Adoption Of Big Data Technologies Research Articles

FinTech Adoption in SMEs and Bank Credit Supplies: A Study on Manufacturing SMEs

Bank lending to SMEs plays a vital role in economic growth, contributing significantly to employment and GDP. Access to bank lending is crucial for small- and medium-sized enterprises (SMEs), as they contribute significantly to global employment and GDP. New financial technologies promise better bank operations, fewer costs, and enhanced credit supply to SMEs. However, there is still a lack of empirical findings on how these technologies can solve demand-side bank lending problems for small- and medium-sized firms. This study gathered data from a sample of 381 respondents, comprising CEOs, managers, officers, loan managers, IT consultants, and other relevant stakeholders. The findings indicate that the adoption of blockchain technologies, as well as the adoption of Big Data technologies encompassing cloud computing, data analytics, algorithms, and programming, along with the adoption of mobile banking technologies, have had a substantial positive impact on bank credit supplies for small- and medium-sized enterprises (SMEs) in Pakistan. This novel study contributes to existing knowledge in two ways. First, it provides knowledge to SMEs looking to adopt new technologies; second, it provides knowledge to a manager looking to finance the SMEs with information asymmetries. This research also provides key findings for researchers and policymakers.

Big data technology adoption in beef production

The United States plays a fundamental role in meeting the global demand for beef. Environmental concerns and volatile weather conditions have increasingly impacted beef production in recent years. To mitigate these issues, the cattle industry has begun to integrate sophisticated technological solutions into production processes. Utilization of these technologies, known as precision livestock farming (PLF) include the use of sensors, robotics, and big data analytics. PLF can enhance production and reduce illness-related losses by providing real-time insights for whole farm operations through the integration and analysis of generated data. PLF technology is still in the early stages of development and implementation, thus little is known about the factors impacting producer adoption in beef production. The objective of this study is to examine factors affecting adoption of PFL, particularly big data technology, in beef cattle production. Results of a binary probit analysis indicate that herd size and prior adoption of sensor-based technology positively affected the adoption of big data technology by U.S. beef producers. It was also found that producer age negatively impacted big data technology adoption. Results of this study were consistent with similar adoption assessments conducted on beef producers, in which farm size and farmer age were found to be common factors affecting technology adoption.

An Evaluation Framework for The Adoption of Big Data Technologies in Higher Educational Institutions

The aim of this study is to develop a framework for the use of Big Data Technology in Higher Educational Institutions (HEIs). The research which employed a mixed method approach, is primarily based on relevant critical analysis and literature review of studies conducted within the Big Data Technology area in higher education institutions. It investigated the processes of monitoring student performance by Namibian HEIs. The challenges faced by Namibian HEIs on the use of BDT. The various methods of data collection by Namibian HEIs and determined the level of readiness to adopt BDT.in Namibian HEIs. The study further undertook quantitative surveys and qualitative interviews with staff of the three (3) higher institutions in Namibia. A sample of 345 participants from International University of Management (IUM), Namibia University of Science and Technology (NUST), and The University of Namibia comprising the study's population (UNAM) were selected for this study using the simple random sampling technique. The Unified Theory of Acceptance and Use of Technology (UTAUT) constructs model was used to analyse to collect and analyse the quantitative data collected in this study. Finally, the study developed a sustainable framework that will guide the use of Big Data Technology in Namibian Higher Educational Institutions (HEIs). The validity of the framework was ascertained by expert reviews to ensure that the framework developed is effective and appropriate in fulfilling the purpose of the study and its objectives.

Big Data Technology Architecture Proposal for Smart Agriculture for Moroccan Fish Farming

As the global population increases rapidly, so does the need for fishing products. Aquaculture is well-developed in Asian countries but is underdeveloped in countries that share Morocco's climate. To meet the rising demands for aquaculture production, it is vital to embrace new digital strategies to manage the massive amount of data generated by the aquaculture environment. By employing Big Data methodologies, aquaculture activity is handled more effectively, resulting in increased production and decreased waste. This phase enables fish farmers and academics to obtain valuable data, increasing their productivity. Although Big Data approaches provide numerous benefits, they have yet to be substantially implemented in agriculture, particularly in fish farming. Numerous research projects investigate the use of Big Data in agriculture, but only some offer light on the applicability of these technologies to fish farming. In addition, no research has yet been undertaken for the Moroccan use case. This study aims to demonstrate the significance of investing in aquaculture powered by Big Data. This study provides data on the situation of aquaculture in Morocco in order to identify areas for improvement. The paper then describes the adoption of Big Data technology to intelligent fish farming and proposes a dedicated architecture to address the feasibility of the solution. In addition, methodologies for data collecting, data processing, and analytics are highlighted. This article illuminates the possibilities of Big Data in the aquaculture business. It demonstrates the technological and functional necessity of incorporating Big Data into traditional fish farming methods. Following this, a concept for an intelligent fish farming system based on Big Data technology is presented.

The UAE Employees’ Perceptions towards Factors for Sustaining Big Data Implementation and Continuous Impact on Their Organization’s Performance

The UAE has officially launched the Big Data initiative in the year 2022; however, the interest in and adoption of Big Data technologies and strategies had started much earlier in the private and public sectors. This research aims to explore the perceptions of the UAE employees on factors needed to implement sustainable Big Data and the continuous impact on their organizational performance. A total of 257 employees were randomly selected for an online survey, and data were collected using a Likert-style five-point scale that was tested for validity and reliability. The findings indicate that employees believe that Big Data Sustainable Implementation leads to Business Performance. Additionally, employees consider factors such as Big Data Architecture Quality, Human Cognitive Factors, and Organizational Readiness to significantly impact on Sustainable Implementation. Further, a moderating impact of Human Cognitive Factors was found on the relationship between Big Data Architecture Quality and Sustainable Implementation. The study provides managerial insights and recommendations for policymaking.

V-Matrix: A wave theory of value creation for big data

This paper examines the “V-Matrix” and provides a wave theory life cycle model of organizations’ adoption of big data. The V-Matrix is based on the big data five “V’s”: Volume, Velocity, Variety, Veracity, and Value and captures and enumerates the different potential states that an organization can go through as part of its adoption and evolution towards big data. We extend the V-Matrix to a state space approach in order to provide a characterization of the adoption of big data technologies in an organization. We develop and use a wave theory of implementation to accommodate a firm’s movement through the V-Matrix. Accordingly, the V-Matrix provides a life cycle model of organizational use of the different aspects of big data. In addition, the model can help organizations’ plan for decision-making use of big data as they anticipate movement from one state to another, as they add big data capabilities. As part of this analysis, the paper examines some of the issues that occur in the different states, including synergies and other issues associated with co-occurrence of different V’s with each other. Finally, this paper integrates the V-Matrix with other data analytic life cycles and examines some of the implications of those models.

Adoption of Big Data Technologies for the Digital Transformation of Public Agencies in the Global South (GS)

Adoption of Big Data Technologies for the Digital Transformation of Public Agencies in the Global South (GS)

When the future meets the past: Can safety and cyber security coexist in modern critical infrastructures?

Big data technologies are entering the world of ageing computer systems running critical infrastructures. These innovations promise to afford rapid Internet connectivity, remote operations or predictive maintenance. As legacy critical infrastructures were traditionally disconnected from the Internet, the prospect of their modernisation necessitates an inquiry into cyber security and how it intersects with traditional engineering requirements like safety, reliability or resilience. Looking at how the adoption of big data technologies in critical infrastructures shapes understandings of risk management, we focus on a specific case study from the cyber security governance: the EU Network and Information Systems Security Directive. We argue that the implementation of Network and Information Systems Security Directive is the first step in the integration of safety and security through novel risk management practices. Therefore, it is the move towards legitimising the modernisation of critical infrastructures. But we also show that security risk management practices cannot be directly transplanted from the safety realm, as cyber security is grounded in anticipation of the future adversarial behaviours rather than the history of equipment failure rates. Our analysis offers several postulates for the emerging research agenda on big data in complex engineering systems. Building on the conceptualisations of safety and security grounded in the materialist literature across Science and Technology Studies and Organisational Sociology, we call for a better understanding of the ‘making of’ technologies, standardisation processes and engineering knowledge in a quest to build safe and secure critical infrastructures.

Big data in agriculture: Between opportunity and solution

CONTEXTBig data applications in agriculture evolve fast, as more experience, applications, good practices and computational power become available. Actual solutions to real-life problems are scarce. What characterizes the adoption of big data problems to solutions and to what extent is there a match between them? OBJECTIVEWe aim to assess the conditions of the adoption of big data technologies in agricultural applications, based on the investigation of twelve real-life practical use cases in the precision agriculture and livestock domain. METHODSWe use a mixed method approach: a case study research around the twelve use cases of Horizon 2020 project CYBELE, varying from precision arable and livestock farming to fishing and food security, and a stakeholder survey (n = 56). Our analysis focuses on four perspectives: (1) the drivers of change that initiated the use cases; (2) the big data characteristics of the problem; (3) the technological maturity level of the solution both at start and end of the project; (4) the stakeholder perspective. RESULTS AND CONCLUSIONSResults show that the use cases’ drivers of change are a combination of data-, technology, research- and commercial interests; most have at least a research drive. The big data characteristics (volume, velocity, variety, veracity) are well-represented, with most emphasis on velocity and variety. Technology readiness levels show that the majority of use cases started at experimental or lab environment stage and aims at a technical maturity of real-world small-scale deployment. Stakeholders’ main concern is cost, user friendliness and to embed the solution within their current work practice.The adoption of better-matching big data solutions is modest. Big data solutions do not work out-of-the-box when changing application domains. Additional technology development is needed for addressing the idiosyncrasies of agricultural applications. SIGNIFICANCEWe add a practical, empirical assessment of the current status of big data problems and solutions to the existing body of mainly theoretical knowledge. We considered the CYBELE research project as our laboratory for this. Our strength is that we interviewed the use case representatives in person, and that we included the stakeholders’ perspective in our results.Large-scale deployments need effective interdisciplinary approaches and long-term project horizons to address issues emerging from big data characteristics, and to avoid compartmentalization of agricultural sciences.We need both an engineering perspective – to make things work in practice – and a systems thinking perspective – to offer holistic, integrated solutions.

Big data analytics for clinical decision-making: Understanding health sector perceptions of policy and practice

The introduction and use of ‘big data and analytics’ is an on-going issue of discussion in health sectors globally. Healthcare systems of developed countries are trying to create more value and better healthcare through data and use of big data technologies. With an increasing number of articles identifying the value creation of big data and analytics for clinical decision-making, this paper examines how big data is applied, or not applied, in clinical practice. Using social representation theory as a theoretical foundation the paper explores people's perceptions of big data across all levels (policy making, planning, funding, and clinical care) of the New Zealand healthcare sector. The findings show that although adoption of big data technologies is planned for population health and health management, the potential of big data for clinical care has yet to be explored in the New Zealand context. The findings also highlight concern over data quality. The paper provides recommendations for policy and practice particularly around the need for engagement and participation of all levels to discuss data quality as well as big-data-based changes such as precision medicine and technology-assisted clinical decision-making tools. Future avenues of research are suggested.

Adoption of big data technologies in smart cities of the European Union: Analysis of the importance and performance of technological factors

Abstract The cities of the European Union are adopting big data technologies in their development towards a smart city. Given that big data technologies are complex and disruptive technologies, it is necessary to determine the importance of factors and their aspects for the adoption of big data technologies in cities. The aim of this paper is to identify the most important aspects of technological factors in the adoption of big data technologies in the cities of the European Union. In order to achieve the goal of the paper a survey was conducted on a sample of European Union cities, and on the collected data, an analysis of the map of importance and performance of factors for the adoption of big data technologies was conducted. The results of the research show that the aspects of absorption capacity and technological readiness of EU cities are of relatively high importance, but with low levels of performance in relation to organizational and environmental factors. The contribution of the paper consists of general guidelines for increasing the level of technological readiness and absorption capacity of cities in order to increase the success of the adoption of big data technologies in the cities of the European Union.

Adoption of Big data technology by water stakeholders in Morocco: An adaptation of the technology acceptance model

Nowadays, big data technologies are becoming increasingly important in the modernization of organizations’ information systems. Indeed, water and climatology data producers and users deal daily with massive data processing. These actors need new technology to overcome the difficulties in data integration, processing and visualization. This paper presents an exploratory study about the intention to use big data technology by the water stakeholders in Morocco; we also present an exploratory review of technology acceptance model theory, a theoretical framework that explains the factors of adopting new technologies by users.

Enablers to supply chain performance on the basis of digitization technologies

PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

Overcoming Resistance to Big Data and Operational Changes Through Interactive Data Visualization.

Research has shown that the use of big data can modify operational processes in organizations. However, little research has been conducted on overcoming resistance to the process changes needed for adoption of big data technologies. In this article, we address this gap in the literature by investigating the impact of interactive data visualization on decision-making around operational process changes with big data. Our goal is to demonstrate how the choice of visualization of workflow and operational processes impacts decisions to embrace real-time, big data technology. To do so, we conduct a case study of patient/provider interactions in a large health care practice and compare the initial state with a revised workflow using a big data, real-time analytics platform. We then investigate the impact of the data visualization strategy on decision-making to implement operational changes caused by big data. The study demonstrates that interactive data visualization of operational processes can be an enabler in overcoming organizational resistance to big data technologies in a change-resistant organization. The concomitant benefit is that big data analytics is placed directly into the hands of primary decision makers.

The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model

Background: Big data technology has been used in several sectors in Indonesia. Adoption of big technology provides great potential for research, especially achievement in the implementation of big data in manufacturing companies. The Data Warehousing Institute (TDWI) Maturity Model is a tool that can be used to measure the state of "As-is" implementation of big data using 5 main dimensions. Maturity level shows the level of organizational ability to adjust big data technology currently.Objective: This study aims to measure the level of maturity in the implementation of big data technology in manufacturing companies PT. XYZ. This measurement is considered very important because it can know the process of managing data that is structured and has a high volume of data and provides more transparent reporting. This can help the company in making decisions that provide good information, so the company can increase the trust of stakeholders.Methods: This study uses qualitative methods to analyze research data using TWDI Maturity Model tools. Interview technique is used to retrieve respondent data where interview preparation guidelines are made by paying attention to 5 dimensions and 50 indicators in TDWI.Results: The research showed that the implementation of big data technology in the company as a whole has reached the level of corporate adoption. Infrastructure, data management, and analytics dimensions have reached the corporate adoption level while the organizational and governance dimensions are still at an early adoption level.Conclusion: To measure the maturity level of adoption of big data technology in manufacturing companies can use qualitative methods with TDWI Maturity model tools, interview guides for data retrieval by considering the 5 dimensions and 50 indicators that exist in TDWI.

Inflation in the Digital Age: Inflation Measurement and Bias in the 21st Century

In a highly timely move, several major central banks have announced a thorough review of their inflation targeting systems in recent months. Official statistics show that consumer inflation in the developed world has remained consistently below central banks’ target rates for almost a decade, in spite of the extraordinary efforts of these central banks. If central banks miss targets permanently, they may incur credibility problems and, over time, this could erode the social acceptance of their decisions. The world and our economies in it are undergoing an extraordinary transformation. 21st century megatrends such as digitalisation, the transformation of globalisation, ageing societies or even climate change are introducing new patterns into how economies function. Closely related to these megatrends, phenomena such as changing consumer habits and an increased importance of services pose new challenges for the measurement of economic processes, including the rate of inflation. One first step in the reconsideration of inflation targeting frameworks can be to correctly identify new types of bias in the measurement of inflation and to understand the new patterns determining the changes in consumer prices. Besides its challenges, technological progress offers new solutions as well. The widespread adoption of big data technologies has also created a significant opportunity for enhancing economic statistics; benefiting from these advantages, economic theory can help economic policymakers again, providing them with more solid support than over the past decade.

Impact of Technological, Organisational and Environmental Factors on Public Companies Ability to Utilise Big Data with Moderating Role of Organisational Culture: A Conceptual Model

The purpose of this research is to conduct a comparative study between Asean and European countries to find out the level of public company’s ability to utilize big data. The research also aims to determine the role that organisational culture plays in the adoption of Big Data technologies. A survey in the form of questionnaire will be distributed through random sampling. Data will be collected from both Asean and EU countries. Smart PLS will be used to test the hypothesis. Recommendations will be made to the Asean and EU government about the policies and strategies to implement so as to encourage more public organisation to adopt Big Data so as to increase their competitiveness and survival rate based on the result of the hypothesis.

Adoption of Big Data Technology for Innovation in B2B Marketing

ABSTRACTPurpose of the paper: The paper explores use of big data in innovation and market leadership in B2B relationships. It provides a framework to analyze the impact of big data, supported by four case studies.Methodology/approach: This is a conceptual paper, supported by case studies, offering the opportunity for concept generalization. The case study organizations are innovating to stay ahead in the market and to expand activity, using big data to exploit marketing opportunities and create new offerings.Findings: Organizations are recipients of and are collectors of big data, partly created by the increasing volume of business and customer transactions online. The qualitative exploration of the cases shows that big data and its analytics and applications can be taken as indicators of organizations’ ability to innovate to respond to market opportunities.Research limitations/implications: The online marketing environment is a dynamic one, where organizations must innovate to survive. A limitation is that the cases in the paper do not deal with those lagging in innovation, for whom survival is an issue. The paper’s examples are limited to successful organizations where big data, data mining and analytics led to strategic business success.Practical Implications: B2B organizations that operate in markets where their own products or services have a significant impact on how the products and services of their B2B customers provide value to end (B2C) customers should, in their innovation activity, engage fully with the end B2C market, together with their B2B customers. This has implications in every area from strategy to human resources. If the B2B organization’s pedigree includes B2C activity parallel to that of their customers but not competitive, this can give them an advantage in marketing, but if their B2C activities start to encroach on the B2C activities of their customers, this may cause conflict and loss of business. This implies that if marketing strategy becomes siloed between the different branches of the business, the potential for conflict increases.Originality/value of the paper: The paper’s value is in contributing to understanding big data’s role in business innovation, specifically in business to business organizations and where their innovation can transform customer experience at the end of the value chain, i.e., with final consumers. Therefore, the paper helps to address a research gap in the literature, which tends to focus on how such innovation affects business-to-business relationships.

Factors Influencing Consumer Behavior and Prospective Purchase Decisions in a Dynamic Pricing Environment — An Exploratory Factor Analysis Approach

The rapid advancements in information and communication technology during the third industrial revolution of the late 20th century has marked the beginning of a new era in the retail sector with the introduction of E-commerce. The dawn of the new century witnessed industry 4.0, revolutionizing all areas of online business by bringing in novel opportunities and possibilities. Despite the progress in technology, the determination of correct pricing on online selling platforms still remains a very complex task. The adoption of big data technology has enabled online sellers to make real-time price changes of high magnitude and proximity. However, with increasing awareness among buyers regarding modern pricing strategies, it is necessary to examine probable changes in consumer behavior when exposed to dynamic pricing scenarios. This study investigates the factors that influence consumer behavior, and their prospective online purchase decisions in a dynamic pricing context, through an exploratory factor analysis approach. A primary research survey was conducted, and 178 samples were finalized for data analysis through a series of web surveys completed by respondents in India. This study identifies, measures and classifies 27 research items into variables, namely shopping experience, privacy concerns, awareness about dynamic pricing, buying strategy, fair price perceptions, reprisal intentions and intentions for self-protection. These seven factors could be used to explain consumer behavior in a dynamic pricing situation.

Factors Influencing Consumer Behavior and Prospective Purchase Decisions in a Dynamic Pricing Environment—An Exploratory Factor Analysis Approach

The rapid advancements in information and communication technology during the third industrial revolution of the late 20th century has marked the beginning of a new era in the retail sector with the introduction of E-commerce. The dawn of the new century witnessed industry 4.0, revolutionizing all areas of online business by bringing in novel opportunities and possibilities. Despite the progress in technology, the determination of correct pricing on online selling platforms still remains a very complex task. The adoption of big data technology has enabled online sellers to make real-time price changes of high magnitude and proximity. However, with increasing awareness among buyers regarding modern pricing strategies, it is necessary to examine probable changes in consumer behavior when exposed to dynamic pricing scenarios. This study investigates the factors that influence consumer behavior, and their prospective online purchase decisions in a dynamic pricing context, through an exploratory factor analysis approach. A primary research survey was conducted, and 178 samples were finalized for data analysis through a series of web surveys completed by respondents in India. This study identifies, measures and classifies 27 research items into variables, namely shopping experience, privacy concerns, awareness about dynamic pricing, buying strategy, fair price perceptions, reprisal intentions and intentions for self-protection. These seven factors could be used to explain consumer behavior in a dynamic pricing situation.