Tag Archives: Gregg

Mashups: A Literature Review and Classification Framework

Brandon A. Beemer and Dawn Gregg
Future Internet, Vol. 1 Issue 1, pp. 59-87.

The evolution of the Web over the past few years has fostered the growth of a handful of new technologies (e.g. Blogs, Wiki’s, Web Services). Recently web mashups have emerged as the newest Web technology and have gained lots of momentum and attention from both academic and industry communities. Current mashup literature focuses on a wide array of issues, which can be partially explained by how new the topic is. However, to date, mashup literature lacks an articulation of the different subtopics of web mashup research. This study presents a broad review of mashup literature to help frame the subtopics in mashup research.

Developing a Collective Intelligence Application for Special Education

Gregg, Dawn G.
Decision Support Systems, Vol. 47 Issue 4, pp. 455-465.

This research uses an action research methodology to develop a web-based collective intelligence application, DDtrac. DDtrac allows special education practitioners to collect data and share insights related to student performance during educational tasks and social interactions and can be used to assess special education student progress and improve decision making. A survey of 40 special education professionals and a four year case study using a single subject both indicate that educators, clinicians, families, parents, or other professionals that work with individuals with developmental disabilities achieve tangible benefit from the real time data tracking and decision support provided by the DDtrac application. The development of the DDtrac application and subsequent end-user evaluation is used to develop a set of six requirements for collective intelligence applications. These requirements can be used to guide future developers seeking to create web-based applications that harness the collective intelligence of groups.

Online Reputation Scores: How well are they Understood?

Gregg, Dawn G.
Journal of Computer Information Systems , Fall, pp. 90-97.

This paper presents a study comparing existing reputation systems to determine if the different reputation system designs are equally capable of eliciting meaningful feedback from users and if the information from these systems is equally useful for evaluating whether or not to purchase from a given seller. A survey of online consumers and data from both the eBay and the Amazon reputation systems are used to determine the impact of reputation system design on overall system effectiveness. Results of this research indicate that the five-star response system used by Amazon may be more useful to users attempting to determine which sellers to buy from.

Dressing Your Online Auction Business For Success: An Experiment Comparing Two E-Bay Businesses

Gregg, Dawn G. and Walczak, Steven
MIS Quarterly, Vol. 32, Issue 3, p. 653-670

Businesses can choose who they want to be online. Product and company attributes that are directly perceivable in the real world can be manipulated to make a favorable impression on online buyers. This study examines whether creating a more professional online e-image can signal consumers about unobservable product or company quality, and whether this signal influences their willingness to transact with the company, and ultimately the prices they are willing to pay for the company’s goods and services. An empirical study is presented that examines two new online auction businesses utilizing different company names and auction listing styles to sell items in parallel over the course of one year. The findings suggest that increasing the quality of an auction business’s e-image does increase consumers’ willingness to transact with the business, and increases prices received at auction. The study also demonstrates the ability to use eBay as an experimental laboratory for testing a variety of hypotheses about purchasing behavior online.

Exploring Information Extraction Resilience

Gregg, Dawn
Journal of Universal Computer Science Vol. 14 Issue 11, p. 1911-1920

There are many challenges developers face when attempting to reliably extract data from the web. One of these challenges is the resilience of the extraction system to changes in the web pages information is being extracted from. This paper compares the resilience of information extraction systems that use position based extraction with an ontology based extraction system and a system that combines position based extraction with ontology based extraction. The findings demonstrate the advantages of using a system that combines multiple extraction techniques, especially in environments where websites change frequently and where data collection is conducted over an extended period of time.

A Typology of Complaints about eBay Sellers

Gregg, Dawn G. & Scott, Judy
Communications of the ACM Vol. 51, Issue 4, p. 69-74

This research shows that reputation systems serve an important function in today’s online world. Results of this study indicate that more than 97% of complaints do allege serious problems with the seller. Comments often indicate that sellers lack business training and clear commerce standards, like proper communication skills (44.2%) and appropriate return policies (10.5%). However, a greater proportion of the complaints contain allegations of fraud. This study shows that 69.7% of negative comments posted in eBay’s feedback forum indicate that the seller may have defrauded the buyer by failing to deliver the item, misrepresenting the item in the product description, selling illegal goods, by adding charges after the close of the auction, or by shill bidding. This rate of fraud is twenty times higher than the rate quoted by eBay. This makes reputation systems important to both online auction houses and to law enforcement as they try to combat rising levels of online auction fraud.

eLearning Agents

Gregg, Dawn G.
The Learning Organization Vol. 14 Issue 4, pp. 300-312

Purpose – This paper illustrates the advantages of using intelligent agents to facilitate the location and customization of appropriate e-learning resources and to foster collaboration in e-learning environments.

Design/methodology/approach – This paper proposes an e-learning environment that can be used to provide customized learning. It utilizes a set of interacting agents that can personalize instruction based on an individual’s prior knowledge as well as their cognitive and learning needs. The e-learning agents monitor the e-learning environment and improve learning and collaboration based on learners’ prior knowledge, social characteristics and learning style.

Findings – e-Learning agents should allow discovery of new learning objects more easily, allow learners to customize materials presented to improve learning outcomes, and improve collaboration in the e-learning environment.

Originality/value – Little prior research has been done on the use of agents in e-learning environments. This paper proposes a set of e-learning agents that, if implemented in online education or training environments, should provide tangible benefits to organizations.

Exploiting the Information Web

Gregg, Dawn G. and Walczak, Steven

IEEE Transactions on Systems, Man and Cybernetics, Part C. Vol. 37 Issue 1 pp. 109-125

The World Wide Web is an increasingly important data source for business decision making; however, extracting information from the Web remains one of the challenging issues related to Web business intelligence applications. To use heterogeneous Web data for decision making, documents containing relevant data must be located and the data of interest within the documents must be identified and extracted. Currently most automatic information extraction systems can only cope with a limited set of document formats or do not adapt well to changes in document structure, as a result, many real-world data sources with complex document structures cannot be consistently interpreted using a single information extraction system. This paper presents an adaptive information extraction system prototype that combines multiple information extraction approaches to allow more accurate and resilient data extraction for a wide variety of Web sources. The Amorphic Web information extraction system prototype can locate data of interest based on domain-knowledge or page structure, can automatically generate a wrapper for a data source, and can detect when the structure of a Web-based resource has changed and act on this to search the updated resource to locate the desired data. The prototype Amorphic information extraction system demonstrated improved information extraction accuracy when compared with traditional data extraction approaches.

Market Decision Making for Online Auction Sellers: Profit Maximization versus Socialization Perspective, A Modified TAM Approach

Walczak, Steven, Gregg, Dawn G. and Berrinberg, Joy

Journal of Electronic Commerce Research Vol. 7, Issue 4, p. 199-220

The purpose of this investigation is to identify factors in the decision making processes used by online auction sellers to select their online auction sales channel. Examining these decision factors will aid in creating a model of online auction seller channel evaluation mechanisms including economic and social factors and may be used by online auction services and intermediaries to maximize their market potential by increasing the perceived value of the various economic or social factors influencing seller outlet selection. An exploratory survey analysis is used to identify the components that online seller’s use for online channel selection.

Adaptive Web Information Extraction

Gregg, Dawn G. & Walczak, Steven

Communications of the ACM Vol. 49, Issue 5, p. 78-84

Extracting information from Web pages for internal applications is difficult. An effective Web information extraction system needs to interpret a wide variety of HTML pages and adapt to changes without breaking. An information extraction system should recognize different Web page structures and act on this knowledge to modify the information extraction techniques employed. In addition, the system should be customizable for a variety of domains and data-object types. This paper examines the characteristics of effective Web information extraction systems. This paper also presents a prototype adaptive Web information extraction system for building intelligent systems for mining information from Web pages.

The Role of Reputation Systems in Reducing Online Auction Fraud

Gregg, Dawn G. & Scott, Judy
International Journal of Electronic Commerce Vol. 10 Issue 3, p. 97-122

Online auctions are one of the most profitablesuccessful types of e-businesses; however, online auctions also provide an avenue for unscrupulous sellers to perpetrate fraud. Online auction fraud is currently the most frequently reported crime committed over the Internet. This research investigates whether online reputation systems are a useful mechanism for potential buyers to avoid fraudulent auctions. Content analysis of complaints posted in an online auction reputation system is used to improve our understanding of online auction fraud and the role of reputation systems in documenting, predicting, and reducing fraud. Results of this study show that the number of fraud allegations found in an online reputation system significantly exceeds the number of fraud allegations made through official channels. It also demonstrates that recent negative feedback posted in an online reputation system is useful in predicting future online auction fraud. Finally, results of this study suggest that experienced online auction buyers are in a better position to use using reputation system data to avoid potentially fraudulent auctions.

Auction Advisor: Online Auction Recommendation and Bidding Decision Support System

Gregg, Dawn G. & Walczak, Steven

Decision Support Systems, Vol. 41 Issue 2, pp. 449-471

Online auctions are proving themselves as a viable alternative in the C2C and B2C marketplace. Several thousand new items are placed for auction every day and determining which items to bid on or when and where to sell an item are difficult questions to answer for online auction participants. This paper presents a multi-agent Auction Advisor system designed to collect data related to online auctions and use the data to help improve the decision making of auction participants. A simulation of applied Auction Advisor recommendations and a small research study that used subjects making real purchases at online auctions both indicate that online auction buyers and sellers achieve tangible benefit from the current information acquired by and recommendations made by the Auction Advisor agents.