Big Data Innovation Research Articles

Utilizing Big Data to Create Educational Information Systems

Background and Aims: Using big data to create educational information systems is critical for customizing learning experiences for individual students while also improving overall educational outcomes. Using data-driven insights, educators and administrators can make informed decisions to improve teaching strategies, predict student performance, and streamline operations, resulting in a more effective and personalized educational experience. The purpose of this paper is to investigate the use of Big Data to create Educational Information Systems. Methodology: The methodology calls for the use of a variety of data sources, including academic journals and case studies, as well as a structured literature review protocol to ensure comprehensive coverage. Data is collected and analyzed thematically to identify key trends, applications, and challenges in big data utilization in education, resulting in a comprehensive overview of its impact. Results: The study discovered that the review emphasizes big data's transformative potential in educational information systems, specifically its ability to improve personalized learning, predictive analytics, curriculum development, and administrative efficiency. Successful implementations, such as those at Georgia State University and Purdue University, highlight the significant benefits of using big data while emphasizing the importance of addressing ethical and privacy concerns. The future of big data in education promises even greater advances thanks to technologies like AI and real-time analytics, but it also necessitates ongoing research into ethical frameworks and their impact on educational equity. Balancing innovation with privacy and ethical concerns will be critical as educational institutions, administrators, and policymakers navigate these changes, allowing big data to reach its full potential while maintaining equitable and effective educational practices. Conclusion: The article emphasizes big data's potential to transform educational systems through personalized learning, predictive analytics, curriculum development, and administrative efficiency. Moving forward, it is critical to strike a balance between technological advancements and ethical and privacy concerns to ensure that big data innovations are used effectively and fairly in education.

How does digital technology administrative penalty affect big data technology innovation: evidence from China

Administrative regulation is an essential institutional arrangement for governing innovation in big data technology. Administrative penalties are one of its main methods. It is worth investigating whether administrative penalties can serve as an institutional safeguard to promote technological innovation. This study collects data on digital technology administrative penalty (DTAP) and big data technology innovation (BDTI) from 2008 to 2020 in 281 cities in China and empirically evaluates the impact and mechanism of DTAP on BDTI. The findings suggest that the normative impact of the DTAP system can foster a conducive business ecosystem for big data innovation. The incentive mechanism motivates firms to increase their long-term investment in technological innovation, while the deterrence mechanism ensures the existence of a regulated competitive market. These mechanisms play a crucial role in facilitating BDTI. Mechanism tests show that the DTAP has the potential to promote novel business models in the digital economy and accelerate progress in industrial digitalisation, which in turn promotes innovation in big data technologies. The impact of the DTAP on promoting BDTI is diverse, with a significantly greater impact in the first-tier cities. DTAP plays a more important role in fostering BDTI in places where the digital factor-driven industry is still in its early stages of development.

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Big data innovation and implementation in projects teams: towards a SEM approach to conflict prevention

PurposeDespite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.Design/methodology/approachThis study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.FindingsResults from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.Research limitations/implicationsThe study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.Practical implicationsThe study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.Social implicationsThe study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.Originality/valueThe study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

Challenges and content structure optimization of ideological and political education in universities based on structural equations

Abstract Big data is a double-edged sword, which brings opportunities and also risks and challenges to the innovation of ideological and political education in colleges and universities, which has led some people to question and deny the integration of big data innovation in ideological and political education in colleges and universities. This paper takes the influence of college ideological and political education environment on education effect as the research object, analyzes the characteristics of college ideological and political education environment, and summarizes the “advantages”, “disadvantages,” and “opportunities” of college ideologic and politic education environment, The research team analyzed the characteristics of the ideologic and politic education environment in colleges and universities and summarized the “advantages”, “disadvantages”, “opportunities” and “challenges”. Then factor analysis was applied to classify the factors influencing the ideological and political education environment of colleges and universities and establish hypotheses. The structural equation modeling method was used to analyze the relationship between the factors influencing the ideological and political education environment in colleges and universities and the relationship between the factors influencing the education environment. The results show that 44.63% of the respondents think there is influence, 27.97% think there is greater influence, and 23.82% think there is an important influence. Therefore, it can be concluded that the severe employment environment has a significant influence on the effect of ideological and political education. This study enhances ideological and political education by integrating big data innovation, which is important for the development of ideological and political education in colleges and universities.

Drivers and Loci of Big Data Innovation: A Review, Synthesis, and Future Research Directions

Despite a drastic increase in the amount of data collected and stored, the exact role that big data plays in innovation remains a subject of a diverging debate. This paper hence employs a systematic literature review and qualitative analysis techniques to identify, analyze and synthesize extant conceptual contributions on how big data drives the innovation process, where this process takes place, and which theoretical perspectives have shaped the research on big data innovation so far. Our findings reveal that big data innovation primarily takes place cooperatively, in the form of open innovation or within innovation networks, and that it is simultaneously driven by a variety of drivers. However, purposeful applications of big data and analytics for innovating in response to existing but unsatisfied market needs ("market pull") remain comparatively underresearched. Concomitantly, the theoretical lenses of “traditional” innovation management have largely framed the research on big data innovation so far. We coherently integrate these findings within an overarching framework and suggest an extensive future research agenda.

Big data analytics and key success factor in achieving competitive advantage and performance of small medium enterprises: literature review

Along with the rapid changes in technology, many small and medium-sized enterprises (SMEs) have leveraged Big Data Analytics and other factors to drive digital transformation to gain a competitive advantage and improve performance. Making smart decisions based on data will help small and medium-sized companies get through the wave toward larger investment targets. The successful implementation of Big Data Analytics and other factors depends on alignment with aspects of an organization, technology, and human resources. Identifying critical success factors (CSFs) in driving Big Data innovation is critical to addressing the challenges surrounding Big Data Analytics and its application. This study aims to examine a systematic literature review that explains the concept of Big Data Analytics and the key success factors in achieving competitive advantage and performance of small and medium-sized companies. By conducting a systematic literature review analysis for studies related to key success factors and Big Data Analytics during the research period on data needs, there were 50 sources of articles analyzed to identify the concept based on its classification. Referring to the descriptive qualitative analysis method for the selected literature section, the findings of this study succeeded in identifying 48 critical success factors from the implementation section of Big Data Analytics by proposing schemes and frameworks in 4 categories, namely: Organization, Technology, Human Resources (People) and Management Data Governance. These findings can be used as a reference for successful strategies and implementation of Big Data Analytics by formulating them into a more effective database form to improve the performance of small and medium-sized companies in achieving competitive advantage.

Enabling adaptive analytics at the edge with the Bi-Rex Big Data platform

Zero Defect Manufacturing (ZDM) is an emergent and disruptive paradigm that aims to optimize industrial process efficiency and sustainability by leveraging innovative and sophisticated data-driven approaches. It is a technology intensive concept that has the ambition of achieving and maintaining “first-time-right” quality goals in spite of varying processes and input material. As a result, developing ZDM applications might become overwhelming for small enterprises due to the multitude of diverse platform, the lack of know-how, and the need to adapt general purpose solutions to meet their needs. The Big Data Innovation and Research Excellence (Bi-Rex) is an Italian consortium that aims to accelerate the industrial innovation process of small enterprises. Within this consortium we developed a Big Data platform that enables adaptive analytics at the IT/OT boundary by leveraging innovative solutions for the safe and automatic deployment of data-driven apps, using MLOps and DevOps techniques and technologies, and evaluated it in real use cases provided by the world leading industrial partners involved in the project.

Implementation of Learning Analytics in Primary and Secondary School: A Systematic Literature Review

Increased adoption of educational technologies, the emergence of digital classroom concepts, and the interest in big data innovations have led to a growing awareness of the potential implementation of learning analytics to support learning development in educational institutions. However, most studies have focused on the implementation of learning analytics in higher education. As a result, research evidence and studies on actual and ongoing implementation in pre-higher education are still scarce. Therefore, there is a need to understand better the implementation of learning analytics at primary and secondary from the current learning analytics literature. This systematic literature review (SLR) aimed to identify learning analytics research that focuses on implementing learning analytics at pre-higher education levels, including in pre-school, primary, or secondary school. This SLR was carried out based on the SALSA framework to determine the protocol, search, appraisal, synthesis, analysis, and reporting approaches. The findings of the SLR support the arguments that the implementation of learning analytics in school is still in its infancy, where the implementation has been observed mostly in developed countries. Most of the Implementation is aimed at descriptive analytics, focusing on the purpose of monitoring, analysis, and feedback. The literature review has shown a lack of research on the implementation of learning analytics at the primary and secondary education levels.

Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?

Data-driven innovations (DDI) have significantly impacted firms’ operations thanks to the massive exploitation of huge data. However, to leverage big data and achieve supply chain innovation, a variety of complementary resources are necessary. In this study, we hypothesise that supply chain innovation (SCI) is dependent on firms’ big data analytics capabilities (BAC). Furthermore, we propose that this relation is mediated by two crucial capabilities of agility and adaptability that enable firms to efficiently meet the challenges of supply chain ambidexterity. Finally, we also test the moderating role of technology uncertainty in our research model. We collected data from 386 manufacturing firms in Pakistan and tested our model using structural equation modelling. The results confirmed our initial hypotheses that agility and adaptability both mediated our baseline relationship of BAC and big data innovation in supply chains. We further found support for the moderating role of technology uncertainty. Furthermore, technology uncertainty moderates the relationship between BAC and SCI. This study extends the current literature on digital analytics capabilities and innovation along the supply chain. Practically, our research suggests that investment in big data can result in affirmative consequences, if firms cultivate capabilities to encounter supply chain ambidexterity through agility and adaptability. Accordingly, we suggest that managers belonging to manufacturing firms need to build up these internal capabilities and to monitor and assess technology uncertainty in the environment.

Big Data Health Care Innovations: Performance Dashboarding as a Process of Collective Sensemaking.

Big data is poised to revolutionize health care, and performance dashboards can be an important tool to manage big data innovations. Dashboards show the progress being made and provide critical management information about effectiveness and efficiency. However, performance dashboards are more than just a clear and straightforward representation of performance in the health care context. Instead, the development and maintenance of informative dashboards can be more productively viewed as an interactive and iterative process involving all stakeholders. We refer to this process as dashboarding and reflect on our learnings within a large European Union–funded project. Within this project, multiple big data applications in health care are being developed, piloted, and scaled up. In this paper, we discuss the ways in which we cope with the inherent sensitivities and tensions surrounding dashboarding in such a dynamic environment.International Registered Report Identifier (IRRID)RR2-10.2196/16779

Big Data Machine Learning Using Apache Spark Mllib

The examination local area has utilized man-made brainpower, and specifically machine learning, in various ways to change various unique and, surprisingly, heterogeneous data sources into excellent realities and information, offering driving capacities to exact example finding. In any case, utilizing machine learning strategies on enormous and convoluted datasets is computationally costly and utilizes a great deal of coherent and actual assets, including central processor, memory, and data record space.In the current study collected the review of different researchers from 2010 to 2022. As how much data produced consistently arrives at quintillions of bytes, it is turning out to be more pivotal than any other time in recent memory to have a vigorous stage for powerful big data examination. Quite possibly of the most notable big datum investigation stages is Apache Spark MLlib, which gives various extraordinary capabilities for machine learning applications like relapse, grouping, aspect decrease, bunching, and rule extraction. This study's hidden reason is that Spark ML's big data execution and precision are fundamentally better than Spark Mllib's. The dataset for bank client exchanges is utilized in the correlation. We are probably not going to have the option to handle the sums and sorts of data we are managing with conventional programming arrangements. Thus, present day big data handling innovations that can disperse and deal with data in a versatile way are either coordinated into or taken over by conventional business knowledge (BI) frameworks. Big data innovation can likewise assist us with learning more about security, which can be found from colossal databases. The big data examination motor Apache Spark is utilized in the review to introduce a security-related data investigation.

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.

Optimization model: the innovation and future of e-ecotourism for sustainability

Purpose This study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology. Design/methodology/approach Data analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness. Findings This study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance. Research limitations/implications This study does not use a primary database, but uses secondary data from official sources that can be accessed by the public. Practical implications The paper includes implications for the development of intelligent technology based on big data and also requires policy innovation. Social implications Sustainable tourism development. Originality/value This study finds the expansion of new theory competitiveness of ecotourism destinations.

Big Data Analytics and Innovation Performance with the Transformational Role of Management Accountant

Big Data Analytics and Innovation Performance with the Transformational Role of Management Accountant

Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics

At present, reducing the impact of the construction industry on the environment is the key to achieving sustainable development. Countries all over the world are using software systems for bridge environmental impact assessment. However, due to the complexity and discreteness of environmental factors in the construction industry, they are difficult to update and determine quickly, and there is a phenomenon of data missing in the database. Most of the lost data are optimized by Monte Carlo simulation, which greatly reduces the reliability and accuracy of the research results. This paper uses Bayesian advanced fuzzy mathematics theory to solve this problem. In the research, a Bayesian fuzzy mathematics evaluation and a multi-level sensitivity priority discrimination model are established, and the weights and membership degrees of influencing factors were defined to achieve comprehensive coverage of influencing factors. With the support of theoretical modelling, software analysis and fuzzy mathematics theory are used to comprehensively evaluate all the influencing factors of the five influencing stages in the entire life cycle of the bridge structure. The results show that the material manufacturing, maintenance, and operation of the bridge still produce environmental pollution; the main source of the emissions exceeds 53% of the total emissions. The effective impact factor reaches 3.01. At the end of the article, a big data sensitivity model was established. Through big data innovation and optimization analysis, traffic pollution emissions were reduced by 330 tonnes. Modeling of the comprehensive research model; application; clearly confirms the effectiveness and practicality of the Bayesian network fuzzy number comprehensive evaluation model in dealing with uncertain factors in the evaluation of the sustainable development of the construction industry. The research results have made important contributions to the realization of the sustainable development goals of the construction industry.

Application of Big Data Innovation in Human Resource Performance Management

Application of Big Data Innovation in Human Resource Performance Management

The role of design thinking in Big Data innovations

ABSTRACT Digital technologies are disrupting the way companies manage business. In today’s society, where a plethora of different digital solutions are booming and the attention of both practitioners and scholars is growing, little is known about how to make the most of such technologies. Given the complexity of the phenomenon, this paper refers to a single digital technology, Big Data. It is commonly recognised that Big Data can represent a competitive advantage source for companies. However, there is still a lack of literature on how to exploit the opportunities provided by this technology, while the amount of data is so vast that their interpretation is complex. The focus on human centricity, iteration, and prototyping of Design Thinking brought the academic world towards the recognition that this approach could be valuable in steering and managing this technology. Thus, the paper investigates how Design Thinking can foster innovation based on Big Data technologies. It leverages three explicative case studies to shed light on a new set of hybrid practices. By comparing and contrasting cases, the four practices of (i) Cropping Big Data Landscapes, (ii) Reframing Big Data applications, (iii) Unveiling Big Data Opportunities, and (iv) Proving Big Data Releases are proposed. These hybrid sets of practices support both practitioners and academics in opening up the black box to interpret and value Big Data.

IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, and evidence-based medical decision support. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data and nonlinearly convert the patterns to high-level medical insights.

The Organization Model of Big Data Innovation Alliance: A Multi-Case Study from the Perspective of Virtual Clusters

The Big Data Innovation Alliance can be understood as a cross-industry and cross-region spatial agglomeration phenomenon based on Big Data. From the two aspects of constituent elements and organizational characteristics, the Big Data Innovation Alliance is found to be a learning organization with a flat management structure. A multi-case study method is used to empirically test the virtual cluster characteristics of the big data innovation alliance organization model using four typical cases, and to analyze its generality and specificity based on the research variables. Several policy implications have been obtained for the big data innovation alliance organization model.