Corporate information systems (IS) functions have become ever closer and more intertwined with firms' marketing functions. Marketing technology and e-commerce implementations require synergy between these functions, which has been reflected in the emergence of researchers and practitioners who can work at the intersection of these disciplines. This article utilizes a systematic literature review to understand this environment and to provide a forward-looking analysis of research at the intersection of IS and marketing. First, a business-focused introduction describes the motivation for the review and puts it into context. This is followed by a bibliographic analysis to select articles at this intersection. A semi-automated content analysis of the selected articles groups them into homogeneous research clusters and further analysis is used to develop cluster themes. This process sheds light on the potential areas of collaboration, offering an in-depth comprehension of their symbiotic relationship. A set of pathways for future research is described based on “collaboration areas” between IS and marketing. These areas, including consumer trust and decision making , social media, online reviews , mobile platforms & apps, and marketing channels, among others, represent the specific areas where marketing and IS overlap and mutually influence each other. Insights are presented on bridging academia and industry and suggestions are proposed for enhancing research at the junction of IS and marketing.

Flexible cultural consensus theory (FlexCCT) provides an integrated framework and tool set for analyzing and aggregating ratings. It utilizes a likelihood-based statistical model to create aggregate ratings weighted for rater competencies and rater biases. It has features for the analysis of multiple rating cultures and for consensus adjusted reliability. Multiple optimization algorithms are implemented for FlexCCT, along with a range of model identifiability options to restrict certain subsets of the model parameters. Bootstrapping- and jackknifing-based methods are implemented to give confidence intervals for parameters. A holdout validation method that generates a measure of “prediction log-likelihood” allows for the testing of solution reliability and stability. Empirical work demonstrates the utility of FlexCCT on unsupervised wisdom of the crowd problems, and an example shows how FlexCCT can be used to analyze educational grading data.

A major concern when incorporating large sets of diverse n-gram features for sentiment classification is the presence of noisy, irrelevant, and redundant attributes. These concerns can often make it difficult to harness the augmented discriminatory potential of extended feature sets. We propose a rule-based multivariate text feature selection method called Feature Relation Network (FRN) that considers semantic information and also leverages the syntactic relationships between n-gram features. FRN is intended to efficiently enable the inclusion of extended sets of heterogeneous n-gram features for enhanced sentiment classification. Experiments were conducted on three online review testbeds in comparison with methods used in prior sentiment classification research. FRN outperformed the comparison univariate, multivariate, and hybrid feature selection methods; it was able to select attributes resulting in significantly better classification accuracy irrespective of the feature subset sizes. Furthermore, by incorporating syntactic information about n-gram relations, FRN is able to select features in a more computationally efficient manner than many multivariate and hybrid techniques.

Multidimensional scaling (MDS) is a technique used to extract a set of independent variables from a proximity matrix or matrices. Applications of MDS are found in a wide range of areas, including visualization, pattern analysis, data preprocessing, scale development, cybernetics, and localization. The overall rationale behind the paper is to help share innovations across disciplines. We survey and synthesize MDS methods from the academic areas of psychometrics, statistics, and computing. We define classical MDS and distance-based MDS. We then introduce basic MDS formulations and functions. We survey MDS techniques designed for nonlinear data and describe distance-based MDS in terms of minimizing the energy function in a spring system. We describe completely nonmetric MDS techniques for ordinal input data and describe how MDS solutions can be compared using ordinal neighborhood information. We describe optimization methods for fitting MDS models, covering both continuous optimization techniques and combinatorial techniques. We give several illustrative applications of MDS from the areas of cybernetics, air traffic control, molecular chemistry, robotics, and network localization. We link this work to the techniques described in the previous sections of the paper. We list a wide range of currently available MDS software and discuss possible future work in the area.