Technological Aspects in the Era of Digital Transformation Leading to the Adoption of Big Data

The purpose of this study is to examine how technical advancements have impacted big data analytics adoption in the age of digital transformation. The study's population consists of Indonesian businesses who have integrated big data into their operational systems, particularly those in Jabodetabek. 205 individuals were included in the study's sample through the use of a purposive sample technique. Primary data from a questionnaire survey are the sort of data used in this study. Using SPSS version 25, several regression approaches were used to examine the data. The study's findings indicate that businesses' interest in implementing big data is positively impacted by relative advantage, compatibility, and complexity. However, security has a negative impact on businesses' willingness to use big data.


INTRODUCTION
The worldwide economy has been significantly impacted by the COVID-19 pandemic throughout 2019.In the sector, big data analytics (BDA), which provides firms with insightful data, is viewed as a new tactical tool, particularly in these difficult times.In recent years, big data has been hailed as a technical triumph [1].Big data analysis is becoming more and more appealing in theory and practice when taking into account the potential advantages and challenges [2].In the business world, Big Data information can be utilized to guide decisions that will ultimately help businesses meet their profit goals [3].Big Data's output can be used by organizations in the public service sector to increase client or customer satisfaction [4].
Ir. Samuel Samson, M. SiA former Serbian ambassador, spoke at the 2021 Goveia Data Web Summit on "Big data & AI Future Ecosystems" and noted that we are currently facing challenges.He noted that working from home during the Covid-19 pandemic has taught us many lessons about how to come up with innovations to overcome all existing limitations [5].The digital transformation period is followed by new breakthroughs [6].The future of these three components-Big Data, Artificial Intelligence (AI), and Ecosystemsshould be predicted in light of the focus of this event [7].The government and digital transformation stakeholders are heavily utilizing big data [8].
Furthermore, Big Data, Cyber Security, and AI technologies are essential for success in the information age, particularly at this period of economic crisis, disruption, and epidemic for businesses, society, and non-governmental organizations alike.Increasing the government's competitiveness and economic growth in the digital sector is now critically needed, both in terms of innovation, collaboration, and synergy between society, industry, Big Data & AI providers, startups, academia, researchers, and the government in general.This phenomenon indicates that technology use will only increase in the future.Of course, if we take advantage of these opportunities and use technology properly, there are plenty of ways it can simplify our lives and careers [9].
Even though big data implementation has many advantages, not all firms use big data, according to numerous studies [10].One of them presents the dubious claim that if companies adopt big data without having defined strategic goals, eighty percent of them will not be able to make use of it.In addition, there are difficulties and barriers with implementing big data.Big Data adoption is frequently the biggest issue that organizations confront, in addition to important issues with technology, structure, procedures, data management, and skills.There are many obstacles to overcome, and Big Data is expanding rapidly, necessitating additional research [11].
A few studies have been conducted using posi big data analytics.The factors that influence technology are as follows: factors related to technology (competitive pressure, government regulation), factors related to organizations (top management support, organizational readiness), and factors related to technology (relative advantage, complexity, compatibility, security) [12].When thinking about implementing new technology, security is a crucial consideration [13].Big data has a significant degree of insecurity, which makes businesses reluctant to embrace it.When it comes to implementing data-related services, business owners' first priority is security.According to earlier studies, security concerns hindered the adoption of big data [14].
The questionnaire employed by Lutfiet al. (2022) was changed for this study, which is an extension of that study.In addition to changing the questionnaire, this study used a different sample than other studies.Lutfi et al. (2022) used a sample of MSMEs in Jordan, whereas this study focused on big data-enabled Indonesian businesses [15].

LITERATURE REVIEW
The process of innovation diffusion has been explained by the diffusion of innovation theory for a long time.This theory addresses how novel concepts and innovations proliferate across a society.Rogers developed the DOI idea after putting it forth in 1962 [16].Originality Adoption is the process by which an innovation spreads throughout a social system over a predetermined amount of time via specific pathways [17].Prior to deciding whether to accept or reject an innovation, an organization needs to understand how the innovation works and then form a positive or negative attitude about it [18].adverse in light of this [19].Thus, the diffusion of information technology is intimately associated not only with its exceptional capacity to address technological issues but also with organizational features, external and internal organizational structures, and the change-averse attitudes of leaders [20].New technologies are adopted because of both organizational traits and innovation.The degree of adoption of innovation can be influenced by five key determinants: trialability, observability, relative advantage, complexity, and complexity.Thus, it can be said that the DOI model emphasizes the part that technological characteristics play in the adoption of IT within the company.
According to the DOI hypothesis, innovation spreads widely throughout a culture [21].Naturally, there are underlying variables that contribute to the development of an innovation [22].Several possibilities from this research can explain this, including

Advantage Relative (RA)
The business always weighs the costs, benefits, and drawbacks of any choice it makes [23].How a technology can serve something better than how it is now implemented is referred to as a relative advantage [24].According to the theory of diffusion of innovation, a business will embrace a technological innovation-in this case, BDA-if it believes that doing so will result in benefits that outweigh those of sticking with the status quo at the time of compatibility (COMP) and complexity (COMX) adoption.Security of Big Data (SECU) [25].Relative advantage can be achieved by improving the efficacy or efficiency of the business's operations, which adds value to the enterprise.Prior studies have indicated that the adoption of big data is positively impacted by relative advantages, as the benefits offered by big data serve as a catalyst for organizations to embrace it [26].Relative advantage is the benefit that a business will experience from implementing technology.Thus, the following is the formulation of the research hypothesis: H1: Relative Advantage positively influences the adoption of big data (BDA) [27].

Conformity (COMP)
The degree of alignment between a new system and an organization's present systems is known as compatibility [28].One of the main factors influencing the adoption of technology is compatibility.Compatibility in technology adoption refers to how well an organization's technology integrates with its business processes and culture.The degree to which the innovation selected by the business complements the internal user experience is another measure of compatibility [29].The Diffusion of Innovation hypothesis states that if a technology-in this case, BDA-is appropriate and aligned with the company's present needs and adhered to values, potential users are more likely to adopt it.Adopting an innovation that goes against the values and needs of potential consumers is likely to present challenges for the company.Contrary to this research, which revealed that compatibility had no effect on big data adoption because BDA was implemented more frequently due to the company's requirement for internal expertise, earlier research indicated that compatibility favorably influences the adoption of big data.Therefore, the research hypothesis is as follows: H2: Compatibility positively affects BDA [30].

Complexity (COMX)
If a new system or technology is thought to be excessively complex or challenging to implement, it may not succeed in spreading.Technological hurdles occur when processes such as collaboration are altered, necessitating that the new technology be simple to use in order to be readily adopted.
Employee comprehension of the innovation must occur quickly because sophisticated technologies introduce unpredictability and complexity into their use.The human element in utilizing technology plays a significant role in determining whether a corporation will accept it or not, in addition to the technology's usability.Previous studies discovered that the adoption of big data was negatively impacted by complexity.This contrasts with studies, which indicates that businesses are more likely to embrace big data if they believe it to be complex.Thus, the research hypothesis H3 is as follows: a high degree of complexity has a detrimental influence on BDA.

Security (SECU)
When opposed to manual storage, it is envisaged that the use of technology in a firm will aid in the functioning of the business in a more structured manner.Technologybased corporate data storage is not only left on its own.Technology adoption companies must take into account the security of the data they store, specifically the degree of security in which they store company information.Businesses are reluctant to embrace big data due to its high degree of insecurity.When it comes to using data-related services, company owners are mostly concerned about security.There are hazards associated with outsourcing that are relevant to security, such as using third-party tools and support to provide big data solutions or adopting cloud computing services.Prior studies have indicated that adoption of big data is adversely affected by security concerns.This leads to the derivation of the following hypothesis: H4: BDA is negatively impacted by low security (security).

Sample and Information Gathering
Primary data were used in this study, and the questionnaire survey approach was employed.Companies in the Jabodetabek region of Indonesia that have incorporated big data into their everyday operations make up the population and sample of this study.Purposive sampling is the method used in this study to acquire data.The research's sample requirements are as follows: workers in the Jabodetabek region who have incorporated big data into their operations must have been employed by the company for a minimum of four years and hold a Diploma or higher in education.It is considered that individuals with four years of work experience and a minimum education level of Diploma 3 have expertise and a thorough understanding of the company environment.

Gathering Information Resources
Comparative Experience If a corporation implements technology, it will reap benefits.There will be five (five) indicators used to measure this variable.The degree to which big data adoption is integrated into the organization's business procedures and culture is known as technology compatibility.Three indicators, total, will be used to measure this variable.Technological complexity is the degree to which employees of a corporation find it challenging to understand, manage, and utilize large data.Three indicators, total, will be used to measure this variable.Technology security is the degree of protection that comes with using big data.It covers issues like privacy and data security, vulnerabilities in access control to business information assets, risks associated with relying too much on vendors, and difficulties putting company policies into practice for employee protection.security and privacy of data.Four (four) indicators will be used to measure this variable.Adoption of big data is the term used to describe data that comes from regular internet use.Using specialized technologies, this data is processed so that the end products may be utilized for decision-making and as assets for the firm.Four (four) indicators will be used to measure this variable.
For every variable, the following are the question items.Six Likert scales are used to measure the variables: Strongly Disagree, Disagree, Agree, Agree Very Much, and Strongly Agree.In order to keep respondents from selecting "Neutral," Scale 6 was used.The responses to each question item will be tallied once each variable has been answered.Relative Advantage has a maximum total score of 30 (equal to six times five statement items), followed by Big Data Adoption (BDA) at 24, Complexity at 18, and Compatibility at 18.

No Variables
Instruments Source 1 Relative Advantage -BD helps our company handle supply chain risks effectively.
-BD helps our company reduce waste of any kind during the warehousing process.
-BD would allow our company to adapt to changing conditions more quickly than competitors.
-BD would enable our company to reduce the overall cost of the product to the end users. -

Modification
After that, the acquired data will undergo data quality testing in the form of reliability and validity tests using Cronbach's Alpha with a limit of >0.6 and Pearson Correlation, respectively.The following equation will be used to perform a multiple regression analysis on the data: BDA is equal to a plus b1.RA, b2.TIBLE, b3.COMPLEX, b4.SECURE, and e.
Where: Big Data Adoption, or BDA Relative Advantage is RA.

TABLE = Equivalency COMPLEX = Complexity SECURE = Security e = Error
The F-test is used for the model fit test, the t-test is used for the hypothesis test, and adjusted R2 is used for the coefficient of determination test.

RESULTS AND DISCUSSION
This study collects data quantitatively, using a questionnaire as the primary tool.The questionnaire, which used a 1-6 Likert scale, was circulated in a closed way using a network of friends and Google Form media in order to guarantee that the respondents who responded were the ones who were expected.The period of data collection was April 25, 2022-July 15, 2022.A total of 251 respondents were received during this time.205 responses were examined, but 46 of them did not match the requirements.The following table provides an explanation of the data selection procedure.Respondents who have worked for less than 4 years 28 5 It is possible to process all data.205 Four responders who did not fit the research criteria and worked for organizations that did not use big data were eliminated.Because they were concerned that they wouldn't comprehend the questionnaire's contents, 14 responders with less than a diploma 3 education were disqualified for not meeting the requirements.Additionally, twenty-eight responders with fewer than four years of work experience were disqualified.

Respondent Profile
The features of respondents, including their gender, age, degree of education, tenure, and position, are explained in this part by way of their demographics.

100%
Table 3 demonstrates that the percentage of respondents who are male and female is evenly distributed; the only variable is the number of respondents who are Pira, which is 103 (50.2%) and women 102 (49.8%).With a bachelor's degree accounting for 99.5% of the respondents' highest level of education, the bulk of respondents were between the ages of 31 and 35.The bulk of responders-88.3%-areemployees, followed by supervisors (11.2%) and middle managers (0.5%).

Hypothesis
A quality check was performed on the gathered data to determine the sincerity of the respondents' responses and to identify contextual variables at the time the study was carried out.The tests that were conducted included reliability tests using Cronbach's Alpha and validity tests using Pearson Correlation.The estimated r value for the validity test was found to be greater than the r table, indicating that the study's assertions were valid and consistent in assessing the variables under investigation.In the meantime, the big data adoption variable has a Cronbach's Alpha value of >0.60 and the value of >0.70 for all independent variables.This demonstrates that the claims made in this study are accurate and dependable for measuring the variables under investigation.The model fit (F test), partial test (t test), and coefficient of determination (adjusted R2) are used in this study's multiple regression analysis to assess the hypothesis.The outcomes of the hypothesis testing are as follows: Tabel 4. Hypothesis Results The study's adjusted R square value is 0.362, meaning that, as a percentage, the independent variable influences the dependent variable by 36.2%, with the remaining 63.8% being influenced by variables unrelated to the research variables.With 205 data and 8 independent variables, the study's F Table value is 1.98, and its Count value is 15.456 with a sig value of 0.000 <0.05 (α=5%).Therefore, it can be said that the dependent variable in this study is significantly influenced by the independent variables overall.

First Hypothesis: Comparative Advantage over BDA
Relative advantage has a positive impact on big data adoption, or H1 is accepted, according to the relative advantage variable's hypothesis testing results, which show a sig value of 0.000 < 0.05.This is a result of the business realizing the advantages and opportunities that come with implementing technology; big data is thought to be able to assist businesses in enhancing their performance.The study's findings are consistent with the DOI theory, which states that a business must consider the financial gains and advantages of implementing a new idea before deciding whether or not to do so, regardless of whether the innovation will satisfy the business.An innovation will proliferate among groups more swiftly the more benefits it is thought to offer.These findings support the findings of earlier researchers, who found that adoption of big data is positively influenced by relative advantage.

Second hypothesis: BDA compatibility
With a sig value of 0.017 < 0.05, the compatibility variable's hypothesis test findings indicate that compatibility has a positive impact on big data adoption, or, to put it another way, that H2 is accepted.The research's significant test results support earlier findings that compatibility positively influences the adoption of big data.However, other research explains that innovation adoption among SMEs is challenging due to the need for internal expertise that can leverage big data.This is in line with the DOI theory, which states that an innovation will be easier for a company to adopt if it is relevant to the needs of the company, fits in with the company's culture, and can be applied internally.This is because the company won't have to spend as much time internally adapting the innovation, which would waste time and be ineffective.Innovations that are incompatible with the social, cultural, and value context of the company, organization, or group will not be embraced at the same rate as those that are appropriate or compatible.

Third Hypothesis: BDA's degree of complexity
With a sig value of 0.065 > 0.05, the complexity variable's hypothesis test findings indicate that complexity has no bearing on the adoption of big data, and hence, H3 is rejected.This can occur because, if a corporation has a plan in place to get past this high degree of complexity, technology, while challenging, is no longer a barrier to adoption.The intricacy of a technology can be surmounted by offering training to potential users, hence facilitating the adoption of new advances by company personnel.According to DOI theory, technology will spread swiftly and be adopted by a larger number of individuals the more tech-savvy they are.These findings are consistent with studies that show complexity increases business decision-making agility (BDA) and that organizations that view big data as very complicated are more likely to embrace it.But these findings don't align with the research

Security Towards BDA Hypothesis
H4 is accepted because of the security variable's considerable negative impact on big data adoption, as indicated by the security variable's hypothesis test findings, which have a sig value of 0.000 < 0.05.Companies are reluctant to embrace technological innovation when security is inadequate.Leakage of firm data is one of the risks associated with insufficient security.This is something that businesses take into account before implementing an invention since it would cost them money.A new technology will be considered for adoption by more companies the safer it is.This is consistent with the DOI hypothesis of innovation diffusion inside an organization.The results of the significance test for this hypothesis are consistent with earlier studies that found low security standards to be detrimental to the adoption of big data.

CONCLUSION
Three of the four hypotheses that have been put forth can be accepted, according to the test results.Relative benefit, compatibility, and security are some of these theories.It was discovered that while security has a detrimental impact on firms' enthusiasm in adopting Big Data, relative advantage and compatibility have a positive impact.It is evident how crucial these elements are to comprehending what influences or prevents businesses from implementing big data.These findings provide a significant contribution to the understanding of technology adoption in the context of digital transformation by demonstrating that, although security concerns may be a limiting factor, the relative benefits and applicability of technologies are potentially crucial drivers.
It's interesting to note that adoption of Big Data was not found to be significantly impacted by complexity, suggesting that other factors like relative advantage and appropriateness may be more important.These results underscore the significance of comprehending the dynamics of certain variables in technological decision making, offering useful insights for businesses aiming to incorporate Big Data into their strategy.

Tabel 3 .
Features of the Respondent