The development of an improved business game for use in Massey University marketing courses : a thesis presented in partial fulfillment of the requirements for the degree of Master of Agricultural Business and Administration in Marketing at Massey University

The thesis is a response to a problem situation in which a business game, having been used in undergraduate courses for several years, was thought to be inadequate by course and game administrators. The problem is first defined and objectives for the study are set. This is followed by a comprehensive overview of business gaming and a more specific review of the processes and problems of business game design. A description of the game in use, MARKSIM, is given. The MARKSIM experience at Massey University is evaluated from the game administrators' and game players' points of view, the latter by a survey of 41 second and third year marketing students. The specifications of a more satisfactory game are derived from this evaluation and alternative means of acquiring such a game are investigated. The solution chosen as most appropriate is to modify the game already in use and this is carried out. Improvements to the game include reparameterization of the game to reflect the New Zealand business environment, adoption of a two-product product mix, inclusion of optional qualitative administrator inputs reflecting advertising efficiency and annual report quality, superimposition of a share market on the model business community, increased market research capabilities, and general improvement of the game's robustness against administrator and player errors. Evaluation of the resultant game in terms of the problem situation is not possible within the time horizon of the thesis. Program listings are appended

Gender and Age Related Effects While Watching TV Advertisements: An EEG Study

The aim of the present paper is to show how the variation of the EEG frontal cortical asymmetry is related to the general appreciation perceived during the observation of TV advertisements, in particular considering the influence of the gender and age on it. In particular, we investigated the influence of the gender on the perception of a car advertisement (Experiment 1) and the influence of the factor age on a chewing gum commercial (Experiment 2). Experiment 1 results showed statistically significant higher approach values for the men group throughout the commercial. Results from Experiment 2 showed significant lower values by older adults for the spot, containing scenes not very enjoyed by them. In both studies, there was no statistical significant difference in the scene relative to the product offering between the experimental populations, suggesting the absence in our study of a bias towards the specific product in the evaluated populations. These evidences state the importance of the creativity in advertising, in order to attract the target population

Evaluating Content-centric vs User-centric Ad Affect Recognition

Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation (ICMI) 201

Advertising in Duopoly Market

The paper presents the dynamics of consumer preferences over two competing products acting in duopoly market. The model presented compared the majority and minority rules as well as the modified Snazjd model in the Von Neumann neighborhood. We showed how important advertising in marketing a product is. We show that advertising should also consider the social structure simultaneously with the content of the advertisement and the understanding to the advertised product. Some theoretical explorations are discussed regarding to size of the market, evaluation of effect of the advertising, the types of the advertised products, and the social structure of which the product is marketed. We also draw some illustrative models to be mproved as a further work

Chance Constrained Optimization for Targeted Internet Advertising

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoretical upper and lower bounds are presented along with a numerical substantiation

Neural Machine Translation with Word Predictions

In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence.These vectors are generated by parameters which are updated by back-propagation of translation errors through time. We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors. In this paper, we propose to use word predictions as a mechanism for direct supervision. More specifically, we require these vectors to be able to predict the vocabulary in target sentence. Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation. It is also helpful in reducing the target side vocabulary and improving the decoding efficiency. Experiments on Chinese-English and German-English machine translation tasks show BLEU improvements by 4.53 and 1.3, respectivelyComment: Accepted at EMNLP201