Generative AI can help small business owners automate tasks, increase efficiency, and improve their bottom line. However, despite the seemingly intuitive design of systems like ChatGPT, significant barriers remain for those less comfortable with technology. To address these disparities, prior work highlights accessory skills -- beyond prompt engineering -- users must master to successfully adopt generative AI including keyboard shortcuts, editing skills, file conversions, and browser literacy. Building on a design workshop series and 15 interviews with small businesses, we introduce BizChat, a large language model (LLM)-powered web application that helps business owners across digital skills levels write their business plan -- an essential but often neglected document. To do so, BizChat's interface embodies three design considerations inspired by learning sciences: ensuring accessibility to users with less digital skills while maintaining extensibility to power users ("low-floor-high-ceiling"), providing in situ micro-learning to support entrepreneurial education ("just-in-time learning"), and framing interaction around business activities ("contextualized technology introduction"). We conclude with plans for a future BizChat deployment.
RegTech is a rapidly rising financial services sector focused on using cutting-edge technology to improve the process of regulatory compliance. RegTech solutions are characterized by numerous features and benefits that can considerably contribute to helping organizations operate effectively in the increasingly regulated environment, when it comes to compliance and risk management. This paper sheds light on why RegTech will be one of the most promising markets, driven by the rising cost of compliance and the growing reliance on technology in crisis management. Moreover, this paper will examine the advantages of using such solutions to strike a balance between compliance and operational efficiencies. This paper will deepen the understanding of regulatory compliance, introduce RegTech, and examine the benefits of using these solutions to achieve compliance.
MSMEs have become a very important sector in the Indonesian economy. One of them is included in the type of MSMEs that refer to micro, small, and medium enterprises which are often identified with the term street vendors or abbreviated as street vendors. In running a business, the important thing to do and apply is financial recording. Good financial records become capital in good business management in order to achieve success in their business. This study aims to measure the extent to which the involvement of street vendors around the governor's office field (Jl. Wolter Monginsidi, Teluk Betung Utara, Bandar Lampung) understands and implements good financial records in their business. This research applies a qualitative approach and uses case studies as a research method. Data collection is done through interviews, observation, and documentation. The data analysis used is qualitative descriptive analysis. From the findings of the research that has been conducted, street vendors who carry out trading activities around the governor's office field (Jl. Wolter Monginsidi, Teluk Betung Utara, Bandar Lampung) do not understand and apply good financial records in their business. Limited knowledge about good financial records is an obstacle for traders to implement good financial records.
Mark Gaynor, Kathleen Gillespie, Allison Roe
et al.
Background: In recent years, blockchain technology has made great strides in diverse industries but has fallen behind within the pharmaceutical industry. The pharmaceutical industry is complex and would benefit greatly from the distributed database and emphasis of information privacy promoted by blockchain technology. This paper identifies the potential best application for blockchain technology in the United States pharmaceutical industry by identifying current trends, companies exploring the possibilities of blockchain technology, and industry concerns with opportunities for improvement.
Methods: We utilized a 4D framework using ease of implementation, novelty, necessity, and fit of the overall industry to examine the adoption of blockchain technology in the pharmaceutical industry. Based on the 2D framework of difficulty and novelty as driving factors for the development of foundational technologies in the world of business by Iansiti and Lakhani in The Harvard Business Review, each application was ranked and scored for the best potential implementation. The potential applications proposed in this paper can be grouped into two main categories. The first category, management, includes best use cases such as health records, clinical trials, and inventory systems. The second category, monitoring, highlights cases such as pharmaceutical products, preventing counterfeits, optimizing supply chains, and addressing prescription misuse and abuse.Results: Each application was ranked by the four metrics in the framework, giving the greatest weight to necessity and ease of implementation. Using the highlighted methodology above, the applications for best implementation include Prescription Drug Misuse and Abuse Prevention, Prevention of Counterfeits, Clinical Trial Outcomes, and Smart Contracts.
Conclusion: Blockchain technology offers a new and promising solution to pharmaceutical industry needs. Each application of blockchain technology must fit within the framework of necessary, ease of implementation, familiarity amongst stakeholders, and fit of the overall industry to promote the most appropriate use. By using the extended framework proposed by Iansiti and Lakhani we show that blockchain, in all these domains, shows promise to improve pharmaceutical industry performance.
Computer applications to medicine. Medical informatics
Cloud-based distributed databases are a popular choice for many current applications, especially those that run over the Internet. By incorporating distributed database systems within cloud environments, it has enabled businesses to scale operations to a global level, all while achieving desired standards of system reliability, availability, and responsiveness. Cloud providers offer infrastructure and management tools for distributed databases as Database-as-a-Service (DBaaS), re-purposing the investment by businesses towards database services. This paper reviews the functionality of these services, by highlighting Amazon Relational Data Service (RDS), suited for handling relational distributed databases.
У статті узагальнено науково-практичні підходи до діджиталізації в сучасній індустрії туризму та гостинності, ідентифіковано проблеми та виокремлено перспективні напрями використання у ній цифрових технологій. Наукова новизна дослідження зумовлена виокремленням та аналізом ключових проблем, пов'язаних з діджиталізацією індустрії туризму та гостинності, зокрема конфіденційності та безпеки даних, «цифрової нерівності», технологічної інфраструктури та цифрової грамотності, можливого знеособлення послуг, залежності від онлайн-відгуків, надмірного туризму та постійної еволюції очікувань клієнтів. Практичне значення дослідження полягає у пропозиції можливих рішень для виокремлених проблем, включаючи застосування розширених заходів кібербезпеки, партнерства між державою та приватним сектором, навчання співробітників, використання гібридних моделей обслуговування, штучного інтелекту та машинного навчання, алгоритмів обробки Big Data, формування чіткого нормативного забезпечення інформаційної безпеки туризму та гостинності, залучення громад та інвестицій в цифрову інфраструктуру. Зроблено висновок про те, що, незважаючи на виклики, діджиталізація пропонує багатообіцяючі перспективи для більш інклюзивного, ефективного та орієнтованого на клієнтів майбутнього в індустрії туризму та гостинності.
Economics as a science, Business records management
Mpyana Mwamba Merlec, Youn Kyu Lee, Seng-phil Hong
et al.
A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.
Vitor Amado de Oliveira Bobel, T. Sigahi, Izabella Rampasso
et al.
The COVID-19 outbreak caused several negative effects in industries of all sizes and in all parts of the world, leading academic and practitioners to ask whether organizations could have been better prepared to face disruptive situations. This paper aims to analyze business continuity practices performed by Brazilian industries. A survey was conducted with academics who work in the field of organizational resilience and business continuity and are familiar with the reality of Brazilian companies in the industrial sector. The participants assessed 16 practices (P) proposed by the ISO 22301:2020, considering two categories: large industries (LI) and small and medium-sized industries (SMI). Data analysis was performed using Hierarchical Cluster Analysis, frequency analysis, Fuzzy TOPSIS and sensitivity analysis. For LIs, P4 (leaders conduct periodic critical analyses of practices) was considered the practice with the best application rate, while for SMIs, P2 (understand stakeholders’ needs and expectations, and use information in business continuity management) was chosen. In all scenarios tested for LIs and SMIs, P8 (well-structured systematic processes to analyze the impact of abnormal situations on their business and the potential risks of a disruption) and P16 (periodic audits of their business continuity management activities to identify opportunities for improvement, and information record) are in the bottom quartile. When compared to LIs in the Brazilian context, SMIs exhibit more profound deficiencies in terms of applying business continuity practices. The findings of this study can be of great value to assist managers in improving organizational resilience. Organizations should be better prepared to face future disruptive events, whether biological, social, technological, or economic.
Marta Pérez Sigüenza, Laura Rodríguez-León Rodríguez, Juan Manuel Ramón Jerónimo
et al.
This study analyses how entrepreneurs adapt or change international control management and organisation structures in response to their resources and capabilities and the context of the situation, from the resource-based theory (RBT) and contingency and effectuation framework approaches, taking the dynamism from knowledge-intensive services (KIS) into consideration. A multiple case study has been performed, based on semi-structured interviews with nine founders (entrepreneurs) of less-than 5-year-old international businesses who are actively involved in the management. All the interviews have been recorded, coded, and analysed through factsheets. The findings suggest that there is a relation between entrepreneurship and the characteristics of the entrepreneur; the character of owners or founders is key to embarking on this kind of business challenge. Furthermore, the age and nature of the manager—entrepreneur or non-entrepreneur—influence the business direction. This research analyses the role of the founder, owner, and/or management depending on the resources, capabilities, and uncertain contexts of the small, young firms. The age of the organisation’s and the degree of professionalism of the management’s impact on the management style and the use of control mechanisms are scarcely analysed yet, which could improve the relationships in MCS to achieve local and global control needs.
In the management of environment the Environmental Management Accounting (EMA) is essential for corporate or companies because corporate sectors are the main parties of environmental humiliation as they are existed in the environment and for protecting environment a branch of accounting is emerged which is called environmental management accounting. The objective of the study is to develop a compliance framework for EMA and appraise the ER practices in selected industries in Bangladesh. In conducting the study, 50 environmental sensitive industries were selected from DSE. A compliance checklist was developed on 75 aspects of EMA and ER under 13 groups. In developing the compliance index binary method is used i.e. 1= if ER practices; 0= if not practices. Further the level of EMR/ER practices have been evaluated in terms of selected independent variables of the company viz. total assets, total sales, return on equity and size of board. The study found that the environmental management accounting in the manufacturing companies is in poor level. The maximum compliance is 67% and the lowest is 20%. The TA, TS BS and SP have been considered to find out the explanatory variables. In most of the cases board size does not play significant role in the practice of EMA in the sampled firms.
Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.
This study aims to design a set of the visual artificial intelligence system based on medical information mining for hospital emergency care management. A visual artificial intelligence emergency first aid nursing management system is designed by analyzing the needs of the emergency first aid nursing management system. The results show that system personnel allocation, comparative management, record management, query management analysis, basic setup analysis, nursing management basis, and nonfunctional requirements all need to be optimized for the emergency first aid management system. In this study, the comparative management module, log management module, and the query management module are designed, and the emergency first aid management system of different APP terminal functions in different modules is described in detail. The nursing document query business is tested, and the corresponding time of query of nursing assessment sheet, nurse shift record, nurse record, and physical sign observation sheet is 375.50 ms, 351.48 ms, 336.36 ms, and 245.57 ms, respectively. It shows that the visual artificial intelligence emergency nursing management system based on medical information mining can provide convenience for clinical work to a large extent and has potential application value in hospital emergency nursing work.
Purpose This paper aims to examine the influence of records management on the performance of procurement management units (PMUs) in Tanzania. Design/methodology/approach To meet the objective of the study, cross-sectional survey design and quantitative approaches were used for data collection and analysis. Preliminary analysis of the data was carried out using descriptive statistics and structural equation modelling was then used to analyse the influence of records management standards on procurement performance. The data were collected from 164 government PMUs in Tanzania. Findings The findings revealed that records management attributes significantly affect procurement performance in Tanzania. Furthermore, government policy and integrity factors confound the association between records management and procurement performance. Practical implications The policymakers and practitioners of public procurement are required to be aware of records management principles and how each related factor influences the performance of PMUs. They should emphasise proper storage and housing, equipment, arrangement and access and implement electronic records management. Originality/value This study shows how the ISO 15489 model can be applied to public procurement processes. This study also provides the lesson that procurement records need to be protected to ensure their authenticity, reliability, integrity and useability.
Information technologies (IT) and information systems (IS) are the backbone of any developed business, and organizations without them cannot compete. In recent decades, many best practices standards, and guides have been made available to project managers and organizations aimed to improve project management. Unfortunately, IS projects continue to show a poor track record, and problems related to project management performance persists. Risk management has a vital role in this context since it can increase the likelihood and impact of positive events, and decrease the likelihood and impact of adverse events in the project. This article presents the results of an international web-based survey, studying if risk management processes are being implemented consistently in IS project management. The obtained results show low levels of risk management processes implementation and reinforce the idea that “it can be risky not to do risk management,” demanding more research in this area.
Deep reinforcement learning (DRL) has been widely studied in the portfolio management task. However, it is challenging to understand a DRL-based trading strategy because of the black-box nature of deep neural networks. In this paper, we propose an empirical approach to explain the strategies of DRL agents for the portfolio management task. First, we use a linear model in hindsight as the reference model, which finds the best portfolio weights by assuming knowing actual stock returns in foresight. In particular, we use the coefficients of a linear model in hindsight as the reference feature weights. Secondly, for DRL agents, we use integrated gradients to define the feature weights, which are the coefficients between reward and features under a linear regression model. Thirdly, we study the prediction power in two cases, single-step prediction and multi-step prediction. In particular, we quantify the prediction power by calculating the linear correlations between the feature weights of a DRL agent and the reference feature weights, and similarly for machine learning methods. Finally, we evaluate a portfolio management task on Dow Jones 30 constituent stocks during 01/01/2009 to 09/01/2021. Our approach empirically reveals that a DRL agent exhibits a stronger multi-step prediction power than machine learning methods.
We analyzed the efficiency of the insurance industry in Bosnia and Herzegovina (BiH) in the period from 2015 to 2019 in order to identify good and bad practices, sources of inefficiency and to propose guidelines for the necessary efficiency improvements based on the results. Efficiency measurement was performed using the nonparametric Data Envelopment Analysis (DEA) technique as the most commonly used tool for efficiency analysis in finance. We used one output and two input variables according to the input-oriented approach assuming a variable return to scale (VRS). Empirical research was conducted on all insurance companies from BiH, which are grouped according to the size of assets, type of insurance, and headquarters in order to determine whether there are differences in the efficiency of insurance companies in terms of their size, type of insurance, or depending on whether it operates in the Federation of Bosnia and Herzegovina (FBiH) or Republic of Srpska (RS). The results of the analysis indicate significant inefficiencies in the insurance sector in BiH, but also differences among the observed groups. The insurance sector is more efficient in FBiH compared to RS, and insurance companies in the composite insurance market are significantly more efficient than companies in the non-life insurance market. Finally, the research has showed a relatively high level of positive correlation between the size of an insurance company and its efficiency. According to all efficiency indicators, there is significant potential for efficiency improvement. Based on the analysis, the main causes of inefficiency were identified and guidelines for improving efficiency were proposed.
Economic theory. Demography, Business records management
Abstract XYZ is a company engaged in the port sector. To support the company’s business processes, XYZ uses two applications to carry out operational activities, namely the CARTOS application to manage invoices and the Finance application to record company costs and revenues. To produce a cost accounting report, XYZ is still processing and visualizing it manually with data sources from the two applications mentioned earlier. This resulted in quite a long time processing data into information. So that reporting to management cannot be done in real time. Therefore XYZ needs a system that can help management to analyze and manage data into information in real time. The Business Intelligence (BI) method is one of the solutions for company needs, especially in analyzing and providing access to data to help make better decisions. This study discusses the design and implementation of business intelligence solutions ranging from architecture, data warehouse, ETL processes and visualization in the form of a dashboard in accordance with the needs of XYZ. The method used in developing business intelligence dashboards refers to the executive information system life cycle method which consists of justification, planning, business analysis, design, construction, and dissemination. The results of this research are dashboard visualization using the Power BI tool that displays information and knowledge needed in the monitoring process and becomes material to produce management decisions related to cost accounting reports.
Adding domain knowledge to a learning system is known to improve results. In multi-parameter Bayesian frameworks, such knowledge is incorporated as a prior. On the other hand, various model parameters can have different learning rates in real-world problems, especially with skewed data. Two often-faced challenges in Operation Management and Management Science applications are the absence of informative priors, and the inability to control parameter learning rates. In this study, we propose a hierarchical Empirical Bayes approach that addresses both challenges, and that can generalize to any Bayesian framework. Our method learns empirical meta-priors from the data itself and uses them to decouple the learning rates of first-order and second-order features (or any other given feature grouping) in a Generalized Linear Model. As the first-order features are likely to have a more pronounced effect on the outcome, focusing on learning first-order weights first is likely to improve performance and convergence time. Our Empirical Bayes method clamps features in each group together and uses the deployed model's observed data to empirically compute a hierarchical prior in hindsight. We report theoretical results for the unbiasedness, strong consistency, and optimal frequentist cumulative regret properties of our meta-prior variance estimator. We apply our method to a standard supervised learning optimization problem, as well as an online combinatorial optimization problem in a contextual bandit setting implemented in an Amazon production system. Both during simulations and live experiments, our method shows marked improvements, especially in cases of small traffic. Our findings are promising, as optimizing over sparse data is often a challenge.
The target of this paper is to present an industry-ready prototype software for general game playing. This software can also be used as the central element for experimental economics research, interfacing of game-theoretic libraries, AI-driven software testing, algorithmic trade, human behavior mining and simulation of (strategic) interactions. The software is based on a domain-specific language for electronic business to business negotiations -- SIDL3.0. The paper also contains many examples to prove the power of this language.