Adoption Of Big Data Analytics Research Articles

Institutional pressure and responsible innovation: how big data analytics adoption drives manufacturing SMEs toward competitiveness

Institutional pressure and responsible innovation: how big data analytics adoption drives manufacturing SMEs toward competitiveness

Big data analytics usage in the banking industry in Tanzania: does perceived risk play a moderating role on the technological factors

PurposeThe main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).Design/methodology/approachThe study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.FindingsTop management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.Originality/valueThis study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

The Integration of Sustainable Technology and Big Data Analytics in Saudi Arabian SMEs: A Path to Improved Business Performance

Big data analytics technology offers significant opportunities for innovation and performance improvement for small- and medium-sized enterprises (SMEs) operating in competitive environments. However, reaping these benefits requires the adoption of such technologies by SMEs. This study investigates the factors influencing the adoption of big data and analytics in Saudi Arabian SMEs in the service and manufacturing sectors, with a particular focus on the role of facilitating sustainable technology in enabling sustainable business performance. Data were collected from managers of SMEs in Saudi Arabia using a quantitative method. The proposed hypotheses were tested using structural equation modeling with SmartPLS 4.0. The findings reveal that big data security and management support significantly influence the perceived ease of use and usefulness of big data analytics in SMEs. Perceived ease of use significantly influences the adoption of big data analytics. Furthermore, facilitating sustainable technology was a significant predictor of sustainable business performance. Additionally, the study revealed that the adoption of big data analytics significantly influenced business performance. The insights obtained from this study can be useful for the service and manufacturing industries operating in Saudi Arabia, particularly regarding the key influencing factor of perceived ease of use that determines the adoption of big data analytics in the Saudi Arabian SME market.

Big Data Analytics Maturity Model for SMEs

Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.

Big Data Analytics Implementation and Practices in Medical Institute Libraries of Pakistan

Abstract The aim of this research was to analyze the contemporary practices of Big Data Analytics (BDA) in medical libraries of Pakistan and to explore ways for its implementation in these libraries. A cross-sectional study was carried out among medical librarians in Pakistan by using a Modified Technology Acceptance Model (MTAM) model, with collected data analyzed using Smart-PLS. The results revealed that lack of resources, technical expertise, and some other factors were significant hurdles in smooth implementation of BDA. It is concluded that the perception of medical librarians about adoption of BDA is very encouraging, which resultantly will improve the quality of healthcare services in the country.

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.

Exploring the dynamics of bigdata adoption in the Indian food industry with fuzzy analytical hierarchical process

PurposeThe main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.Design/methodology/approachThe study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.FindingsThe findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.Research limitations/implicationsThis research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.Originality/valueThe present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

Practices and barriers for big data projects

The adoption of big data analytics is increasing in every major industry, demanding investments in new projects, technologies, architectures, and processes to allow the integration of big data platforms with legacy systems; however, many organizations have failed to incorporate it effectively into their decision-making processes and project benefits have not been adequately captured. This study aims to further investigate how a big data analytics project can be implemented in insurance companies. A case study was conducted on one of the largest insurance companies in Brazil with interviews and document analysis. The study identified five main practices that were adopted to successfully implement a big data analytics project (implement automatic autoscaling alerts, use specialized big data tools, integrate the platform with legacy systems, comply with privacy legislation, and ensure the documentation of technical architecture using business process modeling), as well as four barriers that prevent its proper adoption (complexity of access to multicloud data sources, high processing requirements of unstructured data analysis, failure to attend to business necessities at the right time, and project delays brought by bureaucratic interdepartmental processes); some of these have not previously been identified. Finally, an action plan to address these issues is presented.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability, customer demand forecasting, cheaper development of products, and improved stock control. Small and medium sized enterprises (SMEs) are the backbone of the global economy, comprising of 90 % of businesses worldwide. However, only 10 % SMEs have adopted big data analytics despite the competitive advantage they could achieve. Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics. The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK. This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners. The results of the evaluation are presented with a discussion on the results, and the paper concludes with recommendations to improve the scoring tool based on the proposed framework. The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

The use of big data and analytics in external auditing: Does audit firm size matter? Evidence from a developing country

Purpose: The aims of this research are to investigate the reasons for adopting big data (BD) and big data analytics (BDA), determine their extent of usage, and identify potential obstacles to their adoption in a developing country, Egypt. Motivation: Prior literature criticized the audit profession for the slow adoption of BDA, and little is known about the adoption of BD and BDA in developing countries. The reluctance to incorporate BD and BDA into auditing can be attributed to their potential obstacles. In addition, prior studies focused on the Big-4 audit firms in developed countries with little known about adopting BD and BDA in local audit firms and developing countries. Design/methodology/approach:To achieve the objectives of this study, 16 audit practitioners with various positions, specializations, and experience levels were interviewed. The 16 participants belong to audit firms of different sizes: international audit firms, local audit firms, and a governmental auditing agency. Thematic analysis was employed through using the MAXQDA software package to analyze the data. Main findings: The findings revealed that the reasons for using BD and BDA go beyond improving audit efficiency and effectiveness and satisfying clients. All audit firms collect and analyze large volumes of traditional accounting data. However, the Big-4 firms manage and analyze non-financial data and new data items as complementary audit evidence. Also, it was found that the type of audit firm affects the use of these technologies, with international firms being superior to other firms. The Accountability State Authority lags behind other audit firms in adopting BD and BDA. Furthermore, it was found that some obstacles to adopting BD and BDA arise due to the specific characteristics of the Egyptian context, while others are universal. Practical implications/Managerial impact: Determining the reasons for and obstacles to adopting BD BDA is useful for audit firms and regulators to remove these obstacles and encourage using such new audit technologies. The findings might help developers of BDA software packages to enhance their packages to meet auditor requirements. Moreover, academic scholars can benefit from the findings of this study by gaining an understanding of the main differences between developed and developing countries in relation to adopting BD and BDA. Novelty/Contribution: This study was conducted in Egypt, a developing country with a an underdeveloped audit environment. Therefore, this study enriches the relevant literature by providing information about BD and BDA in an unexplored developing environment. Besides, it delves deeper into the reasons and obstacles to using BD and BDA in external financial auditing in a developing country. Also, it adds to the literature by identifying the most common BDA software packages and techniques that auditors use in a developing country. Moreover, it considers audit practitioners’ viewpoints on audit firms of various sizes.

Big Data Analytics for the Moroccan Water Actors: Towards an Adoption Model

This paper delves into the adoption of Big Data Analytics (BDA) within the Moroccan water management sector, specifically examining data collection, management flow, and decision-making processes. Through insightful interviews with representatives from the public sector responsible for water management, this study investigates the pivotal role of effective data governance in optimizing water resources[1]. Given the centrality of natural resources, particularly water, in national development strategies, the imperative for robust water governance is underscored. The impact of climate change further highlights the need for sufficient data collection and an efficient data management system[2]. In the management of natural resources, the accumulation of extensive data necessitates the application of BDA technology for streamlined processing. While BDA provides a solution for optimal data utilization, this article emphasizes the importance of maintaining control over processing and decision-making to ensure the success of functional and technical designs. The outcomes contribute to the formulation of an adoption model for BDA in the Moroccan water sector, facilitating well-informed and strategic decision-making for sustainable water resource management.

Big data analysis and application of library information resources

Abstract This study explores the application of extensive data analysis to library information resources, focusing on enhancing the efficiency of resource utilization and user satisfaction through personalized recommendation services. We employ cluster analysis algorithms, and least squares support vector machines to model library users’ borrowing behaviors. Additionally, we use the Apriori algorithm and collaborative filtering to recommend books effectively. This study’s experimental outcomes show a notable 11.36% increase in the index of network resource demand satisfaction, indicating a significant enhancement in the library’s ability to meet resource update demands. The adoption of big data analytics has been instrumental in advancing library information management and expanding extensive data services. The findings underscore the pivotal role of big data in enhancing the efficiency of information resource utilization and elevating user satisfaction, mainly through personalized recommendations.

The adoption of big data analytics in Jordanian SMEs: An extended technology organization environment framework with diffusion of innovation and perceived usefulness

While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights.

Big Data Analytics for Crisis Management From an Information Processing Theory Perspective: A Multimethodological Study

COVID-19 pandemic has created disruptions and risks in global supply chains. Big data analytics (BDA) has emerged in recent years as a potential solution for provisioning predictive and pre-emptive information to companies in order to preplan and mitigate the impacts of such risks. The focus of this article is to gain insights into how BDA can help companies combat a crisis like COVID-19 via a multimethodological scientific study. The advent of a crisis like COVID-19 brings with it uncertainties, and information processing theory (IPT) provides a perspective on the ways to deal with such uncertainties. We use IPT, in conjunction with the Crisis Management Theory, to lay the foundation of the article. After establishing the theoretical basis, we conduct two surveys towards supply chain managers, one before and one after the onset of the COVID-19 pandemic in India. We follow it up with qualitative interviews to gain further insights. The application of multiple methods helps ensure the triangulation of results and, hence, enhances the research rigor. Our research finds that although the current adoption of BDA in the Indian industry has not grown to a statistically significant level, there are serious future plans for the industry to adopt BDA for crisis management. The interviews also highlight the current status of adoption and the growth of BDA in the Indian industry. The article interestingly identifies that the traditional barriers to implementing new technologies (like BDA for crisis management) are no longer present in the current times. The COVID-19 pandemic has hence accelerated technology adoption and at the same time uncovered some BDA implementation challenges in practice (e.g., a lack of data scientists).

Analysis of Weather Prediction, Resource Management, and Land Optimization on the Application of Big Data Analytics in Agricultural Land Utilization in Agrarian Areas of West Java

This research investigates the impact of Weather Prediction, Resource Management, and Land Optimization on the adoption of Big Data Analytics in agricultural land utilization within the agrarian region of West Java. Employing a quantitative approach, the study integrates measurement model analysis, structural equation modeling, demographic profiling, and model fit assessment to comprehensively explore the intricate dynamics of technological adoption in agriculture. Results indicate that Land Optimization, Resource Management, and Weather Prediction significantly influence the adoption of Big Data Analytics. Demographic factors such as gender, age, education, and farming experience demonstrate varying correlations with key variables. The model exhibits strong fit, and approximately 60.2% of the variance in Big Data Analytics adoption is explained by the combined influence of the identified factors. This study contributes nuanced insights to inform policy and practice for sustainable and technology-driven agriculture in West Java.

Unified Data Analytics Platform For Financial Sector Using Big Data

Organizations in today's data-driven digital economy are seeking ways to leverage the immense value of massive amounts of information so as to make more informed decisions. They are able to not only uncover new prospects, but also learn more and improve their performance thanks to big data analytics. Although many businesses have poured resources into big data analytics projects, most have failed to reap the benefits. While there has been a lot of research into the topic of big data analytics, relatively little is known about the strategies that organizations use to merge the various aspects of this discipline. The authors of this paper attempt to solve the problem by developing a comprehensive big data analytics maturity model that can be used by executives to evaluate their own proficiency levels and plan for future development. In this research, we combined traditional quantitative techniques with qualitative meta-synthesis. To begin, we conducted an extensive literature search to catalog the skills and procedures associated with big data analytics maturity. Later, using a quantitative survey method, experts' perspectives on the proposed fundamental skills and practices were analyzed and graded. Finally, a focus group was used to allocate the capabilities to maturity levels according to their priority of deployment, taking into account the architecture of the big data analytics maturity model. We propose a model with four primary capabilities, nine key dimensions (KDs), and five stages of development. Its framework is CMMI-based, which stands for "capability maturity model integration." Questionnaires and focus groups were used to illustrate the big data maturity model. As a guide for effective adoption of big data analytics, we provide the capabilities and KDs, together with their suggested deployment order and weight in the maturity model. Using the offered methodology, businesses may assess their current big data analytics capabilities and steer themselves toward the most effective paths for improvement. Because of this, it enables managers to assess their strengths and weaknesses and establish investment objectives. This work uses a meta-synthesis, which hasn't been done in this subject before, to create an extensive maturity model. The proposed method makes substantial contributions to large data research, and it is both descriptive and prescriptive. A framework is presented in this paper for evaluating different big data analytics projects and settling on a coherent plan of action for their future growth and development.

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

The significance of big data analytics (BDA) has benefited the health sector by leveraging the potential insights and capabilities of big data in decision making. However, every implementation of BDA within the healthcare field faces difficulties due to incomplete or flawed information that necessitates attention and resolution. The purpose of this systematic literature review is to accomplish two main objectives. Firstly, it aims to synthesize the various elements that contribute to imperfect information in BDA and their impact on decision-making processes within the healthcare sector. This involves identifying and analyzing the factors that can result in imperfect information in BDA applications. Secondly, the review intends to create a taxonomy specifically focused on imperfect information within the context of BDA in the health sector. The study conducted a systematic review of the literature, specifically focusing on studies written in English and published up until February 2023. We also screened and retrieved the titles, abstracts, and potentially relevant studies to determine if they met the criteria for inclusion. As a result, they obtained a total of 58 primary studies. The findings displayed that the presence of uncertainty, imprecision, vagueness, incompleteness, and complexity factors in BDA significantly impacts the ability to sustain effective decision-making in the healthcare sector. Additionally, the study highlighted that the taxonomy for imperfect information in BDA provides healthcare managers with the means to utilize suitable strategies essential for successful implementation when dealing with incomplete information in big data. These findings have practical implications for BDA service providers, as they can leverage the findings to attract and promote the adoption of BDA within the healthcare sector.

AI and big data-driven decision support for fostering student innovation in music education at private underground colleges

This study investigates the transformative impact of AI-based Decision Support Systems (DSS) and Big Data Analytics (BDA) on student innovation and employability skills in an era of rapid technological advancement, with a focus on the mediating role of technological acceptance and the moderating role of resource availability. This study, which draws on a wide range of educational contexts and data sources, gives complete knowledge of the complex links between technology adoption, student results, and contextual factors. The results of this study show how AI-based DSS and BDA have a significant impact on musical education. These technological advancements enable tailored instruction and foster students' creative thinking. In order to prepare students for a work market that is rapidly changing, they act as a catalyst for improving employability skills. The study, however, emphasizes the complicated dynamics at work. Technological Acceptance emerges as a major mediating component, underlining the significance of students and instructors freely and effectively accepting technological tools. Furthermore, as a moderating factor, Resource Availability takes center stage, emphasizing the need for equitable access to educational resources to ensure that technology-driven advantages are accessible to all. The results of this study have broad repercussions. The adoption of AI and BDA by educational institutions is encouraged as transformative technologies for enhancing the learning process. Policymakers must create regulations that support equal access to technology and promote an innovative culture in the classroom. This study highlights for students how important it is to adopt new technologies, realizing how important they are in determining both their academic and career paths.

Determinants of Big Data Analytics (BDA) Adoption among Small and Medium Enterprises (SMEs)

Businesses must be innovative in the current "knowledge-based economy" if they intend to create and maintain a competitive advantage over rivals. The adoption of big data analytics (BDA) is thus revolutionary for precise decision-making and top performance in the present industrial environment. BDA application has attracted interest particularly due to the prospects and benefits that may be realized from its utilization in both academic and practical circles. Nevertheless, its adoption in small and medium-sized (SME) businesses is unknown thus far, prompting this study. To determine the determinants influencing SME preparedness to adopt BDA, the current study used the Technology Organization Environment (TOE) framework. The results supported the positive effects of BDA adoption on organizational marketing and financial performance in SMEs. Hence, understanding the factors influencing BDA acceptance enables managers to undertake the right initiatives, which are essential for its successful implementation. The outcomes also render it possible for BDA service providers to draw in and spread its prevalence among SME businesses.

Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review

In the current market landscape, enterprises face tremendous pressure to remain competitive and innovative to extend their businesses globally. Thus, a need exists for a new data analysis technique and a tool known as Big Data Analytics (BDA), which refers to massive data sets in light of their volume, velocity, variety, and veracity. Small and Medium Enterprises (SMEs) face challenges in obtaining and utilizing the knowledge derived from big data to make informed decisions regarding market selection and adopting appropriate internationalization strategies. These enterprises encounter resource limitations that hinder their ability to effectively acquire and implement big data insights for strategic decision-making in these areas. This systematic literature review aims to investigate the state of research on adopting big data analytics in SMEs. The study focuses on identifying key factors that influence the adoption of BDA. The study extracted 13 significant factors that are the highest influencers for BDA in SMEs. Those factors are top management support, training, relative advantage, it infrastructure, security, compatibility, complexity, adaptability, government it policies, competency, collaboration, digital transformation tools, and decision quality. The findings of this review are useful for practitioners, researchers, and decision-makers in understanding the factors that influence the adoption of big data analytics and the potential benefits and challenges associated with its implementation. Findings will shed light on the interplay between data governance and other factors influencing adoption, providing valuable insights for organizations seeking to establish robust data governance frameworks that support successful BDA initiatives.