The Effect of Data Characteristics and Top Management

Characteristics on Decision Making Capabilities: The Role of AI and

Business Analytical Capability

MOHAMMED ALAREFI

Department of Management Information Systems, Faculty of Business Administration,

University of Tabuk, Tabuk, King Faisal Road, 47512, SAUDI ARABIA

Abstract: - Data is essential for making decisions. However, these data should be appropriately collected and

analyzed by capable managers. Few studies examined the effect of data characteristics (DC) and top

management characteristics (TMC) on decision-making capability (DMC). In addition, few examine the

application of artificial intelligence enterprise resource planning (AIERP) in this process. The purpose of this

study is to examine the effect of DC and TMC on DMC. Building on existing theories and studies, this study

proposed that DC (data integrity, data quality, data authentication, and data error) significantly affect the DMC.

In addition, TMC (data analytical capability and technological innovation) significantly affect the DMC.

AIERP is predicted to have a mediator role between DC and TMC, and DMC. Business analytical capability

(BAC) is anticipated as a moderating variable. The data was collected from technological companies in the

Gulf Cooperation Council (GCC). A purposive sampling technique was deployed. The findings using

SmartPLS 4.0 showed that DC and its components expect data authentication and TMC and its components

have significant effects on DMC. AIERP mediated the effect of DC and TMC on DMC while BAC did not

moderate the effect of DC and TMC on DMC. Decision-makers have to focus on collecting high-quality data

and ensuring the data is free from error. Decision-makers also have to use technology to enhance the quality

and effectiveness of decisions.

Key-Words: - Data Characteristics, Top management Characteristics, Decision making, Enterprise resource

planning, Artificial intelligence, GCC, Technological

Received: May 27, 2021. Revised: September 2, 2022. Accepted: September 26, 2022. Published: October 25, 2022.

1. Introduction

Decision-making is a fundamental aspect of

business organization. It has the capability in

achieving competitive advantages by making

decisions to create new products or services or

entering a new market, [1]. Managers with decision-

making capabilities (DMC) are able to lead

organizations in highly competitive industries and

are able to make the right decision during times of

uncertainty, [2]. The current environment is

characterized by high uncertainty and the need for

decision-making is critical for the survival and

thriving of organizations. Certain characteristics are

required to be held by managers to face the

increasing uncertainty, [3]. This can be divided into

business analytical capabilities and the ability to

interpret data. Due to the use of big data, the size,

accuracy, and the possibility for data error, [4],

decision-makers have to be aware of the data

analytical capabilities and have to use supporting

technologies that can help in making decisions, [5].

Developing decisions based on data is deemed

beneficial. It is known in decision-making that

accurate data leads to accurate information which in

turn leads to better decision-making, [6]. A good

data characteristic (DC) is integrity, free from error,

high quality, and authenticity. A report by

McKinsey, [4], indicated that benefits are accruing

to companies that base their decision-making

processes on ever-increasing amounts of data.

According to research conducted by McKinsey, [4],

data-driven businesses have a 23 times greater

chance of outperforming their competition in terms

of client acquisition, a nine times greater chance of

retaining consumers, and up to 19 times greater

potential for profit, [7]. Therefore, the necessity of

maintaining the accuracy of data cannot be stressed

because there are many things that depend on the

power of data. A single mistake in a dataset might

cause a domino effect and affect the decisions that

are most crucial to organizations, [8].

Managers with knowledge in the business can

use this knowledge in making decisions that can

improve the achievement of organizations, [2].

Managers’ characteristics such as their knowledge

of the industry as well as their innovation

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capabilities are critical components for decision-

makers and the desire to use technical support for

making decisions, [2], [9]. Technology such as

artificial intelligence (AI) aids decision-makers in

effectively addressing and coping with the present

business difficulties, [10]. With the advance of

technology, decision-making is much based on the

technology that is being used. Software such as

enterprise resource planning (ERP) and AI provide

assistance for decision-makers to make accurate

decisions, [11]. AI is transforming the ERP and

making it more capable of analyzing data and

making decisions, [11].

Nevertheless, there are several issues faced by

organizations in using these technologies. One of

the issues is related to the knowledge of managers

and their innovative capabilities as well as the cost

of acquiring this software. Further, the usage of

these technologies varied among countries.

Developed countries deployed these

technologies while there is less usage of the

technology in developing countries. In the Gulf

Cooperation Council (GCC), technological

companies have started deploying these

technologies. However, it is not known how the user

can affect the decision-making process among these

companies. Accordingly, this study aims to examine

the effect of DC and top management characteristics

(TMC) on the decision-making in GCC

technological companies. The study also aims to

examine the mediating role of AIERP and the

moderating role of business analytical capabilities.

The remaining sections of this article are the

literature review, research methodology, findings,

discussion, implications, and conclusion,

respectively.

2 Literature Review

This section presents the theoretical framework as

well as the development of hypotheses.

2.1 Theoretical Framework

Several theories can be used to explain the decision-

making process. One of these theories is the

resource-based view (RBV). This theory suggested

that a company can create a competitive advantage

and improve decision-making by deploying its

resources and capabilities such as top management

analytical capabilities and the technological

infrastructure, [12]. The technology-organization-

environment framework (TOE), the framework

suggested that there are organizational and

technological aspects that affect the decision-

making to use new technology. The technological

aspect is related to the technological characteristic

such as the mechanism of having accurate,

integrated, authenticated, and free-from-error data.

In addition, it is related to the use of technological

tools such as AI and ERP. Building on these two

theories, this study is deploying the conceptual

framework.

2.2 Critical Analysis

In this study, certain variables are deployed to

enhance the DMC of managers and decision-makers

in technological companies in GCC. The variables

include the DC which includes the data integrity

(DI), data quality (DQ), data authentication (DA),

and data error (DE). In addition, the variables also

include the TMC which includes the data analytical

competency (DAC) and technological innovation

(TI). The study also includes the variable of AIERP

and business analytical capabilities (BAC). In Table

1, the prior literature is examined to understand how

these factors can affect the DMC and to identify the

gaps in the literature.

Table 1. Critical Analysis for Selected Variables

Variables/ studies

TMC

DAC

AIERP

BAC

[13]

✓

[14]

✓

[15]

✓

[16]

✓

[17]

✓

[18]

✓

[19]

✓

[20]

✓

[21]

✓

[22]

✓

[23]

✓

[24]

✓

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As shown in Table 1, none of the reviewed studies

have included all the selected variables. Some

variables such as DAC have received less attention

compared with DC. In this study, these variables are

included.

2.3 Conceptual Framework and Hypotheses

Development

Building on the theory of RBV as well as the TOE,

this study assumes that the effects of variables such

as DC and TMC on DMC are positive. The research

also predicted that the effect of both the DC and

TMC is mediated by AIERP and moderated by the

business analytical capabilities. Figure 1 shows the

conceptual framework.

Fig. 1: Conceptual Framework

2.3.1 DC

DC is defined as the traits of data of being accurate,

reliable, having high quality, and free from

error, [25]. In this study, DC is a second-order

variable, and it consists of data integrity, data

quality, data authenticity, and data error. Prior

literature indicated that the data is essential for

making decisions, [6]. Having accurate, reliable, and

free-of-error data will enable companies to achieve

competitive advantages by making the right

decision, [7]. Therefore, it is proposed that:

H1: DC has a positive effect on DMC.

2.3.2 Data Integrity

In the context of the data lifecycle, data integrity is

described as "the extent to which all data are full,

consistent, and correct at all times", [26]. The

procedures of cleaning, mining, analysis, and other

processing of big data are significantly influenced

by data integrity in a number of ways, [27]. Data

integrity is linked positively to the business to

business transactions, [28]. It also has a positive

impact on the decision to use internal banking, [29].

Data integrity has also a positive effect on customer

trust in online banking, [30]. In this study, data

integrity is expected to affect the DMC of managers

and directors of technological companies in GCC.

H2: Data integrity positively affects the DMC.

2.3.3 Data Quality

The term "data quality" refers to the dependability

and the application efficiency of the data that is

currently available in a system, [31]. Data quality

affected positively the decision-making system,

design, and validation, [32]. In addition, data quality

also affected the business operation, [33] as well as

the performance of a machine learning system, [34].

Therefore, this study proposes a positive link

between data quality and DMC by managers of

technological companies in GCC.

H3: Data quality positively affects the DMC.

Data Characteristic

1. Data Integrity

2. Data Quality

3. Data authentication

4. Data Error

Top management Characteristic

1. Data analytical

competency

2. Technological innovation

Decision Making

capabilities

Artificial intelligence (AI)

ERP (AIERP)

Business analytical Capability

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2.3.4 Data Authentication

The authenticity of data is defined as the

maintenance of the data's integrity in addition to the

possibility of origin verification, [35]. The

legitimacy of the data is the most important factor in

determining the quality of the data. Data

authentication has an impact on the quality of

decision-making, which, in turn, has an impact on

the success of businesses, [36]. Data authenticity

affected the decision to use the internet of things

(IoT), [37]. In this study, data authenticity is

proposed as a predictor of the DMC of managers in

GCC technological companies. Therefore:

H4: Data authenticity positively affects the

DMC.

2.3.5 Data Error

It is not uncommon that there are mistakes made as

a result of the loss of some data, particularly crucial

data, [27]. Data error affects decision-making and

leads to misleading decisions, [2]. Error in data can

lead to poor-quality decision-making, [36]. Data

error can cause loss or damage of information which

leads to less quality decision-making, [37].

Accordingly, in this study, data error is expected to

have a negative effect on the DMC of managers in

GCC.

H5: Data error has a negative effect on the DMC.

2.3.6 TMC

TMC is defined as the competency and the skills of

the top management that helps in making a decision,

[38]. TMC has a significant effect on several

organizational outcomes such as organizational

innovation and business process development, [39].

TMC affected positively rational decision-making,

[40]. TMC also affected the firm performance, [41].

TMC is proposed in this study as a predictor of the

DMC. Therefore, the following is hypothesized:

H6: TMC positively affects the DMC.

2.3.7 Data Analytical Competency

A manager's data analytical capability (DAC) is a

broad description of their expected level of know-

how and ability in this area, [42]. DAC is critical for

decision making and it is found to have a significant

effect on the DMC, [24]. DAC also positively

affected the decision-making quality, [42]. The

decision-making quality is also affected by DAC,

[43]. This study proposed that DAC will affect the

DMC by managers of GCC in technological

companies.

H7: DAC positively affects the DMC.

2.3.8 Technological Innovation

In business, technological innovation refers to the

introduction of novel methods, apparatuses, and

materials that improve upon previous practices by

increasing productivity and efficiency.

Technological innovation positively affected the

DMC, [44]. Technological innovation affected the

decision to use green productivity, [45]. It also

affected the decision to introduce new technology in

organizations, [46] and the technology energy

efficiency in the organization, [47]. In this study,

technological innovation by managers is expected to

have a significant effect on the DMC of

technological companies in GCC.

H8: technological innovation positively affects

the DMC.

2.3.9 Mediating Role of AIERP

The use of technology such as AI and its

combination with ERP has enhanced the DMC of

managers. ERP positively affected business model

innovation, [48]. Additionally, it has mediated the

relationship between the performance of SMEs and

internal and external elements (technical

development, government policy, information

access, organizational culture and structure,

communication process, and IT readiness), [49]. In

India, the adoption of AI-CRM moderated

positively the effect of digital transformation on the

entrepreneurship process of SMEs, [50]. In this

study, the usage of AIERP is expected to mediate

the effect of DC and TMC on the DMC of managers

in technological companies in GCC.

H9: AIERP mediates the effect of DC on DMC.

H10: AIERP mediates the effect of TMC on

DMC.

2.3.10 Moderating the Role of Business

Analytical Capabilities

Business analysis is essential for making decisions.

Business analytical positively affected the firm

performance, [51]. Business management

capabilities and technology capabilities moderated

the effect of business model novelty and efficiency

design with new product development performance,

[52]. Business analytical capabilities moderated the

effect of intelligence dissemination and

responsiveness of managers, [53]. In this study, the

business analytical capabilities of managers of

technological companies in GCC are expected to

have a moderating role between the DC and the

TMC and their impact on DMC. Therefore, the

following is proposed:

H11: BAC moderates the effect of DC on DMC.

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H12: BAC moderates the effect of TMC on

DMC.

3 Research Methodology

This study aims to examine the effect of DC and

TMC on the DMC of technological companies in

GCC. Therefore, the population of this study is the

technology companies in GCC which includes six

countries. The number of technology companies is

1,754 companies, [54]. These companies are the

population of the study, and they are represented by

their managers, executives, and directors. All the

companies are large-scale companies, and they have

the potential to use advanced technology such as

AIERP. However, due to the lack of knowledge

regarding the capabilities of these companies and

contact details, purposive sampling is deployed in

this study. This is because the study set criteria to

ask only those who deploy the AIERP and to ask

only managers or top management-level employees.

Therefore, a question is asked at the beginning of

the online questionnaire regarding the usage of

AIERP. Those who have the technology are only

asked to answer the questionnaire.

The variables of the study were adopted and self-

developed. The adopted variables are data integrity

(4 items), data authentication (4 items), data error (4

items), and data quality (5 items) were adopted

from, [25], [38]. TMC which includes the data

analytical capabilities (6 items) adopted from, [52]

and the technological innovation (4 items) adopted

from, [55]. DMC consists of 8 items and it was

adopted from, [52]. Business analytical capabilities

and AIERP was self-developed based on prior

literature that has deployed similar variables such as

[50], [51], [52], [53]. The questionnaire was

validated by three experts in information technology

and decision-making. A pilot study was conducted

on 34 top management employees that are not

included in the field data and the reliability of the

measurement was established because Cronbach’s

Alpha (CA) is greater than 0.70 and this meets the

assumption of the reliability, [56].

The data was collected from 315 managers and

executives in the GCC technological and

information technology companies. The data

collection took place between December 2021 and

April 2022. The data were checked for missing

values and outliers. This has resulted in removing

13 responses. Further, the normality was assessed

using Skewness and Kurtosis. All values are less

than 1 and this meets the assumption of normal data

distribution. The multicollinearity was not an issue

in this study because the value of variation inflation

factor (VIF) and tolerance (T) is less than 5 and

greater than 0.20 respectively as shown in Table 2.

Table 2. Result of Normality and Multicollinearity

Variable

Skewness

Kurtosis

Tolerance

VIF

Data Integrity

302

-.068

-.481

.796

1.257

Data quality

302

-.531

-.344

.635

1.575

Data error

302

-.660

-.412

.362

2.764

Data authentication

302

-.720

-.473

.332

3.012

Data analytical capability

302

.503

-.533

.640

1.561

Technological innovation

302

-.773

-.068

.454

2.201

AIERP

302

-.409

-.544

.276

3.618

Business analytical capability

302

-.842

-.089

.302

3.311

DMC

302

-.625

-.227

4 Findings

4.1 Background of the Respondents

This study included 302 respondents. The

background of the respondents is shown in Table 3.

The majority are males (78.1%), with ages older

than 35 years (79.1%) and education of bachelor's

degree and above. The respondents have experience

of more than 5 years (97%).

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Table 3. Background of Respondents

Variable

Label

Frequency

Percent

Gender

Male

236

78.1

Female

21.9

Age

26-35

23.8

36-45

108

35.8

More than 45

122

40.4

Education

First university degree

232

76.8

Postgraduate

23.2

Experience

Less than 5 years

3.0

6-10 years

101

33.4

11 to 15 years

138

45.7

16-20 years

12.3

More than 20 years

5.6

4.2 Measurement Model

SmartPLS 4.0 was used to analyze the data for this

investigation, and the measurement model was

evaluated by examining the factor loading

(supposed to be more than 0.70), Cronbach's Alpha

(CA), composite reliability (CR), and validities,

including convergent and discriminant validity. To

increase the validity and reliability, several items

were removed. The values of CA, CR, and

convergent validity were attained, as shown in Table

4. Because the average variance extracted (AVE)

was more than 0.50, the convergent validity was

satisfied.

The discriminant validity was met because the

indicators are greater than the cross-loading of the

variables as shown in Table 5.

Table 4. Results of Assessing Measurement model

Second order

Variables

AVE

AIERP

0.947

0.954

0.862

Business analytical capability

0.962

0.963

0.840

Data analytical capability

CA=0.845

CR=0.851

AVE=0.593

Data Authentication

0.702

0.921

0.648

Data Error

0.612

0.570

0.294

Data Integrity

0.722

0.930

0.681

Data Quality

0.910

0.920

0.736

TMC, CA=0.899, CR=0.901,

AVE=0.671

Data Analytical Capability

0.943

0.945

0.814

Technological Innovation

0.741

0.710

0.579

DMC

0.925

0.926

0.817

Table 5. Discriminant Validity

AIERP

BAC

DAC

DMC

AIERP

0.929

BAC

0.543

0.916

DAC

0.432

0.488

0.902

0.590

0.602

0.515

0.805

-0.557

-0.570

-0.594

-0.613

0.843

0.546

0.585

0.550

0.552

-0.515

0.825

0.462

0.471

0.527

0.458

-0.462

0.539

0.858

DMC

0.578

0.614

0.648

0.554

-0.658

0.646

0.625

0.904

0.540

0.561

0.546

0.478

-0.484

0.637

0.595

0.674

0.892

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4.3 Structural Model

The assessment of the structural model includes the

R-square (R2) which represents the explanatory

power of the models. The value for the direct effect

models was 0.57 indicating that DC and TMC can

explain 57% of the variation in DMC. In the

mediating model, the value increased to 0.59

indicating that adding BAC can increase the

variation in DMC. Further, the R2 increased to 0.62

in the moderation model which added to the

explanation of the DMC. The F-square is acceptable

for all paths except for Data Authentication -> DMC

and the moderation paths. The structural model of

this study is presented in Figure 2. The Figure

shows the mediation and the moderation model.

The results of testing the hypotheses are shown

in Table 6. All the hypotheses related to the DC

except data authentication are significant. Similarly,

the hypotheses related to TMC are significant as

well as the mediator. However, no moderating effect

of BAC was identified in this study.

Fig. 2: Structural Model

Table 6. Results of the Hypotheses

Hypothesis

Path

Std.

Remark

Direct Effect

DC-> DMC

0.390

0.056

7.012

0.000

Sig

0.57

0.08

Data Integrity -> DMC

0.158

0.044

3.565

0.000

Sig

0.03

Data Quality -> DMC

0.176

0.041

4.271

0.000

Sig

0.04

Data Authentication -> DMC

0.022

0.053

0.417

0.677

Not sig

0.00

Data Error -> DMC

-0.267

0.058

4.570

0.000

Sig

0.06

TMC->DMC

0.437

0.052

8.445

0.000

Sig

0.13

Data Analytical Capability -> DMC

0.169

0.084

2.014

0.044

Sig

0.08

Technological Innovation -> DMC

0.237

0.049

4.853

0.000

Sig

0.10

Mediation

DC->AIERP->DMC

0.107

0.043

2.463

0.014

Sig

0.59

0.03

H10

TMC->AIERP->DMC

0.120

0.043

2.825

0.005

Sig

0.04

Moderation

H11

BAC x TMC -> DMC

0.047

0.063

0.755

0.451

Not sig

0.62

0.00

H12

BAC x DC -> DMC

-0.060

0.061

0.988

0.323

Not sig

0.00

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The first hypothesis related to the DC was

confirmed because the effect of DC on DMC is

significant at 0.000 supporting H1. For H2, H3, and

H5, they are supported because the p-value is less

than 0.05. Thus, data integrity, data quality, and data

error are critical for decision-making. However, data

authentication is not important because the p-value

is greater than 0.05. Thus, H4 is rejected. This could

be related to the notion that the data is prepared in

the organizations. For the effect of TMC on DMC,

the findings in Table 6 showed that there is a

positive relationship. Similarly, for H7 and H8, the

effects of BAC and TI are significant. The

mediation effect was examined by using the specific

indirect effect provided by SmartPLS 4.0. The

business analytical capability mediated the effect of

DC on DMC and this supports H9. Similarly, the

H10 is supported because the direct and indirect

effect of TMC on DMC is significant directly and

via the mediator AIERP. Thus, H10 is supported.

The moderation effect of AIERP was found not

significant. The moderator was examined using the

product indicator approach as suggested by, [56].

BAC did not moderate the effect of DC on DMC or

the effect of TMC on DMC. Thus, H11 and H12 are

rejected.

5 Discussion

This study examined the effect of DC and TMC on

the DMC among technology companies in GCC.

The findings showed that both the DC and the TMC

are critical for the DMC. TMC is more critical

compared with DC because the coefficient is higher

than the DC. However, both characteristics are

critical for DMC. These findings are in line with the

RBV which indicated that using the resources and

capabilities of companies can lead to competitive

advantage which ultimately leads to better

performance, [12]. In addition, the TOE pointed out

that the decision of using innovation is dependent on

technological factors such as the DC and its

components. The findings also agree with the

findings of, [25], [7] in terms of the effect of DC on

DMC. The findings of prior literature indicated that

the effect of TMC on DMC is positive, [39], [41].

In terms of the components, the most critical

components are data errors. Error in the data can

lead to a vital effect on the DMC. Wrong data leads

to wrong and misleading decisions which might

affect the performance of technological companies.

These findings in line with the report of, [7] which

indicated that decision-based on accurate data is

critical for the effectiveness of decision-making.

The second important components of DC are data

quality followed by data integrity. These findings

are in line with prior literature because the high

quality and integrity of data lead to better decision-

making [29], [30], [32], [33], [34].

For the TMC, the effect of DAC on decision-

making is important, indicating that managers with

high DAC will make more accurate decisions. This

finding is in agreement with the findings of, [24]

who found that DAC has a positive effect on

decision-making. In addition, technological

innovation is critical for the DAC indicating that

managers with high technological innovation will

make decisions accurately. The findings of prior

literature agree with the findings of this as they

found that technological innovation is important for

the decision to use green products, the introduction

of new technology in the organization, and the use

of technology effectively, [45], [46], [47].

The mediating role of AIERP was confirmed in

this study. This mediation is partial which indicates

that part of the relationship between DC and DMC

as well as between TMC and DMC can be explained

by the AIERP. This indicates that using the AIERP

can help greatly in making the decision by

technological companies in GCC. These findings

are in line with the findings of previous studies such

as, [49] which found that internal and external

factors can be mediated by ERP. Lastly, the

moderating effect of BAC was not confirmed in this

study. This might be due to the notion that this

software helps more in providing information for

decision-making where the need for the managers'

capabilities is reduced.

6 Implications

This study has examined the effect of DC and TMC

on DMC. The findings referred to the importance of

both characteristics. Decision makers in the GCC

must focus on the capabilities of managers in

making decisions. The ability to understand the data

and interpret data in a scientific manner can help in

making accurate and effective decisions. Data error

is catastrophically harmful to decision-making.

Therefore, decision-makers have to make sure that

the data is accurate and free from error. This can be

ensured by paying attention to the quality of data as

well as its integrity. The findings also showed that

technological innovation is important for decision-

making. Policymakers should focus on selecting

managers who have high technological innovation

and are open to the usage of new technology that

can help in forming better decisions that will have

an impact on decision-making. The use of AIERP is

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critical for organizations to identify errors and make

decisions using advanced technology. Decision

makers have to launch training courses to enhance

the knowledge of managers regarding the usage of

AIERP. Having adequate knowledge will enhance

technological innovation which will lead to more

usage of advanced technology and ultimately lead to

enhanced decision-making.

Business analytical capability did not moderate

the effect of DC and TMC and this could be due to

the fact that this study selected companies that have

ERP systems which mean that the knowledge of

business analytics is similar among the respondents.

This might explain the insignificant effect.

However, those in the position of developing the

policy are advised to conduct an assessment of the

understanding of managers regarding the industry

and choose those who have a high level of

knowledge. This study has contributed to the

literature by examining the DC and TMC in the

context of GCC. This research added to the existing

body of knowledge by investigating the moderating

effect of BAC and the mediating effect of AIERP.

Another contribution was made by integrating the

RBV and the TOE in the context of decision-

making. This integration has helped in explaining a

large portion of the variation in decision-making.

7 Conclusion

This study was conducted to examine the DMC of

managers in technological companies in GCC.

Managers should understand the importance of the

DC and its components. TMC is critical when it

comes to making decisions in a highly competitive

market. Using technology such as AIERP will give

companies a competitive advantage and enhance

their ability to make decisions. The study was

conducted on technological companies in GCC

using the information provided by the top

management of these companies. In addition, the

study deployed purposive sampling. Thus, the study

is limited to the companies that have participated in

this study. To extend the findings of this study,

future work is recommended to examine the effect

of DC and TMC in other industries such as the

service industry or manufacturing industry. Future

work is advised to focus on other emerging

economies. Future studies also are advised to

include other variables such as technological

uncertainty. Control variables such as age and size

of the company as well as the cost of using

technology can be employed by future research to

explain decision-making. It is advised for decision-

makers to train senior management staff members

and expand the use of cutting-edge technologies to

improve DMC.

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