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The possibility of an established firm repelling a newcomer's cost reducing technical advances by providing the newcomer access to its currently superior technology, is explored. The oldtimer is supposed to offer his technology in return for the newcomer either ceasing R&D or sharing her findings. It is found that newcomers with the R&D potential to drive the oldtimer out of business cannot be coopted, but that less potent newcomers can. Whenever newcomers are deterred, the product price is higher and technical advance lower than it would be in the absence of a deal.

The purpose of this paper is to examine the operation of the Institute of Chartered Accountants in Ireland's (ICAI) complaint process from the complainant's perspective. The findings are interpreted drawing on key elements of Parker's private interest model of professional accounting ethics, particularly the private interest roles of professional authority and professional insulation.

The primary evidence used is drawn from numerous sources. These include: extensive “private” documentation comprising original correspondence between the complainant in the case examined (or his advisors) and various representatives of the ICAI spanning a five‐year period; detailed supporting documentation included with this correspondence; Independent Experts' Reports on the complaints submitted; and in‐depth interviews with the complainant prior to, during, and post the examination of the documentary evidence.

The paper reveals how high levels of professional authority and professional insulation worked in tandem to prevent complaints entering the complaint process and deny the complainant reasons for decisions taken. It demonstrates how a key structural barrier in the complaint process, the screening role of the professional accounting body's secretary, created a complainant impression of a process concerned primarily with protecting members' interests. Subsequent to complaint process changes, an erosion of professional insulation is unveiled. However, this proves fleeting and, in response to persistent complainant challenges to heightened demonstrations of professional authority, the degree of professional insulation intensifies further.

The paper focuses on a specific case where the complainant was dissatisfied with the ICAI's procedures. It reveals the extent to which complainants using professional body complaints procedures may, often by virtue of the structures in place, feel that profession protection motives are overriding purported concerns for society protection.

The paper extends and advances the literature examining professional accounting body disciplinary and complaint procedures. Prior research investigating the operation of these procedures has neglected to examine complaint processes in depth to inform their evaluations, particularly from the perspective of potential users of these processes.

Purpose and approach – We examine theoretically and experimentally how unequal abilities to contribute affect incentives and efficiency when players compete for membership in stratified groups based on the contributions they make. Players have either a low or a high endowment. Once assigned to a group based on their group contribution, players share equally in their group’s collective output. Depending on the parameters, the mechanism has several distinct equilibria that differ in efficiency.

Findings – Somewhat counter to conventional expectation our theoretical analysis indicates that as long as certain assumptions are satisfied, efficiency increases rather than decreases the more abilities to contribute differ. The analysis also suggests various follow-up experiments about equilibrium selection, tacit coordination, and the effect of unequal abilities in systems with endogenous grouping. We conduct an experiment that shows that subjects tacitly coordinate the mechanism’s asymmetric payoff-dominant equilibrium with precision; this precision is robust to a change in the structure and complexity of the game.

Implications – The results suggest that people respond to merit-based grouping in a natural way and that competitive contribution-based grouping encourages public contributions even when abilities to contribute differ, which is the case in all communities and societies.

A large number of microprocessors (μPs) have been widely used in many practical fields and the demand for improvement of their reliabilities have recently increased. Watchdog processors (WDPs) are small and simple coprocessors that can detect errors by monitoring the behavior of μPs. This paper formulates three reliability models of μP systems with WDPs: model 1 considers the system where a main processor (MPu) has n WDPs with self checking. Next, model 2 considers the system with upper limit number of resets. Further, model 3 discusses the system with limit processing time. The expected costs of each model are derived and the optimal policies which minimize them are discussed analytically. Finally, the numerical examples are given.

Constructing a simple dynamic North–South model in which factors of production are internationally immobile and there is no international credit market, it is possible that a persistent and unilateral foreign aid makes both North and South better off. We also show that the Pareto-improving transfer involves local indeterminacy.

This is the second in a series of papers focused on alcohol and substance abuse rehabilitation centers. Centers face the ongoing challenge of validating outcomes to meet the burden of evidence for insurance companies. In the first paper, data mining was used to establish baseline patterns in treatment success rates, for the Futures: Palm Beach Rehabilitation Center, that have a direct impact on a client’s ability to receive insurance coverage for treatment programs. In this paper, we examine 2016 outcomes and report on facility efficacy, alumni progression and sobriety, and forecast treatment success rates (short and long term) in support of client insurability. Data collection has been standardized and includes admissions data, electronic medical records data, satisfaction survey data, post-discharge survey data, Centers for Disease Control (CDC) data, and demographic data. Clustering, partitioning, ANOVA, stepwise regression and stepwise Logistic regression are applied to the data to determine statistically significant drivers of treatment success.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

Examines trend rules in conjunction with other well‐known supplementary runs rules to assess their impact when used in control charting. Focuses on a set of 613 trend rules deemed as potential candidates to increase the sensitivity of the control chart. The examined rules are viewed in the light of a stable environment, which determines the false alarm rate, and then in an environment in which the process mean is subjected to drift. Results indicate that there are subsets of trend rules that aid in the detection of out‐of‐control conditions depending on the severity of the drift and the number of zonal‐based supplementary runs rules used.

Presents a repetitive group sampling plan for three‐attribute classes where items are classified into three categories, namely good, marginal and bad. Derives various performance characteristics of interest to quality control engineers and plan designers through the approach of the Graphic Evaluation and Review Technique (GERT). In order to facilitate the operation and construction of the plan, tabulates Poisson unity values for a useful subset of the proposed plan.

THE 28th International Air and Space Show at Le Bourget will be the largest yet held. One hundred and twenty‐five British companies will be taking part and this number represents well over 90 per cent of the British aerospace industry's production and research capacity. The theme of British participants will be ‘Aerospace through the Seventies’ and displays will include illustrations of projects for the next decade, as well as current products and research programmes. The Salon has been organized so that each day will be devoted to emphasizing a particular aspect of aeronautical activity: 29th May Press Preview; 30th May Official Opening Day; 31st May Philatelists Day and Aerospace Orientation Day; 1st June General and Business Aviation; 2nd June Aeromedical Aviation; 3rd June Electronics Industry; 4th June Equipment Industry; 5th June Rotary Wing Industry and Special Steel Studies; 6th June Foreign Missions Day; 1th June International Flying Display; 8th June International Air Display. The Show will be open to the public every day from 9.30 a.m. to 6.30 p.m., except on Friday 6th June which will be Foreign Missions Day and admission will then be by invitation only.