The Effect of Business Intelligence on Management Accounting Information System

In today's business world, we are faced with high volumes of data. New developments in IT provide organizations with effective and efficient access and storage of information. In any case, there is a long distance between the mass of data and its use. Management accounting information system has changed as a key to success in today's business environment. In the field of management accounting, if the accounting information system is not capable of providing information to business managers timely and quickly, organizations' success will be threatened in the competitive environment. To cope with competitors and growth of long-term strategies, the accounting information system should benefit from business intelligence techniques to provide timely and effective financial information. The important competitive advantage against opponents and business competitors in the market is the most important reason to create intelligent systems. The purpose of business intelligence is to help control the flow and resources of business information within and around the organization. In this study, based on the research objectives, using a meta-analysis, some of the applied criteria and parameters of accounting information systems were examined based on business intelligence features. In addition, a model was proposed based on four categories of relationships and inferences, warning and reporting systems, and tools for effective analysis and decision-making. Among the criteria in the literature review are group decision-making, optimization, integration, simulation, traffic reports, prototyping based on the original version, two-way argument process, awareness technology, informing on the content, fuzzificatio, data mining, data storage, real-time analysis process, establishing communication  channels, creating intelligent factors etc. Therefore, the necessity to use a business intelligence-based model in management accounting information system is proposed

Customer Centricity & Competitive Intelligence Performance in the Insurance Industry in Western Kenya, East Africa

Globally, inability to analyze information, to see past customer life cycle disruptions and dispassionately interpret events to anticipate competition threatens performance in the insurance industry. Organizations fail to respond and offer solutions desired by their customers, instead sticking to their traditional products not highly of customer choice. It was against this gap that this paper discussed Customer Centricity and its influence on competitive intelligence in Insurance Firms in Kenya. The objectives of this study were to determine effects of Customer Centricity on Competitive Intelligence of insurance firms in Kenya; to investigate whether Customer Life Cycle practices employed by insurance firms have effects on Competitive Intelligence; to assess whether the Customer Value practices affect Competitive Intelligence of insurance firms and establish the effects of Customer Experience on Competitive Intelligence of insurance firms in Eldoret Town in Kenya, East Africa. A mixed method design was used to study 250 selected from 600 employees of insurance firms in Eldoret. A semi-structured questionnaire and an interview guide were used to collect data. Data were analyzed using selected descriptive and inferential statistics The results of applying Spearman and Friedman tests showed that customer centricity and its dimensions  significantly affected competitive intelligence, with customer life  as the most important, indicated by the  Entropy technique. Customer experience emerged top when the Binomial test was applied. A recommendation is made that Managers in the insurance industry embrace all dimensions of customer centricity, especially the dimension of customer life cycles, in order to improve their competitive intelligence. Keywords: Customer Centricity, Competitive Intelligence, Customer Life Cycle, Customer Value, Customer Experience DOI: 10.7176/EJBM/11-10-07 Publication date: April 30th 201

The Effect Of Mobile BI On Organisational Managerial Decision-Making

Managerial decision-making has always involved the use of numerous distinct information resources. Modern managerial decision-making processes require a wealth of information that is enhanced and transformed into knowledge in order to take effective action. Mobility in business is increasingly exercising influence on core business processes of organisations. Recent advances in wireless technologies coupled with the rapid growth of mobile devices in business have led to a new era in business computing. Mobile Business Intelligence (Mobile BI) is a system that has been conceived to assist, accelerate and to enhance the managerial decision-making processes. Drawing from an array of previous studies that attempted to measure the value of Business Intelligence (BI) and other IT systems in organisations, this study develops a new kind of measure which is based on an understanding of the distinct properties of Mobile BI systems in an organisational-oriented context

The Effect of Business Intelligence on Management Accounting Information System

In today's business world, we are faced with high volumes of data. New developments in IT provide organizations with effective and efficient access and storage of information. In any case, there is a long distance between the mass of data and its use. Management accounting information system has changed as a key to success in today's business environment. In the field of management accounting, if the accounting information system is not capable of providing information to business managers timely and quickly, organizations' success will be threatened in the competitive environment. To cope with competitors and growth of long-term strategies, the accounting information system should benefit from business intelligence techniques to provide timely and effective financial information. The important competitive advantage against opponents and business competitors in the market is the most important reason to create intelligent systems. The purpose of business intelligence is to help control the flow and resources of business information within and around the organization. In this study, based on the research objectives, using a meta-analysis, some of the applied criteria and parameters of accounting information systems were examined based on business intelligence features. In addition, a model was proposed based on four categories of relationships and inferences, warning and reporting systems, and tools for effective analysis and decision-making. Among the criteria in the literature review are group decision-making, optimization, integration, simulation, traffic reports, prototyping based on the original version, two-way argument process, awareness technology, informing on the content, fuzzificatio, data mining, data storage, real-time analysis process, establishing communication  channels, creating intelligent factors etc. Therefore, the necessity to use a business intelligence-based model in management accounting information system is proposed

Of BI research : a tale of two communities

The Business intelligence (BI) literature is in flux, yet the knowledge about its varying theoretical roots remains elusive. This state of affairs draws from two different scientific communities (informatics and business) that have generated multiple research streams, which duplicate research, neglect each other’s contributions, and overlook important research gaps. In response, we structure the BI scientific landscape and map its evolution to offer scholars a clear view of where research on BI stands and the way forward. For this endeavor, we systematically review articles published in top-tier ABS journals and identify 120 articles covering 35 years of scientific research on BI. We then run a co-citation analysis of selected articles and their reference lists. This yields the structuring of BI scholarly community around six research clusters: Environmental Scanning (ES), Competitive Intelligence (CI), Market Intelligence (MI), Decision Support (DS), Analytics Technologies (AT), and Analytics Capabilities (AC). The Co-citation network exposed overlapping and divergent theoretical roots across the six clusters and permitted mapping the evolution of BI research following two pendulum swings. Our article contributes by 1) structuring the theoretical landscape of BI research, 2) deciphering the theoretical roots of BI literature, 3) mapping the evolution of BI scholarly community, and 4) suggesting an agenda for future research.© 2020 Emerald Publishing Limited. This manuscript version is made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY–NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model,  measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery. Keywords: business intelligence system, data mining, extract transform load, on line analitical process cub

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model,  measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery. Keywords: business intelligence system, data mining, extract transform load, on line analitical process cub

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model, measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery

Identifying customer priority for new products in target marketing: Using RFM model and TextRank

Target marketing is a key strategy used to increase the revenue. Among many methods that identify prospective customers, the recency, frequency, monetary value (RFM) model is considered the most accurate. However, no RFM study has focused on prospects for new product launches. This study addresses this gap by using website access data to identify prospects for new products, thereby extending RFM models to include website-specific weights. An RF model, built using frequency and recency information from website access data of customers, and an RwF model, built by adding website weights to frequency of access, were developed. A TextRank algorithm was used to analyze weights for each website based on the access frequency, thus defining the weights in the RwF model. South Korean mobile users’ website access data between May 1 and July 31, 2020 were used to validate the models. Through a significant lift curve, the results indicate that the models are highly effective in prioritizing customers for target marketing of new products. In particular, the RwF model, reflecting website-specific weights, showed a customer response rate of more than 30% among the top 10% customers. The findings extend the RFM literature beyond purchase history and enable practitioners to find target customers without a purchase history