JRSSEM 2023, Vol. 02, No. 7, 1416 – 1425

E-ISSN: 2807 - 6311, P-ISSN: 2807 - 6494

DOI : 10.36418/jrssem.v2i07.385 https://jrssem.publikasiindonesia.id/index.php/jrssem/index

DIGITAL COMPETENCIES AND EXPERIENCE IN PARTNERSHIP

PROGRAM ON SMEs PERFORMANCE

Muhammad Reza Aulia

University of Teuku Umar, Indonesia

e-mail: muhammadrezaaul[email protected]

*Correspondence: muhammadrezaaul[email protected].id

Submitted

:15

January 2023

Revised

: 09

February 2023

Accepted

: 24

February 2023

Abstract: Small medium enterprises need to improve their digital competencies but the lack of

awareness of digital services and outcomes is a concern in business environments is one of the

business units that help the country a lot in reducing the unemployment rate. The main objective of

this study is to determine the direct and indirect effects of experience, digital competencies,

partnership program on the SMEs performance. The approach in this research is causal research

(cause and effect). The population in this study is the SME business actor who are fostered. While the

sampling technique used a purposive sampling where sample used in this study is 241 SMEs business

actors who are participating in the partnership program. Data collection techniques used a

questionnaire with an ordinal measurement scale. Data analysis used Smart PLS 4 based Partial Least

Square. Based on the results of the direct effect analysis, it shows that Digital Competence has a

significant effect on the Partnership Program and SMEs Performance; Experience has a significant

effect on Digital Competence, Partnership Programs, and SMEs Performance; and the Partnership

Program has a significant effect on SMEs Performance. Meanwhile, the results of the indirect effect

analysis show that Digital Competence has no significant effect on performance (only direct

influence); Experience has a significant influence on the Partnership Program and SMEs Performance.

Keywords: Digital Competencies; Partnership Program; SMEs Performance; PLS.

Muhammad Reza Aulia | 1417

INTRODUCTION

SMEs are the key sources for

entrepreneurial ideas (Suh & Kim, 2014). A

large body of literature has documented

the importance of small and medium-sized

firms (SMEs) in the economic development

of most countries throughout the world

over the last two decades or more (Kraja &

Osmani, 2015).

SMEs have an extraordinary influence

on the economy of a country, both

developed and developing countries. It is

certain that micro, small and medium

enterprises (SMEs) play an important role in

supporting the economy in a better

direction (Ochinanwata et al., 2021). SMEs

are the largest contributor to the formation

or growth of the gross domestic population

(GDP) and contribute the most to

employment than large businesses, this

shows the importance of SMEs for the

country's economy (Franco & Haase, 2020).

SMEs are also inextricably related to the

economic well-being of their owners

(Tehseen et al., 2019). By focusing on

entrepreneurs as owner-managers

(Beckman et al., 2012); (Ghobakhloo &

Tang, 2013), the results provide a wealth of

evidence about the context of digital

entrepreneurship (Ngoasong, 2018).

SMEs need to improve their digital

competencies (Hamburg, 2020);

(Oberländer et al., 2020) but the lack of

awareness of digital services and outcomes

is a concern in business environments

(Horváth & Szabó, 2019); (Lehner &

Sundby, 2018); (Srivastava & Shainesh,

2015).

Small and medium firms risk their

competitiveness, growth, and profitability if

they fail to embrace digital transformation

(Ulas, 2019); (Li et al., 2018), often

renouncing digital initiatives because they

are unaware of how to incorporate them

into their operations (Lehner & Sundby,

2018); Reis 2018]. The main reason why

small and medium firms experience a

digital divide is not the lack of access to

digital technology but rather the firm’s lack

of relevant knowledge and education

(Horváth & Szabó, 2019); (Lehner &

Sundby, 2018); Reis 2018]. Since digital

competencies and transformation are

perceived as crucial lifelong learning and

development challenges [Hamburg, 2020;

(Oberländer et al., 2020), universities and

research centres should support firms in

developing their competencies toward

digital skills and transformation (Azevedo

& Almeida, 2021), as e-learners that require

digital knowledge are proactive learners

and tend to make good use of what they

learn (Azevedo & Almeida, 2021).

SMEs performance is the result or

evaluation of company work achieved by

both individuals and groups obtained

based on their roles and responsibilities

towards tasks that have been given and

determined by the company in a certain

period of time (Mutegi et al., 2015).

However, so far the performance of SMEs

has not been able to achieve maximum

results, especially in the failed assisted

SMEs.

Management of SMEs should not be

carried out carelessly and without good

management due to the strategic role of

SMEs and the limited ability of SMEs to be

able to grow (Hendratmoko, 2021). This

partnership pattern is one of the solutions

for improving the performance of SMEs

1418 | Digital Competencies and Experience In Partnership Program on SMEs Performance

(Wulandari & Nadapdap, 2020).

Partnership between the government

and business actors is a form of

cooperation between the government and

the business sector, which allows them to

share resources, risks and mutual benefits

aimed at changing existing environmental

practices with innovation findings (Lin,

2016). It is necessary to examine the role of

the partnership program on digital

entrepreneurship

MATERIALS AND METHODS

This study uses an associative

approach, namely research that seeks to

establish a relationship between two or

more variables. With this research, a theory

can be developed that can explain, predict,

and control a phenomenon or event.

The data used is quantitative data

collected through survey methods. This

study will take respondents from a

population and will collect main data

through questionnaires and direct

interviews with respondents.

The population according to (Lubis,

2021) are all research objects that have

certain traits and characteristics

determined by a researcher as a data

source and then draw conclusions based on

the data collected. The population in this

study are SMEs assisted at PT. Perkebunan

Nusantara III with a total of 241 SMEs

business actors.

According to (Lubis, 2021) means that

the sample is part of the population that

can represent the population, selecting the

sample according to a certain procedure or

method. One type of sampling technique is

purposive random sampling. Purposive

random sampling is a sampling technique

with consideration of certain criteria and

the process of selecting respondents is

carried out randomly (Sugiyono, 2016). The

reason for using this purposive sampling

technique is because it wants to focus on

UKM players who are smooth in making

payments. Therefore the sample used in

this study is 241 SMEs business actors who

are smooth in making installment

payments.

It is very important to determine the

right data collection technique, this is

because it can determine whether a study

is good or bad. Data collection is an effort

made to obtain real and accountable

information or information and truth. In

conducting this research using several

methods, namely:

1. Observation

Observation is an activity to provide

observation or attention to the research

object by utilizing the five senses. This

observation is also known as observation.

In this study, observations were made by

looking directly at the conditions in the

field, especially the partners in the

partnership program from PT. Nusantara

Plantation III.

2. Questionnaire / Questionnaire

The questionnaire is one of the media

used to obtain some news that is known by

the informants. This questionnaire can be in

the form of written questions.

There are two sources of data needed

in this study, namely primary data which is

also called primary data and secondary

data which is also called secondary data.

Primary data or primary data derived from

questions or Questioners addressed to the

partnership of PT. Nusantara Plantation III.

Muhammad Reza Aulia | 1419

Secondary data obtained through PT.

Perkebunan Nusantara III is a list of

partners participating in the partnership

program

The combination of regression with

path analysis was chosen in an effort to test

the hypothesis about the direct or indirect

relationship between the complexity of the

existing variables so that it can use the

Partial Least Square analysis method. PLS

can describe all the relationships of the

dependent and independent variables in

one analysis.

PLS is a program that can be used to

analyze existing data with the SMART PLS 4

program. All relationships that occur can be

described in the model through theory. The

influence of Digital Competence and

Experience on the Partnership Program and

its relationship with SMEs Performance can

be well analyzed with this program.

Several advantages of PLS according to

Hair et al. (2014) include: (1) being able to

operate complex models, a large number of

dependent and independent variables is

not a problem; (2) being able to process

data that has multicollinearity among

independent variables; (3) able to process

even if there is missing data or abnormal

data, the results are still solid and good; (4)

on data that has reflective and formative

constructs can be applied; (5) data that is

classified as a small sample and data that

does not have to be normally distributed

can also be operated. (6) different scaled

data types such as nominal, ordinal and

continuous data can also be used.

Digital competence in this study is

measured by networking, social media, web

design, communication, business

applications, inventory management, e-

commerce, team and time management,

project management.

Experience in this study is measured by

length of work, knowledge, dexterity,

mastery of equipment, mastery of a job,

calmness of work.

The partnership program in this study

is measured by business development,

mutually beneficial business and

management relationships, trust,

cooperation, participation of business

actors, and the role of partners.

The performance of SMEs in this study

was measured by increasing sales volume,

increasing the number of customers,

increasing profits, smooth repayments,

branding image, customer satisfaction.

RESULTS AND DISCUSSION

In the process of evaluating the

manifest variable (indicator) measurement

model, it is necessary to measure latent

variables (constructs) correctly. By

evaluating the level of validity of the

manifest variable through the value of the

loading factor (λ) if the value is greater than

0.7 then the manifest variable is declared

valid, but if it is less than 0.7 then the latent

variable is considered immeasurable and

must be discarded.

All values that lead to the yellow box

(loading factor value) have a value above

0.7, which means that all manifest variables

are declared valid and ready to be used in

the model

This study found reliable results

because they met the requirements with

AVE values and reliability > 0.5 and > 0.7.

This also indicates that all the indicators

used can be used to measure the construct.

1420 | Digital Competencies and Experience In Partnership Program on SMEs Performance

To test whether there is a

multicollinearity problem, look at the VIF

Inner Model value. Hair (2014) said that a

VIF value below 5 indicates no

multicollinearity problem.

The process is continued with an

evaluation through discriminant validity

values. This is useful for validating that

different constructs may not be highly

correlated. Discriminant validity testing

uses the Heterotrait-Monotrait Ratio

(HTMT) and Fornell and Larcker (1981)

methods where the cross loading value

must be greater than 0.50 which means that

it must have at least 50 percent variation

from the indicators that can be explained.

Based on the data above, it is known

that the final model is in accordance with

the existing requirements so that the

resulting manifest variable is said to meet

the requirements.

In order to evaluate the structural

model through observations on

endogenous variables and estimated

values of path parameter coefficients. If it is

known that the R-square is 0.75 then the

model is categorized as a strong model, but

if it has 0.50 then it will be categorized as a

medium model, and it is said to be a weak

model if the R-square is 0.25.

All measurements are correct.

Furthermore, an analysis of the relationship

between variables can be carried out.

Hypothesis testing can use a t-value below

1.645, a p-value below 0.05. Shown in

Figure 1 and Table 1 is the result of PLS

analysis.

Figure 1. PLS Output

Table 1. Result

Variable

Std

Beta

Std.

Error

t-value

value

Decision

Direct Effect

Digital Competencies -> Partnership

Program

0.204

0.065

3.14

0.002

Accepted

Digital Competencies -> Performance

0.093

0.046

1.993

0.046

Accepted

Experience -> Digital Competencies

0.676

0.054

12.583

0.000

Accepted

Experience -> Partnership Program

0.712

0.061

11.67

0.000

Accepted

Experience -> Performance

0.652

0.119

5.497

0.000

Accepted

Partnership Program -> Performance

0.251

0.096

2.597

0.009

Accepted

Indirect Effect

Digital Competencies -> Performance

0.051

0.03

1.729

0.084

Rejected

Experience -> Partnership Program

0.138

0.046

2.969

0.003

Accepted

Experience -> Performance

0.276

0.095

2.902

0.004

Accepted

Muhammad Reza Aulia | 1421

Based on the results of the direct effect

analysis, it shows that Digital Competence

has a significant effect on the Partnership

Program and SMEs Performance;

Experience has a significant effect on

Digital Competence, Partnership Programs,

and SMEs Performance; and the

Partnership Program has a significant effect

on SMEs Performance. Meanwhile, the

results of the indirect effect analysis show

that Digital Competence has no significant

effect on performance (only direct

influence); Experience has a significant

influence on the Partnership Program and

SMEs Performance.

Digital Competencies

The results of the analysis show that

the higher the digital competence, the

more performance also increases. This is in

line with Marguna's research (2020) with a

study of the performance of librarians,

stating that the higher the digital

competence (e-Skills) of librarians, the

more the performance of librarians at the

Unhas Library UPT also increased.

Digital competence is part of

entrepreneurial competence, the two

cannot be separated. Drydakis (2022) states

that indicators of digital competencies are

networking, social media, customer

relations management, communication,

accounting-finance, managing inventory,

team management, time management,

project management. Meanwhile, (Aulia,

2020a), (Aulia, 2020b) states that

entrepreneurial competence includes

managerial abilities, conceptual abilities,

social skills, decision-making abilities, and

time management skills.

Based on the results of data analysis,

digital competence has a significant effect

on SMEs performance, the same is true of

entrepreneurial competence which has a

significant influence on SMEs performance

(Aulia, 2018); (Hasanah et al., 2018); (Aulia

et al., 2021).

Experience

Experience can be increased through

mentoring and training programs, and the

element of experience must be an aspect in

determining which SMEs qualify for the

partnership program.

Directly the experience of Human

Resources has a significant influence on the

partnership program. The results of this

research are similar to previous research

conducted by (Tambunan, 2019) which

suggests that business experience has a

significant influence on SMEs performance.

Opinion from (Cowling et al., 2018)

someone who has experience in doing

business is the best predictor of success,

especially if the new business is related to

previous business experience (Iskandar &

Safrianto, 2020). When managing a

business, what must be needed is to

increase the complexity of the environment

using business experience obtained from a

lot of learning related to what data is

needed and used during the decision-

making process.

Based on the results of data analysis, it

shows that experience significantly

influences the partnership program. This

means that experience has a significant

influence on the partnership program. It is

understandable how much depends on the

progress of an organization because the

implications of the findings in this study are

1422 | Digital Competencies and Experience In Partnership Program on SMEs Performance

that business experience will direct SMEs to

continue to develop their business without

having to focus on participating in mutually

beneficial partnership programs for both

SMEs and corporate partners. The

following results are the same as research

conducted by (Trisnawati et al., 2020) which

states that the level of experience in

entrepreneurship is able to increase the

ability of SMEs in business development.

Furthermore, (Iskandar & Safrianto,

2020) research entitled the influence of

entrepreneurial skills and business

experience on entrepreneurial success

states that experience can lead SMEs to

avoid the risk of business failure. Then

(Vasan, 2020) states that the experience of

Human Resources has a significant

influence on partnerships. The relevance of

the findings in the following research is that

so far the experience possessed by SMEs

has only been limited to the results of

hereditary experiences. The business that is

run by SMEs is still based on suggestions

and levels from the family. So that this

successful or good experience cannot be

separated from the family. The existence of

a partnership program also does not have

a major impact on the experience of SMEs

in conducting business development. The

existing partnerships are still considered to

be activity programs that are considered

not to play a big role in the progress of the

SMEs business.

Partnership Program

The results of the analysis show that

the partnership program directly has a

significant effect on the performance of

SMEs. The following research results are in

line with the results of previous research

conducted Ghoniyah (2019) which stated

that partnership programs play a major role

in helping SMEs to progress and develop.

Partnerships are able to improve the

financial performance of SMEs to make

them safer from competitive pressures.

Furthermore, research Chinomona (2019)

states that partnerships have a significant

effect on SMEs performance. Then the

results of research Baroncelli (2020) state

that mutually beneficial collaboration can

increase the competitiveness of SMEs. The

partnership program is considered the

most effective in increasing the SME class

to become a business unit capable of

competing with other business units.

Figure 2. Digital Competencies in

Partnership Program Model

Figure 2 describes the digital

competency model in the partnership

program to improve SME performance.

There are several things that can be used as

the focus of training in partnership

programs, namely networking, social

media, web design, communication,

business applications, inventory

management, e-commerce, team and time

management, project management.

Implication

This research makes an important

contribution to enrich our understanding

Muhammad Reza Aulia | 1423

and research on Digital competence in

program partnerships. First, this research is

empirical validate the newly developed

predictive model in digital content content.

This study shows the importance of digital

competence in program partnerships to

improve SME performance. Second, this

research provides a deeper understanding

of how competence is digitally mediated by

experience influencing program

partnerships and SME performance. We

suggest that the creation of the digital

competence design part in the program

partnership should be simple, useful and

according to the needs and expectations of

young people.

There needs to be further study on

digital competencies, especially those

related to behavior, as an He’s (2019)

research shown digital competence has the

most significant effect on students' digital

informal learning behavior.

CONCLUSIONS

Experience and digital competence can

be used as a selection requirement to join

the partnership program as well as a focus

on competency development to improve

performance.

Digital competencies that can be

developed are networking, social media,

web design, communication, business

applications, inventory management, e-

commerce, team and time management,

project management.

The partnership program is urgently

needed by SMEs as a medium for

increasing digital competence. The existing

partnership program has only been limited

to providing capital and tools, there are no

supporting facilities such as digital

competency training for SMEs.

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