Informed Business Decisions Research Articles

Costs function assessment: an empirical business analytics approach for decisional purposes

The aim of the present paper is, without any pretension of to be exhaustive, to propose a business analytics contribution in the elaboration of a reliable and accurate assessment of firm’s costs function for decision purposes. As a matter of fact, the analyst/consultant, the entrepreneur itself or also the manager has not always the possibility to access to the wide range of all the needed useful information to deal with this task. Income statements or general accounting documents could not record - for an appropriate and sound assessment - all relevant figures. In these (not-extraordinary) cases, supplementary and accurate data gathering is both extremely hard to carry on and not synchronized with entrepreneurial needs. Hence, an analysis developing informed business decisions seems not possible except of roughly approximations. Considering these binding constraints, business analytics tools by recurring to their interdisciplinary versatility can surely support managerial decision-making widening and strengthening possibilities to enhance the value of retrievable insights from traditional firm’s accounting data. Under this perspective, management accounting combines its traditional informational power with sound scientific support for a renewed, more efficient and multidisciplinary approach to business.

Costs function assessment: an empirical business analytics approach for decisional purposes

The aim of the present paper is, without any pretension of to be exhaustive, to propose a business analytics contribution in the elaboration of a reliable and accurate assessment of firm's costs function for decision purposes. As a matter of fact, the analyst/consultant, the entrepreneur itself or also the manager has not always the possibility to access to the wide range of all the needed useful information to deal with this task. Income statements or general accounting documents could not record - for an appropriate and sound assessment - all relevant figures. In these (not-extraordinary) cases, supplementary and accurate data gathering is both extremely hard to carry on and not synchronised with entrepreneurial needs. Hence, an analysis developing informed business decisions seems not possible except of roughly approximations. Considering these binding constraints, business analytics tools - by recurring to their interdisciplinary versatility - can surely support managerial decision-making widening and strengthening possibilities to enhance the value of retrievable insights from traditional firm's accounting data. Under this perspective, management accounting combines its traditional informational power with sound scientific support for a renewed, more efficient and multidisciplinary approach to business.

Patterns of Care for Biologic-Dosing Outliers and Nonoutliers in Biologic-Naive Patients with Rheumatoid Arthritis.

Although most biologic medications for patients with rheumatoid arthritis (RA) have recommended fixed dosing, actual biologic dosing may vary among real-world patients, since some patients can receive higher (high-dose outliers) or lower (low-dose outliers) doses than what is recommended in medication package inserts. To describe the patterns of care for biologic-dosing outliers and nonoutliers in biologic-naive patients with RA. This was a retrospective, longitudinal cohort study of patients with RA who were not pregnant and were aged ≥ 18 and < 90 years from an integrated health care delivery system. Patients were newly initiated on adalimumab (ADA), etanercept (ETN), or infliximab (IFX) as index biologic therapy between July 1, 2006, and February 28, 2014. Outlier status was defined as a patient having received at least 1 dose < 90% or > 110% of the approved dose in the package insert at any time during the study period. Baseline patient profiles, treatment exposures, and outcomes were collected during the 180 days before and up to 2 years after biologic initiation and compared across index biologic outlier groups. Patients were followed for at least 1 year, with a subanalysis of those patients who remained as members for 2 years. This study included 434 RA patients with 1 year of follow-up and 372 RA patients with 2 years of follow-up. Overall, the vast majority of patients were female (≈75%) and had similar baseline characteristics. Approximately 10% of patients were outliers in both follow-up cohorts. ETN patients were least likely to become outliers, and ADA patients were most likely to become outliers. Of all outliers during the 1-year follow-up, patients were more likely to be a high-dose outlier (55%) than a low-dose outlier (45%). Median 1- and 2-year adjusted total biologic costs (based on wholesale acquisition costs) were higher for ADA and ETA nonoutliers than for IFX nonoutliers. Biologic persistence was highest for IFX patients. Charlson Comorbidity Index score, ETN and IFX index biologic, and treatment with a nonbiologic disease-modifying antirheumatic drug (DMARD) before biologic initiation were associated with becoming high- or low-dose outliers (c-statistic = 0.79). Approximately 1 in 10 study patients with RA was identified as a biologic-dosing outlier. Dosing outliers did not appear to have better clinical outcomes compared with nonoutliers. Before initiating outlier biologic dosing, health care providers may better serve their RA patients by prescribing alternate DMARD therapy. This study was sponsored by Janssen Scientific Affairs. It is the policy of Janssen Scientific Affairs to publish all sponsored studies unless they are exploratory studies or are determined a priori for internal use only (e.g., to inform business decisions). Meyer is an employee of Janssen Scientific Affairs and a stockholder in Johnson and Johnson, its parent company. Delate and Jenkins have nothing to disclose. Study concept and design were contributed by Delate and Meyer. Delate took the lead in data collection, along with Jenkins. All authors participated in data analysis. The manuscript was written primarily by Delate, along with Meyers and Jenkins, and was revised by Meyer, along with Delate and Jenkins.

Digital Embeddedness and its Effect on Organizational Purchase Decisions

Business scholars and thought leaders suggest that increased reliance on digital sources (e.g., blogs, social media, and online forums) exhibited by the emerging millennial workforce may affect procurement decisions. These arguments contend with research that indicates that organizational buyers rely on decision shortcuts (e.g., brand, loyalty, and peer opinions) to mitigate risks or to simplify purchase decisions. One can argue that buyers gain more information from digital sources, which may attenuate their reliance on heuristics. At the same time, digital sources may strengthen prior beliefs, which can increase reliance on decision proxies. This study draws from information processing theory to understand the role of digital sources of information in decision-making, in the context of business purchasing. Based on the Accessibility-Diagnosticity framework, we introduce a novel Digital Embeddedness construct to conceptualize the extent to which information from digital sources is integral to an individual’s decision-making. Analysis of survey responses from 196 purchasing managers suggests that more digitally embedded buyers are more willing to adopt innovations, yet interestingly, rely more on decision shortcuts such as brand and peer opinions. We also find that while more digitally embedded buyers feel more attached to their existing vendors, they are less likely to re-purchase from them. We discuss implications for B2B collaboration, including customer relationship management. This research elucidates individual-level differences in the use of digital sources, and thus has implications for future work that examines the interpretation of digital information to inform business decisions.

Book review. Microeconometrics in Business Management, 2016. By Jerzy Witold Wiśniewski. Reviewed by J. Kordos

This book introduces the application of micro-econometric methods for modeling various aspects of economic activity for small- to large-sized enterprises, using methods that are based on both time-series and cross-section approaches. The information obtained from using these estimated models can then be used to inform business decisions that improve the efficiency of operations and planning. Basic models used in the modeling of the business (single-equation and multiple-equation systems) are introduced whilst a wide range of economic activity including major aspects of financial management, demand for labour, administrative staff and labour productivity is also explored.

Is it efficient to single-handedly run a multi-machine harvesting operation? A case study from the Northeast United States

ABSTRACTHarvest operations carried out by a single logger operating more than one machine are common, yet little is understood of their productivity and costs. A case study of three single-logger operations (SLO) located in New York, USA was conducted to assess their harvest costs and productivity. Daily activity and production data were collected from each harvest operation and analyzed in conjunction with machine rate calculations to estimate system productivity and costs for each SLO. Based on these data, deterministic simulations were conducted to assess how the addition of second and third loggers affected harvest system efficiency. Operator and equipment utilization rates were calculated at the systems level and compared among the observed SLOs and simulated two- and three-logger simulations. In most cases, the introduction of additional loggers reduced operator utilization due to the introduction of system delays, and therefore, increased labor costs. However, this investment in labor often led to greater equipment utilization and lower production costs. Overall, system productivity increased by between 77% and 145% when additional loggers were added to the system, while costs per cubic meter (m3) decreased by as much as 28%. The results of this study will inform business decisions regarding the efficiency of SLO and the potential gains associated with investing in crew sizes greater than one.

Reverse logistics in Malaysia: The Contingent role of institutional pressure

Reverse logistics is a primary component of green supply chain management initiatives and is often analysed from a broad perspective. Rooted in the resource-based view of the firm, this empirical study decomposes reverse logistics into five commonly adopted disposition options (repair, recondition, remanufacture, recycle, and disposal) to examine the effects of using each option on measures of environmental performance, profitability, and sales growth. Considering institutional theory, this study also investigates the moderating role of both regulatory and ownership pressure on the relationship between each reverse logistics disposition option and levels of performance. Using survey data collected from managers at 89 ISO14001 certified electrical and electronic equipment manufacturing firms, regression models test a series of hypothesized relationships. Results suggest that under the presence of institutional pressure, use of disposition options results in increased levels of performance in some cases. The recovery of valuable components during product recondition and remanufacture activities contributes to enhanced environmental and economic benefits. Conversely, product recycling and disposal activities are not necessarily performance-inducing initiatives in the face of regulatory pressures The findings of this study can be used to inform business decisions regarding the adoption and use of reverse logistics strategies. Legislative frameworks regarding extended producer responsibility are recommended in order to motivate the implementation of reverse logistics product disposition activities.

Managing uncertainty in electricity generation and demand forecasting

The most readily accessed and abundant renewable energy sources—wind, hydro, and solar—are weather dependent and are therefore inherently intermittent. Energy demand is also dependent on the weather as well as many other difficult-to-model factors. In fact, there are many uncertainty factors that affect the modeling of both energy demand and renewable energy sources. In this paper, we begin with an overview of several of the underlying changes taking place in the energy industry and then specifically consider the uncertainty inherent in the forecasting of energy demand and wind power. We conclude with a discussion of techniques to integrate energy demand and the renewable energy supply, each with their associated uncertainty factors, in a stochastic environment to enable better informed business decisions.

Environmental Performance Measures for State Departments of Transportation

Transportation supports ease of movement, access, and economic health for communities, but transportation also has indisputable impacts on air, water, and natural ecosystems. Although state departments of transportation (DOTs) are increasingly harmonized in their approaches to performance measurement for areas such as infrastructure preservation and congestion, there has been little consensus or direction on approaches for measuring environmental performance. This paper describes the identification and testing of a suite of core environmental performance measures designed to help state DOTs better understand and communicate the impacts of transportation on the environment and ultimately to support more informed business decisions. The measures reflect five major focus areas: air quality, energy and climate, materials recycling, stormwater, and wildlife and ecosystems. The measures discussed in this paper were subject to proof of concept testing with available data gathered from state DOTs. Twenty-seven states participated in the testing to some degree. The performance measures recommended in this paper should be considered a sound starting point for continued efforts in assessing the impacts of DOTs on the environment and not perfect or permanent metrics.

Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model

Supply Chain consists of various components like supplier, manufacturer, factories, warehouses, distribution agents, customers, etc. Supply Chain Management encompasses all the activities from moving goods from sourcing to consumption. Sales forecasting is a part of downstream activity of supply chain and is the process of predicting future sales of the product. It helps in making informed business decisions. In this paper a study of various sales forecasting algorithms is done and results of sales of oral-care products are calculated using Back-Propagation Neural Network Model. The error rate for different products is also calculated.

Using Competitive Intelligence Instruction to Develop Practice-Ready Legal Professionals

Competitive intelligence is the identification and use of internal and external information to inform business decisions. Integrating competitive intelligence instruction into the law school curriculum is another option to use when preparing “practice-ready” legal professionals. First, it broadens the curriculum experience beyond the courtroom with its introduction of basic corporate, regulatory, and transactional documents, and second, introduces students to the critical decision-making and business-development skills lawyers use to identify, investigate, and develop business leads necessary for a financially viable legal practice.

Influence of evoked contexts on consumers' rejection of two products: Implications for shelf life estimation

Consumers' increasing demand for fresh, safe and high quality products requires food companies to accurately estimate sensory shelf life and tolerance limits for sensory defects. Sensory shelf-life and acceptance limits for sensory defects have been estimated with consumers' rejection data using survival analysis without considering contextual variables, which could have a major influence on consumers' perception. The aim of the present study was to study how consumers' rejection is affected by different evoked contexts in a laboratory setting. Two studies were carried out to study the influence of evoked contexts on consumers' rejection. In the first study consumers' rejection for consumption at home and purchase at a supermarket was compared using orange juice samples with different storage times. In the second study, consumers' rejection for consumption at home and purchase at a supermarket for brands of different familiarities was compared using dulce de leche with different plastic flavour intensities as samples. Consumers' rejection data were analysed using survival analysis. The use of written scenarios to evoke different contexts affected consumers' rejection of products with different storage times and intensities of a sensory defect. Shelf lives that were estimated based on consumers' rejection to purchase were shorter than those estimated considering consumers' rejection to consume. In the second study, consumers' rejection under the consumption at home evoked context was similar to rejection to repeat purchase of a usual brand. However, a large difference existed between the evoked contexts that involved a usual and a new brand. Consumers were harsher when considering to repeat purchase of a new brand compared to their usual brand. These results suggest that the consideration of evoked contexts could contribute to increase the accuracy of sensory shelf life estimation and acceptance limits of sensory defects, leading to more informed business decisions.

Good sampling is good business: reconciling economic drivers of productivity and quality using fundamentals of sampling theory

Author Summary: Iron Ore supply is outpacing global demand, reinforcing the importance of product quality and reliability as critical factors that distinguish Iron Ore producers in a competitive market. This expectation calls for a shift in industry attitudes toward sampling in bulk commodities, beginning with a greater emphasis on optimisation of sampling processes from Exploration to Port. Business initiatives aimed at optimising processes often call upon technological innovation, such as mobile sampling and analysis modules at the drill rig. Such technologies indeed represent an exciting frontier in the business of minerals exploration; however their merits must be critically compared to existing sampling protocols before implementation if sample quality is to be maintained. Quantifying the Fundamental Sampling Error (FSE) of the sampling protocol is a minimum requirement to achieve this and should be preceded by experimental calibration of the sampling constant K and the exponent alpha. Here, we present a case study in which the Segregation Free Analysis (SFA) calibration methodology proposed by Minnitt et al. was used to determine K and alpha for a Channel Iron Deposit (CID) and a Brockman Iron Formation-hosted Bedded Iron Deposit (BID) from the Pilbara region of Western Australia. Following three experimental calibrations of K and alpha, liberation size was calculated for iron oxides and deleterious minerals using Gy&#x2019;s formula. Validation of liberation size is critical if the resulting FSE calculation is to inform business decisions. Electron beam instruments such as QEMSCAN have been proposed as a relatively quick and low cost way to estimate liberation size. An &#x201C;off-the-shelf&#x201D; QEMSCAN analysis was trialled as a validation method against the SFA calibration results. Good agreement was achieved between liberation sizes determined by the SFA calibration method and the QEMSCAN analysis. Furthermore, the QEMSCAN results proved to be a beneficial source of supplementary information, in the form of particle size analysis, which indicates the degree of aggregation that persisted in the calibration material despite best efforts to eliminate it, as well as mineral abundance analyses, which either confirmed or highlighted uncertainty around critical mineralogical assumptions made in the calibrations. These observations emphasise the importance of validation when assessing FSE. The case study presents an industry perspective on the applications of sampling theory in response to an increasingly competitive Iron Ore market.

Evolution of the Internet Economic Ecosystem

The evolution of the Internet has manifested itself in many ways: the traffic characteristics, the interconnection topologies, and the business relationships among the autonomous components. It is important to understand why (and how) this evolution came about, and how the interplay of these dynamics may affect future evolution and services. We propose a network-aware, macroscopic model that captures the characteristics and interactions of the application and network providers, and show how it leads to a market equilibrium of the ecosystem. By analyzing the driving forces and the dynamics of the market equilibrium, we obtain some fundamental understandings of the cause and effect of the Internet evolution, which explain why some historical and recent evolutions have happened. Furthermore, by projecting the likely future evolutions, our model can help application and network providers to make informed business decisions so as to succeed in this competitive ecosystem.

Accounting as Personal Apology

ABSTRACT What is the role of accounting? Typically, accounting is viewed as a technology to inform business decisions, such as allocation of economic resources within the marketplace. In contrast, public interest scholars emphasize the social role of accounting. For example, accounting mediates relationships among various parties, impacts social outcomes, and justifies the distribution of economic rewards. This paper contributes to the public interest perspective by exploring the origin of double-entry accounting (DEA) as a form of personal apologia. To develop the thesis that DEA originated as a form of personal apology, this paper draws from modern and medieval scholars. During the medieval period, profit seeking and markets were deeply suspect and, thus, medieval merchants occupied a precarious social and moral position. The Catholic Church was active in determining the “just price” for goods. Personal morality and just, balanced relationships were primary factors in the development of DEA. Confession and penance, sacraments of the Catholic Church, may have provided the model. The thesis that DEA originated as a form of personal apology is plausible. The contemporary, widespread use personal carbon-offset accounting (PCOA) illustrates that accounting retains the power to help construct the moral self and to mediate the individual's status within the community.

A Fine-Grain Price Analysis for Online Transaction Data

Because an accurate price analysis is very important for business, there are different kinds of price analysis systems in China. In this paper, we focus on the price analysis of agricultural products. According the examination of those price analysis systems; we find that most of them cannot provide enough information for business decisions. In this paper, we first choose to use online transaction data because they often provide rich information. We then use an agricultural ontology and a GIS (Geographic Information System) technology in our price analysis. With the GIS technology, we can bridge the price of an agricultural product and its producing areas. With the agricultural ontology, we can make a fine-grain price analysis. Our analysis shows that more exploration is needed for price analysis.

Monthly industrial output in China 1980–2012

Monthly economic indicators are used for a variety of purposes, from studying business cycles to determining economic policy and making informed business decisions. China's published monthly industrial output statistics could hardly be more confusing, with changes in variables, in coverage, in measurement, and in presentation. This paper reviews the available official data and proceeds to construct a monthly industrial output series in nominal terms and in real terms for the period 1980–2012, economy-wide and for the public sector.

Paired preference data with a no-preference option – Statistical tests for comparison with placebo data

It is well-established that when respondents are presented with identical samples in a preference test with a no preference option, a sizable proportion of respondents will report a preference. In a recent paper (Ennis, D. M., & Ennis, J. M. (2012a). Accounting for no difference/preference responses or ties in choice experiments. Food Quality and Preference, 23, 13–17) noted that this proportion can depend on the product category, have proposed that the expected proportion of preference responses within a given category be called an identicality norm, and have argued that knowledge of such norms is valuable for more complete interpretation of 2-Alternative Choice (2-AC) data. For instance, these norms can be used to indicate consumer segmentation even with non-replicated data. In this paper, we show that the statistical test suggested by Ennis and Ennis (2012a) behaves poorly and has too high a type I error rate if the identicality norm is not estimated from a very large sample size. We then compare five χ2 tests of paired preference data with a no preference option in terms of type I error and power in a series of scenarios. In particular, we identify two tests that are well behaved for sample sizes typical of recent research and have high statistical power. One of these tests has the advantage that it can be decomposed for more insightful analyses in a fashion similar to that of ANOVA F-tests. The benefits are important because they enable more informed business decisions, particularly when ingredient changes are considered for cost-reduction or health initiative purposes.

Monthly Industrial Output in China Since 1983

Monthly economic indicators are used for a variety of purposes, from studying business cycles to determining economic policy and making informed business decisions. China’s published monthly industrial output statistics could hardly be more confusing, with changes in variables, in coverage, in measurement, and in presentation. This paper reviews the available official data and proceeds to construct a monthly industrial output series in nominal terms and in real terms for the period since May 1983, economy-wide and for the state sector.

Dynamic AI-Geo Health Application based on BIGIS-DSS Approach

Public health organizations use GIS on a daily basis for input, storage, manipulation, and output of geographic information which provides a powerful tool for public health assessment and monitoring in remote locations and developing countries (1). Information technology is taking on a new level of importance due to its business intelligence application solutions. Our proposal is aimed at integrating these two different worlds in a single dynamic framework using web technology at health sector. Dynamic AI-Geohealth application based on BIGIS_DSS approach uses GIS tools and techniques to convert geographic information and analysis of data that becomes valuable information to make more informed business decisions and rational. Thus, the approach is not just a technical or business analytical process; But discipline which includes both. This paper explores the adopting Business Intelligence (BI) with Geographic Information System (GIS) to build a spatial artificial intelligence and predictive analytical approach. Also this paper will explores case research for implementation of proposed approach in developing countries like Egypt at health sector as dynamic health map web application. The proposed approach helps in solving spatial problem which faces decision makers at health