feature O

User satisfaction increases when such features are provided and decreases when

the

are not.

Essential feature M

When such functions are provided, user satisfaction will not be significantly

improved, but when such functions are not provided, the satisfaction will be greatly

reduced, which is a basic requirement that must be guaranteed.

Indifference

Feature

That is, there is no significant change in user satisfaction with or without such

functionality. Under limited conditions, such functionality may not be provided as

a priority.

Reverse Feature R

That is, users do not have this function, if provided, it will lead to a decline in

satisfaction.

In general, the priority of product features is:

required feature M> desired feature O> attractive

feature A> indifference feature I, and reverse feature

R is not necessary to develop.

The Kano model measures different features

through the scales in the structured questionnaire. In

the scale, each feature is asked by positive and

negative questions, and the options are set according

to whether it is satisfied or not. The respondents have

five options to choose, specifically: dislike, tolerable,

whatever, necessary and like. Each option was given

a corresponding score from 1 to 5 to explore the

responses of respondents with and without the five

features (Table 2).

Table 2: Schematic design of Kano model questionnaire

Negative question

Like (5) necessary (4) Whatever (3) Tolerable (2) Dislike (1)

Positive

question

(5)

Q A A A O

necessary (4) R I I I M

Whatever (3) R I I I M

Tolerable (2) R I I I M

Dislike (1) R R R R Q

A stand for attractive feature; O for desired

characteristic; M for required feature; I for

indifference feature; R is for the reverse

characteristic; Q is for question results, which means

respondents do not understand or answered

incorrectly.

The Kano model tells us: Firstly, not all of product

features are related to user satisfaction, and some

features are not. Secondly, the required features

cannot improve user satisfaction, which is just the

basic needs of users. If the basic needs are not met,

the satisfaction will be reduced. Thirdly, the

improvement of desired features and attractive

features can effectively improve user satisfaction,

which is also a key attribute to optimize the user

experience of a product.

3 RESEARCH DESIGN

3.1 KANO Questionnaire Design

In the questionnaire, the features of universal

knowledge audio and video products were used as

variable indexes, and the positive and negative

aspects of each feature were questioned (Table 3).The

actual questionnaire consists of two parts: one is the

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basic information of the respondents, including four

basic questions: gender, age, education background

and occupation; the second part is the core part of

Kano questionnaire, which contains 4 screening

questions and 26 positive/negative questions.

Table 3: Examples of forward and reverse questions

How would you feel if a pan-knowledge audio and

video

roduct had the followin

features?

Positive question

Providing references

Background music

Answer options (points)

Like (5 points)

Necessary (4 points)

Whatever (3 points)

Tolerable (2 points)

Dislike (1 point)

Negative question

No references are

provided

No background music

3.2 Kano Scale Design

Referring to the research of many scholars, this paper

proposes four first-level indicators of content feature,

presentation feature, interaction feature and service

feature, and 13 second-level indicators (Table 4).

Table 4：Variable indicators of the characteristics

First Level

indicators

Second level

indicators

Index

sign

Index

source

content

unusual theme

summary

references

graphic pages

5 minutes or

less

Qian-min

Wang

2018

Dai-li

Wang

2021

presentation

background

music

visual effects

editing

professionally

Duan

2021

interaction

annotation

tool

notes

interactive

discuss

Liu 2019

service

ratings

Bai el.

2019

4 DATA ANALYSIS

4.1 Descriptive Statistical Analysis

In order to ensure the accuracy and objectivity of the

data, we distributed questionnaires to people who

used and had access to the knowledge sharing

platform, such as students and employees, and finally

171 valid questionnaires were collected. As shown in

Table 5, data such as age, occupation and educational

background were consistent with the user portrait of

the knowledge share platform.

Table 5：Demographic characteristics of respondents

Variable Cat e

Percenta

gender

male 38.60%

female 61.40%

educational

background

high school and

elow

1.17%

college degree 1.75%

achelor degree 89.47%

master's degree or

above

7.60%

age

18 ~ 24 56.73%

25 to 30 years ol

34.50%

31 ~ 40

ears ol

4.09%

41 to 50

ears ol

4.09%

e 51 and olde

0.58%

profession

student 27.49%

government civil

servants/institute

staff

4.68%

enter

rise em

ees 48.53%

professional (such as

doctors/teachers,

etc.

)

7.02%

freelance

12.28%

4.2 Data Reliability and Validity

Analysis

The reliability and internal consistency of the

questionnaire can be judged only after reliability and

validity analysis of the questionnaire data. The

reliability index is generally tested by Cronbach

Alpha coefficient. When the Cronbach Alpha

coefficient is greater than 0.6, the reliability of the

questionnaire can be judged to be reliable. The KMO

value is generally used as the validity index, and a

KMO value greater than 0.7 indicates that the validity

of the questionnaire is qualified. The online data

analysis platform SPSSPRO is used to analyze the

reliability and validity of the data. As shown in Table

6, Table 7 and Table 8, the Cronbach Alpha

coefficient of positive questions is 0.799, the

Cronbach Alpha coefficient of negative questions is

0.877, and the overall Cronbach Alpha coefficient of

the questionnaire is 0.784, indicating that the internal

consistency of the questionnaire is good. The data are

reliable. As shown in Table 9, Table 10, and Table 11,

User Requirements of Audio and Video Products with Pan-Knowledge Based on Kano Model

605

the KMO value of the positive question is 0.756, the

KMO value of the negative question is 0.874, and the

overall KMO value of the questionnaire is 0.753. The

Bartlett spherical test reaches the significant level (P

<0.001), which meets the requirements of statistical

validity test. This indicates that the reliability and

validity of the Kano questionnaire are good, and the

data can be further analyzed.

Table 6: Reliability analysis of positive questions

Cronbach's

alpha

coefficient

Standardized

Cronbach's α

coefficients

Item Samples

0.799 0.803 13 171

Table 7: Reliability analysis of negative questions

Cronbach's

alpha

coefficient

Standardized

Cronbach's α

coefficients

Item Samples

0.877 0.880 13 171

Table 8: Reliability analysis of the questionnaire

Cronbach's

alpha

coefficient

Standardized

Cronbach's α

coefficients

Item Samples

0.784 0.781 26 171

Table 9: Validity tests for positive questions

Table 10: Validity tests for negative questions

KMO test and Bartlett's test

KMO value 0.874

Bartlett's test for

sphericity

The approximate

chi-s

uare

977.824

df 78.000

0.000 * * *

Table 11: Overall validity test of the questionnaire

KMO test and Bartlett test

KMO value 0.753

Bartlett's test for

sphericity

The approximate

chi-square

1944.270

df 325.000

0.000 * * *

4.3 Kano Model Analysis

According to the analysis of Kano tool provided by

SPSSPRO (Table 12), 10 of the 13 variable indicators

in the questionnaire belong to the indifference

characteristic, two indicators belong to the expected

characteristic, and one indicator belongs to the

attractive characteristic.

Table 12：KANO evaluation Table

tor

Characteristics

A O M I R Q

33.9

18%

2.92

1.75

25.7

31%

4.67

30.9

94%

23.3

92%

15.7

89%

16.3

74%

38.5

96%

1.17

4.67

29.2

21.0

53%

9.94

35.0

88%

0.58

4.09

26.9

01%

30.4

09%

11.1

11%

28.0

0.0

3.50

28.6

55%

10.5

26%

5.84

45.6

14%

5.26

4.09

26.9

01%

7.01

2.33

52.0

47%

5.84

21.6

37%

14.0

35%

8.18

49.1

23%

4.09

2.92

19.2

98%

31.5

79%

20.4

68%

25.7

31%

0.58

2.33

30.9

94%

15.7

89%

5.84

42.1

05%

1.17

4.09

35.6

73%

17.5

44%

3.50

40.3

51%

0.58

2.33

24.5

61%

12.2

81%

2.92

52.6

32%

5.26

2.33

9.94

4.09

5.26

70.7

5.84

4.09

26.9

01%

14.6

9.94

45.0

29%

0.58

2.92

4.4 Better - Worse Coefficient

Satisfaction coefficient (Better coefficient)

=(A+O)/(A+O+M+I) 

Dissatisfaction coefficient (Worse coefficient)

= -1*(O+M)/(A+O+M+I) 

The Better-worse values of each indicator are

calculated by equations  and  as shown in Table

13. The value of the better coefficient is usually

positive. The larger the positive value or the closer it

is to 1, the stronger the effect of improving user

satisfaction will be, and the faster the satisfaction will

KMO test and Bartlett's test

KMO value 0.756

Bartlett's test for

sphericity

The approximate

chi-square

597.313

df 78.000

0.000 * * *

Note: ***re

resents the 1% level of si

nificance

NMDME 2022 - The International Conference on New Media Development and Modernized Education

606

rise. The worse coefficient is usually negative, with a

smaller negative value or closer to -1 indicating the

greatest impact on user dissatisfaction. The stronger

the effect of decreasing satisfaction, the faster the

decline.

Table 13: Better-Worse coefficient

indicators category Better

coefficient

Worse

coefficient

A1 A 0.5727 0.0727

A2 I 0.5276 0.3252

A3 I 0.4162 0.3416

A4 O 0.5939 0.4303

A5 I 0.4323 0.1807

B1 I 0.3841 0.1060

B2 I 0.3837 0.2390

B3 O 0.5241 0.5361

C1 I 0.4938 0.2284

C2 I 0.5482 0.2169

C3 I 0.3987 0.1646

D1 I 0.1558 0.1039

D2 I 0.4303 0.2545

total

average

0.4509 0.2462

Build a scatter plot corresponding to all the

feature coefficients. Using the absolute value of the

Worse coefficient as the abscissae and the better

coefficient as the ordinate, the scatter plot is divided

into four quadrants, expecting the feature to fall in the

first quadrant (better>0.5, worse>0.5), Attractive

features fall in the second quadrant (better>0.5,

worse<0.5), indifference features fall in the third

quadrant (better<0.5, worse<0.5), and necessary

features fall in the fourth quadrant (better<0.5,

worse>0.5), as shown in Figure 1.

Figure 1: Plot of Better-Worse coefficients

5 CONCLUSIONS

According to the analysis of Kano model, the unusual

theme (A1), introduction or summary (A2), graphic

page (A4), note function (C2) belong to the attractive

feature, and editing professionally(B3) belongs to the

desired feature. Providing reference materials (A3),

within 5 minutes (A5), background music (B1),

visual effects (B2), annotation tools (C1), discussion

(C3), rating and evaluation (D1), and sharing (D2)

belong to the indifference features.

The research results have the following

management implications for audio and video

platforms and bloggers:(1) unusual themes can get

more page views and attention. The platforms can

also recommend products with new themes to users

to gain their attention. Products can be attached with

profiles or summaries to help users match their needs,

enhance user engagement, and improve user loyalty.

At the end of the video, the content can be

summarized in the form of graphic pages to improve

user learning experience. (2) Well-made and vivid

pictures can make products more competitive.

Platform enterprises should avoid the listing of pan-

knowledge audio and video products that are poorly

made when reviewing products, because the

unprofessional products will bring users a sense of

dissatisfaction. (3) The platforms can provide notes

User Requirements of Audio and Video Products with Pan-Knowledge Based on Kano Model

607

and other learning tools for pan-knowledge video

products, which can improve user experience and

highlight the characteristics and competitiveness of

the platforms.

ACKNOWLEDGEMENTS

This paper is supported by Dongguan Social Science

Association under grant No. 2022CG82 and

Department of Education of Guangdong Province

under grant No. 2019J054.

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