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Journal Of Economics, Technology, and Business (JETBIS)

Volume 2, Number 2 February 2024

p-ISSN 2964-903X; e-ISSN 2962-9330

MARKETING AND BUSINESS DEVELOPMENT STRATEGIES TO IMPROVE

THE QUALITY OF DIGITAL MARKETING AND DIGITAL INNOVATION IN

MICRO, SMALL, AND MEDIUM ENTERPRISES OMAH KEPITING IN

SURABAYA

Upik Purwanti

, Rini Adiyani

, Kurniawati Darmaningrum

Univesitas Tunas Pembangunan Surakarta, Indonesia

Email: nderun31@gmail.com

[email protected]

,kurniawa[email protected]

KEYWORDS:

Digital Marketing, Business

Development, Digital

Innovation, Micro, Small and

Medium Enterprises (Umkm),

Omah Kepiting

ABSTRACT

A business that can build a ready innovation ecosystem will tend to

have better digital marketing capabilities than other competitors.

Digital marketing adoption indicates the level of use of digital

marketing technology in an organization. The research aims to

determine the effect of digital marketing adoption on digital

marketing capabilities in Omah Kepiting Micro, Small, and Medium

Enterprises in Surabaya. In this research, the data were analyzed

using the PLS-SEM statistical method to determine the weight of

each research indicator. The PLS-SEM method was chosen because

there was an abnormality in the distribution of the data and the

research required latent variable scores for further analysis. These

findings are interesting because although the readiness of the

innovation ecosystem does not have a direct effect on digital

marketing adoption (β= 0.114; P-value > 0.05), digital marketing

adoption turns out to have a significant positive impact on the

company's digital marketing capabilities (β= 0.125; P- value < 0.05).

Conclusion of digital transformation in improving the company's

digital marketing capabilities, which can be the main driver in facing

increasingly complex market dynamics and increasingly fierce

competition.

INTRODUCTION

Marketing management is a process of determining marketing objectives for a company

or organization by considering internal resources and creating, and maintaining profitable

exchanges with target markets that intend to achieve the company's main objectives. In

marketing, there are concepts in carrying out its activities, including the concept of production,

product concept, sales concept, marketing concept, social marketing concept, and global

marketing concept. Along with the advancement of technology, science, and economic

development, it is so rapid that it creates fierce competition. To be able to survive and be able

to compete in this competition, a business or company must pay attention to effective service

and efficiency in utilizing its resources to achieve predetermined business or company goals.

Micro, Small, and Medium Enterprises (MSMEs) have an important role in improving

the regional economy and the economy of a country. According to (T. Tambunan, 2012) in

Indonesia, MSMEs have proven to have an important role in overcoming the consequences and

impacts of the economic crisis that occurred in 1997 when many large companies went

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bankrupt, while MSMEs were able to survive the crisis conditions. In addition, this sector can

increase the per capita income or Gross Domestic Product (GDP) of the community because it

can absorb a large number of workers (Alfrian & Pitaloka, 2020). Small businesses are

productive economic businesses that stand alone, carried out by individuals or business entities

that are not subsidiaries or branches of companies that are owned, controlled, or are part of

either directly or indirectly from micro businesses or large businesses that meet the criteria for

small businesses as referred to in the law (Was’an et al., 2023).

Medium-sized enterprises are productive economic businesses that stand alone, carried

out by individuals or business entities that are not subsidiaries or branches of companies that

are owned, controlled, or part of either directly or indirectly micro, small, or large businesses

that meet the criteria for medium-sized businesses as referred to in Law Number 20 of 2008

concerning MSMEs (Wibowo, 2021). In the law, the criteria used to define MSMEs as stated

in Article 6 are net worth or asset value, excluding land and buildings of the place of business,

or annual sales proceeds. With this criterion, a micro-enterprise is a business unit with an asset

value of IDR 50,000,000 or annual sales revenue of IDR 300,000,000 at most. Small businesses

with an asset value of more than IDR 50,000,000.00 up to a maximum of IDR 500,000,000.00

or with annual sales revenue of more than IDR 300,000,000.00 up to a maximum of IDR

2,500,000,000.00. Medium-sized enterprises are companies with a net worth of more than Rp

500,000,000,000.00 to a maximum of Rp 10,000,000,000,000.00 or have annual sales results

above Rp 2,500,000,000,000.00 to a maximum of Rp 50,000,000,000.00 (T. Tambunan, 2012).

According to (T. T. H. Tambunan & Purwoko, 2002), the quality of human resources,

including the behavior of business actors, can affect business success. a business can be said to

be successful if business actors have honest, creative, and innovative personality characteristics

to build trust with consumers, the more motivation and good behavior a business actor has, the

more likely an MSME can develop. To develop an MSME so that the business is successful,

business actors must be able to think creatively and innovatively to further develop their

business. According to (T. T. H. Tambunan & Purwoko, 2002), capital strength can affect

business success.

Business capital is necessary to conduct business activities. Therefore, a certain amount

of funds is needed as a basis for the business's financial size. Sources of business capital can

be obtained from own capital, government assistance, and financial institutions both banks and

non-bank institutions. Capital is a business factor that must be available before carrying out

activities. The size of the capital will affect the development of the business in achieving

income (Riyanto et al., 2001).

The government pays great attention to the development of MSMEs to survive the global

crisis. Various initiatives are always sought by the government through the Ministry of

Cooperatives and Small and Medium Enterprises so that more and more individuals want to

pursue entrepreneurship in the form of establishing MSMEs (Holiseh & Izzatusholekha, 2023).

The government's huge attention to MSMEs is the right strategic step needed by the Indonesian

people. The seriousness of the government's concern for MSMEs with programs to grow

MSMEs in Indonesia. Although the Indonesian government's support is very large to make

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MSMEs succeed and develop, it does not mean that it is without obstacles. One of the obstacles

is the digital transformation that has begun to enter the business world (Utama, 2019). This

transformation involves inevitable difficulties, which are rooted more deeply in the realities of

small businesses. Factors such as lack of funding, high labor costs, structural problems, slow

bureaucracy, and limited technology diffusion, can slow down the evolutionary path of Micro,

Small, and Medium Enterprises (MSMEs). The use of technology to improve efficiency in the

organization is a major problem that these enterprises must overcome. Lack of knowledge

about the development and application of technology in the business world, one of which is in

terms of marketing. Therefore, actors must make a complicated choice, continue to apply

strategies and techniques that have been effective so far or accept change through new products

that allow them to fully develop and thrive thanks to the digital transformation (Karyani et al.,

2021).

Businesses as producers, facing a new marketing paradigm, must adapt to unprecedented

changes in the marketing landscape. The new marketing landscape has brought companies to

conditions that require them not only to exist in the conventional competitive arena but also in

the digital-based marketing arena. Businesses are preparing themselves to provide the right

answers to consumers' needs and interests, businesses are also learning how to reach and

engage their consumers through valuable information.

In other literature, digital marketing capability is the competence of businesses in using

the Internet and other information technologies to facilitate in-depth interactions with

customers. These interactions give customers access to company resources and information

and provide companies with information about their customers. Based on this explanation, it

can be concluded that digital marketing is not only about the adoption of digital marketing

technology, but also about how companies can plan, implement, and manage their digital

marketing. Companies must have digital marketing capabilities because they can improve

business performance. According to Field's findings, businesses can achieve impressive results

by improving their digital marketing capabilities, lowering campaign costs by 30%, and

increasing revenue by 20%. Even when companies use advanced technology and human

resources with superior supervisory skills, campaign performance can improve by 30%. A

research model for improving a company's digital marketing capabilities has been established.

The model states that an enterprise's digital marketing capabilities can be improved through

innovation ecosystem readiness, digital marketing technology adoption, and digital

transformation.

Innovation ecosystem readiness shows the level of readiness of the business innovation

ecosystem in responding to changes in the company's external and internal business

environment. A business that can build innovation ecosystem readiness will tend to have better

digital marketing capabilities than other competitors. Digital marketing adoption indicates the

level of use of digital marketing technology in the organization. A business that can adopt

effective digital marketing technology will tend to have a higher level of digital marketing

capabilities. Finally, digital transformation shows the process undertaken by businesses to

integrate technology into their business processes.

To create an MSME such as a catering business at Omah Kepiting which provides a

variety of foods with certain characteristics, both traditional and modern foods. Omah Kepiting

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strives for several innovations and creations in food to attract consumers. Omah Kepiting also

always tries to improve the quality of food products so that consumers are satisfied with the

taste of food and service. In addition, they also utilize technology as a strategy to improve their

business development.

The purpose of the study was to determine the effect of digital marketing adoption on

digital marketing capabilities at Omah Kepiting Micro, Small, and Medium Enterprises in

Surabaya. The benefits of this research With this research, it is hoped that it can provide

meaningful input for business progress related to digital transformation, ecosystem innovation

readiness, and digital marketing so that it can evaluate and improve marketing capabilities and

digital innovation.

RESEARCH METHODS

In this study, the data were analyzed using the PLS-SEM statistical method to determine

the weight of each research indicator. According to Rambut, the PLS-SEM method was chosen

because there was an abnormal distribution of data and the research required latent variable

scores for further analysis. latent variables for further analysis. PLS-SEM is a statistical method

to understand, describe, and determine the role of each indicator that accompanies it. that

accompanies it. The number of samples or respondents obtained in this study This study

exceeds the recommended sample size for PLS-SEM analysis given by Cohen and

recommended by Rambut. given by Cohen and recommended by Hair. on statistical power

analysis. If the maximum number of independent variables is six, and a statistical power of

80% is required to detect a minimum R2 of 0.10 with a 1% chance of error, then a minimum

sample of 179 samples is required. sample of at least 179 samples. A larger sample size will

increase the precision of the PLS-SEM coefficient estimates (Sri & Ahmad, 2017).

RESULTS AND DISCUSSION

PLS-SEM tests and estimates whether the data collected can explain the causal

relationship that occurs in the model. The quality of the PLS-SEM results model will be

explained by evaluating the measurement model and structural model. Measurement model

testing will refer to the results of empirical measurements of the relationship between

indicators and their constructs (variables), while structural model testing will explain the

relationship between constructs.

Furthermore, the PLS-SEM evaluation summary table displays the internal consistency

reliability, convergent validity, discriminant validity, collinearity, magnitude and

significance of path coefficients, magnitude and significance of external loading, and

coefficient of determination, which are all testing criteria for the reflective measurement

model and structural measurement model.

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Table 1

Summary of PLS-SEM Evaluation

Evaluation

Criteria

Output

Results

Step 1: Reflective Measurement Model Evaluation

Internal consistency

reliability

Convergent validity

Discriminant

validity

Cronbach's alpha =0.6-

0.9 or less than 0.95

0,89 – 0,905

Supported

Composite reliability

>0.70

0,915 – 0,921

Supported

Outside loading >

0.708

0,574–0,848

Supported

Average Variance

Extracted (AVE) >0.5

0,544–0,624

Supported

Heterotrait-Monotrait

Ratio (HTMT) < 0.90

0,371–0,894

Supported

Step 2: Structural Model Evaluation

1. Collinearity

Size and

significance of

path coefficients

Coefficient of

determination (R2)

Variance Inflation

Factor (VIF) = 0.20-5

P-value <0.05

1.227–2.615

Supported

Weak = 0.25-0.50,

medium = 0.5-0.75,

strong > 0.75

P-value <0.05

Supported

0,185–0,695

The weak and

medium

category

Step 3: Model Fit Evaluation

Standardized root

mean square

residual (SRMR)

SRMR < 0,08

0,067

Supported

The table above presents a summary of the results of testing the PLS-SEM model in the

study. The standardized root mean square residual (SRMR) value = 0.067 obtained in this study

shows a good fit model because the SRMR value is less than 0.08, which means it meets the

criteria for evaluating the fit model. The standardized root mean square residual is a measure

of model fit, defined as the root mean square difference between the observed correlation and

the correlation implied in the model. Since SRMR is an absolute measure of fit, a value of zero

indicates a perfect fit.

Evaluation of the Reflective Measurement Model

Based on Table 1, it can be concluded that all components of the reflective model

evaluation test provide results that support this research. This can be seen from the internal

consistency reliability value, convergent validity, and discriminative validity that meet the test

criteria.

All indicators in the study show the significance and relevance of outer loading on each

research construct. The results show that the weights of all indicators on each construct are

significantly different from zero, as evidenced by aPnilnya less than 0.05, as shown in Table 1.

Organizational readiness is the most important indicator in building innovation

ecosystem readiness. Meanwhile, the most important indicator in the construction of digital

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transformation is operational improvement (question: Our company's technological

innovations have enabled customers to interact with our operational processes in new ways).

Based on these indicators, we can conclude that the readiness of the innovation ecosystem is

largely determined by organizational readiness, namely the readiness of resources, structures,

systems, culture, skills, and organizational strategies in adopting digital marketing innovations.

Digital transformation is largely determined by the ability of MSME players to utilize their

core operational strengths, which means that MSME players can look for bottlenecks and

inefficiencies in their business processes and can consider and apply digital technology to help

achieve better business process analysis

(Firmansyah et al., 2022).

In general, indicators with an outer loading value of less than 0.4 can be eliminated from

the model immediately, while values between 0.40-0.70 can be removed from the measurement

model if and only if their removal results in an increase in their composite reliability value. The

results of this study show that some indicators have outer loading values between 0.4 and 0.7,

so it is necessary to consider including or eliminating these indicators in each of the

accompanying constructs. Technology leadership indicators in the digital transformation

construct with an outer loading value of 0.688 are some of the indicators that must be

considered to be included or removed from the accompanying construct. In addition,

observability, opinion leadership, competitive pressure, and innovation infrastructure

indicators with outer loading values of 0.685, 0.610, 0.590, and 0.574 on the innovation

ecosystem readiness construct also need to be reconsidered.

Table 2

PLS-SEM Results: Size and Significance of External Loading

Items and Construction

Outside Loading

Statistic T

P Values

Innovation Ecosystem

Readiness

1. Employees find it easy to

implement digital

marketing innovations

2. Digital marketing

innovation benefits

employees' work

3. Businesses are ready to

implement digital

marketing innovations

4. Business management

actively introduces digital

marketing innovations

5. Customers are already

using digital marketing

innovations

0,779

0,815

0,842

0,834

0,590

0,748

24,145

27,153

38,648

34,101

20,131

10,665

0,000

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6. Competitors will be

superior if they do not

implement digital

marketing innovations

7. The government has built

adequate infrastructure to

support the

implementation of digital

marketing innovations in

Indonesia

8. In general, opinion

leaders and the media talk

about digital marketing

innovations very often

9. Digital marketing

innovation is easy to

implement in business

processes

10. It's easy to distinguish

how digital marketing

works from conventional

marketing.

0,574

0,610

0,825

0,685

11,048

12,086

29,340

16,191

0,000

Digital Transformation

1. We use digital channels to

market our products and

services

2. Our technological

innovations have enabled

customers to interact with

our operational processes

in new ways

3. We have launched a new

business model using

digital technology

4. Our management has a

vision to digitally

transform the business for

the future

5. We promote the cultural

change required for

digital transformation

6. We define it with clear

0,723

0,843

0,808

0,736

0,797

0,838

18,755

35,629

30,292

19,060

18,033

34,731

0,000

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roles and responsibilities

between departments to

execute digital initiatives.

7. The performance of the

information technology

unit has been able to

fulfill the need to carry

out digital transformation

0,688

14,460

0,000

Digital Marketing Adoption

Does your business use

digital marketing techniques

and methods?

1.000

About the indicators mentioned above, we re-analyzed the PLS-SEM model, i.e. the

modified model, by removing the indicators whose values ranged from 0.4 to 0.7, and then

compared the new composite reliability value with the old composite reliability value. Based

on the comparison of the composite reliability values in Table V.3, removing these indicators

does not affect the composite reliability values. As a result, it can be determined that there are

not enough compelling reasons to remove the indicator from the original model, as doing so

would change the content validity of the accompanying construct but not improve the

composite reliability (CR).

Tabel 3

Nilai Reliabilitas Komposit Lama dan Baru

Variabel

CR lama

CR baru

Cronbach Alfa

Jalan

Kesiapan ekosistem inovasi

0,921

0,928

0,905

0,544

Transformasi digital

0,915

0,913

0,890

0,606

Indeks adopsi pemasaran

digital1

1.000

Kemampuan pemasaran digital

0,920

0,913

0,899

0,624

Manifest variable

This study shows a high level of internal consistency reliability of all research variables,

indicated by a Cronbach's alpha value greater than 0.89 and a Composite Reliability value

greater than 0.90 but less than 0.95, indicating that the indicators in this research questionnaire

can be relied upon to measure the same thing in a construct. Furthermore, this study has good

convergent validity, evidenced by AVE values greater than 0.50, indicating that the constructs

of this study explain more than 50% of the diversity contained in the research indicators. The

digital marketing adoption index variable, as the only manifest variable in the study, has a

value of one for CR, Cronbach's alpha, and AVE. This is understandable given that the digital

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marketing adoption index only has one response value. The HTMT values are also between

0.371 and 0.894, less than 0.90. This means that based on these indicators, each construct has

shown good discriminant validity, or each variable has its uniqueness. Table V.4 shows the

specific discriminant validity results for each variable.

Table 4

Heterotrait - Monotrait Ratio (HTMT)

Variable

DMA

IER

DMC

Digital marketing adoption index

(DMA)1 Innovation ecosystem

readiness (IER)

0,371

Digital marketing capabilities

0,486

0,790

(DMC) Digital Transformation (DT)

0,446

0,834

0,894

Structural Model Evaluation

Based on Table 2, it can be concluded that all test components for structural

model evaluation provide results that support this research. This can be seen from the

value of collinearity, the coefficient of determination, and the significance of the path

coefficient that meets the test criteria. The average VIF value in this study is less than

3, which indicates that there is no collinearity between constructs, and path coefficient

measurement bias can be avoided.

Each relationship in the structural model also has a significant effect on improving

digital marketing capabilities. Table 5 displays the magnitude and significance of the

path coefficients in the structural model for each relationship while answering various

hypotheses in Masrianto's previous research [6]. The readiness of the innovation

ecosystem has a significant positive effect on the company's digital marketing

capabilities (H3: β = 0.259; P-value < 0.05), as does digital transformation (H5: β =

0.555; P-value < 0.05), and digital marketing adoption (H6: β = 0.125; P-value < 0.05).

The findings of this hypothesis testing support the authors' opinion that innovation

ecosystem readiness, digital transformation, and digital marketing as components of

DMUI are factors that can significantly improve digital marketing capabilities.

Table 5

PLS-SEM Results: Size and Significance of path coefficients

Hypothesis

Path

Coefficient

Statistic T

P Values

Results

0,772

24,719

0,000

Supported

0,114

0,999

0,318

Not Supported

0,259

3.440

0,001

Supported

0,336

3,102

0,002

Supported

0,555

7,405

0,000

Supported

0,125

3,015

0,003

Supported

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The hypothesis was developed from Masrianto's research.

Other hypothesis testing results show that the readiness of the innovation ecosystem

has a direct influence on digital transformation (H1: β = 0.772; P-value < 0.05), and digital

transformation has a direct influence on the adoption of digital marketing (H4: β = 0.336;

P-value < 0.05). The findings of this study are interesting because innovation ecosystem

readiness does not have a direct effect on digital marketing adoption (H2: β = 0.114; P-

value > 0.05). MSME players with mature innovation ecosystem readiness will not adopt

digital marketing before digital transformation; they understand that good digital

marketing capabilities will never be achieved without going through digital

transformation.

The coefficient of determination R2 shows a value of 0.695 for digital marketing

capabilities, which means that the explanatory power of the model is relatively moderate.

Hair states that the value of R2 which is between 0.50-0.75 indicates that endogenous

variables can be explained by exogenous variables with a moderate level of determination

(Simamora & Saputra, 2023).

Rumus Indeks Pemanfaatan Pemasaran Digital (DMUI)

The Digital Marketing Utilization Index (DMUI) is introduced in this study to

measure the level of digital marketing utilization by a company using three aspects of

digital marketing capability improvement, namely: innovation ecosystem readiness, digital

transformation, and digital marketing adoption. The three aspects of improving a

company's digital marketing are derived from the digital marketing capability

improvement model of a company.

As mentioned earlier, digital marketing capabilities are focused on strategy rather

than technology. The three fundamental aspects of improving digital marketing

capabilities discussed in this study are closely related to the relationship between digital

marketing planning, implementation, and management. MSME players can identify the

factors that contribute to high and low levels of digital marketing capability, allowing them

to take corrective actions to improve their digital marketing capability immediately. DMUI

is presented to fulfill such requirements.

The 32 indicators used to calculate the digital marketing utilization index come from

10 indicators that show the readiness of the innovation ecosystem, 15 indicators that show

the adoption of digital marketing, and 7 indicators that show digital transformation, as

shown in the figure below DMUI is a composite index that measures three aspects of a

company's digital marketing capabilities.

The first step in calculating DMUI is to create three separate indices for each

dimension. These index dimensions (one for innovation ecosystem readiness, one for

digital marketing adoption, and one for digital transformation) are then used to calculate

the total DMUI using geometric means. The loading factor of each significant indicator

generated by PSL-SEM is used to analyze the index value of each variable, such as the

innovation ecosystem readiness index, digital transformation index, and digital marketing

capability index. The loading factor determines the absolute contribution of an indicator

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to a construct. The absolute contribution of each reflective model indicator then becomes

an item weight to produce a variable score. Furthermore, for the weighted digital marketing

adoption indicator, the digital marketing usage index approach is used. The max-min

procedure is then used to convert each score on each dimension into an index score with a

value distribution ranging from 0 to 100.

Figure 1

DMUI components and subcomponents

The basic formula for converting indicator score values (V) into index scores (I) is:

SAYA=100×

V- minute_value

, (1)

max_value - min_value

Where value_min is the lowest possible score (lower limit) and value_max is the

highest possible score (upper limit).

The second step is to calculate the total DMUI, which is the result of multiplying the three

indices and then calculating the geometric mean to produce the final DMUI number. The

formula for calculating DMUI is:

√

DMUI :

3SAYA

SAYA

×SAYA

, (2)

Where SAYA

SAYA

is the innovation ecosystem readiness index, SAYA

is the digital

transformation index, and

SAYA

is the adoption of digital marketing index.

Economic Ecosystem Readiness Index

The innovation ecosystem readiness index is obtained from the exogenous latent

variable of innovation ecosystem readiness measured by indicators that characterize

ecosystem readiness in using digital marketing innovations. Innovation ecosystem

readiness is measured using ten key indicators derived from five interacting ecosystem

dimensions, namely employee perceptions and attitudes, internal characteristics and

external characteristics, industry characteristics, country and society characteristics, and

digital marketing technology characteristics. Based on the PLS-SEM weighting results, we

applied the following to each indicator: organizational readiness and manager innovation

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were assigned a weight of 0.12; perceived ease of use, perceived usefulness, and flexibility

were assigned a weight of 0.11; customer needs were assigned a weight of 0.10;

observability was assigned a weight of 0.09; and residual competitive pressure, innovation

infrastructure, and opinion leadership were assigned a weight of 0.08.

Innovation ecosystem readiness indicators are measured using a Likert scale with

values ranging from 1 (strongly disagree) to 5 (strongly agree). The total score of

innovation ecosystem readiness is then calculated by weighting each indicator.

Furthermore, the maximum-minimum procedure is used to convert the total score of

innovation ecosystem readiness into a 0-100 score index.

The innovation ecosystem readiness index value obtained from MSME actors

included in this study based on the score index calculation steps above shows that the score

index value ranges from 33 to 100. A total of 25% of companies have an innovation

ecosystem readiness score index value of less than 67, 50% have a score index value of

less or more than 78, and the remaining 25% have a score index value greater than 92.

Business Digital Transformation Index

The digital transformation index is obtained from the endogenous latent variable

of digital transformation measured by indicators that characterize the digital

transformation of a company. Digital transformation is measured using seven key

indicators derived from two interacting dimensions of digital transformation, namely

building digital capabilities and building leadership capabilities. Based on the PLS-

SEM weighting results, we applied the following to each indicator: operations

improvement, business model reinvention, organizational engagement, and

transformation setting were assigned a weight of 0.15; digital vision was assigned a

weight of 0.14, and customer experience and other technology leadership were

assigned a weight of 0.13.

Digital transformation indicators are measured using a Likert scale with values

ranging from 1 (strongly disagree) to 5 (strongly agree). The total digital transformation

score is then calculated by giving weight to each of its indicators. Furthermore, the

minimum maximum procedure is used to convert the total digital transformation score into

a 0-100 score index.

The digital transformation index value is obtained from all companies in

Indonesia included in this study using the index score calculation steps above, which

shows that the index score value ranges from 14 to 100. A total of 25% of companies

have a digital transformation score index value of less than 61, 50% have a score index

value of less or more than 75, and the remaining 25% have a score index value greater

than 93.

Business Digital Marketing Adoption Index

Digital marketing adoption was measured using fifteen key indicators derived from

digital marketing techniques used by MSME players, namely (1) websites, (2)

eCommerce, (3) social media marketing, (4) email marketing, (5) SEO, (6) SEM, (7)

digital PR, (8) digital advertising, (9) digital CRM, (10) content marketing, (11) affiliate

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marketing, (12) online newsletters, (13) display advertising, (14) mobile marketing, and

(15) digital analytics. For each indicator, we applied the digital marketing usage index

approach as follows: content marketing is weighted 0.09; search engine optimization,

search engine marketing, social media marketing, affiliate marketing, mobile marketing,

digital analytics, and digital customer relationship management are weighted 0.08; digital

advertising, digital public relations, display advertising, and eCommerce are weighted

0.06; email marketing is weighted 0.05; newsletters are weighted 0.04, and other websites

are weighted 0.02.

Digital marketing adoption indicators are measured using binary data with values

ranging from 0 (not used) to 1 (used). The total digital marketing adoption score was then

calculated by weighting each of the indicators. Next, the maximum-minimum procedure

was used to convert the total digital marketing adoption score into a 0-100 score index.

The digital marketing adoption index value was obtained from the MSME players

included in this study using the index score calculation steps above which showed that the

index score value ranged from 0 to 100. A total of 25% of companies have digital

marketing adoption index score values of less than 49.5, 50% have index score values of

less or more than 74, and the remaining 25% have index score values greater than 91.

After the three indices in the DMUI component are calculated, the DMUI value

can be calculated using the formula previously presented. The digital marketing

utilization index value obtained from MSME players included in this study using the

above DMUI score index calculation steps shows that the score index value ranges

from 27 to 100. A total of 25% have a digital transformation score index value of less

than 60, 50% have a score index value of less or more than 74, and the remaining 25%

have a score index value greater than 84.

Based on the clustering results above, further investigation of the constituent

components of DMUI is conducted. According to Table V.6, companies with a low

category of digital marketing utilization index will also have a low level of innovation

ecosystem readiness, digital transformation, and digital marketing adoption.

Similarly, companies with a high digital marketing utilization index are better

prepared for innovation ecosystem readiness, digital transformation, and digital marketing

adoption.

Table 6

Average Index Score

Component 1

Average

Innovation Ecosystem Readiness Index

Digital transformation index

Digital marketing adoption index

Digital marketing utilization index

DMUI Implementation

The previous discussion explains how the index score is calculated. This research

produces a DMUI index score calculator that is useful for identifying a company's

level of digital marketing usage, predicting the company's level of digital marketing

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capability, and then parsing the causes of the company's high level of digital marketing

capability to identify things that the company should improve to enhance its digital

marketing capability.

Based on Table 6, the digital marketing capability index is 100, with a digital marketing

utilization index of 100 as well. The digital value of the marketing capability index and the

digital marketing utilization index of Omah Kepiting MSMEs in Surabaya is classified as a

high category (very good) because both DMUI>84.

According to the DMUI index score calculator, the high digital marketing capability of

Omah Kepiting MSMEs in Surabaya is due to the high utilization of digital marketing in all

aspects of its digital marketing capability. In this example, it can be concluded that Omah

Kepitiong MSMEs in Surabaya are very good at managing every aspect that can improve their

digital marketing capabilities.

This study found that digital transformation has a direct impact on digital marketing

adoption as well as digital marketing capabilities, but similar findings were also found in other

studies. Furthermore, contrary to previous studies, innovation ecosystem readiness does not

have a direct impact on digital marketing adoption. The readiness of the innovation ecosystem

of Omah Kepiting MSMEs in Surabaya does not guarantee the adoption of digital marketing

but provides more awareness to Omah Kepiting MSMEs in Surabaya to carry out digital

transformation so that Omah Kepiting MSMEs in Surabaya can choose digital marketing

adoption methods that are more in line with the business processes of Omah Kepiting MSMEs

in Surabaya. Therefore, the role of digital transformation in mediating the readiness of the

innovation ecosystem and digital marketing adoption is very important.

This research can formulate and apply a digital marketing utilization index to evaluate

and improve the digital marketing capabilities of Omah Kepiting MSMEs in Surabaya. DMUI

can be created by combining the innovation ecosystem readiness index, digital transformation

index, and digital marketing adoption index. Based on this research, all of these variables have

a direct impact on the company's digital marketing capabilities.

This research also identifies and presents practical ways to improve the digital marketing

capabilities of Omah Kepiting MSMEs in Surabaya. DMUI Index score calculator can calculate

and then identify the level of digital marketing capability of Omah Kepiting MSMEs in

Surabaya. DMUI Calculator can also show the factors that contribute to the level of digital

marketing capabilities of Omah Kepiting MSMEs in Surabaya. This will certainly help Omah

Kepiting MSMEs in Surabaya identify and understand their weaknesses, allowing them to take

corrective actions. DMUI differs from existing digital capability indices in that it involves the

overall adoption and techniques of digital marketing, and links it to digital transformation and

ecosystem readiness.

CONCLUSION

Based on the data analysis, we conclude that innovation ecosystem readiness has a

significant direct effect on digital transformation (β = 0.772; P-value < 0.05). This finding is

interesting because although the readiness of the innovation ecosystem does not directly affect

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the adoption of digital marketing (β = 0.114; P-value > 0.05), the adoption of digital marketing

has a significant positive impact on the company's digital marketing capabilities (β = 0.125; P-

value < 0.05). In addition, digital transformation is also proven to have a positive and

significant direct influence on the company's digital marketing capabilities (β = 0.555; P-value

< 0.05), and has a direct influence on digital marketing adoption (β = 0.336; P-value < 0.05).

This illustrates the importance of digital transformation in improving the company's digital

marketing capabilities, which can be a key driver in facing increasingly complex market

dynamics and increasingly fierce competition (Rasid & Rizal, 2021).

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