INCOMPATIBLE BUSINESS BEHAVIOR TOWARD ISLAMIC ETHICS: A STUDY ON THE PRACTICES OF RIBA AND BANK INTEREST

Various kinds of interpretations of usury and interest in modern financial institutions (banks), both from jurists and Muslim economists, seem to occur because the 'illat usury put forward by the jurists is seen as inaccurate in the development of Islamic legal thought. Gharar which is translated as speculation is equated with gambling because of the uncertainty for the parties (seller and buyer). This research uses a literature study, which is carried out by collecting, studying and reviewing books, scientific magazines and related documents such as theses and scientific journals. Many modern societies carry out such practices, such as buying and selling agricultural products that are still on the land with a wholesale system. When viewed from the ethical side of Islamic transactions, both usury, interest and gharar violate the ethics of transactions. Ethical considerations prohibiting usury, interest and gharar, due to unfairness, exploitation and unproductiveness. While the system of economic ethics emphasizes products, fairness and honesty in trade, and fair competition. In this case, good ethics will give good business, good ethics: good business. Ethics that is in the spotlight in this study, especially ethics in the operations and transactions of Islamic financial institutions. Then the sharia financial institutions in question include sharia banking, sharia pawnshops, sharia insurance, and sharia capital markets, all of which are some forms of representation of sharia macro financial institutions that are certain. have a significant impact on the nation's economic development. So that behavior that is not in accordance with Islamic ethics, such as related to usury and bank interest, must be avoided for Muslims.

Business Behavior in the Face of Severe Weather: Studying the Effects of Deterministic and Probabilistic Warning Systems

Tornadoes are nature’s most violent storm and annually cause billions in damage along with the threat of fatalities and injuries. To improve tornado warnings, the National Weather Service is considering a change from a deterministic to a probabilistic paradigm. While studies have been conducted on how individual behavior may change with the new While studies have been conducted on how individual behavior may change with the new businesses. This project is a response to the Weather Research and Forecasting Innovation Act of 2017, H.R. 353, which calls for the use of social and behavioral science to study and improve storm warning systems. The goal is to discuss business response to probabilistic tornado warnings through descriptive and regression-based statistics using a survey administered to businesses in North Texas. Prior to release, the survey was vetted by a focus group comprised of businesses in Grayson County, TX who assisted in the creation of a behavior ranking scale. The scale ranked behaviors from low to high effort. Responses allowed for determining if the business reacted to the warning in a passive or active manner. Returned surveys came from large and small businesses in North Texas and represent a wide variety of industries. Regression analysis explores which variables have the greatest influence on businesses’ behavior and show that beyond increases in probability from the probabilistic warnings, trust in the warning provides the most significant change to behavior.

Strategic corporate changes under the conditions of developing digital twin management systems

The key postulates of company management, strategic changes in business activity management are considered. Regularities of business behavior, conditions of achievement of business efficiency and limitations of functioning, tasks of macro- and micro-management, principles of centralization and decentralization of management, methods of evaluation of effectiveness of strategy of changes, organizational mechanisms of corporate system action in conditions of innovative transformations are consistently deployed. The necessity of using a digital twin of the management system for solving planning tasks and compliance with proportions and ratios at different phases of the company’s development cycle is substantiated.

Characteristics of methods of evaluation of personnel of forensic expert institutions.

The article states that to determine the ability of a specialist to perform official duties and his professionalism, evaluation of employees is used, which allows to identify their professional abilities, highlights personal qualities and prospects, which is especially important for forensic activities. It is emphasized that depending on the purpose and objectives of evaluation in some institutions use different types of evaluation of employees, which are classified on many grounds. The article presents the classification of evaluation methods on such grounds as: features of the subject and the evaluation process itself; by its content; by regularity; depending on the evaluation period; depending on the evaluation criteria; by systematic evaluation; by the subject of evaluation; according to the professional and functional structure of the evaluated. It is stated that the structure of comprehensive evaluation of employees of forensic institutions can be presented in the form of a model that covers three groups of characteristics: the employee in terms of his business and personal qualities; labor (business) behavior; performance of work, its results and outlines the components of each of them. It is established that the method of comprehensive assessment can be used to assess all categories of employees: workers, specialists and managers. However, it is noted that the evaluation of the work of different categories of employees of the forensic institution has its own specific tasks, indicators, methods of identifying and measuring the results of work. It is noted that these problems can be solved using evaluation methods: direct – they are used to evaluate the direct results of the work of specialists and managers, or the results of the work of their subordinate team; indirect – when it is not the result of the activity that is evaluated, but the course of performance of tasks by employees, their realization of their functional responsibilities.

“Dodging the bullet”: are effectual managers better off in a crisis? A case of Ghanaian agricultural SMEs

Purpose This study aims to investigate the effectiveness of causation decision-making and effectual decision-making logic in challenging economic situations within agricultural small and medium-sized enterprises in Ghana. Design/methodology/approach The authors collect and derive composite variables from effectuation, causation, financial capital availability (FCA) and crisis performance data through a randomized system based on literature precedents. This study analyzes the data using descriptive analysis, confirmatory factor analysis and ordinary least squared regression through STATA 15. Findings The authors find that effectual managers are indeed better off during crisis conditions. Also, there is a significant moderation relationship between the effectuation, FCA and crisis performance. Originality/value From strategic management and entrepreneurial process standpoint, the effectuation theory thrives within uncertain business environments. This notwithstanding, literature has often focused on hypothetical, uncertain business environments. The authors present plausible evidence of effectual business behavior from a real crisis, from small agriculture firms’ perspectives and an emerging economy context.

Research on Business Environment Risk Governance Based on Occupational Claims: 1784 Cases of Food Safety Disputes

There are disputes on the legal acceptance of occupational claims and the risk of occupational claim spoiling business environment is skyrocketing. How to manage the risk of occupational claims in the business environment is the subject of urgent research, especially COVID-19 is ravaging the world and the risk of economic crisis is increasing dramatically. In this study, we collected 1784 cases of food occupational claims from the Chinese legal documents website. Using Excel, Review Manager 5.0 and SPSS 19.0, the number of cases prosecuted as “consumers” were obtained by means of textual analysis, and the development process was directly influenced by occupational claims, with 2017 as a great value point (watershed); the rate of losing occupational claim cases has been climbing since 2018; and the risk of losing occupational claims is higher than that of true meaningful consumer advocacy cases (Z = 6.99, p < 0.001), and in 2019, the risk of losing a case was 33.34 times higher than that of an ordinary consumer. The proportion of occupational claims in the total number of food safety disputes is positively correlated with official protective behavior; the failure rate of occupational claims is positively correlated with official regulatory behavior. The results show that occupational claims are being reexamined by society; the continued rise in the number of unsuccessful occupational claims cases indicates a lack of regulatory guidance for their profit-making behavior, the existence of malicious reporting complaints against business behavior, damage to the business environment, and a certain lack of legality. Therefore, it is proposed that laws and regulations should be in line with international standards, highlight the legal thinking and the concept of the rule of law, return to the original legislative intent, and build a five-in-one mechanism of “consumers, operators, media, government, and justice” to coordinate the management of occupational claims, crack down on occupational claims, and contribute to the creation of a good business environment.

Analisis Stok dan Penjualan Kaftan Menggunakan Metode Backpropagation (Studi Kasus Lala New Fashion)

INTISASITeknologi data mining sangat berguna dalam membantu penjualan dalam menemukan informasi yang sangat penting dari gudang data mereka. Data mining mengeksplorasi basis data untuk menemukan pola-pola yang tersembunyi, meramalkan tren dan sifat-sifat perilaku bisnis yang sangat berguna untuk mendukung pengambilan keputusan, pencarian informasi dalam memprediksi yang mungkin saja tidak dilihat atau dilupakan oleh pelaku bisnis karena di luar dari ekspektasi mereka. Analisis yang diotomatisasi yang dilakukan oleh data mining menjawab pertanyaan-pertanyaan bisnis yang dengan cara tradisional memerlukan cukup banyak waktu dalam menjawabnya. Hasil dari analisis adalah menggali informasi dari data penjualan sehingga dapat memprediksi seberapa besar stok yang harus disiapkan untuk nantinya ditaruh di toko sehingga tidak ada penumpukan stok yang berlebih. Pada penelitian ini, akan dilakukan analisis jaringan saraf tiruan untuk memprediksi stok yang akan disiapkan. Algoritma yang akan digunakan adalah backpropagation untuk kemudian dapat menjadi keluaran angka yang dapat di tentukan besaran stok yang akan disiapkan kepada penjual. Diharapkan analisis yang dibuat dalam memprediksi stok dan penjualan menggunakan metode backpropagation dapat memprediksi stok sesuai dengan data yang ada. Kata Kunci: stok, penjualan, backpropagation, prediksi, data mining.. ABSTRACT Data mining technology is very useful in helping sales find important information from their data warehouses. Data mining explores databases to find hidden patterns, predict trends and traits of business behavior that are very useful to support decision making, search for information in predicting that may not be seen or forgotten by business people because it is outside of their expectations. Automated analysis performed by data mining answers business questions that traditionally require considerable time in answering. The result of the analysis is to dig information from sales data so that it can predict how much stock should be prepared to later be placed in the store so that there is no excess stock buildup. In this research, an analysis of artificial neural networks will be conducted to predict the stock that will be prepared. The algorithm that will be used is backpropagation to then be able to output numbers that can be determined the amount of stock to be prepared for the seller. It is expected that the analysis made in predicting stock and sales using the backpropagation method can predict stock in accordance with existing data. Keywords: stock, sales, backpropagation, prediction, data mining..

Penerapan Data Mining untuk Memprediksi Minat Nasabah Terhadap Produk Asuransi Meninggal Dunia dengan Metode Naïve Bayes (Studi Kasus : PT. BNI Life Insurance)

INTISASIPendapatan untuk perusahaan asuransi ditentukan oleh jumlah premi yang dibayar oleh nasabah. faktor penting nasabah berupa premi, premi ditentukan dalam persentase atau tarif tertentu. Pada perusahaan asuransi pasti memiliki jumlah data, dan data tersebut sangat penting bagi perusahaan untuk mengetahui kriteria nasabah yang berminat pada asurnsi yang dipasarkan. Dengan adanya informasi dari data nasabah yang ada, perusahaan asuransi dapat mengambil suatu keputusan dalam menerapkan stragi perusahaan diantarnya yaitu menjual produk- produk promo untuk meninggatkan pendapatan perusahaan. Data mining merupakan suatu teknologi yang dapat membantu perusahaan dalam menemukan suatu yang sangat penting dari sekumpulan data. Data mining dapat membentu sautu pola atau membuat suatu sifat perilaku bisnisa yang berguna untuk pengambilan keputusan. Dengan menggunakan metode algoritma Naive Bayes diharapkan bisa membantu perusahaan dalam pengelolaan data nasabah dengan cara mengklasifikasi data nasabah untuk memprediksi minat nasabah dengan tingkat akurasi melebihi 80% dalam memilih produk asuransi meninggal dunia. Kata Kunci: asuransi, baïve bayes, prediksi, data mining. ABSTRACTIncome for insurance companies is determined by the amount of premium paid by the customer. Important factors for customers in the form of premiums, premiums are determined in certain percentages or rates. The insurance company certainly has the amount of data, and the data is very important for companies to know the criteria of customers who are interested in the insurance marketed. With the information from existing customer data, the insurance company can make a decision in implementing the company's strategy, which is to sell promo products to increase company revenue. Data mining is a technology that can help companies find a very important set of data. Data mining can form a pattern or create a nature of business behavior that is useful for decision making. By using the Naive Bayes algorithm method, it is expected to be able to assist companies in managing customer data by classifying customer data to predict customer interest with an accuracy rate exceeding 80% in choosing a death insurance product. Keywords: insurance, baïve bayes, predictions, data mining..