In theories and studies of persuasion, people's personal knowledge about persuasion agents' goals and tactics, and about how to skillfully cope with these, has been ignored. We present a model of how people develop and use persuasion knowledge to cope with persuasion attempts. We discuss what the model implies about how consumers use marketers' advertising and selling attempts to refine their product attitudes and attitudes toward the marketers themselves. We also explain how this model relates to prior research on consumer behavior and persuasion and what it suggests about the future conduct of consumer research.

The Persuasion Knowledge Model: How People Cope with Persuasion Attempts

Since the original Data Envelopment Analysis (DEA) study by Charnes et al. [Measuring the efficiency of decision-making units. European Journal of Operational Research 1978;2(6):429–44], there has been rapid and continuous growth in the field. As a result, a considerable amount of published research has appeared, with a significant portion focused on DEA applications of efficiency and productivity in both public and private sector activities. While several bibliographic collections have been reported, a comprehensive listing and analysis of DEA research covering its first 30 years of history is not available. This paper thus presents an extensive, if not nearly complete, listing of DEA research covering theoretical developments as well as “real-world” applications from inception to the year 2007. A listing of the most utilized/relevant journals, a keyword analysis, and selected statistics are presented.

Evaluation of research in efficiency and productivity:a survey and analysis of the first 30 years of scholarly literature in DEA

The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91p on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.

Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums

The bulk of our understanding of teams is based on traditional teams in which all members are collocated and communicate face to face. However, geographically distributed teams, whose members are not collocated and must often communicate via technology, are growing in prevalence. Studies from the field are beginning to suggest that geographically distributed teams operate differently and experience different outcomes than traditional teams. For example, empirical studies suggest that distributed teams experience high levels of conflict. These empirical studies offer rich and valuable descriptions of this conflict, but they do not systematically identify the mechanisms by which conflict is engendered in distributed teams. In this paper, we develop a theory-based explanation of how geographical distribution provokes team-level conflict. We do so by considering the two characteristics that distinguish distributed teams from traditional ones: Namely, we examine how being distant from one's team members and relying on technology to mediate communication and collaborative work impacts team members. Our analysis identifies antecedents to conflict that are unique to distributed teams. We predict that conflict of all types (task, affective, and process) will be detrimental to the performance of distributed teams, a result that is contrary to much research on traditional teams. We also investigate conflict as a dynamic process to determine how teams might mitigate these negative impacts over time.

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Out of Sight, Out of Sync: Understanding Conflict in Distributed Teams

The purpose of this paper is to briefly trace the evolution of DEA from the initial publication by Charnes et al. (1978b) to the current state of the art (SOA). The state of development of DEA is characterized at four points in time to provide a perspective in both directions—past and future. An evolution map is provided which illustrates DEA growth during the 17-year period, the timing of the major events, and the interconnections and influences between topics. An extensive DEA bibliography is provided.

Data envelopment analysis: The evolution of the state of the art (1978–1995)

Outcome satisfaction in negotiation: A test of expectancy disconfirmation.

Our need to understand the impact of communication media on negotiation is growing as technological advances offer negotiators more communication options. As access to technologies such as computer chat and videoconferencing increases, negotiators are choosing to use or to avoid these media without knowing the impact of their choices on negotiations. This research assesses objective and subjective negotiation outcomes, such as profit and outcome satisfaction, across four communication media with varying levels of media richness (face‐to‐face, videoconference, telephone, and computer‐mediated communication). A conceptual framework is offered to illustrate how media richness impacts objective and subjective outcomes. Results suggest that media richness affects required bargaining time, outcome satisfaction and the desire for future negotiation interaction. Thus, the communication media for negotiations should be chosen with care.

The impact of communication media on negotiation outcomes

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, theK-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.

A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning: A Comparison with K-Means Clustering.

Comparative performance of the FSCL neural net and K-means algorithm for market segmentation

In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operator's process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairly large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options.

P.V. (Sundar) Balakrishnan

Papers

Outcome satisfaction in negotiation: A test of expectancy disconfirmation.

The impact of communication media on negotiation outcomes

A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning: A Comparison with K-Means Clustering.

Comparative performance of the FSCL neural net and K-means algorithm for market segmentation

Development of hybrid genetic algorithms for product line designs