Hasil untuk "Business records management"

Lei Zhang, B. Liu

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

Ali Zeytoon-Nejad

Business growth is a goal of great importance for its both private and social benefits. Many firms view business growth as an imperative for their survival, stability, and long-term success. Business growth can be socially beneficial, too, as it enables businesses to expand into new territories where they can stimulate economic growth and development, creates more jobs, increase living standards, and better serve their communities by giving back more through Corporate Social Responsibility initiatives. Business growth must be planned reasonably and optimally so that it can effectively achieve its critical ambitions in business practice. The current common practices for planning the supply side of business growth are usually ad-hoc and lack well-established mathematical and economic foundations. The present paper argues that business growth planning can be pursued more structurally, reliably, and meaningfully within the framework of Growth Accounting (GA), which was first introduced by Economics Nobel Laureate Robert Solow to study economic growth. It is shown that, although GA was initially put forth as a procedure to explain "economic growth" ex-post, it can similarly be used to plan "business growth" ex-ante when a general backward approach is taken in its procedure-called Backward Growth Accounting (BGA) in this paper. Taking this well-established economic-mathematical approach to planning business growth will enhance the current practices conceptually and structurally, as it is built on the basis of economic logic and mathematical tools. BGA can help businesses identify and plan for key drivers of output growth and assess shortcomings in the growth process, such as poor productivity, inadequate labor utilization, or insufficient capital investment. The paper outlines an eight-step procedure for planning business growth using BGA and includes appendices with real-world examples.

Chia-Fu Liu1, Chia-Wei Hsu, Ali Mostafavi

The recovery of businesses after a disaster is vital to community economic resilience, yet the network dynamics influencing the speed and spillover effects of recovery remain poorly understood. Understanding these dynamics is essential for characterizing economic resilience and informing more effective recovery policies. This study explores the extent to which post-disaster business recovery is shaped by network diffusion processes within pre-disaster business dependency networks, driven by visitation behaviors among business points of interest (POIs). We developed a network diffusion model to simulate recovery across businesses in the Louisiana Gulf Coast following Hurricane Ida (2021) and assessed its performance using empirical data. Our analysis focuses on four key areas: (1) the presence of a diffusion process influencing recovery across the business network; (2) variations in how different business types depend on others for recovery; (3) identification of recovery multiplier businesses that accelerate regional recovery; and (4) differences in recovery multipliers across high- and low-income areas. The findings reveal that business recovery is governed by diffusion dynamics in these behavior-based networks, with recovery speed closely linked to pre-disaster visitation patterns. Retail and service businesses are identified as key recovery multipliers whose rapid recovery accelerates the broader business network's recovery, enhancing economic resilience. Additionally, recovery multipliers vary between high- and low-income areas. This study enhances our understanding of network mechanisms in post-disaster recovery and offers valuable insights for improving recovery policies.

Bo Li

Empirical business cycle studies using cross-country data usually cannot achieve causal relationships while within-country studies mostly focus on the bust period. We provide the first causal investigation into the boom period of the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research design, we show that credit expansion in private-label mortgages causes a differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the house-related industries in the high net-export-growth areas. Most importantly, our unique research design enables us to perform the most comprehensive tests on theories (hypotheses) regarding the business cycle. We show that the following theories (hypotheses) cannot explain the cause of the 1999-2010 U.S. business cycle: the speculative euphoria hypothesis, the real business cycle theory, the collateral-driven credit cycle theory, the business uncertainty theory, and the extrapolative expectation theory.

Jinyang Li

In this research paper, we investigate into a paper named "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" [arXiv:1706.10059]. It is a portfolio management problem which is solved by deep learning techniques. The original paper proposes a financial-model-free reinforcement learning framework, which consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. Three different instants are used to realize this framework, namely a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). The performance is then examined by comparing to a number of recently reviewed or published portfolio-selection strategies. We have successfully replicated their implementations and evaluations. Besides, we further apply this framework in the stock market, instead of the cryptocurrency market that the original paper uses. The experiment in the cryptocurrency market is consistent with the original paper, which achieve superior returns. But it doesn't perform as well when applied in the stock market.

Suresh Kamath

The development of an IT strategy and ensuring that it is the best possible one for business is a key problem many organizations face. This problem is that of linking business architecture to IT architecture in general and application architecture specifically. In our earlier work we proposed Category theory as the formal language to unify the business and IT worlds with the ability to represent the concepts and relations between the two in a unified way. We used rCOS as the underlying model for the specification of interfaces, contracts, and components. The concept of pseudo-category was then utilized to represent the business and application architecture specifications and the relationships contained within. The linkages between them now can be established using the matching of the business component contracts with the application component contracts. However the matching was based on manual process and in this paper we extend the work by considering automated component matching process. The ground work for a tool to support the matching process is laid out in this paper.

Chu AMY, Kwok PWH, Chan JNL et al.

Amanda MY Chu,1 Patrick WH Kwok,1 Jacky NL Chan,2 Mike KP So2 1Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong; 2Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong KongCorrespondence: Amanda MY Chu, Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong, Email amandachu@eduhk.hkBackground: The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries’ pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era.Objective: We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning.Methods: This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts.Results: Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles.Conclusion: Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.Keywords: meta-analysis, coronavirus, pandemic risk management, WHO COVID-19 research database, data visualization

R. Sollmann, Azlan Mohamed, H. Samejima et al.

Lei Zhao, Miaomiao Zhang

This article mainly introduces how to use various basic emulators to form a combined emulator in the Jiutian Intelligence Network Simulation Platform to realize simulation service functions in different business scenarios. Among them, the combined emulator is included. The business scenarios include different practical applications such as multi-objective antenna optimization, high traffic of business, CSI (channel state information) compression feedback, etc.

Martiana Andri, Yunita Ani, Jauhari Sobar M. et al.

Most MSMEs, especially micro-enterprises, do not have good business accounting records, making it difficult for owners/managers to measure the profitability of their companies. Internal control management has a significant influence in reducing fraud in Micro Enterprises. The more principles-based accounting rules, the shorter the delay in annual reporting disclosures. It is important to have a good financial reporting system that can be utilized by Micro Enterprises and its cooperation partners who provide and distribute funds. ISSuFiRs is a web-based application that enables Islamic Financial Cooperatives to track the business progress of their financing partners. With this application, micro businesses can disclose their business activities in these financial records in accordance with the SAK-ETAP national accounting standards. Reporting on this system includes balance sheet, profit and loss, cash flow, sources, use of ZIS funds, and comments on financial reports. This study presents a prototyping framework that enables user value perception, evaluation, and continuous improvement of ISSuFiR design quality through co-creation methods. It is hoped that a simple and effective financial reporting system, ISSuFiRs, will be useful for Micro Enterprises and Islamic Financial Cooperatives to monitor business developments. This study proposes a prototyping framework that enables the user's perception of value. The framework is expected to be implemented in industrial product service systems, evaluated and then concluded. This research is ongoing, with a new prototype being rebuilt based on the evaluation results.

Adrian POP

The purpose of this study is to carry out a diagnostic analysis of the quality management system (QMS) in the Romanian call center industry In order to achieve this goal, the method of selective research was used, carrying out an investigation in the relevant companies. The sample was made up in such a way that the condition of representativeness in relation to the general community of the call-center industry is fulfilled. The questionnaire contained questions regarding the elements (processes) from which is compose quality management system, because the diagnosis must be made by analyzing the elements that from which the QMS is compose. In the second chapter, the QMS diagnosis was presented, analyzing the four elements that, according to the specialized literature, make the quality management system The third chapter analyzes the current status of quality management system implementation in the call center industry. Through the diagnostic analysis, the QMS dysfunctions most frequently faced by the call center industry were identified. Knowing the causes that determine nonquality, remedial and quality improvement solutions presented in the conclusions section were formulated.

Muslikhun Muslikhun, Tri Wahjoedi

The objective of this study is to investigate the impact of financial literacy and financial attitudes on consumptive behavior, with financial behavior acting as a mediating factor among residents of Sidoarjo, Indonesia. The research was conducted using a quantitative approach, and data collection was done through a questionnaire administered via Google Form. The study included a sample of 96 respondents, determined using the Lemeshow formula due to the unknown population size. The data was analyzed using the SmartPLS-3 software, applying the structural equation modeling (SEM) technique. The findings of this study reveal several significant outcomes. Firstly, financial literacy is found to have a direct significant influence on both consumptive behavior and financial behavior. Secondly, financial attitudes also have a direct significant impact on consumptive behavior and financial behavior. However, it is observed that financial behavior does not have a significant effect on consumptive behavior. Furthermore, the study concludes that financial behavior does not act as a mediating factor in the relationship between financial literacy and financial attitudes on consumer behavior. Overall, this research contributes to the existing knowledge by providing insights into the interplay between the discussed variables.

Wiwin Juliyanti

This study aims to examine the relationship between the use of social media on economic literacy and its implications for the lifestyle of the millennial generation in the city of Surakarta, Indonesia. Classified as a field research that uses quantitative descriptive methods, the questionnaire is used as a primary data collection instrument from purposive sampling technique of respondents consisting of students in Surakarta, Indonesia with the age criteria as Y and Z generations who actively use internet-based social media, so that 120 respondents were obtained. The data analysis used is descriptive and hypothesis testing using path analysis with the assistance of SPSS version 17 software. The results presentation that the use of Social Media (X) has a direct effect on Economic Literacy (Z), the higher the use of social media as measured using the parameters of Perceived Usefulness and Perceived Ease of Use from TAM theory, the higher the level of economic literacy owned by students, the Economic Literacy variable (Z) also affects Lifestyle (Y), besides that the test results also explain that the use of Social Media (X) has an indirect effect on Lifestyle (Y) through Economic Literacy (Z). The implication provides evidence that the perception of the benefits and convenience of social media has indirectly changed the lifestyle of students as measured using Activity, Interest and Opinion (AIO) psychographics.

Hisham Noori Hussain Al-Hashimy

In order to price construction in a way that ensures the highest degree of performance and prevents loss, this research based on businesses' accounting will utilize Building Information Management (BIM) to deal with engineer projects. The current study focus on establish a distinctive record for quantity pricing in engineering projects applied in Iraqi construction enterprises. For the sake of cost, it has been linked to the engineering contract construction licenses. Then, by balanced scorecard of each contract item, it was connected and priced using BIM. How to analyze the rotation of engineering projects placed out to bid using a balanced scorecard. Experience in pricing is necessary to apply the balanced scorecard idea to prices. This way of fusing technical and administrative labor must be understood and studied by engineers as well. This strategy supports the balanced scorecard approach to bid pricing in strategic management accounting. How is it possible for the balanced scorecard to make the four prices follow its guidelines, provide the predicted projected profit for this bid and for each component, and ensure that the outcomes follow a preset and authorized strategy in engineering pricing?

Fernando López-Martínez, E. Núñez-Valdéz, Vicente García Díaz et al.

Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population health include better healthcare outcomes, improvement of clinical operations, reducing costs of care, and generation of accurate medical information. Several machine learning algorithms implemented by the authors can use the large standardized datasets integrated into the platform to improve the effectiveness of public health interventions, improving diagnosis, and clinical decision support. The data integrated into the platform come from Electronic Health Records (EHR), Hospital Information Systems (HIS), Radiology Information Systems (RIS), and Laboratory Information Systems (LIS), as well as data generated by public health platforms, mobile data, social media, and clinical web portals. This massive volume of data is integrated using big data techniques for storage, retrieval, processing, and transformation. This paper presents the design of a digital health platform in a healthcare organization in Colombia to integrate operational, clinical, and business data repositories with advanced analytics to improve the decision-making process for population health management.

A. Yadav, S. Shikha, Suraj Gupta et al.

This research presents a scalable property/land registration framework. It is based on blockchain technology, where all registry offices come together on a single framework based on blockchain to improve the registration process. This paper mainly focuses on the problems in the land record and revenue sectors. In this paper, we explore the use of blockchain technology in current business scenarios. We discuss issues such as data security, privacy, and, more significantly, the absence of a shared forum amongst the involved organizations. This research proposes an efficient consensus mechanism to evaluate the performance of a blockchain-based implementation of a land revenue & recording automation system. The proposed Modified Round Robin Consensus Algorithm (MRRCA) reduced the 30% - 40% message overhead in committing a transaction to the network and thus adding the block to the blockchain.

Alistair P. Barros, Arthur H. M. ter Hofstede, Henderik A. Proper

The workflow concept, proliferated through the recently emergent computer supported cooperative work (CSCW) systems and workflow systems, advances information systems (IS) implementation models by incorporating aspects of collaboration and coordination in business processes. Under traditional implementation models, applications are partitioned into discrete units of functionality, with (typically) operational procedures used to describe how human and computerised actions of business processes combine to deliver business services. In this paper, a number of essential modelling concepts and features for business transaction workflows are developed.

Parantap Chatterjee

A learning management system (LMS) is an educational framework used to plan, implement, and assess a specific learning process which is specially used for e-learning education courses or training programs. We begin with the argument that the Technology Acceptance Model (TAM) is more applicable in predicting intention to use and usage for users than non users. This paper looks at the acceptability of TAM in predicting intention to use MOODLE Online Examination System among current users and future users where MOODLE is a learning platform designed to provide educators, administrators and learners with a single robust, secure and integrated system to create personalised learning environments. The place of the study was based on a Pvt. Management College of Kolkata, West Bengal, India and sample size was 55. For conducting the Online Examination in MOODLE platform, sample questions along with the correct answers has to be uploaded in the MOODLE local server with a few supported specific formats like – Aiken format, Gift format, Black board format, WebCT format or MOODLE XML format etc. Now it is a tedious job among the Faculty members of different departments to submit all the questions along with the appropriate answers with that specific MOODLE supported format, specially for whom who does not have so much experience in IT or Web based Technology. To resolve the problem, we practically implemented a template (namely Moodle_Quiz_V09) in the MOODLE platform which specifically can convert any .doc format (simple Word format) questions to MOODLE XML format. After conversion of such a questionnaire, a mock test was conducted to test the acceptability of the template. Based on the practical implementation, a proposed theoretical framework has been designed from the original TAM Model. A pilot survey was conducted among the 55 Faculty members of the College to collect their feedback and incorporated the same into the proposed framework which shows the acceptance of MOODLE Online Examination system with the specified Moodle_Quiz_V09 template.

Fatima Lampreia Carvalho

The present article aims to explain why community-based natural resource management and tourism certification are the main concerns in academic literature on tourism sustainability implementation. The method of choice is a systematic review of literature based on the Prisma Statement for Systematic Reviews. Sources of interest were identified within the Web of Science Core Collection and other repositories. From a total of 430 records screened, 106 stable documents were selected and submitted to content analysis to create a matrix coding of mentions of sustainable tourism implementation in highly cited publications. A content analysis revealed that sustainable tourism implementation encompasses eight sub-categories of interest in current research outputs. Those sub-categories are: (1) Adaptive resource management (ARM), (2) Carbon mitigation approach; (3) Community-based Conservation Areas (CCAs) and Community -based ecotourism; (4) Community-based natural resource management (CBNRM); (5) Multiobjective Optimization model (6) Social reinvestment strategy; (7) Tourism Sustainability Certification and (8) Transition Management. The analysis revealed that implementation strategies such as Community-based natural resource management and the Tourism Certification Approach, covered 60 percent of all mentions of methods of sustainability implementation in the literature selected and should be treated as leading accelerators of tourism sustainability, yet much work needs to be done explain how and why a certain destination or tourism business meet set standards over time and across national contexts.

Ziming Gao, Yuan Gao, Yi Hu et al.

Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest.