Emerging Industrial Internet of Things (iIoT) platforms generate cross-company added value, providing functionalities and technologies for a variety of digital services in the industrial engineering. iIoT platforms integrate various stakeholders, such as end customers and complementors and build iIoT ecosystems. Earlier research has recognized boundary resources as an emergence and governance mechanism for software ecosystems. In this study we apply the boundary resources for iIoT by exploring the longitudinal case study of the Siemens MindSphere ecosystem. The goal of this exploratory paper is to show which boundary resources are currently used in iIoT ecosystems and how do they impact the development of iIoT ecosystems.
Cloud computing and the service-oriented business model are transforming the way software is developed, delivered, and used. Servicization of digital products creates new challenges for software companies to be able to reap the benefits of this change. Pricing is recognized as an essential component of software product management that affects the overall performance of the product and the company. Existing academic research on SaaS pricing is fragmented and separated from a wide range of practitioner literature. As a result, a comprehensive body of knowledge on SaaS pricing is missing. Our paper reports the results of a multi-vocal literature review on 76 identified pieces of ``white'' academic literature and 149 items that belong to ``grey'' literature. The goal of our study is to answer the question on whether we know how to price SaaS by summarizing the existing knowledge from different research areas and practice on SaaS pricing, and identify the areas which require further investigation.
Recent studies have proposed the use of experiments to guide software development in order to build features that users really want. In this context, product assumptions should be taken as hypotheses to be tested through experiments. User stories (US) are broadly used in the agile context but current guidelines to write them, like INVEST (Independent, Negotiable, Valuable, Estimable, Small and Testable), are not suitable in a experiment-driven context. In this paper, we present the first cycle of a design science research to tackle this problem. We proposed QUESt, a quality guideline recommending US to have a Questioning sense, be Updatable, Evaluable and Straightforward, and a new template to write user stories. To evaluate these new artifacts, we performed a think-aloud evaluation with software development practitioners. Although the results do not confirm the artifacts effectiveness, they indicate that they have value and should be tested within a comprehensive framework complemented by other practices.
Through increasing market dynamics, rapidly evolving technologies and shifting user expectations coupled with the adoption of lean and agile practices, companies are struggling with their ability to provide reliable product roadmaps by applying traditional approaches. Currently, most companies are seeking opportunities to improve their product roadmapping practices. As a first challenge they have to assess their current product roadmapping capabilities in order to better understand how to improve their practices and how to switch to a new approach. The aim of this article is to provide an initial maturity model for product roadmapping practices that is especially suited for assessing the roadmapping capabilities of companies operating in dynamic and uncertain market environments. Based on interviews with 15 experts from 13 various companies the current state of practice regarding product roadmapping was identified. Afterwards, the model development was conducted in the context of expert workshops with the Robert Bosch GmbH and researchers. The study results in the so-called DEEP 1.0 product roadmap maturity model which allows companies to conduct a self-assessment of their product roadmapping practice.
Startups seek to create highly scalable business models. For startups, growth is thus vital. Growth hacking is a marketing strategy advocated by various startup practitioner experts. It focuses on using low cost practices while utilizing existing platforms in creative ways to gain more users for the service. Though topics related to growth hacking such as marketing on a general level have been extensively studied in the past, growth hacking as a practitioner-born topic has not seen much interest among the academia. To both spark interest in growth hacking, and to facilitate teaching growth hacking in the academia, we present two board games intended to serve as an engaging introduction to growth hacking for students.
Time-bounded events such as hackathons, code fests and others have become a global phenomenon. Entrepreneurial hackathons in particular have gained wide spread popularity because they come with the prospect to being the grounds where the next billion dollar enterprise is born. There is however limited insight into whether and how hackathons participants and start-up founders are connected beyond studies on singular events focusing on hackathons as a starting point for start-ups. To address this gap we conducted a study on a dataset covering 44 hackathons over three years and 489 start-ups in the North-Eastern European country of Estonia. Our findings indicate that hackathons are not always the start of an entrepreneurial endeavor but can also be useful through later stages as a means to develop future products, find future employees and others. The results presented in this paper are based on an initial analysis of this rich dataset and thus present the starting point of a larger study on the connection between the hackathon and start-up communities which is currently in planning.
Artificial intelligence (AI) has been recognized to be the most disruptive technology in the next ten years. The disruptive potential of AI is based on enhanced data processing capabilities which enable broader task automation but also allows AI to change its behavior based on user input. Simultaneously with AI development new platform-based business structures have gained traction and disrupted traditional pipeline business models. Platform business models rely on digital infrastructures to connect the supply and demand. AI has great potential to enable efficient resource allocation in these kinds of systems and in that way enhance the potential of value creation. Despite this complementary condition between AI and platform-based business, no academic understanding concerning the intertwinement of AI technologies and platform structures has yet been published. This position paper introduces five research areas which help us to understand AI enhanced value creation in B2B sales platforms through technology interaction.
The software business is moving from product business to Soft-ware-as-a-Service (SaaS). In addition to business changes, this also changes how productization should be done. This paper explores the required levels of productization in different phases of SaaS business. The research is based on a case study in a company and in a context of enterprise software and B2B business. This study focuses on how to build an enterprise SaaS solution, with excellent viable product–market fit, and con-currently, before larger productization investments, must be sure that demand exists. As a result of the study a productization model including three productization levels is presented. In this study we refer to products as software products and services.
Hub companies (e.g. Amazon, Facebook, Apple, Twitter and Google) rule the internet. They are de facto monopolies in their area of operations. They shape the future in which we live. And, it seems there is nothing we can do about that, as traditional economies of extreme scale – in which eventually the size of a corporation starts to be a hindrance, rather than an advantage – do not apply to them. On the contrary, they keep growing and growing and thus gaining stronger and stronger strangle hold over their respective areas of commerce and influence. This leads to unethical results, where the corporations spin out of any control, national or international. In this paper we give reasons to this phenomenon and lament the future of the internet – unless something drastic is done to change this.
Software start-ups face fierce competition in the market forcing them to release their products quickly and often under tough time constraints. To meet their deadlines, start-ups take shortcuts in software development leading to the accumulation of technical debt. They are able to put their product in users hands faster, get feedback, and improve at the expense of quality issues in the long run. As a start-up evolves through inception, stabilization and growth this debt will have to be managed. Technical debt management and software product line engineering techniques have some similar benefits of increased productivity and reduced time-to-market. Our aim is to check whether software product line engineering can be a candidate technique for start-ups to employ in managing technical debt as a response to their life-cycle phase goals and challenges. We conducted expert interviews with nine start-up professionals to identify the strategies applied in relation to technical debt management and software product lines engineering and other software engineering practices in start-ups. By analyzing the responses from the interviews we found that depending on the life-cycle phase of the start-up software product line engineering proved effective in managing technical debt and helped the start-ups to advance through the life-cycle phases.