Predicting Organizational Success within a Project-Based Joint Venture Alliance

Project-based joint ventures (a special type of alliance) are an often-used method to form competitive organizational structures in the global market; they allow participants to quickly assemble project-needed assets on a short-term basis without substantial investment. However, published discussions of joint ventures describe mixed success and focus on factors that affect the ventures after they are formed. This paper exposes factors, which may be observed at joint venture inception, that are predictive of organizational success within the joint venture. The survey research of U.S. architecture/engineering/construction (A/E/C) firms indicates that smaller partners in a joint venture experience more market growth and are more successful. Also, a firm with strength in legitimacy (client trust) is more likely to gain in short-term income but will likely suffer some long-term market loss in comparison to its less-legitimized partners. It is further concluded that culture match among partners plays a significant role in ensuring profitable joint venture returns.     

A modified Lotka–Volterra model for competition forecasting in Taiwan’s retail industry

The retail industry is an important component of the supply chain of the goods and services that are consumed daily and competition has been increasing among retailers worldwide. Thus, forecasting the degree of retail competition has become an important issue. However, seasonal patterns and cycles in the level of retail activity dramatically reduce forecasting accuracy. This paper attempts to develop an improved forecasting methodology for retail industry competition subject to seasonal patterns and cycles. Using market share data and the moving average method, a modified Lotka–Volterra model with an additional constraint on the summation of market share is proposed. Furthermore, the mean absolute error is used to measure the forecasting accuracy of the market share. Real Taiwanese retail data from 1999 is used to validate the forecasting accuracy of our modified Lotka–Volterra model. Our methodology successfully mitigates errors from seasonal patterns and cycles and outperforms other benchmark models. These benchmarks include the Bass and Lotka–Volterra models for revenue or market share data, with or without using the moving average method. Our methodology assists the retail industry in the development of management strategies and the determination of investment timing. We also demonstrate how the Lotka–Volterra model can be used to forecast the degree of industry competition.     

对德化传统陶瓷花卉捏塑艺术的发展探讨

优质的德化陶瓷材质与娴熟的塑造技艺,造就陶瓷花卉捏塑艺术发展的新纪元;工艺品市场的巨大需求,促进陶瓷花卉捏塑艺术的繁荣发展;作为一种独立的艺术形式,德化陶瓷捏塑在发展中应提炼其艺术审美价值,从而保持陶瓷花卉捏塑艺术的创新和发展。     

The social implications of casual online gaming: Examining the effects of competitive setting and performance outcome on player perceptions

The theory of bounded generalized reciprocity has recently been applied to the study of video games, particularly those that offer cooperative and competitive settings. Emphasizing casual online gameplay, this study investigates how individuals respond to manipulated performance feedback in either a competitive or cooperative game play setting. An interaction between competitive setting and performance feedback was detected on measures of interpersonal liking and perceived competence. Specifically, perceptions of partners and competitors were relatively favorable in the cooperation/success and competition/failure conditions, respectively. On the other hand, participants rated their partners and competitors less favorably during cooperative failure and competitive success, possibly reflecting a unique self-serving bias. The results also suggest that individuals in a cooperative setting experience greater enjoyment than those in a competitive setting. The effectiveness of the two manipulations in this study may also have important implications for the design of serious or educational games, which often have the goal of strategically motivating players in an attempt to promote desired outcomes.     

The British utility regulation model: Its recent history and future prospects

This paper considers the evolution of the British model of independent utility regulation from the publication of the 1983 Littlechild Report on telecom regulation. Over the last ten years, the model has faced some threats to its pro-competition emphasis, notably in energy regulation and particularly in electricity. These threats have arisen since 2008 and the onset of the Great Recession. However, with the partial exception of energy, challenges to the independence of regulators and to the role of competition have not been acute. The model seems to have generally been able successfully to meet the challenges faced and, relative to other countries, remains intact, if less distinctive. The main continuing issue is how best to handle repeat price-setting regulatory reviews where new initiatives have been tried but have not as yet reached a settled framework within which to handle the underlying strategic gaming problems.     

The taxation effect on gasoline price asymmetry nexus: Evidence from both sides of the Atlantic

This paper explores the degree of competition in various gasoline markets and infers possible causes of price asymmetry across the globe. For this purpose we use the Dynamic Ordinary Least Square method in order to estimate price asymmetry in twelve European countries and the United States for a sample of weekly observations which spans the period from June 1996 to August 2011. The results indicate the common perception that less competitive gasoline markets exhibit price asymmetry, while highly competitive gasoline markets follow a symmetric price adjustment path. Finally, the inclusion of taxes (VAT and excise tax) into retail gasoline prices, supports the existence of price asymmetry in many European countries.     

Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm

In an electricity market generation companies need suitable bidding models to maximize their profits. Therefore, each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. In this paper optimal bidding strategy problem is solved using a novel algorithm based on Shuffled Frog Leaping Algorithm (SFLA). It is memetic meta-heuristic that is designed to seek a global optimal solution by performing a heuristic search. It combines the benefits of the Genetic-based Memetic Algorithm (MA) and the social behavior-based Particle Swarm Optimization (PSO). Due to this it has better precise search which avoids premature convergence and selection of operators. Therefore, the proposed method overcomes the short comings of selection of operators and premature convergence of Genetic Algorithm (GA) and PSO method. Important merit of the proposed SFALA is that faster convergence. The proposed method is numerically verified through computer simulations on IEEE 30-bus system consist of 6 suppliers and practical 75-bus Indian system consist of 15 suppliers. The result shows that SFLA takes less computational time and producing higher profits compared to Fuzzy Adaptive PSO (FAPSO), PSO and GA.     

The (In)efficiency of the retail-minus rule

The Retail-Minus Rule (RMR) specifies that the rate for a mandated wholesale service is set by subtracting from the vertically integrated provider's (VIP's) retail price the costs it avoids by selling the wholesale service rather than the retail service. While the RMR offers regulators an expedient method for setting wholesale prices, we show that it is efficient only under restrictive conditions. The proper test for a price squeeze inquires whether the VIP's retail price is greater than or equal to the sum of the direct cost and the opportunity cost of supplying the retail service. The RMR passes this test only when the competing retail services are perfect substitutes.     

An adaptive modeling and asset allocation approach to financial markets based on discrete microstructure model

In previous works, it was verified that the discrete-time microstructure (DTMS) model, which is estimated by training dataset of a financial time series, may be effectively applied to asset allocation control on the following test data. However, if the length of test dataset is too long, prediction capability of the estimated DTMS model may gradually decline due to behavior change of financial market, so that the asset allocation result may become worse on the latter part of test data. To overcome the drawback, this paper presents a semi-on-line adaptive modeling and trading approach to financial time series based on the DTMS model and using a receding horizon optimization procedure. First, a long-interval identification window is selected, and the dataset on the identification window is used to estimate a DTMS model, which will be used to do asset allocation on the following short-term trading interval that is referred to as the trading window. After asset allocation is over on the trading window, the length-fixed identification window is then moved to a new window that includes the previous trading window, and a new DTMS model is estimated by using the dataset on the new identification window. Next, asset allocation continues on the next trading window that follows the previous trading window, and then the modeling and asset allocation process will go on according to the above steps. In order to enhance the flexibility and adaptability of the DTMS model, a comprehensive parameter optimization method is proposed, which incorporates particle swarm optimization (PSO) with Kalman filter and maximum likelihood method for estimating the states and parameters of DTMS model. Based on the adaptive DTMS model estimated on each identification window, an adaptive asset allocation control strategy is designed to achieve optimal control of financial assets. The parameters of the asset allocation controller are optimized by the PSO algorithm on each identification window. Case studies on Hang Seng Index (HSI) of Hong Kong stock exchange and S&P 500 index show that the proposed adaptive modeling and trading strategy can obtain much better asset allocation control performance compared with the parameters-fixed DTMS model.