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Firms and individuals are interacting online on an unprecedented scale. These interactions may lead to new digital products, services and practices, all of which are manifestations of digital innovation. This process relies on records users leave on various digital platforms which carry information about their activities – digital trace data. The data is generated on a massive scale, yet is just data until it is confronted with meaning – its value remains latent. Digital trace data is agnostic about future use, it carries records of interactions with digital artifacts and is available to wide numbers of actors to reinterpret them as sources of innovation and value creation. Online communities where data is generated can be sources of innovation, but are also extremely vulnerable. Digital trace data are not finitely expendable but may be used and passed along to any other individuals, partners, customers, or suppliers. To remain competitive, firms increasingly need to manage dynamic interactions of online community members, confront digital trace data with meaning, and facilitate innovation that is decentralized and requires heterogeneous knowledge resources.

This dissertation explores how digital innovation can be leveraged in the context of online communities. It is based on four empirical investigations in the context of firms interacting with online communities that are rich with digital trace data. Collectively, these studies illustrate the potential utility of digital trace data generated by online communities for digital innovation, and suggest possible strategies for effective management of digital innovation for value creation. The dissertation contributes to both theoretically and empirically oriented discourses on the use of digital trace data. Specifically, it does so by providing propositions for dealing with digital trace data through platform design, community sociality, and narration for digital innovation.

The purpose of this study is to examine the co-branding activity on social media platforms, particularly in regard to company-employee relationship. We conducted a case study of co-branding on Instagram involving the soccer club Manchester United and the soccer player Zlatan Ibrahimović. We performed sentiment and emotional tone analysis, assessed intersection of the audience and illustrated non-verbal communication used by social media users. We demonstrated how the soccer club failed to capitalize on co-branding activity as measured through consolidating the audience, generating consistent emotional response, and creating a coherent message. This paper contributes to social media management research by illustrating the difficulties associated with co-branding between personal and corporate brands as well asynchronous communication. Further, our use of digital traces and computational analysis illustrates how access to social media can illuminate research activities and provide insight about online communication.

In this study, we investigate value proposition offered by big data evangelists – zealous advocates for big data analytics. By applying Competing Value Framework, we analyze the communications of the market leader in data analysis and visualization applications. Surprisingly, our analysis shows how big data evangelist relies on a "safe" value proposition by focusing on potential gains in operational efficiency, rather than addressing more ambitious categories, such as flexibility, learning, and cohesion. We describe potential challenges in the “safe” approach to big data value proposition and suggest promising directions for future research.

Due to the potential to extract useful knowledge by means of data mining and statistical analysis, there has been a significant increase in education that teach data science courses, issues degree certificates, and offers master’s programs. Such programs focus on three skill sets: 1) information technology skills necessary for accessing and working with data (e.g. relational databases, OLAP); 2) analytical skills drawing from various disciplines that enable data analysis (e.g. statistical analysis, machine learning, econometrics); 3) business and communication skills that facilitate appropriate problem formulation and value extraction from data science solutions. Successful educational programs need to synthesize and balance these three aspects.However, recent reports show that academic programs consistently underperform in preparing data science professionals. We studied the challenges and opportunities of teaching data science skills to students of diverse backgrounds (often the case in Master’s level programs). Our preliminary findings suggest that to balance the three skill sets educators need to recognize their distinct learning cycles. A learning cycle consists of exploration, concept introduction, and application and the emphasis on each stage varies greatly across skill sets. Technical data science skills rely on a multitude of separate systems and technologies involving numerous concepts that require continuous exploration, short cycles of learning (and assessment). Analytical skills require deeper engagement, hence longer learning cycles with an emphasis on concept introduction and formal instruction. Business and communication skills rely on engagement with high-level social systems (e.g. an organization, a market) and require longer involvement with the learning context, almost exclusively achieved by simulation projects or projects with industry partners. Our study indicates that data science education must consider the distinct learning cycles students experience, and suggests methods for doing so.

Business incubators are institutions that link capital, competence and entrepreneurial talent with a goal to accelerate business development. While experiencing a set of institutional pressures, incubators navigate their environment and find different means to assist startups in exploitation of entrepreneurial opportunities. We conducted a multiple case study of incubators in Northern Sweden in which we: (1) distinguish four different incubation modes, with the reference to institutional logics and entrepreneurial opportunities; (2) we identify key barriers and gateways to digital entrepreneurship: pursue of entrepreneurial opportunities that lead to products and services that are exclusively digital; (3) and we show how the plural logics are not necessarily logics in conflict, but rather in fact complementing each other at an aggregate level.