Jesse Bockstedt

Making sense of technology trends in the information technology landscape: a design science approach

A major problem for firms making information technology investment decisions is predicting and understanding the effects of future technological developments on the value of present technologies. Failure to adequately address this problem can result in wasted organization resources in acquiring, developing, managing, and training employees in the use of technologies that are short-lived and fail to produce adequate return on investment. The sheer number of available technologies and the complex set of relationships among them make IT landscape analysis extremely challenging. Most IT-consuming firms rely on third parties and suppliers for strategic recommendations on IT investments, which can lead to biased and generic advice. We address this problem by defining a new set of constructs and methodologies upon which we develop an IT ecosystem model. The objective of these artifacts is to provide a formal problem representation structure for the analysis of information technology development trends and to reduce the complexity of the IT landscape for practitioners making IT investment decisions. We adopt a process theory perspective and use a combination of visual mapping and quantification strategies to develop our artifacts and a state diagram-based technique to represent evolutionary transitions over time. We illustrate our approach using two exemplars: digital music technologies and wireless networking technologies. We evaluate the utility of our approach by conducting in-depth interviews with IT industry experts and demonstrate the contribution of our approach relative to existing techniques for technology forecasting.

The Move to Artist-Led On-Line Music Distribution: A Theory-Based Assessment and Prospects for Structural Changes in the Digital Music Market

New forms of digital distribution are dramatically transforming market structures in the recorded music industry value chain. We propose a model and theoretical perspective that take account both of the music industrys traditional value chain and distribution network, and the product characteristics of digital music as related to consumer value creation. The model highlights changes in the market structure from the perspective of the players in the music industry value chain. Utilizing a series of propositions, we characterize the forces at work in the market transformation and show how each players role in the industry value chain is likely to change. We also examine the effects of market structure changes on intellectual property rights issues. Finally, we present a series of mini-cases that provide evidence in support of the proposed theoretical perspective.

Technology roles and paths of influence in an ecosystem model of technology evolution

We propose a new conceptual model for understanding technology evolution that highlights dynamic and highly interdependent relationships among multiple technologies. We argue that, instead of considering technologies in isolation, technology evolution is best viewed as a dynamic system or ecosystem that includes a variety of interrelated technologies. By considering the interdependent nature of technology evolution, we identify three roles that technologies play within a technology ecosystem. These roles are components, products and applications, and support and infrastructure. Technologies within an ecosystem interact through these roles and impact each others’ evolution. We also classify types of interactions between technology roles, which we term paths of influence. We demonstrate the use of our proposed model through examples of wireless networking (Wi-Fi) technologies and a business mini-case on the digital music industry.

The Move to Artist-Led Online Music Distribution: Explaining Structural Changes in the Digital Music Market

We propose a model for understanding the transformation of the market structure in the recorded music industry value chain due to new forms of digital distribution. It takes into account the traditional music industrys value chain and distribution network, and the product characteristics of digital music as they relate to consumer value creation. We showcase changes in market structure from various perspectives of the players in the music industry value chain. With this as background, we then present propositions that highlight forces at work in market transformation and how each players role in the digital music industry value chain is likely to change. We note the new tensions that arise around intellectual property rights for digital music with market structure changes. We support the propositions with illustrative mini-cases.

Modeling Supply-Side Dynamics of IT Components, Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression

Prior IS research on technological change has focused primarily on organizational information systems and technology innovation; however, there is a growing need to understand the dynamics of supply-side forces in the introduction of new technologies. In this paper we investigate how the interdependencies among information technology components, products, and infrastructure affect the release of new technologies. Going beyond the ad hoc heuristic approaches applied in previous studies, we empirically validate the existence of several patterns of supply-side technology relationships in the context of wireless networking. We use vector autoregression (VAR) to model the comovements of new component, product, and infrastructure introductions and provide evidence of strong Granger-causal interdependencies. We also demonstrate that substantial improvements in forecasting can be gained by incorporating these cross-level effects into models of technological change. This paper provides some of the first research that empirically demonstrates these cross-level effects and also provides an exposition of VAR methodology for both analysis and forecasting in IS research.

Seller Strategies for Differentiation in Highly Competitive Online Auction Markets

We explore the issue of seller differentiation in competitive auction environments, where most sellers have a high percentage of positive feedback. Analyzing a set of eBay auction listings for identical products, we find evidence that the use of visibility-enhancing and quality-signaling discretionary auction attributes affects auction outcomes throughout the auction process (i.e., listing views, bids, and price premiums). We also find strong evidence that the number of reputable sellers in an auction marketplace moderates the effects of these discretionary attributes on auction outcomes. Specifically, as auction environments become more competitive, these attributes become more effective tools for differentiation, whereas seller feedback scores become less effective. Furthermore, sellers appear to select their strategies for employing these discretionary attributes based on both their prior experience and the number of experienced reputable sellers in the market. These findings suggest that in addition to relying on feedback scores, online sellers must take a more strategic approach in the selection of discretionary attributes in their auction listings.

The Framing Effects of Multipart Pricing on Consumer Purchasing Behavior of Customized Information Good Bundles

Applying behavioral economic theories, we hypothesize that consumers have sticky reference prices for individual information goods, but their perceived value for customizable bundle offers can be significantly influenced by the framing of a multipart pricing scheme. The potential impacts of these framing effects are measurable changes in consumer behavior and sales outcomes. We conducted a series of behavioral experiments and a large-scale natural field experiment involving actual purchases of customized information good bundles to confirm and analyze the hypothesized effects. The results demonstrate a consumers willingness to purchase a customized bundle of information goods, the size of the resulting bundling, and the consumers perceptions of the transaction are each significantly influenced by the design of the multipart pricing scheme. These results hold even when the final price and size of a customized bundle are the same across differing schemes. We discuss the potential tradeoffs in economic outcomes that result from price framing (e.g., likelihood of sale versus size of purchased bundles) and the implications for information good retailers.

C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multiattribute (multidimensional) and temporal in nature. Data. mining and business intelligence, techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique for representing trends in multiattribute temporal data using a clustering- based approach. We introduce Cluster-based Temporal Representation of EveNt Data (C-TREND), a system that implements the temporal cluster graph construct, which maps multiattribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time. In this paper, we present our temporal clustering-based technique, discuss its algorithmic implementation and performance, demonstrate applications of the technique by analyzing data on wireless networking technologies and baseball batting statistics, and introduce a set of metrics for further analysis of discovered trends.

Understanding evolution in technology ecosystems

There has been extensive research on the nature of innovation and there are many theories and methods for technological forecasting. A critique of these models, however, is that technologies are often considered individually. With today’s highly interconnected technology systems running the world’s organizations, it is necessary to consider a system of interrelated technologies and factors that influence the evolution and development of one another. We propose the technology ecosystem model for representing the dynamic nature of technological evolution. The model is designed to help firms identify the important relationships between the multiple technologies that potentially influence their operations and strategic decisions. The model outlines the three specific roles that technologies can play within an ecosystem and the nine paths of influence that describe the types of interactions technology roles have with one another.

Understanding Patterns of Technology Evolution: An Ecosystem Perspective

Understanding the dynamics of technology evolution — whether for the purposes of forecasting new product or technology infrastructure developments, or identifying the basis for future digital convergence in the global market— is a key challenge for innovators, senior managers, and policymakers. This research provides an overview of a new ecosystem model of technology evolution, the purpose of which is to structure these kinds of assessments and suggest reusable analysis structures to ensure that total environment of technological innovation is considered. We use examples from the end-user computing context and the electronics industry to identify five patterns of technology evolution that commonly occur. We also develop a state diagram-based approach to demonstrate the cyclical nature of technology evolution. Finally, we illustrate our findings using a case study on digital music technologies.