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Predicting the inactivity and the repeat transaction frequency of a firm's customer base is critical for customer relationship management. The literature offers two main approaches to such predictions: stochastic modeling efforts represented by Pareto/NBD and machine learning represented by neural network analysis. As these two approaches have been developed and applied in parallel, this study systematically compares the two approaches in their prediction accuracy and defines the relatively appropriate implementation scenarios of each model.

By designing a rolling exploration scheme with moving calibration/holdout combinations of customer data, this research explores the two approaches' relative performance by first utilizing three real world datasets and then a wide range of simulated datasets.

The empirical result indicates that neither approach is dominant and identifies patterns of relative applicability between the two. Such patterns are consistent across the empirical and the simulated datasets.

This study contributes to the literature by bridging two previously parallel analytical approaches applicable to customer base predictions. No prior research has rendered a comprehensive comparison on the two approaches' relative performance in customer base predictions as this study has done. The patterns identified in the two approaches' relative prediction performance provide practitioners with a clear-cut menu upon selecting approaches for customer base predictions. The findings further urge marketing scientists to reevaluate prior modeling efforts during the past half century by assessing what can be replaced by black boxes such as NNA and what cannot.

This research aims to investigate how entrepreneurial orientation influences a firm’s differentiation–cost advantage ambidexterity (DCAA) and performance indirectly through dynamic capabilities, while also investigating the impact of the interactive effect of the level and consistency of entrepreneurial orientation on dynamic capabilities. The goal of this study is to better understand the importance of consistently following an entrepreneurial orientation, as well as the linkage of such a consistently implemented strategy upon gaining both a cost and differentiation positioning enhancement.

Two empirical studies are conducted to test the proposed hypotheses – one longitudinal with multiple forms of data (i.e. text data, survey data and archival data) from 100 Taiwanese electronics firms and the other using primary data from a survey of senior managers.

Entrepreneurial orientation improves dynamic capabilities, which in turn promote superior DCAA and enhanced firm performance. In addition, as captured through a unique measure of consistency allowed through computer-aided text analysis, the results indicate that the effect of entrepreneurial orientation on dynamic capabilities is amplified when a firm consistently adopts entrepreneurial decisions and actions.

The specific context was a sample of electronics firms in Taiwan. This limits the generalization of findings, as would be possible with assessing the hypotheses in other industries.

This research clearly demonstrates the significance of consistency in pursuing a strategic orientation. The consistent support and deployment of resources facilitates an organization’s achieving positive outcomes associated with an entrepreneurial orientation.

While entrepreneurial orientation contributes to firm performance, extant knowledge on the internal process through which entrepreneurial orientation affects performance is relatively limited. The findings not only highlight the full mediating role of dynamic capabilities and DCAA, but also shed light on the importance of consistency in entrepreneurial orientation over time.

There has been considerable discussion of various aspects of the “Web 2.0” concept in the past several years. However, the Web 2.0 concept as a whole has not been analysed through the lens of the Web 1.0 metrics on which managers rely heavily for planning and evaluation. This paper aims to analyse the relationships among a site's audience metrics and its degree of Web 2.0‐ness.

Data collected from an online panel's clickstreams were aggregated to derive the web audience metrics. A web site's degree of Web 2.0‐ness was evaluated through a three‐step procedure by a series of binary criteria as to whether the site accommodates popular Web 2.0 applications. Pearson and Spearman correlations were conducted for the empirical analysis of data consisting of clickstreams gathered from an online panel coupled with expert scoring of web sites.

It was found that the size of a web site's visitor base is positively associated with the average number of page views per visitor. The average number of page views per visitor is in turn positively associated with the speed at which the visitors consume the site's content. Furthermore, a site's degree of Web 2.0‐ness is positively associated with the average number of page views per visitor and the speed of content consumption on the site.

First, the “double jeopardy” phenomenon of small brands found in the consumer package goods market is also observed for small sites in cyberspace in terms of audience metrics. Second, the accommodation of more Web 2.0 applications in a web site enhances the site's attractiveness so that its visitor base grows and its visitors will have a deeper relationship with the site.

This paper examines the Web 2.0 phenomenon through the Web 1.0 lens by exploring the relationships among web audience metrics and the degree of Web 2.0‐ness across web sites. It characterises the relationships among a web site's audience metrics and those between such metrics and the site's degree of Web 2.0‐ness. In addition this study fills an important gap in the literature and could serve as a stepping‐stone for further exploration of Web 2.0 issues from the market perspective.

The current research aims to explore variables that explain the differences in online reviewers' behavior.

The authors' panel dataset is compiled from Amazon.com's book section and uses publicly available information about reviewers in combination with the reviews they wrote. The authors utilize the Pareto/NBD model with time-invariant covariates. The model's parameters are estimated using maximum likelihood estimates (MLE) with MATLAB software.

This study contributes to the literature by exploring how the characteristics of reviews and the reviewers might shape consumer review frequency and continuity. Specifically, the authors' results show that review ratings, comments on a review, and helpful votes have a positive association with review frequency and continuity. Furthermore, the length of the textual review has a positive relationship with review frequency, but a negative relationship with review continuity. Relative to anonymous reviewers, people who write reviews and use their real names post reviews less often, but their review continuity is longer.

This paper is the first to identify empirically variables that explain review frequency and continuity, thus enabling companies to gain a better understanding of their reviewer base and hence to manage online word-of-mouth more efficiently. It is the first study to apply a well-known behavioral model to address online review behavior.