Business Database Research Articles

Geographical Entity Management Model Based on Multi-Classification

Scientific and logical classification is crucial for efficient information storage, management, and sharing. However, there are numerous existing classification systems for geographical entities, and the categories to which the same geographical entity belongs are often different in the business databases constructed according to different classification systems, which brings great obstacles to the management and sharing of geographical information. This study analyzes the complexities of multiple classifications of geographical entities and proposes a multi-classification model for geographical entities based on directed hypergraph theory. This model integrates and transforms different classification systems for the same geographical entity, creating a unified method for expressing multiple classifications. We also designed a data structure to support this unified expression. By implementing this model, the study enables the effective management of geographical entity data, facilitating improved sharing and the exchange of geographical information across different industries and applications. In practical, the multi-classification model proposed in this paper allows geographical entities from different classification systems to be stored and managed within a single geographical database. Data views are then used to provide tailored services to various industry sectors and business applications. This approach effectively reduces data duplication and enhances the efficiency of managing and sharing geographical information. Using land use classification as an example, this study constructs a unified expression of three different land use classification systems based on the multi-classification model. An experiment managing land use data for a specific city was conducted using this model in PostgreSQL. The results indicate that the proposed method not only reduces data redundancy but also improves the query efficiency by over 10% on average compared to the mainstream relational database management mode. This confirms the effectiveness and practical value of the proposed method.

Using SaTScan to identify and map nightlife areas in Philadelphia, PA

ABSTRACT Many cities have promoted nightlife or entertainment districts – concentrations of restaurants, bars, and other entertainment-related businesses – in order to revitalize declining neighborhoods. While entertainment districts can boost economic growth, they can also contribute to public health risks including violent crime, traffic accidents, and other harms. With data from the National Establishment Time Series (NETS) business database, we developed methods to use SaTScan cluster detection software to identify entertainment districts, and applied the method in a case-study of Philadelphia, Pennsylvania. Using SaTScan, we identified and mapped 101 spatial clusters of entertainment businesses in the city. Our approach is scalable and does not require prior local knowledge about entertainment areas. The results add to a small but growing literature about the use of SaTScan to map neighborhood features. Placing entertainment districts in spatial context can inform how the built environment might amplify or minimize the potential health risks of these districts.

Tax Complexity and Firm Tax Evasion: A Cross-Country Investigation

This paper endeavours to investigate whether a complex tax system influences firms’ propensity toward tax evasion across countries. To achieve the objectives of this study, we utilised the World Bank Enterprise Survey and the World Bank’s Doing Business databases covering more than 46,000 companies from 83 countries. Our study revealed that the increased time required to pay taxes and higher total tax payments were associated with a greater extent and incidence of tax evasion among firms. The consistency of these benchmark regression results was proven through endogeneity analysis and several robustness tests. Furthermore, our heterogeneity analyses showed that the effect of tax complexity on firm tax evasion was more prominent in low- and lower-middle-income countries and also in the primary industry. These findings offer promising evidence for policymakers, particularly in low- and lower-middle-income countries where the majority of companies operate in the primary industry. Addressing the complexity of the tax system could potentially mitigate the adverse impact on tax evasion levels in these countries. Furthermore, our spatial analysis provides valuable insights, emphasising the potential impact of tax complexity in neighbouring countries and underscoring the necessity for policymakers in the home countries to strategise on harmonising and streamlining their tax systems.

Judicial efficiency, debt structure, and cost of debt

AbstractIn the framework of law and finance literature, this study investigates the link between judicial efficiency and financial debt cost in 1487 non‐financial firms across the euro zone from 2010 to 2021. Utilizing qualitative (rule of law) and quantitative (judicial proceeding length) indicators from the Worldwide Governance Indicators and Doing Business databases, our findings support the hypothesis that efficient justice lowers financial debt cost. Additionally, we observe a moderating effect of debt structure, specifically that private non‐bank debt enhances the reduction impact of judicial efficiency on debt cost, unlike bank and corporate bonds debt.

Estimating the effect of latent time-varying count exposures using multiple lists.

A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same subject due to different source credibilities. As on-site verification is not feasible for historical data, we suggest combining multiple databases to correct the bias in health effect estimates due to measurement error in any 1 datasource. We propose a joint model for the time-varying health outcomes, observed count exposures, and latent true count exposures. Our model estimates the time-specific quality of sources and incorporates time dependence of true count exposure by Poisson integer-valued first-order autoregressive process. We take a Bayesian nonparametric approach to flexibly account for location-specific exposures. By resolving the discordance between different databases, our method reduces the bias in the longitudinal health effect of the true exposures. Our method is demonstrated with childhood obesity data in California public schools with respect to convenience store exposures in school neighborhoods from 2001 to 2008.

A Systematic Review of the UTAUT and UTAUT2 Among K-12 Educators

Aim/Purpose: The purpose of this systematic literature review is to evaluate the application of UTAUT and UTAUT2 frameworks in K-12 education. Background: This study included an analysis of peer-reviewed empirical studies that use the UTAUT and UTAUT2 frameworks to understand the factors that influence technology adoption among K-12 educators. New constructs were identified, and core determinates of the models were studied. Methodology: The researchers used a systematic literature review. Inclusion criteria required articles to be written in English, non-duplicate, peer-reviewed, focused on K-12 educators, and employing quantitative methods to test or extend the UTAUT or UTAUT2 models. We used a strategic search string to conduct standardized searches across multiple databases in education, psychology, business, engineering, and multi-disciplinary publications. We conducted a screening process on the initially identified 98 articles. We kept 14 articles for final analysis, as they met the inclusion criteria. Contribution: This study contributes to the fields of (a) information science, (b) information technology (IT), and (c) education by offering a more detailed analysis and understanding of the use of UTAUT and UTAUT2 in studying technology adoption among K-12 educators. Findings: The results show performance expectancy and social influence are the most used core factors. A total of 27 new variables were identified and organized into a taxonomy. Discrepancies in the application of the models were identified and further discussed. The use of UTAUT and UTAUT2 in K-12 education is minimal. Recommendations for Practitioners: Findings inform stakeholders in education (e.g., directors of technology operations, administrators, educators) and educational technology businesses (e.g., software engineers, interface and content designers, and user experience designers) how to create and integrate products that fit the true needs of the end-users. Technology must align with the tasks needed to accomplish educational objectives (performance expectancy), and certain environmental conditions are needed to support technology usage. Educators should also be aware their decisions regarding technology adoption are influenced by social factors, such as their superiors and peers. Recommendation for Researchers: Researchers need to conduct more studies using UTAUT and UTAUT2 within K-12 education. It is likely that teachers are influenced by their students, although little research exists to study this relationship through the lens of these models. Moreover, consideration should be given when renaming variables, using certain relationships, and developing research models. Impact on Society: Findings support our understanding of educational technology adoption, which benefits our education system, especially teachers and students alike, when done successfully. Future Research: Future research should include a meta-analysis exploring the statistical extent of relationships and new variables identified.

South African Manufacturing: The challenge of growth with jobs

This paper evaluates South African manufacturing performance over 25 years. It reports the evolution of five basic variables: employment, mark-ups, exports, domestic sales, and investment, using an aggregated sample of firms from a reliable business database. The approach differs from other economic models by the complementary use of management perceptions in estimated equations.The results reinforce some previous findings; for example, wage pressure tends to constrain domestic sales while the relative wage incentivizes capital investment. The exchange rate matters for exports and investment is constrained by skill shortages. Other findings add evidence to contested issues such as the importance of the interest rate level for investment and exchange rate stability for export growth.Relationships between key variables can help to identify growth constraints and the potential for manufacturing jobs. In particular, the lack of transmission from exports to employment or to domestic sales and the response of the mark-up to investment suggest institutional failures in coordinating market activity.

Quantitative Evaluation of the Impact of Artificial Intelligence on the Automation of Processes

Introduction: In the current era, Artificial Intelligence (AI) has profoundly transformed the operation and management of business processes, being essential for competitiveness. This article focuses on quantitatively evaluating the impact of AI on the automation of business processes, seeking to support decision making.Objective: This study aims to carry out a quantitative evaluation of the impact of AI on business processes. Robust methods are used to measure and analyze key variables related to AI adoption.Methods: The methodology combines secondary data and company surveys. Public business databases are accessed and financial data is collected, in addition to analyzing Key Performance Indicators (KPI). A random selection of companies is made for the surveys, a structured questionnaire is used and the data is subjected to rigorous statistical analysis.Result: Quantitative results show significant impact of AI on business processes. The average reduction in operating costs reaches 26%, the improvement in the quality of products and services is 30%, and an average increase of 20% in profit margins is observed. Possible moderators that influence these results are identified.Conclusion: This quantitative study supports the strategic importance of AI in business, demonstrating substantial improvements in efficiency, quality and decision making. Despite its limitations, it offers a solid framework for decision-making and future research in the field of AI and business automation.

Data on Black, Indigenous, and People of Color (BIPOC)-Owned Businesses: What's Out There and What Can Be Done?

Decades into the digital era, publicly available data on Black, indigenous, and people of color (BIPOC) owned businesses are still remarkably lacking. In this commentary piece, the authors cover recent literature on the importance of BIPOC-owned businesses for equitable growth and BIPOC wealth-building. The authors then examine the sources of data that are available – public and private – for tracking the success or failure of such businesses. A BIPOC business database was constructed from a variety of sources and used by the authors to examine the potential pitfalls of such exercises. The authors close with a call for better data collection on BIPOC businesses.

The geography of commercial activities in business parks in Cape Town

ABSTRACT Business parks have increasingly become part of Cape Town’s industrial built environment over the last 25 years. It signifies a shift from large industrial premises to smaller units in communal industrial settings. This paper investigates the types of business activities found in the business parks using the conceptual underpinnings of location theory and industrial cluster theory. Online and fieldwork surveys of the businesses found in 49 business parks in Cape Town, South Africa, were conducted to compile a database of businesses and their activities. Each business was classified using the South African Standard Industrial Classification (SIC) codes to establish which economic sectors are represented in the business parks. It was found that the predominant economic sectors in business parks were the wholesale and retail trade, which included the repair of motor vehicles, motorcycles and personal and household goods and catering and accommodation, as well as the financial sector consisting of banking, insurance, real estate and business services. Manufacturing activities and commercial, social and personal services were also well represented. The findings indicate that business parks are the popular location for retail-orientated businesses and small manufacturing concerns and affirm the growing importance of the professional and business service sectors. The paper lays the groundwork for further investigation into the multi-scalar economic linkages of firms in business parks in Cape Town.

Debt Maturity and Institutions: Does Creditor Protection Matter?

This study aims to investigate the relationship between creditor protection and the debt maturity structure of corporations in the Gulf Cooperation Council (GCC) countries. The GCC countries enjoy large GDPs, growing capital markets, especially the Islamic bonds (Sukuk) market, and negligible tax environments. Nonetheless, the GCC countries’ financial systems are still dominated by banks, and their private investments are held by concentrated investors. The study utilizes firm-level financial data and country-level institutional data obtained from the World Bank Governance Indicators and Doing Business databases and applies the two-stage least square estimator to test its hypotheses. The findings indicate that stronger regulatory effectiveness is associated with long debt maturities, while better creditor protection is associated with short debt maturities. The latter finding suggests that managers and owners have incentives to utilize short-term debt in economies characterized by stronger liquidation and insolvency rules to avoid the loss of control in the case of a firm default. This finding has policy implications in terms of the importance of considering the dual influence of institutional reforms on the supply of and demand for long-term capital.

Risky Business: The database of insurance prices (circa 1400 to circa 1900)

The Risky Business database is a newly created database that contains information regarding insurance contracts from the fifteenth to the nineteenth century from markets in Italy, the Netherlands and the United Kingdom. The database, which is accessible to everyone, is populated with data from existing data sets as well as data collected from primary sources. It includes information on the routes for which insurance could be bought, the premium percentages, and the types of cargo and ships. In addition to an interactive map that graphically shows the various routes, there is a search function to raise queries. The results can be downloaded and links to the original sources are provided.

What’s Left in Your List? An Analysis of Business Database Holdings, Funding Sources, Cancelations, and the Impact of COVID-19 at Top Ranking U.S. Universities

Business and economics related databases and data sets are the most vital resources for supporting scholarly research and the curriculum at colleges and schools of business, and these resources evolve rapidly and are subject to significant price fluctuations. In this study, the top-ranking U.S. universities according to the U.S. News and World Report with Association to Advance Collegiate Schools of Business (AACSB) accreditation were surveyed about their subscriptions to databases, WRDS data sets, and Bloomberg Terminals to create a benchmark. In this survey, these institutions were also asked to provide information about funding source or cost-sharing changes and cancelations brought forth by budgetary pressures from COVID-19. The intent was to provide a snapshot of the impact of COVID-19 but to also provide guidance to institutions facing similar budgetary pressures in a future financial crisis.

Natural resources, quality of institutions and entrepreneurship activity

It is well-established that entrepreneurs are agents of change if natural resources are exploited and used judiciously. While entrepreneurial activities may foster the accumulation of resource rents, the quality of institutions matters in this matrix of relationships. Therefore, research on natural resource availability and entrepreneurship has been consolidated over the last decade. However, the evidence on the role of institutional quality in facilitating entrepreneurship across countries with different resource endowments remains limited. Thus, we investigate the natural resource-institutions-entrepreneurship nexus using The World Bank data portals—the Worldwide Governance Indicators, World Development Indicators, Doing Business Database and the Global Entrepreneurship Monitor data from 2006 to 2015. We use a mix of estimation strategies, accounting for potential endogeneity and managing autocorrelation across panels. To a large extent, we find resource rent to increase the quality and growth of business formation in sub-Saharan Africa. More importantly, we observe a significant and positive synergy effect of the quality of institutions in the relationship between natural resource rent and business formation. Our contribution to resource policy is twofold. First, we examine the effect of a rise in natural resource rent on entrepreneurial activity using the context of sub-Saharan Africa– a region rich in natural resources. Second, we demonstrate that facilitating institutional quality in a country will also shape how the availability of natural resources affects entrepreneurial activity. Our findings provide policy and managerial implications.

Red tape and industry dynamics: a cross-country analysis

In this paper, we analyze the relationship between the stringency of red tape and the entry and exit rates of firms. To do so, we use a panel dataset on firm entry and exit rates, obtained from Eurostat Business Demography, covering 22 European countries and 14 manufacturing sectors observed over the period 2013–2019. We complement the information on firm dynamics with country-level data on red tape from the World Bank’s Doing Business database. Five dimensions of red tape are considered: regulations for starting a business, construction permits, procedures for getting electricity, registering property, and contract enforcement. In addition, both the cost and the time taken to complete the administrative procedures are used as metrics of red tape. Using a difference-in-difference approach à la Rajan and Zingales (1998), we find a negative effect of administrative burdens on the entry and exit rates of firms across European countries. Moreover, we find that the negative effect is more pronounced for smaller firms. Finally, the time taken to comply with bureaucratic procedures is a greater impediment to smooth entry and exit than the monetary costs associated with these administrative barriers.

UACA: Unified Access Control Approach For Heterogeneous Database Based-on Service Data Object

Abstract: The requirement for data interchange, access, and retrieval from diverse databases. This requirement is getting more and more crucial. However, retrieving this data from a heterogeneous database necessitates juggling many kinds of query languages, data models, and structure. The goal of this project is to create a unified access control approach (UACA), which will be managed by service data object (SDO) technologies and will be based on an agent mechanism for application access to business databases. To create a unified data access control, applications must be submitted through a UACA. To prevent a direct link to the database and application security threats, unified access control isolates applications from databases. Application systems submit a request for identity authentication to the database access control system using the data access protocol of the access control system. Data are finally transmitted to the application system after identity authentication, data gathering, and data transmission. This investigation led to the creation of a client-middle layer, which is also in charge of transformation and optimization, to ensure uniform data access control and access to the heterogeneous database. It also manages system communication using the TCP/IP protocol to guarantee the accuracy and reliability of the connection. SDO data retrieval offers strong adaptability and system compatibility.

An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis. The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives. Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields. Various subject matter can be encountered on social media platforms, such as movie product reviews, consumer opinions, and testimonies, among others, which can be used for sentiment analysis. The rapid uncovering of these web contents contains divergence of many benefits like profit-making, which is one of the most vital of them all. According to a recent study, 81% of consumers conduct online research prior to making a purchase. But the reviews available online are too huge and numerous for human brains to process and analyze. Hence, machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels, game companies, and so on. Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions, referral systems, and market research. Therefore, this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews. A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words. The dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this study. The performance evaluation of the model shows a test accuracy of 96.08%, an F1-score of 96.00%, a precision of 96.00%, and a recall of 96.00%. The results were compared with state-of-the-art classifiers and showed a promising performance and much better in terms of performance metrics.

DETERMINANTS OF ENTREPRENEURIAL DYNAMICS: THE CASE OF THE EUROPEAN UNION

Since economic theory identifies new enterprises as one of the basic drivers of economic growth, it brings to the forefront the need to understand the existing dynamics of entrepreneurship. Consequently, the identification of the basic entrepreneurship’s determinants is a central issue from both a theoretical and a practical point of view in contemporary literature. A better understanding of the factors influencing the dynamics of entrepreneurship is a basic precondition for creating effective policies aimed at encouraging the creation of new companies, and consequently, the creation of new jobs. Therefore, this paper addresses several important issues. From the theoretical standpoint, the role of entrepreneurial determinants in creating a new business is examined. From a practical standpoint, the basic formal institutional factors influencing the birth rate of new companies are analyzed. The analysis was conducted on a sample of European Union countries for the period from 2010 to 2019 using data from the World Bank’s Doing Business database, as well as data on business dynamics from the Eurostat database. A panel data regression analysis using the fixed-effects estimation procedure with Driscoll-Kraay standard errors was conducted, and the results indicate that the dynamics of new enterprises’ foundation are negatively affected by the total tax burden, as well as the cost of property registration, while the amount of initial capital required to open a business has a positive impact. The results of the research can serve as a basis for fine-tuning policies that will facilitate and encourage the creation of new enterprises.

Improving Digital Booking Skills Accurately For Students Of Smk Negeri 1 Garut

Economic growth in various sectors including the sub-sector of industry, trade and the development of information technology, robotics, computerization and other automation has encouraged the transfiguration of information technology in the accounting world, making the education system in the world of education move quickly and efficiently. This chain then has an impact on vocational learning in Indonesia. Students in the era of society 5.0 must qualified friendly with technology related to accounting in the industrial revolution era 4.0 is like software or accounting robots and big data. Based on initial observations, it was found that 100% of class XI students at SMK Negeri 1 Garut had never operated Android-based digital bookkeeping. To promote the applied profession of society 5.0 , it is necessary to conduct training on android-based accounting applications. The purpose of this service is to increase understanding in recording, presenting, and reporting financial statements that are faster, more precise, and accurate in order to equip students to enter the world of work. The method of activity consists of the stages of preparation, initial observation, program implementation with lectures and practical simulations directly on students using accounting software Accurate Lite. Simulation of bookkeeping practices in trading businesses is carried out by creating a business database, recording purchase and sales transactions, accounts payable and receivables, recording expenses, inventory and bank history and presenting reports. The results obtained are 89% of students are able to operate the bookkeeping system using Accurate Lite and present financial reports according to the provisions.

Design and Implementation of Port Data Analysis and Visualization System

Under the background of big data era, ports have realized the informatization of transport data, and has accumulated a large amount of transport data. The potential value contained in the port transportation data needs to be excavated, and a large number of external data also needs to be obtained. However, the traditional statistical analysis system can no longer meet the demand. In order to fully tap the potential value of port data, this paper designs and implements a port data analysis and visualization system to help enterprises master the trend of material flow. The system development is based on Python language. Pandas is used for data analysis, Flask is used to implement the MVC framework, ECharts is used for chart display, and data is imported from the business database to the MySQL database.