Identifying Effective Online Service Strategies: The Impact of Network Externalities and Organizational Lifecycle Stage

This study presents a framework for identifying effective online transaction-based service strategies that incorporates network externalities and organizational life cycle theories. The framework considers changes in marginal costs, marginal revenues, and service value as the company moves through its initial three life cycle stages (start-up, growth, and maturity). Propositions describe potentially effective strategies for service sites in each lifecycle stage. Each of the propositions is supported by real-world strategy examples and research related findings from three industries – online auctions, online career services and online travel services. Start-up strategies must focus on increasing the number of service users, growth companies need to differentiate themselves from their competitors, and large mature service providers can take advantage of their financial resources and service value by raising entry barriers to maintain their dominant position. This study provides a multi-theory perspective that can be used as a basis for further study of strategies used in these industries

University Opportunities, Abilities and Motivations to Create Data Analytics Programs

Some US colleges and universities have developed undergraduate and graduate data analytics programs in the past five years, but not all universities appear to have sufficient resources and incentives to venture into this multidisciplinary academic area. The purpose of this study is to identify the characteristics of schools that have developed data analytics programs. The study utilizes the motivation-ability-opportunity (MAO) theoretical framework to identify factors that increase the likelihood that a university will develop a data analytics program. An analysis of 391 regional master’s universities in the US finds that schools with data analytics programs are more likely to be in larger cities and have larger student enrollments, better educational quality rankings, and an existing statistics and/or actuarial science program. These findings support the idea that data analytics programs are more likely to be created when universities have opportunities to access a larger number of businesses and governmental organizations, and sufficient resources to support program development, while also having abilities associated with innovation and faculty resources. Preliminary results also indicate that there are two motivations – need to increase student enrollment and need to maintain an up-to-date curriculum

Introduction to Marketing and Consumer Behavior in Electronic Markets

In this paper we discuss three issues that are relevant to understanding the current state of marketing and consumer behavior in online sales channels and electronic marketplaces enabled by the Internet and World Wide Web (Web). First, how is the Internet used for marketing? It can be used as a new tool for market research, new product creation, product advertising and distribution, and developing consumer relationships. The second issue is how much money is currently spent by companies for Web advertising and by consumers for purchasing products and services online? Statistics illustrate explosive growth in each of these areas in the past few years. And the third issue is what factors affect consumer purchase behavior in electronic sales channels and markets? Three studies that represent current research in this area are discussed. Each study uses a different approach to study online consumer behavior in industries such as books, travel, and financial services. Online consumer behavior is an important issue for companies because they need to identify the characteristics of their potential online customers and use this information to effectively design their Web-based customer interface to succeed in this highly competitive new market

Will Products Liability Litigation Help Protect IoT Users from Cyber-Physical Attacks?

While there is an identifiable trend towards protecting consumers from data breaches and data misuses related to IoT devices through new legislation, new regulations, government enforcement actions, and private lawsuits, there has been little progress towards creating similar legally enforceable standards of care for “cyber-physical device security.” This article explores this underdeveloped area of academic inquiry into cyber-physical device security within the context of product liability litigation in the United States. The two questions addressed in this article are: (1) Have there been any successful products liability court decisions in the United States that have held IoT manufacturers liable for creating IoT products with inadequate cyber-physical device security; and (2) Is it likely that product liability litigation will soon lead to significant change in IoT cyber-physical device security? Analysis of laws, regulations, and court cases shows that the answer to both questions is negative. These findings have implications for IoT device users, device manufacturers, and the government agencies whose job it is to deter data breaches and other IoT-related cyberattacks

Online Investment Banking Phase I: Distribution via the Internet and Its Impact on IPO Performance

In the past few years, there has been a growth in Internet markets run by online investment bankers, where companies and investors can buy and sell initial public offerings (IPOs) of corporate stock. In this study, we confine our examination to the first of what we anticipate will be several phases in the evolution of Internet IPOs: the online distribution of shares. This implies the beginning of a general disintermediation in the IPO process where traditional roles of investment banks are being circumvented via the Internet as participants search for greater market efficiency. This is an important research area because potentially it affects all public companies, or companies considering going public, the investment banking industry, and all stock investors. We address two research issues not considered by previous studies. What factors affect organizational choice of online vs. traditional IPO distribution? What are the financial performance differences for IPOs distributed using online and traditional processes? These issues were addressed using company characteristic and financial performance data from 27 IPOs from the last half of 1998. We find that the Internet IPO firms are larger, have younger CEOs, choose more reputable investment banks and are more likely to be involved in a Web-based business, directly employing the Internet in their product or service, than the firms that choose the traditional method of going public. In addition, market performance, both initially and over the first three months of trading, is significantly greater for Internet IPOs

Using Computer Resources for Personal Activities at Work: Employee Perceptions of Acceptable Behavior

Employees use computer resources at work for personal activities and the implementation of countermeasures has not reduced this behavior. In this study we investigate the extent to which an employee’s ethical orientation and supervisory role have an impact on their perceptions regarding these behaviors. We find that employees assess acceptability using a utilitarian orientation. The more money and time involved in an activity, the more employees perceive them to be unacceptable. We also find that supervisors view these activities as less acceptable than do non-supervisor employees. Demographics have little to do with explaining perceptions. Research and managerial implications are discussed

A Review of Machine Learning Approaches for Real Estate Valuation

Real estate managers must identify the value for properties in their current market. Traditionally, this involved simple data analysis with adjustments made based on manager’s experience. Given the amount of money currently involved in these decisions, and the complexity and speed at which valuation decisions must be made, machine learning technologies provide a newer alternative for property valuation that could improve upon traditional methods. This study utilizes a systematic literature review methodology to identify published studies from the past two decades where specific machine learning technologies have been applied to the property valuation task. We develop a data, reasoning, usefulness (DRU) framework that provides a set of theoretical and practice-based criteria for a multi-faceted performance assessment for each system. This assessment provides the basis for identifying the current state of research in this domain as well as theoretical and practical implications and directions for future research