Enterprise Domain Research Articles

Does data asset disclosure affect bank lending acquisition?

The Provisional Regulations stipulates that data resources are recognized as assets, thus deemed integral to financial statements in China. This mandates a readiness for data disclosure within the enterprise domain. This study examined a sample of Chinese A-share listed companies from 2012 to 2022 to investigate the repercussions of data asset information disclosure on bank lending acquisition. The findings underscore that enterprise data asset information disclosure level positively affects strengthening bank lending. This effect can be primarily attributed to heightened information transparency and long-term investors' attention. Heterogeneity analysis indicates that enterprise data asset disclosure significantly augmented the prospects of securing bank loans in non-state-owned enterprises, non-Internet communication enterprises, and enterprises involved in economic policy. The finding illustrates the pivotal role of strengthening enterprise data asset disclosure. These can serve to refine credit service precision, steering the bidirectional integration of financial technology and informatization.

A Systematic Review of Fast, Scalable, and Efficient Hardware Implementations of Elliptic Curve Cryptography for Blockchain

Blockchain technology entered the enterprise domain under the name of permissioned blockchains and hybrid or verifiable database systems, as they provide a distributed solution that allows multiple distrusting parties to share common information. One drawback of these systems is the overhead added by the cryptographic functions which impacts the throughput in terms of transactions per second and increases the latency of transaction processing. Many of the cryptographic functions and protocols used in blockchains are based on Elliptic Curve Cryptography (ECC). Unfortunately, ECC operations such as modulo inverse or scalar point multiplication have considerable latency which causes the slowdown of the entire system. In such situations, reconfigurable computing architectures, such as FPGAs, can be used to offload these tasks to overcome the performance loss. This survey analyzes the current state-of-the-art designs and implementations of ECC from a hardware perspective. We use a PRISMA-based approach to filter recent publications and to reduce their number from over 16,000 to only 43 highly relevant designs. In the end, we show that very few designs are able to fulfill all three properties of high performance, scalability, and efficiency.

Analysis of Enterprise Internet of Things Maturity Models: A Review

Maturity models are valuable tools when assessing the readiness and progress of technology incorporation in organizations, providing information for decision-making, resource allocation, and competitive advantage. The Internet of Things is a technology paradigm of global importance, especially for organizations, as it supports productivity improvements, real-time analysis, and customer satisfaction. Therefore, adopting and implementing this technology in enterprises brings several challenges, such as technological, organizational, security, and maturity issues. However, secondary studies that systematically compile the existing literature on these specific mechanisms for the enterprise domain are still being determined. This article aims to address this knowledge gap by conducting a review to deepen and synthesize the existing knowledge. This research followed established methodologies and protocols to synthesize and analyze the state of the art in the area; 489 documents were retrieved from seven bibliographic databases, and, applying inclusion and exclusion criteria, 36 primary studies were selected. The results indicate that the typical structures of maturity models incorporate technological, organizational, human, performance, and security dimensions through graded levels that denote the sophistication of the Internet of Things. Measurement techniques and metrics vary from model to model. There are few empirical validations or standardized improvement frameworks. The main conclusion is that there is a diversity of models, dimensions, indicators, and methods and a need for more comprehensive, adaptable, and user-friendly tools to help companies assess their Internet of Things maturity and inform future development strategies.

Architectural Patterns and Best Practices for Scalable Enterprise Applications with Angular

Angular has emerged as a leading framework for building scalable and maintainable enterprise applications. This article delves into the architectural patterns and best practices that harness Angular's robust ecosystem for developing enterprise-level applications that are not only scalable but also efficient and reliable. Through a detailed examination of module-based architectures, microfrontend architectures, and the monorepo approach, we uncover strategies that facilitate the development of large-scale applications, addressing common scalability challenges. Additionally, the article discusses key practices in lazy loading, state management, and component design, aimed at optimizing performance and enhancing the user experience. Security, an imperative aspect of enterprise applications, is addressed by outlining Angular-specific best practices for safeguarding applications against prevalent threats. Automated testing strategies and the role of continuous integration and deployment (CI/CD) in maintaining high-quality codebases are also explored. Drawing on real-world case studies, this article highlights the practical applications and successes of these architectural patterns and best practices, offering insights into overcoming typical challenges faced by developers. By providing a comprehensive guide to scalable application development with Angular, this article aims to equip software architects and developers with the knowledge to leverage Angular effectively in the enterprise domain, fostering innovation and efficiency in web application development.

Graphene based waveguide fed hybrid plasmonic terahertz patch antenna

Abstract The terahertz (THz) technology has fascinated lot of attention due to its enormous potential for a wide range of applications in the public, private and enterprise domains. This research work demonstrates the design structure and analysis of graphene based waveguide fed hybrid plasmonic THz patch antenna (HPTPA) constructed at around 3 THz. Hybrid plasmonic THz waveguide (HPTW) as a feeding line for the proposed THz patch antenna can increases antenna efficiency. The graphene is sandwiched between gallium arsenide (GaAs) and silver (Ag) to confine THz waves efficiently. Using mode analysis in finite element method for the proposed HPTW the propagation length, effective refractive index has been thoroughly examined. Based on the finite element method (FEM) approach, the results of the designed graphene-based waveguide fed HPTPA shows a high effective refractive index of 2.9, large propagation length of 230 µm, gain of 2.29 dBi, bandwidth of 200 GHz and efficiency of 85 % has obtained. The proposed graphene-based waveguide fed HPTPA could be beneficial to enable several photonic integrated circuit applications in next-generation wireless communication.

Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry

Since its introduction in 2006, differential privacy has emerged as a predominant statistical tool for quantifying data privacy in academic works. Yet despite the plethora of research and open-source utilities that have accompanied its rise, with limited exceptions, differential privacy has failed to achieve widespread adoption in the enterprise domain. Our study aims to shed light on the fundamental causes underlying this academic-industrial utilization gap through detailed interviews of 24 privacy practitioners across 9 major companies. We analyze the results of our survey to provide key findings and suggestions for companies striving to improve privacy protection in their data workflows and highlight the necessary and missing requirements of existing differential privacy tools, with the goal of guiding researchers working towards the broader adoption of differential privacy. Our findings indicate that analysts suffer from lengthy bureaucratic processes for requesting access to sensitive data, yet once granted, only scarcely-enforced privacy policies stand between rogue practitioners and misuse of private information. We thus argue that differential privacy can significantly improve the processes of requesting and conducting data exploration across silos, and conclude that with a few of the improvements suggested herein, the practical use of differential privacy across the enterprise is within striking distance.

Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

Service exposure and automated life-cycle management: The Key Enablers for 5G Services

The digitalization of society and the growing popularity of 5G-enabled use cases are creating new business opportunities for communication service providers (CSPs) to utilize 3GPP and cloud-based technologies in the enterprise domain.

An Algorithm-Centric Approach to Enhance Business Process Compliance Management

The diversity of enterprise domains is one of the significant factors that add to the challenges confronting organizations when managing compliance, and it, therefore, contributes to the cost of compliance. In particular, there is an indication that the inadequacy of a general approach to analyzing compliance documents impedes adequate compliance management practice. Taking an algorithmic-centric approach to tackling this challenge, this paper proposed algorithms and techniques intended to aid useful extraction, classification and analysis of dataflow, control flow, and time constraints in compliance management domain for different stakeholders. The algorithms and techniques were implemented and validated through two case studies and afterward evaluated for its adoption in practice.

Engaging Workers Broadening Work Meaningfulnes: Social Purpose, Job Satisfaction, and Performance

Despite many efforts and organizational investments, results in terms of workers’ engagement are still poor and questions regarding its antecedents continue to attract researchers’ attention. Leveraging the literature on engagement and the recent research development on meaningfulness, our study aims to investigate the role of the perceived social impact of work as a source of meaningfulness, which may enhance workers’ engagement. This project is based on a survey of 484 workers of 24 Italian social enterprises, a privileged research context in which social mission and market sustainability are joined together. Our results, developed through a multiple regression analysis, provide empirical support for the mediated positive effects of social meaningfulness of work on job satisfaction and individual performance through job engagement. They corroborate the fruitful possibility of combining apparent dichotomies, such as work and life domains, social and economic dimensions, individual and organization perspectives. They also demonstrate the appropriateness of the role of social issues as relevant sources of work meaningfulness for diverse kinds of workers. The empirical evidence contributes to a better understanding of the role of social significance in work relationships with relevant theoretical and managerial implications both for social enterprise domain and for for-profit organizations.

Dalykinės srities žinių turinio atnaujinimo valdymo informacinių sąveikų pagrindu modelis

The hierarchical Detailed Value Chain Model and the Elementary Management Cycle model of educational domain knowledge content updating are formally described in this paper, wherein computerized process measures are also proposed. The paper provides a method for updating the knowledge of the analyzed domain, referred to as the “enterprise domain,” based on enterprise modelling in terms of management information interactions. A method was designed, the formal DVCM and EMC descriptions of which are provided in the BPMN notation, allowing to develop a two-level (granular) model for describing the knowledge of educational domain management information interactions. In implementing this model and its algorithms in technological terms, a subsystem of enterprise knowledge has been created in a knowledge-based CASE system (computerized knowledge-based IS engineering), which performs the function of a domain knowledge database.

A developed software agent-knowledge-assisted procurement management tool for retailing enterprise

PurposeThis study aims to develop a software agent-knowledge procurement management tool to address uncertainties from external and internal environments, such as record failure, slow logistics auditing and distribution delay toward improving procurement management in retailing enterprises.Design/methodology/approachQuantitative methodology was used to collect data using a self-administered survey from randomly selected procurement staffs, marketers and customers to measure their perception regarding the feasibility and acceptance of the implemented agent-knowledge-based procurement management tool.FindingsResults from empirical analysis reveal that the implemented tool facilitates collaboration and interaction among buyers, sellers and procurement managers toward enhancing procurement managers’ flexibility to handle unexpected exceptions. In addition, results confirm the feasibility of the implemented tool in supporting procurement management toward handling inventory failure exception, which occurs in traditional procurement approaches. Moreover, descriptive results from user acceptance test verify that the tool was accepted by the respondents.Research limitations/implicationsThe limitation of this study is that the implemented tool is evaluated using data collected from respondents in Malaysia retailing enterprise only; thus, the results cannot be generalized to other enterprises and country. In addition, research implications from this study design a methodological and comprehensive software agent-knowledge-based model that support buyers, sellers and procurement managers with information to facilitate buying and selling operations.Practical implicationsPractically, the designed software agent-knowledge-based model describes how software agents collaborate with each other to facilitate procurement tasks and also use the knowledge base in the implemented tool to provide information sharing platform that manages the dynamics of procurement operations.Social implicationsThis research integrates software agents which are autonomous programs that carryout pre-defined task on behalf of end users. Socially, this study would be useful for procurement managers in developing mechanisms for instilling insights in retailing operations.Originality/valueThis research is among the first to attempt to develop a software agent-knowledge-based model to support procurement management in the retailing enterprise domain. It contributes to promote e-procurement practices by implementing a software agent-knowledge-oriented tool to address uncertainties experienced in retailing enterprise. It is envisaged that this study will provide basis for future research into e-procurement practices for retailing businesses in Malaysia and beyond.

COMPARATIVE ANALYSIS OF RANKING FUNCTIONS FOR RETRIEVING INFORMATION FROM MEDICAL REPOSITORY

Current and forthcoming Information Retrieval algorithms demand high mean average precision with contemporary high recall rates in the technical literature. Nevertheless, the existing state-of-the-art is still not optimized for speed, average query latency, and performance. The previous researchers presented various information retrieval models in the literature but the user search led to a ranking of documents that were hopeful to be relevant. In this paper, an evaluation of various information retrieval models is presented with a range of algorithms. The aim is to elaborate and review the current information retrieval function in the context of enterprise domain- specific search. Experiments were conducted on the OHSUMED benchmark data set from MEDLINE, a medical information database. The experimental results demonstrate that BM25F ranking function outperforms other extensively used ranking functions such as BM25, TFIDF, and Cosine on precision and recall measures.

Enhancing Privacy in Wearable IoT through a Provenance Architecture

The Internet of Things (IoT) is inspired by network interconnectedness of humans, objects, and cloud services to facilitate new use cases and new business models across multiple enterprise domains including healthcare. This creates the need for continuous data streaming in IoT architectures which are mainly designed following the broadcast model. The model facilitates IoT devices to sense and deliver information to other nodes (e.g., cloud, physical objects, etc.) that are interested in the information. However, this is a recipe for privacy breaches since sensitive data, such as personal vitals from wearables, can be delivered to undesired sniffing nodes. In order to protect users’ privacy and manufacturers’ IP, as well as detecting and blocking malicious activity, this research paper proposes privacy-oriented IoT architecture following the provenance technique. This ensures that the IoT data will only be delivered to the nodes that subscribe to receive the information. Using the provenance technique to ensure high transparency, the work is able to provide trace routes for digital audit trail. Several empirical evaluations are conducted in a real-world wearable IoT ecosystem to prove the superiority of the proposed work.

Towards the deep, knowledge-based interoperability of applications

The interoperability of enterprise applications in a dynamic environment is a complex issue. New methodological approaches and solutions are required. The methodological background of our approach is the internal modeling paradigm integrated with MDA approach. The modified MDA schema includes the new layer of the domain knowledge discovery, frameworks for internal modeling of enterprises. The peculiarity of the modified MDA is a focus on the cross-layer transferring of domain causality. The presented frameworks will help to trace the domain causal dependencies across the layers of the software system development, and they will aid in determining the influence of domain causality to the integrity and interoperability of the application. Researchers consider that the dynamic enterprise domain must be a goal-driven and self-managed system. The management transaction concept uses the internal modeling of the enterprise, which reveals the goal-driven information transformations inside the enterprise management activity (deep knowledge). This approach is combining the business process modeling and control theory principles, enterprise architecture modeling and autonomic computing concepts. The ArchiMate enterprise architecture modeling language is used for illustrating the cross-layer transferring of domain causality. Finally, we developed the architecture of the interoperable enterprise applications with the autonomic integration component.

Unlocking the potential of rural social enterprise

In this paper, we argue that social enterprise could represent a means of tackling rural challenges of providing sustainable economic development, addressing the withdrawal of public services and promoting community cohesion. The paper draws upon a review of existing academic as well as policy literature and develops a conceptual framework that helps to understand how to unlock the potential contribution of social enterprises to rural development. Drawing on an exploratory study conducted in two rural areas of Scotland we use interview data from social enterprise stakeholders to populate the conceptual framework and its rural (geographic), policy and social enterprise domains. Our study suggests that social enterprises can potentially enable an integrated approach to addressing local issues at the local level. They can create locally responsive services that fit the rural context. However, unlocking the potential of rural social enterprise may require moving beyond traditional policy silo approaches that treat economic development, community cohesion and public services as separate and disconnected since national policy-making frameworks have not always translated into practice at the rural level. Additionally, policy treatment of social enterprise needs to move beyond efforts to ‘scale up’ and achieve economies of scale. Collaborations between groups of social enterprises, and between social enterprises and public authorities can lead to economies of scope, particularly where strong trust-based relations within communities harness self-help and the co-production of services. With appropriate guidance and support, many rural challenges and needs could be transformed into opportunities for social enterprise development. In highlighting the opportunities and challenges faced by rural social enterprises, the paper suggests potential research gaps that, if filled, could contribute towards recognising and unlocking their full potential.

Actor diversity and interactions in the development of banana hybrid varieties in Uganda: implications for technology uptake

ABSTRACTPurpose: We examine the nature of networks through which new hybrid banana varieties (HBVs) in Uganda are developed, and how different actors engage in the technology development process.Design/methodology/approach: We collected the data through 20 key informant interviews and 5 focus group discussions with actors involved in the process. We analysed the data with NVivo and Social Network Analysis software.Findings: The process of developing HBVs involves many actors with a diversity of roles and interests. The network density was 29.4, implying existence of only 29.4% of the possible direct linkages. Strong ties mainly existed among research-oriented actors, with other actors in the periphery.Practical implications: The current position of smallholder farmers and other non-research actors inhibits their influence on decisions in the technology development process. We recommend that smallholder farmers should be empowered through better organization so that they can influence the process of developing HBVs. Researchers, supported with appropriate policies, should engage more with actors in the intermediary, enterprise and support service domains for a more vibrant agricultural research system.Theoretical implications: Application of the systems approach to technology development requires a dynamic process that supports involvement of a diverse range of actors. However, close attention to context is important in dealing with issues of power asymmetries and determining to what extent various actors engage in the process.Originality/value: This work contributes to literature on systems approaches to agricultural and rural development. The paper demonstrates that actor diversity and interactions are critical in technology development and uptake.

Modelling Academic Resources: An Apriori Approach

Data Mining or Knowledge Discovery is the process of discovering patterns in large data sets” [1] in the form databases and data warehouses in structured or unstructured manner. Association Rule Mining (ARM) is primarily focused on analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most meaningful relationships. In the area of academic data mining, it concerns with developing methods for discovering knowledge from data that come from Academic Enterprise Domain. There are several data mining algorithms pertaining to association rules used both offline and online platforms. One of the most popular and classical is Apriori algorithm that is used to extract frequent itemsets from large database and generating the association rule for discovering the knowledge. In the proposed research we have implemented an Apriori Algorithm implementation using Matlab and Dot Net Technologies using an academic examination registration dataset. The various Association Rules have been used to mine valuable knowledge regarding present, past and future course selection trends on subjects selected by the students at undergraduate level. The results will provide an insight in making future decisions regarding proposing academic infrastructure pertaining to human resource development/management, building of new departments/ centers, enhancing/ reducing intake capacity for a course/subject etc in an optimized manner.

Using Contribution Analysis to evaluate small & medium enterprise support policy

This research illustrates a pragmatic approach to evaluating the small and medium sized enterprise policy support in small or intermediate N research situations. A public interest perspective is taken. The suggested approach is most appropriate in settings where, for a variety of reasons, a study is unable to meet the requirements of a true experimental design (e.g. random assignment, establishment of counterfactuals, control groups, random samples, etc.). This approach will deliver findings on the contribution of the multiple factors influencing a result and will show whether the implicit theory of change for the policy under evaluation makes the expected contribution to the observed result and in what way. The Contribution Analysis finds that the theory of change for the policy under analysis was not verified in the study period. The outputs and outcomes of the analysis broadly support this conclusion. Moreover, the study finds that the primary limiting factor on profitable small and medium sized enterprise growth was the ‘limited endowments of managerial resources’ (Penrose, 1959) to manage the value-adding growth process and the lack of ‘well thought out, well managed projects’ (Walsh, 1985) and not lack of access to equity finance as surmised by policymakers. Whilst Ireland is used as the case example in the study, there are significant lessons for policy evaluators in other late-developing states and in regions with policy autonomy in the enterprise domain.

A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management

ABSTRACTThe accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprise-specific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.