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A critical concern for corporations test marketing new products involves test market city evaluation. In order for test marketing to be successful, corporations must identify cities that offer a good fit with the firm’s overall product strategy. Unfortunately, little has been written to aid corporations in making complex test city selection decisions. Presents a model that combines the concepts of marketing, the management science technique of goal programming, and microcomputer technology to provide managers with a more effective and efficient method for evaluating test cities and making selection decisions. Extends the existing literature on test market evaluation by applying a computer optimization model to test market evaluation in a way that has not been done before.

This paper briefly introduces the concept of model selection, reviews recent development in the area of econometric analysis of model selection and addresses some of the crucial issues that are being faced by researchers in their routine research problems. The paper emphasizes on the importance of model selection, particularly the information criteria and penalty functions based model selection procedures which are useful for economists and finance researchers.

The purpose of the current study was to examine university students' knowledge, confidence and experience of popular graduate employer selection tests.

A cross-sectional self-report survey was administered to gather a sufficient number of quantitative responses from undergraduate students. A total of 241 students completed the survey with most of them being psychology students from Swansea University. Four key variables were examined: (1) students' experience, (2) confidence and (3) knowledge of selection tests and (4) their desire for more information about selection tests as part of their degree. An audit of selection tests used by the Times Top 100 graduate employers was also conducted.

Students tended to misjudge how often selection tests were used by employers, and generally lacked experience with these tests. Students' confidence in completing each test varied as a function of the selection test; however, prior experience with these tests positively predicted confidence. Additionally, over 70% of students reported a desire for further information about selection tests as part of their degree.

These novel findings suggest that students could benefit from further information about selection tests as part of their degree programme which would be of benefit to both students and universities.

These findings are, to the authors knowledge, the first to explicitly assess second- and third-year undergraduate students' knowledge, experience and confidence with popular graduate employer selection tests and demonstrate that students would like more information about these tests on their programme.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

In today's societies, work environment and customers' expectations change on a daily manner. Consequently, it is crucial for companies to find a way for adapting themselves to new requirements. For this purpose, reengineering projects have been introduced and evolved in different companies with different responsibilities over the past decades. However, the risk associated with these projects is inevitable and is a huge obstacle on the way of their implementation. This study, in line with previous studies, contributed in this context by proposing a new methodology for selecting suitable processes and adopted best practices candidate for business process reengineering (BPR). The proposed methodology aims to achieve lower risk and higher probability of success for BPR projects.

This objective is achieved by integration of the concept of portfolio selection problems (PSP) into the organizational decision making concerning BPR project. A model for selection of most appropriate reengineering scenarios, which is a combination of processes and best practices, is adopted and proposed. This model by putting additional constraints on risks associated with a BPR project and increasing its return identifies the most prosperous portfolio of scenarios for a reengineering project. The proposed model is tested step-by-step through a case study in order to validate its outcome and justify its practicality.

In this paper, a new methodology is proposed containing a model as a managerial tool for conducting more successful reengineering projects. The applicability of the methodology is tested in one of the largest metallurgical laboratory and research centers of Iran. Four strategic processes were selected and several best practices customized, after screening all processes of the case study. Accordingly, in total, 15 different scenarios were explored for the reengineering project in which four of them identified by the model as the processes with the highest possibility of success through the BPR project.

This methodology suggests a novel way to benefit from PSP for process selection problems by putting additional control on implementation risk of reengineering project. While the urge of using reengineering project exists within the current companies, the high level of risk of these projects is considered as a huge obstacle in conducting this project. This study, by proposing a new method, aims to address this issue as well as point to the practicality of integrating PSP model in organizational contexts.

Surrogate-assisted evolutionary algorithms (SAEAs) are the most popular algorithms used to solve design optimization problems of expensive and complex engineering systems. However, it is difficult for fixed surrogate models to maintain their accuracy and efficiency in the face of different issues. Therefore, the selection of an appropriate surrogate model remains a significant challenge. This paper aims to propose a dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm (AHSM-PSO) to address this issue.

A dynamic adaptive hybrid selection method (AHSM) is proposed. This method can identify multiple ensemble models formed by integrating different numbers of excellent individual surrogate models. Then, according to the minimum root-mean-square error, the best suitable surrogate model is dynamically selected in each generation and is used to assist PSO.

Experimental studies on commonly used benchmark problems, and two real-world design optimization problems demonstrate that, compared with existing algorithms, the proposed algorithm achieves better performance.

The main contribution of this work is the proposal of a dynamic adaptive hybrid selection method (AHSM). This method uses the advantages of different surrogate models and eliminates the shortcomings of experience selection. Furthermore, the empirical results of the comparison of the proposed algorithm (AHSM-PSO) with existing algorithms on commonly used benchmark problems, and two real-world design optimization problems demonstrate its competitiveness.

The purpose of this paper is to explore the determinants, transactional alignment and performance outcomes of franchise contract length using transaction costs theory (TCT) and resource-based theory (RBT).

The author hypothesizes that franchisors choose contract length according to TCT and RBT arguments. TCT explains the safeguarding function of contracts: the franchisors will offer longer contracts when franchisees’ specific investments are high and environmental uncertainty is low. RBT highlights the knowledge leverage function of contracts: the franchisors will offer longer contracts when the brand name and intangible knowledge assets are high. Franchise companies that design contract length aligned with transactional attributes will perform better. The author tests the misalignment hypothesis and comparative performance of franchise contracts by estimating two-stage least squares regression and Heckman two-stage procedure that control for endogeneity and self-selection.

Empirical data from the German franchise sector support the hypotheses. In addition to the safeguarding function, franchise contracts have an important knowledge leverage function. Longer contracts perform better due to the development of relational strategic assets and stronger commitment.

Franchisors must offer longer contracts when specific investments of franchisees, brand name, intangible knowledge assets are high, and environmental uncertainty is low. Franchisors should invest in the development of relational strategic assets and offer longer contracts for the benefit of superior performance.

The study addresses the significant question of transactional alignment and comparative performance of franchise contracts. It empirically confirms the importance of RBT in explaining contractual choices and performance.

The dissolving trade barriers, financial deregulation, hyper‐mobility of capital and the rapid diffusion of new information technologies have ushered the Australian economy into the borderless world. The orthodoxy that states that centralised wage‐fixing in Australia has impeded wage flexibility and resulted in high unemployment is unconvincing. Partly, this is because in the 1980s Australian labour market institutions have been decentralised and decollectivised in response to pressures from the borderless world. The insights garnered from cross‐sectional comparative statics that, first, skill‐biased Schumpeterian technological change was the major cause of labour immiserisation and, second, adverse Stolper‐Samuelson trade played an insignificant effect need to be reviewed. Parsimonious dynamic time‐series models of trade and technology have been formulated using general‐to‐specific methods after taking account of stochastic trends through unit root and cointegration tests. Granger causality and non‐nested tests applied to these models support the contention that both trade and technology contributed to increasing wage disparity during the borderless era. Moreover the supply side factors such as female participation, immigration and institutional factors such as deunionisation have also increased wage disparity. The deregulation of the Australian labour market by the Workplace Relations Act, whilst an inevitable response to achieve competitiveness in the borderless world market, would exacerbate wage inequality. Policies aimed at skill accumulation on the one hand, and social welfare policies involving negative income taxes on the other may have to be implemented to mitigate the deleterious social effects of rising wage inequality.

This paper aims to assess all the Brazilian electric power transmission line auctions occurred between 1999 and 2017.

A copula-based Roy/endogenous switching regression model is used. The suitability of this model is twofold: it takes into account the selection bias problem involving auctions data and it allows more flexibility in modeling the joint distribution between the unobserved components of the selection and outcome equations; thus, normal distribution assumptions are not needed.

The main results suggest that stated-owned companies have the highest probability of winning an auction, and there is a non-competitive behavior among the players in the auction. The results also suggest some departure from joint normality in the data.

The copula-based sample selection approach used in this paper is consistent under non-normality and allows one to address different types of nonlinearities in the data such as asymmetry and heavy tails.

In what seems as an infinitely ongoing debate regarding the purchasing power parity (PPP) theory, this paper seeks to question the strength of the scientific “evidence” put forward by the PPP revisionists

In this paper, the validity of the PPP revisionists' scientific evidence supporting long‐run PPP is questioned based on the replication of an influential review study that is considered by PPP revisionists to exhibit “some of the strongest evidence” in favour of the PPP theory.

By simulation experiments it is demonstrated that the traditional PPP unit root tests are non‐robust to the empirically identified (G)ARCH distortions. Due to (G)ARCH distortions, over‐rejections for the traditional unit root tests are shown to be a problem that potentially misleads researchers to believe that long‐run PPP holds under circumstances when it is in fact not valid. As a potential remedy to this problem, a new unit root test is introduced which is robust to conditional heteroscedasticity disturbances, and in contrast to traditional unit root tests, it exhibits no significant empirical support for the PPP theory.

The study illustrates that the PPP revisionists' unit root tests cannot reliably test the PPP hypothesis in the presence of (G)ARCH distortions, due to bad power and size properties. Perhaps it is time to conclude that, based on the currently existing research, it is virtually impossible to empirically come to a credible conclusion regarding whether long‐run PPP holds or not.