Maturity Assessment for Implementing Digital Technologies

in SMEs

Li Pan

Institute of Economics and Management, National Research Tomsk State University, 36 Lenin Ave, Tomsk, Russia

Keywords: Digital Technology, Maturity Assessment, Reference Framework, Labor Productivity, Economic

Profitability.

Abstract: More and more companies have been tapping into digital technologies to improve their labor productivity and

economic profitability. To assess the maturity level of implementing digital technologies is paramount for

SMEs to embark on the digital transformation journey. There is a lack of maturity assessment reference on

implementing digital technologies in SMEs. Through defining a set of digital technology indices, this paper

proposes a maturity assessment reference framework for SMEs to implement digital technologies. By using

the proposed reference framework, the maturity assessment is conducted for two case study companies. The

maturity assessment helps the two companies to clearly understand their current status of digital technology

implementation and confidently know how to move forward on the digital transformation journey so that their

labor productivity and economic profitability can be further improved and their businesses can be more

competitive in the digital economy.

1 INTRODUCTION

Digital technologies have become imperative for

working, learning, entertaining, socializing, shopping

and accessing everything from healthcare to culture

and life style. Specially, in the COVID-19 pandemic,

the digital technologies have been radically changing

and shaping the world and economy, which are the

disruptive innovation since ever. In 2020, affected by

the epidemic, the major economies in the world

showed negative growth. Against this trend, the

digital economy with digital technologies as an

important driving force, increased by 3% year on

year, becoming a key force to effectively hedge the

epidemic and boost the global economy (CAICT,

2021).

The utilization of digital technologies is an

effective way to improve labor productivity and

economic profitability. The digital technologies such

Mobile, Cloud Computing, Internet of Things (IoT),

Artificial Intelligence (AI) have facilitated sharing

economy, crowdsourcing, and network collaboration

to reconstruct the labor and production relationships.

The new transaction and consumption models

redefine the business models, organizational models

and working modes, which have a positive impact on

productivity and profitability of enterprises.

According to the research of American consulting

institutions, the labor productivity of the enterprises

with high information utilization rate is

about 60~90%

higher than that of the enterprises with low

information utilization rate. In the service industry,

the contribution rate of capital utilization of digital

economy to the industry is 2~3 times higher than that

of traditional capital utilization (The State

Information Center, 2018).

Digital technologies also have a deep impact on

the number and quality of employment and how work

is organized. From 2006 to 2016, of the 380 million

additional jobs in the OECD region, about 40% came

from higher digital-intensive industries. From 2011 to

2017, for every 10 additional jobs created in Europe,

four were ICT work-intensive jobs (OECD, 2019). In

the digital economy, workers increasingly need to

adapt their skills to new requirements, especially for

generic skills such as communication, teamwork,

problem solving, and creativity. Digital technologies

also offer new opportunities for how they work. The

increase in flexible work and employee discretion in

the work model is the key to the digital development

of work style (Greenan, Napolitano, 2021).

In the post-epidemic era, embracing digital

technologies has been becoming essential for many

Pan, L.

Maturity Assessment for Implementing Digital Technologies in SMEs.

DOI: 10.5220/0011157300003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 61-66

ISBN: 978-989-758-593-7

businesses. Among them, SMEs play a central role in

this shift, representing about 90% of the world and

providing more than 50% of jobs. In OECD

countries,

SMEs account for 75 percent of jobs in the sectors

most severely affected by the epidemic, and micro-

enterprises with fewer than 10 employees account for

about 30 percent of jobs in those industries. In

emerging economies, formal SMEs account for 40%

of GDP, creating 7 of the 10 jobs (OECD, 2021).

Therefore, SMEs using digital technologies to

innovate the production modes and management

concepts, and promote sustainable development are

the key to unleashing the global economic potential.

In fact, there is few research on digital technologies

for SMEs, and the digitalization of many companies

is still in the initial stage, and most companies know

little about digital technologies and have no clue

about how to implement digital technologies to

survive. It motivates the study of this paper to

propose the maturity assessment reference

framework to assist companies to embark on the

digitalization journey.

The paper is organized as follows. Section II will

conduct the literature review. Section III will propose

the maturity assessment reference framework.

Section IV will conduct the case studies for two

companies by applying the proposed maturity

assessment reference framework. Section V will

conclude the paper with remarks.

2 LITERATURE REVIEW

This section reviews the literature on digital

technologies and digital transformation maturity

assessment models, and identifies the research gaps

in the literature, which motivate the research works in

this paper.

The essence of digital transformation is that

organizations use the corresponding digital

technologies to respond to changes, which is the

source of disruption. In this process, digital

technologies play a central role in the creation and

reinforcement of disruption that occurs in the society

and industry. Digital technologies create the drive

that drives organizations to implement responses to

gain or maintain their competitive advantages. Vial

classified 282 digital transformation documents and

found that most digital technologies are related to

social, analysis, mobile, Internet of Things, cloud

(Vial, 2019), consistent with Zhu et al. on 865 digital

transformation documents from 2000 to 2020 and

moreover, digital platforms are an important category

(Zhu, Ge, Wang, 2021). The business competition

increasingly relies on the ability to use digital

technologies. In defining digital technologies, Vial

observed that combinations of technologies are

particularly relevant in the context of digital

transformation. For example, the ability to implement

algorithmic decisions may depend on the ability of

companies to analyze big data collected by

individuals using social media through their mobile

phones. In the study of Zhu et al. on the thriving stage

from 2018 to 2020 of the digital transformation

literature, the research on digital technologies

compared to digital business strategy, digital

transformation of manufacturing (Industry 4.0),

digital enterprise architecture and other fields is the

least (Vial, 2019).

The findings by DeStefano et al. suggested that

younger companies using cloud computing are more

likely to increase jobs and sales, and that cloud

computing and fiber-optic infrastructure enable

younger companies to scale up without increasing

their geographic footprint. In addition, cloud

computing improves employee mobility between

institutions within the enterprise. Cloud technology

reduces fixed IT costs for enterprises, and can

technically replace their own IT devices, facilitating

start-ups to grow (DeStefano, Kneller & Timmis

2020). From the perspective of the strategic choice of

Russian enterprises, Lezina et al. diagnosed the

digital maturity preparation model of system

management, company structure, business process,

data management and personnel preparation through

the form of questionnaires. The model created by the

authors is universal and targeted at all kinds of

enterprises (Lezina, et al, 2019). Yezhebay et al. used

the SWOT analysis method to define the

characteristics of advantages and disadvantages for

SMEs in Kazakhstan, and developed the digital

maturity model for SMEs in Kazakhstan. The model

consists of six dimensions that are strategy,

leadership, personnel, product, operations and

technology, and the corresponding 15 sub-

dimensions (Yezhebay, et al, 2021).

In the literature, the maturity models for SME

digitalization are mostly based on the Industry 4.0

model. The most maturity models did not consider the

specific requirements and challenges of SMEs and

cannot reflect the actual status of digital technologies

in SMEs (Mittal, et al, 2018). As such, SMEs are

unable to use those maturity models to assess their

digital technology level. Thus, it is significant to

create an appropriate maturity assessment framework

for SMEs to assess their maturity level for

implementing digital technologies and make a right

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

decision while embarking on the digitalization

journey.

In this paper, through defining a set of digital

technology indices, a maturity assessment reference

framework is proposed for SMEs to assess their

maturity level for implementing digital technologies.

By applying maturity assessment reference

framework, SMEs are able to understand their current

status of digital technology implementation and clear

about how to move forward on the digital

transformation journey so that their labor

productivity and economic profitability can be further

improved and their businesses can be more

competitive in the digital economy.

3 MATURITY ASSESSEMNT

REFERENCE FRAMEWORK

The research indicates that mobile technology, social

media, cloud computing and data security are the first

digital technologies that SMEs consider to adopt

(DELL, 2021). During the outbreak of COVID-19 in

China at the beginning of 2020, the Internet of Things

(IoT) and Artificial Intelligent (AI) technologies are

more conducive to production recovery. The research

data shows that SMEs using high-level digital

technologies can mobilize internal and external

resources faster to promote production recovery and

digital response (

Renmin University of China, 2020).

Mobile Technology is the technology that goes

where the user goes. It consists of portable two-way

communications devices, computing devices and the

networking technology that connects them. Mobile

Technology is typified by internet-enabled devices

like smartphones, tablets and watches. The adoption

of Mobile Technology by SMEs can improve the

efficiency of mobile and collaborative working.

Leveraging various forms of social media can

improve product promotion, outreach and

conversion, and facilitate SME business model

optimization within the constraints of a company's

size and financial status. Through mobile applications

or by providing a digital workplace, employees can

perform decentralized online tasks (crowd work)

(Wood, et al, 2019).

Cloud Computing Technology gives users access

to storage, files, software, and servers through their

internet-connected devices: computers, smartphones,

tablets, and wearables. Cloud computing providers

store and process data in a location that’s separate

from end users. Cloud Computing is the ability to

store and access data and programs over the internet

instead of on a hard drive. This means businesses of

any size can harness powerful software and IT

infrastructure to become bigger, leaner, and more

agile, as well as compete with much larger

companies. Regardless of the size of the business,

cloud computing is a major technological priority.

For SMEs in particular, it is the basis for future

changes in the form of work and the reshaping of

business processes (CISCO, 2020). The emergence of

the epidemic has accelerated the development of

cloud services such as telecommuting and

videoconferencing. Cloud technologies allow

workers to connect from everywhere at any time,

shared documents, and exchange information about

their work in an easy way. Employees become more

and better informed that promote workers’ discretion,

autonomy and empowerment, usually entail lower

direct supervision and control over employees. In

return, workers are required to interact, communicate,

and cooperate more (Wood, et al, 2019).

Internet of Things (IoT) is the network of physical

objects “things” that are embedded with sensors,

software, and other technologies for the purpose of

connecting and exchanging data with other devices

and systems over the internet. The potential economic

value that the Internet of Things can release is huge.

The greatest potential for value creation in the factory

setting will be optimizing operations in

manufacturing—making the various day-to-day

management of assets and people more efficient.

Internet of things facilitates several advantages in

day-to-day life in the business sector. For example, as

devices of IoT interact and communicate with each

other and do a lot of tasks, then they minimize human

effort (Behura, et al, 2022).

Artificial Intelligence (AI) is a wide-ranging

branch of computer science concerned with building

smart machines capable of performing tasks that

typically require human intelligence. AI is used for

enterprises to drive modern decisions. Advanced AI

technologies such as machine learning, deep learning,

computer vision, and natural language processing

while basic technologies include data management,

digital assistants, and robotic process automation

(ThoughtLab, 2020).

With four digital technologies Mobile, Cloud

Computing, IoT and AI, the maturity assessment

reference framework is proposed in Table 1. For each

technology, there are three levels and six grades to

describe its maturity. For each grade at every level, a

set of reference indices are defined in each grade to

assess the technology maturity and determine the

corresponding maturity point. For example, there are

Maturity Assessment for Implementing Digital Technologies in SMEs

four reference indices defined for Mobile Technology

in Grade 1, and two reference indices are defined in

Grade 2. Grade 1 is the lowest grade where the

company is in a very preliminary state for

implementing the respective technology. Grade 6 is

the highest grade where the company is fully mature

in terms of implementing the respective technology.

The four digital technologies are presented in the

order of advance in the reference framework. Mobile

is the primary digital technology and AI is the

advanced digital technology.

Table 1. Reference framework for implementing digital technology maturity assessments in SMEs

4 CASE STUDIES

In this section, we use the maturity assessment

reference framework proposed in Section III to assess

the maturity for implementing the digital

technologies in two case study companies, Company

A and Company B. Company A is a manufacturing

company which produces dried fruits, bread, sea-

buckthorn juice and other foods with 30 employees

including 10 administrative personnel and 20

workshop workers. Since the pandemic in 2020, the

company has used various social media such as

WeChat, Mini Programs, Douyin to sell its products.

The sales of sea buckthorn juice is now number one

in China on the Douyin platform. Company B is a

new media company whose main business is video

shooting, promoting and branding through new

media. It also helps other companies to advertise their

products in the new media space and develop their

live streaming businesses. The company currently has

6 people.

According to the maturity assessment reference

framework proposed in Section III, by interviewing

the technical heads of the two companies, we

obtained the points in Table II, where 1 represents all

the indices defined in the corresponding grade are

met, 2/3 represents two of three indices are met, and

0 represents none of indices are met. The maturity

score S is calculated by (1).

𝑆=

∑

𝑤



𝑝







(1)

where 𝑤





is the weight assigned to Grade i, and

𝑝



is the point obtained in the corresponding grade

for the respective technology, 𝑖 = 1, 2, … , 6.

Mobile Cloud Computing IoT AI

Grade 1

•

Internet/wireless connection

•

Paperless documents

•

Networked computers/machines

•

Product Platform (Social media)

•

Data storage in the cloud

•

Files shared in the cloud

•

Single node/device

• Low cost and low complexity

•

Developing plans for AI

Grade 2

•

Product Platform (Website)

•

Product d igital spe c ifica tions

•

Public c loud

•

Community cloud

• Single node/device

• Low volume

•

Local data

• Building internal support for AI

(working closely with business

teams to identify use cases)

•

Simple rule based decision

Grade 3

•

CRM

•

O rd er management Syste m

•

Pub lic cloud environment

•

Mobile workplace

•

Community cloud

•

Single node/device

•

Big volume of data

• Intensive computng

•

Machine learning

• Computer vision

Grade 4

•

ERP

•

Financial management system

•

Hybrid cloud environment

•

Collaborative office

•

Expanding on the cloud

•

Data cybersecurity

•

Multiple e nd node s/device s

•

Big volume of data

• Intensive computng

• Multiple Lo ca l d ata

•

Local analysis

•

Bring in a richer set of data to

drives higher AI performance

(psychographic, geospatial and

real-time)

•

ML based decision

Grade 5

•

Multi- screen office

•

Product branding and Digital

mark eting

•

Digital business process

•

Hybrid cloud

•

Data cybersecurity

•

IaaS

•

Multiple e nd node s/device s

• Coordinator node/device

• Multiple Infinite sensing netwo rk

• Natural language processing

• Deep learning

• Using AI for parts of business

Grade 6

•

5G connection

•

Secure cloud environment

•

Cloud Optimisation

•

Services Secure cloud

•

SaaS

•

PaaS

•

Multiple indep e ndent e nd

nodes/devices

•

Visualize d applicatio ns

• Widely using AI to generate to

transform business

• Training and hiring people

Level 1

Level 2

Level 3

Digital

Tochnoligies

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

Table 2. Assessment Results.

Based on the maturity scores in Table 2, we are

able to know which digital technology the company

has been implementing at which maturity level. From

Table 2, in Company A, Mobile and Cloud

Computing technologies have been implemented

with maturity levels 0.68 (i.e., 68%) and 0.19 (i.e.,

19%), respectively. In Company B, and Cloud

Computing technologies have been implemented

with maturity levels 0.35 (i.e., 35%) and 0.17 (i.e.,

17%), respectively. From our interviews with these

two companies, Company A indicated that IoT

technology has been considered but not yet

implemented, and AI technology is not very relevant

for its business. Company B does not consider to

implement IoT and AI technologies at this moment.

We noted a lack of understanding of IoT and AI in

both companies, which led to missing strategic

considerations. This may be also due to the current

scale of their businesses. Although these two

companies in our case studies are not involved in IoT

and AI technologies, it’s valid for us to keep these

two technologies in the reference framework. Those

companies with larger sizes or more advanced

development may engage in IoT and AI, where the

reference framework will be applicable.

For SMEs under great pressure to survive, from

the perspective of saving operational costs, we

recommend to share technologies, data, human

resources, marketing channels, infrastructure and

other resources by using a shared platform.

5 CONCLUSIONS

The use of digital technologies has become a critical

turning point for all businesses. SMEs have not fully

embraced digital technologies due to their constraints

on survival, costs and sizes. This paper created the

maturity assessment reference framework for SMEs

to assess the maturity level of digital technology

implementation. By conducting the case studies for

the two companies, the proposed reference

framework is applied to provide an opportunity for

the companies to understand their current states of

digital technology adoption and subsequent

development. In the near future, we shall be

conducting more case studies for companies from

various industries and apply the proposed maturity

assessment reference framework to assess their

maturity of digital technology implementation and

provide them decision support for their embarking on

the digital transformation journey so as to further

improve their labor productivity and economic

profitability and maintain their competitiveness in the

digital economy.

ACKNOWLEDGMENT

The author would like to thank the two case study

companies for their valuable information and

feedback.

REFERENCES

Behura A, Satpathy S, Mohanty S N, et al. (2022) Internet

of Things: Basic Concepts and Decorum of Smart

Services. In: Nandan Mohanty S., Chatterjee J.M.,

Satpathy S. (Eds), Internet of Things and Its

Applications. Springer, Cham, Switzerland. pp. 3-36.

CAICT (2021). White Paper on the Global Digital

Economy.http://www.caict.ac.cn/kxyj/qwfb/bps/20210

8/P020210913403798893557.pdf. Accessed 20 Dec

2021

CISCO (2020). Asia Pacific SME Digital Maturity Report.

https://www.cisco.com/c/dam/global/zh_cn/solutions/

small-business/digitalmaturity/cisco-smb-digital-

maturity-ebook.pdf. Accessed 23 Dec 2021

DELL (2021). White Paper of the Digital Initialization

Index in SMEs.

https://i.dell.com/sites/csdocuments/App-

Merchandizing_Documents/zh/cn/DELLDigitalization

WP%202.0_Lite.pdf?ref=cptl_white-

paper2tiles1rows1_cta_primary_. Accessed 23 Dec

2021

DeStefano T., Kneller R. & Timmis J. (2020). Cloud

computing and firm growth. J. Munich Society for the

Promotion of Economic Research, CESifo Working

Paper No. 8306

Greenan N. & Napolitano S. (2021). Why Do Employees

Participe in Innovation? Skills and Organisational

Design Issues and the Ongoing Technological

Transformation. J. halshs-03270141v

Lezina T., Stoianova O, Ivanova V, et al. (2019).

Assessment the company’s readiness for digital

transformation: Clarifying the issue. In: International

Conference on Digital Economy. Beirut. pp. 3-14.

Mittal S, Khan M A, Romero D, et al. (2018). A critical

review of smart manufacturing & Industry 4.0 maturity

models: Implications for small and medium-sized

Maturity Assessment for Implementing Digital Technologies in SMEs

enterprises (SMEs). J. Journal of manufacturing

systems, 49: 194-214.

OECD (2019). Measuring the Digital Transformation: A

Roadmap for the Future. OECD Publishing, Paris.

OECD (2021). OECD SME and Entrepreneurship Outlook

2021. OECD Publishing, Paris.

Renmin University of China. (2020) COVID-19 and Digital

Transformation of Chinese Small and Medium

Enterprises.https://www.rmbs.ruc.edu.cn/uploadfile/ed

/upload/file/20200311/1583923008453554.pdf.Access

ed 25 Dec 2021

The State Information Center. (2018). A profound impact

of the digital economy on social and economic

development.http://www.sic.gov.cn/News/611/9743.ht

m. Accessed 22 Dec 2021

ThoughtLab. (2020). AI: From Data to ROI.

https://thoughtlabgroup.com/ wp-content/ uploads/

2020/09/ai-from-data-to-roi-codex5984.pdf. Accessed

21 Dec 2021

Vial, G. (2019). Understanding digital transformation: A

review and a research agenda. J. The journal of strategic

information systems, 28(2): 118-144.

Wood, A. J., Graham M, Lehdonvirta V, et al. (2019). Good

gig, bad gig: autonomy and algorithmic control in the

global gig economy. J. Work, Employment and

Society, 33(1): 56-75.

Yezhebay A, Sengirova V, Igali D, et al. (2021). Digital

Maturity and Readiness Model for Kazakhstan SMEs.

In International Conference on Smart Information

Systems and Technologies. Nur-Sultan.

Zhu X., Ge S. & Wang N. (2021). Digital transformation:

A systematic literature review. J. Computers &

Industrial Engineering, 162, 107774.

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