Zhengrui Jiang

Speed Matters: The Role of Free Software Offer in Software Diffusion

Many software products are available free of charge. While the benefits resulting from network externality have been examined in the related literature, the effect of free offer on the diffusion of new software has not been formally analyzed. We show in this study that even if other benefits do not exist, a software firm can still benefit from giving away fully functioning software. This is due to the accelerated diffusion process and subsequently the increased net present value of future sales. By adapting the Bass diffusion model to capture the impact of free software offer, we provide a methodology to determine the optimal number of free adopters. We show that the optimal free offer solution depends on the discount rate, the length of the demand window, and the ratio of low-valuation to high-valuation free adopters. Our methodology is shown to be applicable for both fixed and dynamic pricing strategies.

A Generalized Norton--Bass Model for Multigeneration Diffusion

The Norton-Bass (NB) model is often credited as the pioneering multigeneration diffusion model in marketing. However, as acknowledged by the authors, when counting the number of adopters who substitute an old product generation with a new generation, the NB model does not differentiate those who have already adopted the old generation from those who have not. In this study, we develop a Generalized Norton-Bass (GNB) model that separates the two different types of substitutions. The GNB model provides closed-form expressions for both the number of units-in-use and the adoption rate, and offers greater flexibility in parameter estimation, forecasting, and revenue projection. An appealing aspect of the GNB model is that it uses exactly the same set of parameters as the NB model and is mathematically consistent with the later. Empirical results show that the GNB model delivers better overall performance than previous models both in terms of model fit and forecasting performance. The analyses also show that differentiating leapfrogging and switching adoptions based on the GNB model can help gain additional insights into the process of multigeneration diffusion. Furthermore, we demonstrate that the GNB model can incorporate the effect of marketing mix variables on the speed of diffusion for all product generations.

Postrelease Testing and Software Release Policy for Enterprise-Level Systems

Prior work on software release policy implicitly assumes that testing stops at the time of software release. In this research, we propose an alternative release policy for custom-built enterprise-level software projects that allows testing to continue for an additional period after the software product is released. Our analytical results show that the software release policy with postrelease testing has several important advantages over the policy without postrelease testing. First, the total expected cost is lower. Second, even though the optimal time to release the software is shortened, the reliability of the software is improved throughout its lifecycle. Third, although the expected number of undetected bugs is higher at the time of release, the expected number of software failures in the field is reduced. We also analyze the impact of market uncertainty on the release policy and find that all our prior findings remain valid. Finally, we examine a comprehensive scenario where in addition to uncertain market opportunity cost, testing resources allocated to the focal project can change before the end of testing. Interestingly, the software should be released earlier when testing resources are to be reduced after release.

A Framework for Reconciling Attribute Values from Multiple Data Sources

Because of the heterogeneous nature of different data sources, data integration is often one of the most challenging tasks in managing modern information systems. While the existing literature has focused on problems such as schema integration and entity identification, it has largely overlooked a basic question: When an attribute value for a real-world entity is recorded differently in different databases, how should the “best” value be chosen from the set of possible values? This paper provides an answer to this question. We first show how a probability distribution over a set of possible values can be derived. We then demonstrate how these probabilities can be used to solve a given decision problem by minimizing the total cost of type I, type II, and misrepresentation errors. Finally, we propose a framework for integrating multiple data sources when a single “best” value has to be chosen and stored for every attribute of an entity.

How to give away software with successive versions

Free software offer as a promotional tool has been employed by software firms of all sizes. In this research, we propose an extended multi-generation diffusion model that separates substitution from switching, and develop methodologies to help a firm determine the optimal number of free adoptions for each version. Our analyses show that due to the word-of-mouth effect, free offer can help increase a firms total profit for all versions of a product. Furthermore, we find that in the presence of low-valuation free adopters, the optimal number of high-valuation free adopters decreases, the total number of free adopters increases, and the total profit improves substantially as a result.

A Decision-Theoretic Framework for Numerical Attribute Value Reconciliation

One of the major challenges of data integration is to resolve conflicting numerical attribute values caused by data heterogeneity. In addressing this problem, existing approaches proposed in prior literature often ignore such data inconsistencies or resolve them in an ad hoc manner. In this study, we propose a decision-theoretical framework that resolves numerical value conflicts in a systematic manner. The framework takes into consideration the consequences of incorrect numerical values and selects the value that minimizes the expected cost of errors for all data application problems under consideration. Experimental results show that significant savings can be achieved by adopting the proposed framework instead of ad hoc approaches.

A Markov-Based Update Policy for Constantly Changing Database Systems

In order to maximize the value of an organizations data assets, it is important to keep data in its databases up-to-date. In the era of big data, however, constantly changing data sources make it a challenging task to assure data timeliness in enterprise systems. For instance, due to the high frequency of purchase transactions, purchase data stored in an enterprise resource planning system can easily become outdated, affecting the accuracy of inventory data and the quality of inventory replenishment decisions. Despite the importance of data timeliness, updating a database as soon as new data arrives is typically not optimal because of high update cost. Therefore, a critical problem in this context is to determine the optimal update policy for database systems. In this study, we develop a Markov decision process model, solved via dynamic programming, to derive the optimal update policy that minimizes the sum of data staleness cost and update cost. Based on real-world enterprise data, we conduct experiments to evaluate the performance of the proposed update policy in relation to benchmark policies analyzed in the prior literature. The experimental results show that the proposed update policy outperforms fixed interval update policies and can lead to significant cost savings.

Quality, Pricing, and Release Time: Optimal Market Entry Strategy for New Software-as-a-Service Vendors

As a new software licensing model, software-as-a-service (SaaS) is gaining tremendous popularity across the globe. In this study, we investigate the competition between a new entrant and an incumbent in a SaaS market, and derive the optimal market entry strategy for the new entrant. One interesting finding is that, when the new entrant’s product is fully compatible with that of the incumbent, but has a significantly lower quality, the new entrant should adopt an instant-release strategy, i.e., releasing its product at the start of the planning horizon. If the initial quality gap of the two products is small, the new entrant is better off adopting a late-release strategy, i.e., deferring the release of the new product until its quality surpasses that of the existing product. When the two competing products are partially compatible, in addition to instant-release and late-release, an early-release strategy, i.e., spending some time improving the quality of the new product but releasing it before its quality reaches that of the existing product, can also be optimal. Furthermore, under full product compatibility, higher quality leads to higher price for the new product, whereas under partial product compatibility, higher quality does not always go in tandem with higher price.

T-Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data Publishing

Privacy-preserving data publishing has received much attention in recent years. Prior studies have developed various algorithms such as generalization, anatomy, and L-diversity slicing to protect individuals’ privacy when transactional data are published for public use. These existing algorithms, however, all have certain limitations. For instance, generalization protects identity privacy well but loses a considerable amount of information. Anatomy prevents attribute disclosure and lowers information loss, but fails to protect membership privacy. The more recent probability L-diversity slicing algorithm overcomes some shortcomings of generalization and anatomy, but cannot shield data from more subtle types of attacks such as skewness attack and similarity attack. To meet the demand of data owners with high privacy-preserving requirement, this study develops a novel method named t-closeness slicing (TCS) to better protect transactional data against various attacks. The time complexity of TCS is log-linear,...

How to Deal with Liars? Designing Intelligent Rule-Based Expert Systems to Increase Accuracy or Reduce Cost

Input distortion is a common problem faced by expert systems, particularly those deployed with a Web interface. In this study, we develop novel methods to distinguish liars from truth-tellers, and redesign rule-based expert systems to address such a problem. The four proposed methods are termed split tree (ST), consolidated tree (CT), value-based split tree (VST), and value-based consolidated tree (VCT), respectively. Among them, ST and CT aim to increase an expert system’s accuracy of recommendations, and VST and VCT attempt to reduce the misclassification cost resulting from incorrect recommendations. We observe that ST and VST are less efficient than CT and VCT in that ST and VST always require selected attribute values to be verified, whereas CT and VCT do not require value verification under certain input scenarios. We conduct experiments to compare the performances of the four proposed methods and two existing methods, i.e., the traditional true tree (TT) method that ignores input distortion and the...