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In recent years, many new and interesting business models for Internet-based selling have emerged with the advent of electronic commerce, one of which is the Internet-based group-buying. Since group-buying can be quickly built and removed, and the consumers can pay a lower price for the product through it, the group-buying can be a new online promotion form. In this chapter we build up a two-period pricing model for a supply chain when a supply chain member utilizes group-buying program to promote its products. In detail, we consider a supply chain consisting of a supplier and a retailer, where the supplier or retailer may launch a group-buying program to promote the products via a group-buying web site in the promotion period (i.e., the first period), as well as the supplier may sell its products through the retailer traditionally in both periods. Utilizing game theory, we derive the equilibrium decisions of the two supply chain members in three different scenarios, that is, (i) there is no group-buying program, (ii) the supplier launches a group-buying program, and (iii) the retailer launches a group-buying program. Analysis of the equilibrium decisions illustrates the following results: (i) both, the supplier and the retailer, will set low prices in the promotion period and high prices in the regular period; (ii) this trend will be enhanced when a group-buying program occurs, especially when such program is launched by the supplier; (iii) while the retailer can always benefit from a group-buying program, the supplier’s profit may be reduced under certain conditions; and (iv) in spite of the fact that the supplier’s profit may be damaged by the group-buying program, when the two supply chain members decide to launch a group-buying program, the unique equilibrium is that the supplier will launch such a program.

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.

Processes, in practice, may involve more than one quality characteristic that are of interest. It is quite possible for such quality characteristics to not be independent of each other since the magnitude of one of the characteristics may influence the magnitude of the other characteristics. Under this setting, it is of interest to determine the optimal settings of the process parameters (usually the process mean and the process standard deviation of each quality characteristic) under various objectives. Some of the objectives may be conflicting to each other. In general, it may be possible for the decision-maker to prioritize the objectives. Using such a prioritized scheme, it is of interest to determine the optimal settings of the process mean and standard deviation for each quality characteristic that is being monitored. Such solutions could be labeled as “satisficing” solutions. Sensitivity analyses of the decision variables to the chosen objectives and parameter values are also investigated.

Research in entrepreneurship is increasingly exploring how archetypes, taxonomies, typologies, and configurations can help scholars understand complex entrepreneurial phenomena. We illustrate the potential for set-theoretic methods to inform this literature by offering best practices regarding how qualitative comparative analysis (QCA) can be used to explore research questions of interest to entrepreneurship scholars. Specifically, we introduce QCA, document how this approach has been used in management research, and provide step-by-step guidance to empower scholars to use this family of methods. We put a particular emphasis on the analytical procedures and offer solutions to dealing with potential pitfalls when using QCA-based methods and highlight opportunities for future entrepreneurship research.