Understanding Ecosystem Data

There is a growing body of empirical studies on business ecosystems. Driven by different questions these studies typically employ a wide variety of data sources – ranging from open to proprietary, structured to unstructured – that contain a broad range of entities, relationships, activities, and issues of interest. Individually, these data sources offer the ability to investigate very targeted business ecosystem questions. However, when linked and combined these data sources can potentially open up many new lines of inquiry. The purpose of this study is to provide an overview of the scope and complexity of the business ecosystem data landscape, discuss what type(s) of information is captured in them, identify how data sources overlap and differ, discuss strengths and weaknesses, and suggest new types of analyses that could be generated when combined. In doing so this study aims to help researchers and practitioners with the data identification and selection process and stimulate novel data-driven ecosystem intelligence. The study concludes with theoretical and managerial implications

The Ecosystem of Machine Learning Methods

Machine learning (ML) is a rapidly evolving field and plays an important role in today’s data-driven business environment. Many digital innovations in domains as diverse as healthcare, banking, energy, and retail are powered and enabled by ML. Examples include search engines, recommendation systems, pattern recognition, computer vision, and natural language processing. A key element in ML innovation is the advancement of the underlying methods, which specify how machines should algorithmically process, derive patterns, and learn from data for a given decisioning task. The speed at which this is happening is exponential, with researchers leveraging and building upon existing building blocks as well as introducing entirely new methods. Given the speed, scale, and complexity, understanding this complex evolving ML method space can be challenging. What methods are core and peripheral to ML? Which methods span task areas? How are ML methods evolving? In this exploratory research paper, I address these questions by (1) framing the ML method space and (2) visualizing the evolving structure of the ML methods ecosystem. The results reveal several foundational ML building blocks, different coupling levels between ML areas, and variable speeds of evolution. The study also provides insights into how digital innovation evolves at an algorithmic level. I discuss the implications of the findings and describe opportunities for future ML ecosystem-focused research

Visualizing the Alliance Network Structure of Service Industries

A growing body of research focuses on the structure of interfirm value co-creation. Despite this emphasis, little is known about the variation in interfirm collaboration across different service industries. Building on prior work in service value networks and business ecosystems, we analyze the structural characteristics of 11 service industries using a data-driven visualization approach. We first examine the alliance network structure of each service industry individually and differentiate the nature of collaboration using an exploration/coopetition lens. Second, we examine service industries integratively, thereby exploring the extent to which service industries are converging and traditional industry boundaries are blurred. Our results reveal significant structural differences in alliance network structures between service industries as well as diverse value co-creation orientations. Our macro analysis reveals an overall core-periphery structure and different service industry coupling levels, with actors in the ICT industry playing a particularly central role across subclusters. We frame our findings in terms of industry robustness, openness, and embeddedness. We conclude the paper with theoretical and practical implications for understanding and managing service ecosystems and suggest future research opportunities

Visualizing Interfirm Collaboration in the Microservices Ecosystem

The shift from monolithic software solutions to a microservices architecture is fundamentally changing the way software is developed, deployed, and managed. In this paper, we aim to uncover the collaborative fabric of the microservices ecosystem using a data-driven visualization approach of 2,608 software firms. Our visual analysis reveals a core-periphery structure with several subcommunities, suggesting both complementary and competing arrangements between software vendors. Theoretically, our paper contributes to our understanding of interfirm relationships in a software context. Managerially, our results show that there are wide range of partnership strategies that shape the microservices ecosystem. Methodologically, we demonstrate how a data-driven ecosystem visualization approach can help decision makers augment their sensemaking capability of emerging software ecosystems. The paper concludes with opportunities for future research

Modeling and Analysis of Complex Technology Adoption Decisions: An Investigation in the Domain of Mobile ICT

Mobile information and communication technologies (ICT) promise to significantly transform enterprises, their business processes and services, improve employee productivity, effectiveness, and efficiency, and create new competitive advantages and business agility. Despite the plethora of potential benefits, however, widespread enterprise adoption of mobile ICT has not been as extensive as initially anticipated. Drawing on the extant information systems, technology management, and organizational innovation literature, this dissertation investigates the salient drivers and inhibitors of emerging ICT adoption, in general, and mobile ICT in particular, and develops an integrative ICT adoption decision framework. From this synthesis we identify four broad elements that influence an enterprise s decision to adopt mobile ICT: (1) business value, (2) costs and economics, (3) strategic alignment, and (4) enterprise readiness. The latter decision element has received only little theoretical and practical attention. In order to fill this gap, this dissertation explored the concept of enterprise readiness in further detail and identified eight key dimensions and their associated assessment indicators. Using a two-stage expert study and experimental design approach, we empirically validated these dimensions and determined their relative importance. Results indicated that leadership readiness followed by technology, data and information, and resource readiness, contributed the most to enterprise readiness for mobile ICT. The results are implemented into a web-based readiness diagnostic tool (RDT) that enables decision makers to assess an enterprise s readiness for mobile ICT. The benefits of the RDT are multifold: first, it navigates the decision maker through the complex readiness assessment space; second, it identifies potential organizational deficiencies and provides a means to assess potential sources of risks associated with the adoption and implementation of mobile ICT; and third, it enables decision makers to benchmark their level of readiness against other organizations. The dissertation concludes by highlighting both theoretical and practical implications for emerging and mobile ICT adoption management and suggesting directions for future research.Ph.D.Committee Chair: Rouse, William; Committee Member: Cross, Steve; Committee Member: Cummins, Michael; Committee Member: DeMillo, Richard; Committee Member: Vengazhiyil, Rosha

Structural Analysis and Visualization of Ecosystems: A Study of Mobile Device Platforms

Platforms enable and drive the creation of new products and services; they also shape and transform industries in fundamental ways. Consequently, platforms have become a core feature of many emerging business models. The success of a platform is inextricably linked to its network, or ecosystem, of enablers and complementors. Drawing on models and theories of complex systems, innovation, and network analysis, this study analyzes the evolving structure of interfirm relations in the mobile device platform ecosystem. This domain is of particular interest due to the emergence of promising new platforms and competition for platform leadership between open and closed business models. The visual approach presented in this study provides insights to the complexity of interfirm relations in the mobile device platform ecosystem, determines a platform’s competitive position, and identifies structural configurations that characterize various types of business strategies. Both theoretical and practical implications are discussed

Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we developed Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2018

Assimilation of Tracking Technology in the Supply Chain

While tracking technology has become increasingly accessible, firms still struggle with deploying these technologies into the supply chain. Using the complementary perspectives of transaction cost and institutional theory, we develop an understanding of how supply network, product, and environmental characteristics jointly impact tracking technology assimilation. We empirically test our model on a global dataset of 535 supply chain executives and decision makers. The results suggest that assimilation is frequently initiated by an external stakeholder in a firm\u27s supply chain and that firms must develop strong collaborative ties with their partners in order to take full advantage of this technology