206702052
Comparing Customer Trust in Virtual Salespersons With Customer Trust in Human Salespersons
Virtual salespersons (computer agents) act in a similar role in online stores as human salespersons act in physical stores. Customer trust in a salesperson is key in generating transactions and managing customer relationships. In this exploratory study, 44 participants used the services of both virtual and human salespersons in the same commercial store. Written protocols were collected by asking the participants open-ended questions regarding their comparative trust. This paper finds that similar to trust in a human salesperson, trust in a virtual salesperson contains trust in competence, benevolence, and integrity; however, the formation processes of trust in virtual salespersons, trust in human salespersons, distrust in virtual salespersons, and distrust in human salespersons are different. This paper theoretically outlines to what extent research on trust in computer agents can draw from literature on interpersonal trust. It practically contributes to our understanding of how to better design trustworthy virtual salespersons.
13974173
An empirical investigation of intelligent agents for e-business customer relationship management: a knowledge management perspective
Izak Benbasat MIS Division, Faculty of Commerce and Business Admin. University of British Columbia 2053 Main Mall, Vancouver, BC, V6T1Z2, Canada Phone: (604)822-8396, Fax: (604)822-0045 Email: benbasat@commerce.ubc.ca Abstract: Using a knowledge management perspective, this paper investigates new and efficient ways of applying intelligent agents to e-business customer relationship management. Intelligent agents, as well as knowledge-based systems or expert systems, as a branch of applied artificial intelligence not only predate the recent surge of interest in knowledge management, but also stand out as a well-established means for implementing certain aspects of knowledge management. Intelligent agent technologies make it easier to codify, store, share, and transfer certain kinds of knowledge. Based on the IS literature on explanations and decisional guidance for knowledge-based systems, this paper argues that transferring appropriate knowledge from an organization’s staff to its partners and customers can facilitate efficient customer relationship management (e.g., improving customer trust). It is suggested that three types of knowledge – “How Explanations”, “Why Explanations”, and “Decisional Guidance” – be embedded in intelligent agents, and transferred to the agent’s users. A laboratory experiment is proposed to test if, in which aspects, and to what extent these types of knowledge will increase customer trust.
14240267
Multimedia-Based Interactive Advising Technology for Online Consumer Decision Support
This paper proposes a new method for online consumer decision support: Multimedia-based Interactive Advising (MIA). The method is based on current internet technologies, particularly multimedia, intelligent agent and advising technology. The discussion of the benefits and design of MIA will illuminate particular areas of research and development, with the goal of improving consumers’ choices in e-commerce environments.
14446380
OP-cluster: clustering by tendency in high dimensional space
Clustering is the process of grouping a set of objects into classes of similar objects. Because of unknownness of the hidden patterns in the data sets, the definition of similarity is very subtle. Until recently, similarity measures are typically based on distances, e.g Euclidean distance and cosine distance. We propose a flexible yet powerful clustering model, namely OP-cluster (Order Preserving Cluster). Under this new model, two objects are similar on a subset of dimensions if the values of these two objects induce the same relative order of those dimensions. Such a cluster might arise when the expression levels of (coregulated) genes can rise or fall synchronously in response to a sequence of environment stimuli. Hence, discovery of OP-Cluster is essential in revealing significant gene regulatory networks. A deterministic algorithm is designed and implemented to discover all the significant OP-Clusters. A set of extensive experiments has been done on several real biological data sets to demonstrate its effectiveness and efficiency in detecting coregulated patterns.
8724139
News Content Highlight via Fast Caption Text Detection on Compressed Video
Captions present in video frames play an important role in understanding video content. This paper presents a fast algorithm to automatically detect captions in MPEG compressed video. It is based on statistics features of caption text's chrominance components. The paper also discusses its principle and speed-up mechanism in detail. We have successfully exploited the technique to automatically construct the pictorial catalogue, a new content representation. Experiment results show the proposed caption detection algorithm has not only the ideal accuracy 96.6% and recall 100%, but also a detection speed of faster than real time.
2768791
Finding representative set from massive data
In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. The massive data volume poses challenges to both human users and computers. In this project, we propose a new model for identifying representative set from a large database. A representative set is a special subset of the original dataset, which has three main characteristics: It is significantly smaller in size compared to the original dataset. It captures the most information from the original dataset compared to other subsets of the same size. It has low redundancy among the representatives it contains. We use information-theoretic measures such as mutual information and relative entropy to measure the representativeness of the representative set. We first design a greedy algorithm and then present a heuristic algorithm that delivers much better performance. We run experiments on two real datasets and evaluate the effectiveness of our representative set in terms of coverage and accuracy. The experiments show that our representative set attains expected characteristics and captures information more efficiently.
19971560
Successfully Adopting IT for Social Welfare Program Management
In DGO 2003, we first introduced the long-standing Work First project that is inter-disciplinary between social work and computer science at the University of North Carolina at Chapel Hill (UNC-CH) with the collaboration of the North Carolina Department of Health and Human Services (NC DHHS). Based on the success in our Work First project, the NC DHHS and UNC-CH have extended the collaboration to work on a similar project to assist in the evaluation of changes to the Food Stamp employment and training (E&T) program in North Carolina. In this paper, we reflect on our experience to discuss some important aspects of successfully adopting information technology in government agencies in order to replicate them in the new Food Stamp project.
10163897
Accelerating approximate subsequence search on large protein sequence databases
In this paper, we study the problem on how to build a persistent index structure for protein sequences to support approximate match. The suffix tree has been proposed as a solution to index sequence database and has been deployed on organizing DNA sequences (Hunt et al. (2001)). Unfortunately, it suffers from the problem of "memory bottleneck" that prevents it from being applied efficiently to a large database. The performance even degrades further for protein database due to a larger fanout at each node. Here, we employ an indexing structure, called BASS-tree, to support approximate match in sublinear time on a large protein database. We call this indexing method the sequence approximate match index method. The search of approximate matches can be properly directed to the portion in the database with a high potential of matching quickly. It is demonstrated in our experiments that the potential performance improvement is in an order of magnitude over alternative methods such as the BLAST algorithm and the suffix tree.