Kaisa Still

Understanding Business Ecosystem Dynamics: A Data-Driven Approach

Business ecosystems consist of a heterogeneous and continuously evolving set of entities that are interconnected through a complex, global network of relationships. However, there is no well-established methodology to study the dynamics of this network. Traditional approaches have primarily utilized a single source of data of relatively established firms; however, these approaches ignore the vast number of relevant activities that often occur at the individual and entrepreneurial levels. We argue that a data-driven visualization approach, using both institutionally and socially curated datasets, can provide important complementary, triangulated explanatory insights into the dynamics of interorganizational networks in general and business ecosystems in particular. We develop novel visualization layouts to help decision makers systemically identify and compare ecosystems. Using traditionally disconnected data sources on deals and alliance relationships (DARs), executive and funding relationships (EFRs), and public opinion and discourse (POD), we empirically illustrate our data-driven method of data triangulation and visualization techniques through three cases in the mobile industry Google’s acquisition of Motorola Mobility, the coopetitive relation between Apple and Samsung, and the strategic partnership between Nokia and Microsoft. The article concludes with implications and future research opportunities.

Insights for orchestrating innovation ecosystems: the case of EIT ICT Labs and data-driven network visualisations

This paper explores opportunities for supporting the orchestration of innovation ecosystems, hence contributing to a fundamental capability in the networked world. We present analysis, evaluation and interpretation toward the objective of decision support and insights for transforming innovation ecosystems with a case study of EIT ICT Labs, a major initiative intended to turn Europe into a global leader in ICT innovation. Towards this, we use a data-driven, relationship-based and network centric approach to operationalise the ‘innovation ecosystems transformation framework’. Our results indicate that with coordinated and continuously improved use of visual and quantitative social network analysis, special characteristics, significant actors and connections in the innovation ecosystem can be revealed to develop new insights. We conclude that the IETF transformation framework can be used to develop shared vision and to support the orchestration of innovation ecosystem transformations.

Visual decision support for business ecosystem analysis

Comparative evaluation of the effectiveness of three ecosystem visualization methods.Extend cognitive fit theory to examine impact of ecosystem complexity and task type.Ecosystem complexity significantly influences decision performance.Contribute to our understanding of visual business ecosystem intelligence tools. This study comparatively evaluates the effectiveness of three visualization methods (list, matrix, network) and the influence of data complexity, task type, and user characteristics on decision performance in the context of business ecosystem analysis. We pursue this objective using an exploratory study with 14 prototypical users (e.g. executives, analysts, investors, and policy makers). The results show that in low complexity contexts, decision performance between visual representations differ but not substantially. In high complexity contexts, however, decision performance suffers significantly if visual representations are not appropriately matched to task types. Our study makes several theoretical and practical contributions. Theoretically, we extend cognitive fit theory by investigating the impact of business ecosystem task type and complexity. Managerially, our study contributes to the relatively underexplored, but emerging area of the design of business ecosystem intelligence tools and presentation of business ecosystem data for the purpose of decision making. We conclude with future research opportunities.

Ostinato: The Exploration-Automation Cycle of User-Centric, Process-Automated Data-Driven Visual Network Analytics

Network analysis is a valuable method for investigating and mapping the social structure driving phenomena and sharing the findings with others. The interactive visual analytics approach transforms data into views that allow the visual exploration of the structures and processes of networks represented by data, therefore increasing the transparency of editorial processes on social media as well as networked structures in innovation ecosystems and other phenomena. Although existing tools have opened many new exploratory opportunities, new tools in development promise investigators even greater freedom to interact with the data, refine and analyze the data, and explore alternative explanations for networked processes. This chapter presents the Ostinato Model—an iterative, user-centric, process-automated model for data-driven visual network analytics. The Ostinato Model simultaneously supports the automation of the process and enables interactive and transparent exploration. The model has two phases, Data Collection and Refinement and Network Creation and Analysis. The Data Collection and Refinement phase is further divided into Entity Index Creation, Web/API Crawling, Scraping, and Data Aggregation. The Network Construction and Analysis phase is composed of Filtering in Entities, Node and Edge Creation, Metrics Calculation, Node and Edge Filtering, Entity Index Refinement, Layout Processing and Visual Properties Configuration. A cycle of exploration and automation characterizes the model and is embedded in each phase.

Visual Network Analysis of Twitter Data for Co-organizing Conferences: Case CMAD 2013

The aim of this research is to explore what kinds of insights information visualization of social media data can provide for co-organizing conferences. Our paper focuses on Twitter use before, during and after conference. We present a case study based on an conference of Community Manager Appreciation Day (CMAD 2013). With the process of data-driven visual network analysis, we used Twitter data to analyse the network of conference participants and the conferences discussion topics. We were able to identify e.g. influential conference participants, most interesting presentations and discussions, similarities between interests of the conference participants. Hence, several development and information needs of conference co-organization were derived from the information visualizations, which have implications for improving the planning and co-organizing of conferences, as well as for Twitter use in conference communication.

Alumni network analysis

Alumni connections are important resources that contribute to university evaluation. Even though alumni connections represent networks, they have been mostly evaluated as tabular data (e.g. by providing average salary, employment rate, etc.). This ironically disregards all qualities of a network, from which an alumni network gets its name. It is desirable to evaluate an alumni network as a network, because networks have the potential to provide very insightful information. Evaluation of alumni networks as a network has not been feasible in the past due to data fragmentation (neither universities nor companies willing to share meaningfully significant data in its entirety). Recently the feasibility of such analysis has changed, due to new trends towards democratization of information, accelerated by the Web 2.0 user-generated content phenomenon and crowd-sourcing mentality. Utilizing web-crawlers, we actively harvested data and assembled a dataset on alumni in leadership positions in technology-based industries. Moreover, we included a high proportion of startup companies, which allowed us to evaluate alumni networks with respect to entrepreneurial as well as technology involvement. We show that by analyzing alumni connections as networks, it is possible to uncover new patterns, as well as provide a new way of examining the old.

Visualizing the Geography of Platform Boundary Resources: The Case of the Global API Ecosystem

Platform boundary resources play an increasingly transformative role in the global digital ecosystem. In this study, we focus on one type of platform boundary resource, namely application programming interfaces (APIs). Guided by two competing assumptions—1) that geographic boundaries are blurred and potentially less important in a digitally connected world, and 2) that geographic proximity matters for co-innovation—we investigate the global footprint of APIs. Using a datadriven visual network analysis approach to examine more than 15,000 APIs and mashups, we first map the global locations of where APIs are being created. We then examine how API mashups connect these locations globally and regionally. Our results show that while APIs are globally distributed, they are mainly concentrated in major entrepreneurial regions. We also find that there is a skewed distribution, with the U.S. and Silicon Valley in particular leading the way. We conclude with both theoretical and managerial implications.

Innovation Ecosystems vs. Innovation Systems in Terms of Collaboration and Co-creation of Value

In this paper, we explore the relevance of the term “innovation ecosystem” to describe dynamic collaborative networks of people and organizations formed around projects with an innovation objective. We present a survey of literature reviews on ecosystems studies to clarify typical features and interpretations of innovation ecosystems, and we highlight differences in terms of collaboration and value co-creation. We explore ecosystem thinking and illustrate patterns of innovation ecosystems by describing the structure of regional clusters, global value chains and platforms. We offer policy insights on the role of governments in stimulating innovation ecosystems and innovationconducive environments.

Insights from Social Network Analysis -- Case Board Interlocks in Finnish Game Industry

In the world of networked innovation and ecosystems, flows of resources into the organization as well as between organizations are emphasized. This allows for using network measures for better understanding. In this study, we look at the case context of Finnish game industry, concentrating especially on inter-organizational flows of board networks or interlocks, and the possibilities of using SNA metrics for insights. The game industry has been very successful, with a turnover of 1.8 Billion Euros in 2014, and we explore the role of board interlocks in it. Our findings of the formal board member networks indicate that in contrast to the assumptions of high actor or node level metrics (degree and betweenness centrality) and the network level metrics (density and clustering), board interlocks are limited.

Business Model Innovation of Startups Developing Multisided Digital Platforms

Platforms are defined as multisided marketplaces with business models that enable producers and users to create value together by interacting with each other. In recent years, platforms have benefited from the advances of digitalization. Hence, digital platforms continue to triumph, and continue to be attractive for companies, also for startups. In this paper, we first explore the research of platforms compared to digital platforms. We then proceed to analyze digital platforms as business models, in the context of startups looking for business model innovation. Based on interviews conducted at a technology startup event in Finland, we analyzed how 34 startups viewed their business model innovations. Using the 10 sub-constructs from the business model innovation scale by Clauss in 2016, we found out that the idea of business model innovation resonated with startups, as all of them were able to identify the source of their business model innovation. Furthermore, the results indicated the complexity of business model innovation as 79 percent of the respondents explained it with more than one sub-construct. New technology/equipment, new processes and new customers and markets got the most mentions as sources of business model innovation. Overall, the emphasis at startups is on the value creation innovation, with new proposition innovation getting less, and value capture innovation even less emphasis as the source of business model innovation.