Zhongju Zhang

Feeling the Sense of Community in Social Networking Usage

In recent years, social networking systems have become quite popular, and have been established for a variety of purposes. However, it is still not well understood if sense of community (SOC) contributes to an individual users continued usage of these systems. This paper presents a theoretical model combining key constructs from the SOC framework and the information systems usage/success models to evaluate social networking usage. We surveyed users from popular social networking sites to test the validity of the research model. Our results indicate that while user satisfaction is still the most salient determinant for system usage, SOC also plays a significant role in the users online social interaction process. Besides its direct influence on usage, SOC also indirectly influences usage through user satisfaction. In addition, we show that SOC is a multidimensional construct that should be measured using several components. We also demonstrate that the quality of the information contained in the communities has a significant impact on SOC, but system quality does not seem to influence it. Theoretical and practical implications of the study are discussed.

ExpertRank: A topic-aware expert finding algorithm for online knowledge communities

With increasing knowledge demands and limited availability of expertise and resources within organizations, professionals often rely on external sources when seeking knowledge. Online knowledge communities are Internet based virtual communities that specialize in knowledge seeking and sharing. They provide a virtual media environment where individuals with common interests seek and share knowledge across time and space. A large online community may have millions of participants who have accrued a large knowledge repository with millions of text documents. However, due to the low information quality of user-generated content, it is very challenging to develop an effective knowledge management system for facilitating knowledge seeking and sharing in online communities. Knowledge management literature suggests that effective knowledge management should make accessible not only written knowledge but also experts who are a source of information and can perform a given organizational or social function. Existing expert finding systems evaluate ones expertise based on either the contents of authored documents or ones social status within his or her knowledge community. However, very few studies consider both indicators collectively. In addition, very few studies focus on virtual communities where information quality is often poorer than that in organizational knowledge repositories. In this study we propose a novel expert finding algorithm, ExpertRank, that evaluates expertise based on both document-based relevance and ones authority in his or her knowledge community. We modify the PageRank algorithm to evaluate ones authority so that it reduces the effect of certain biasing communication behavior in online communities. We explore three different expert ranking strategies that combine document-based relevance and authority: linear combination, cascade ranking, and multiplication scaling. We evaluate ExpertRank using a popular online knowledge community. Experiments show that the proposed algorithm achieves the best performance when both document-based relevance and authority are considered.

What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings

In the blizzard of social media postings, isolating what is important to a corporation is a huge challenge. In the consumer-related manufacturing industry, for instance, manufacturers and distributors are faced with an unrelenting, accumulating snow of millions of discussion forum postings. In this paper, we describe and evaluate text mining tools for categorizing this user-generated content and distilling valuable intelligence frozen in the mound of postings. Using the automotive industry as an example, we implement and tune the parameters of a text-mining model for component diagnostics from social media. Our model can automatically and accurately isolate the vehicle component that is the subject of a user discussion. The procedure described also rapidly identifies the most distinctive terms for each component category, which provides further marketing and competitive intelligence to manufacturers, distributors, service centers, and suppliers.

Web server load balancing: A queueing analysis

Over the last few years, the Web-based services, more specifically different types of E-Commerce applications, have become quite popular, resulting in exponential growth in the Web traffic. In many situations, this has led to unacceptable response times and unavailability of services, thereby driving away customers. Many companies are trying to address this problem using multiple Web servers with a front-end load balancer. Load balancing has been found to provide an effective and scalable way of managing the ever-increasing Web traffic. However, there has been little attempt to analyze the performance characteristics of a system that uses a load balancer. This paper presents a queuing model for analyzing load balancing with two Web servers. We first analyze the centralized load balancing model, derive the average response time and the rejection rate, and compare three different routing policies at the load balancer. We then extend our analysis to the distributed load balancing and find the optimal routing policy that minimizes the average response time.

Price competition with service level guarantee in web services

Web services have become quite popular over the last few years as they allow easier development and integration of business applications. Unlike traditional software systems, web services are self-contained modular software components that are delivered over a network (such as the Internet) and executed on a remote system hosting the requested services. However, the network and processing overhead associated with web services have also presented a significant challenge to its performance. As a result, a web service provider often announces a service-level agreement when launching a service. The service-level agreement provides a guarantee to the consumers that they can get the service they pay for at an assured level of quality. In this paper, we study the competition between two such providers offering functionally the same web services. Each provider needs to decide a service level (standard or premium) she would offer and a corresponding price for the selected service level to meet the QoS guarantee (in terms of an average response time of the service). We first analyze the case where the providers choose service levels and prices simultaneously, and then extend it to a sequential-move situation. Finally, we examine strategic choices of providers when the processing capacity is endogenized into the model.

Design and Use of Preference Markets for Evaluation of Early Stage Technologies

In the work presented here, we develop and apply preference markets in evaluating early stage technology. Partnering with a Fortune 5 company, we developed and implemented two internal preference markets (field experiments). In both cases, nonmonetary (play money) incentives were utilized, but one market provided additional nonmonetary (play money) incentives. Working with the partner company, our investigation started with seven emerging technologies and expanded to a total of 17 emerging technologies. Our results suggest that even a simple form of additional nonmonetary play money incentive yielded greater price convergence, increased spread across final market prices, and greater consistency with a costly expert panel that was set up by the partner company. Based on the outcomes of our analyses, the partner company is investing in developing extended applications of preference markets as a potentially scalable approach for dealing with its ongoing and expanding strategic identification of promising emerging technologies.

Price competition in e-tailing under service and recognition differentiation

The Internet has significantly increased the bargaining power of consumers. Many online shopping search engines allow consumers to find most retailers that sell a specific product, compare product prices, and review detailed store ratings. With competition just a click away, online retailers have little control over where consumers would shop. Offering the lowest price alone does not always guarantee that consumers will come and buy at your site. Other non-price attributes, such as service quality and a merchants brand recognition, also play important roles in helping online retailers to build competitive advantages. In this paper, we present a model of price competition that assumes e-tailers can mainly differentiate themselves by providing different levels of service and by establishing a different online recognition. Closed-form equilibrium solutions are obtained for the different scenarios that may arise in this model. Based on such solutions, we give managerial insights on how e-tailers should position themselves when parameters such as service cost, service levels, and recognition are varied.

Optimal Synchronization Policies for Data Warehouses

The notion of a data warehouse for integrating operational data into a single repository is rapidly becoming popular in modern organizations. An important issue in this context is how often one should synchronize the data warehouse to reflect the changes in the constituent operational data sources. If the synchronization is performed very frequently, the associated cost might be quite high, although the data warehouse would only have a small amount of stale data. On the other hand, if the data warehouse is synchronized infrequently, it might result in costly errors in business decisions arising from the stale data. This paper examines the trade-off between the synchronization and staleness costs and derives the optimal synchronization frequency.

A Finite Mixture Logit Model to Segment and Predict Electronic Payments System Adoption

Despite much hype about electronic payments systems (EPSs), a 2004 survey establishes that close to 80% of between-business payments are still made using paper-based formats. We present a finite mixture logit model to predict likelihood of EPS adoption in business-to-business (B2B) settings. Our model simultaneously classifies firms into homogeneous segments based on firm-specific characteristics and estimates the models coefficients relating predictor variables to EPS adoption decisions for each respective segment. While such models are increasingly making their presence felt in the marketing literature, we demonstrate their applicability to traditional information systems (IS) problems such as technology adoption. Using the finite mixture approach, we predict the likelihood of EPS adoption using a unique data set from a Fortune 100 company. We compare the finite mixture model with a variety of traditional approaches. We find that the finite mixture model fits the data better, controlling for the number of parameters estimated; that our explicit model-based segmentation leads to a better delineation of segments; and that it significantly improves the predictive accuracy in holdout samples. Practically, the proposed methodology can help business managers develop actionable segment-specific strategies for increasing EPS adoption by their business partners. We discuss how the methodology is potentially applicable to a wide variety of IS research.

Balancing IT with the Human Touch: Optimal Investment in IT-Based Customer Service

To cut costs, companies have chosen to deliver a variety of service offerings online. However, the digital systems providing such services (e-service) have always been complemented with or supported by human-based service (h-service). Whereas h-service has total costs that increase with the demand for services, e-service mainly requires a fixed investment upfront, which can be amortized over the totality of customers served. Considering the different nature of the costs of h-service and e-service and the heterogeneity of customer preferences for services, we derive the optimal mix of h-service and e-service for a service-providing company vis-a-vis its competitor. Our theoretical analysis finds the subgame-perfect Nash equilibria that determines the optimal positions in a duopoly setting. We further study the competitive dynamics of the system to examine how firms stay on the equilibrium paths. Using simulation, we investigate the effects of starting positions, small adjustments in h-service and/or e-service, and monotonic expansions of e-service on the final positioning and profits of the firms. Our results demonstrate that when firms follow a local best-reply strategy, they may end up in a position of low profitability, and when only monotonic expansions of e-service are allowed, both firms may end up overinvesting in e-service.