Arho Suominen

Clustering scientific documents with topic modeling

Topic modeling is a type of statistical model for discovering the latent “topics” that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities.

Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification

The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised‐learning classification, and discuss the advantages and disadvantages of this approach vis‐à‐vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoning‐based classification frameworks of scientific knowledge, as they typically try to fit new‐to‐the‐world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large‐scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large‐scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.

Revenue Models of Application Developers in Android Market Ecosystem

Mobile application ecosystems have growth rapidly in the past few years. Increasing number of startups and established developers are alike offering their products in different marketplaces such as Android Market and Apple App Store. In this paper, we are studying revenue models used in Android Market. For analysis, we gathered the data of 351,601 applications from their public pages at the marketplace. From these, a random sample of 100 applications was used in a qualitative study of revenue streams. The results indicate that a part of the marketplace can be explained with traditional models but free applications use complex revenue models. Basing on the qualitative analysis, we identified four general business strategy categories for further studies.

Wealthy, Healthy and/or Happy — What does ‘Ecosystem Health’ Stand for?

The health of a software ecosystem is argued to be a key indicator of well-being, longevity and performance of a network of companies. In this paper, we address what scientific literature actually means with the concept of ‘ecosystem health’ by selecting relevant articles with systematic literature review. Based on the final set of 38 papers, we found that despite a common base, the term has been used to depict a wide range of hoped characteristics of a software ecosystem. However, the number of studies addressing the topic is shown to grow while empirical studies are still rare. Thus, further studies should aim to standardize the terminology and concepts in order to create a common base for future work. Further work is needed also to develop early indicators that warn and guides companies on problems with their ecosystems.

Young mobile users: Radical and individual - Not

The use of mobile phones by youth has stirred a plethora of research in different fields. Literature has analyzed in length the changes and adoption patterns related to the evolving telecommunications industry. This body of knowledge often makes assumptions on the changes in consumer profiles and the value of different features. In this study we take an longitudinal approach by analysing the results of 1928 responses to an online questionnaire conducted in Finland to students of a university in the Spring 2012 and on against the reanalysis of the responses of the Finnish students of upper secondary schools in the Spring 2001 and study on undergraduate students in 2006-2007. The results indicate that the youth and young adults of Finland, often argued to be an advanced country for mobile services, are surprisingly conservative towards new mobile devices and services. The changes in technology and service offering in a decade, has had a limited impact in attitudes and feature valuation, which sets significant implications to increasing adoption and usage.

Sources of value in application ecosystems

Abstract Mobile application stores have revolutionised the dynamics of mobile ecosystems. Research on mobile application ecosystems has been significantly driven by data that is focused on the visualisation of an ecosystems dynamics. This is a valuable step towards understanding the nature of the ecosystems, but it is limited in its explanatory power. Thus, a theory-driven approach is needed to understand the overall dynamics of such systems. This study applies a theoretical framework of value creation in e-business in the context of mobile application ecosystems, with a focus on application developers. A qualitative research strategy is employed in testing operationalisation in a sample of developers. The sample comprises 27 application developers from the three leading mobile application ecosystems. The results show that efficiency is the main source of value, products seldom create value through complementarities, and approaches towards lock-in and novelty seem to vary among application developers. The managerial and theoretical implications of such biased value creation in mobile ecosystems are considered.

The influence of developer multi-homing on competition between software ecosystems

Developers multi-home by offering apps in competing mobile application ecosystems.If developers multi-home, several competing ecosystems can survive.We analyzed multi-homing in three mobile application ecosystems with 1.3 million apps.3% of developers generate 80% of installations in a marketplace.The majority of all apps single-home, but the most popular apps multi-home. Having a large number of applications in the marketplace is considered a critical success factor for software ecosystems. The number of applications has been claimed to determine which ecosystems holds the greatest competitive advantage and will eventually dominate the market. This paper investigates the influence of developer multi-homing (i.e., participating in more than one ecosystem) in three leading mobile application ecosystems. Our results show that when regarded as a whole, mobile application ecosystems are single-homing markets. The results further show that 3% of all developers generate more than 80% of installed applications and that multi-homing is common among these developers. Finally, we demonstrate that the most installed content actually comprises only a small number of the potential value propositions. The results thus imply that attracting and maintaining developers of superstar applications is more critical for the survival of a mobile application ecosystem than the overall number of developers and applications. Hence, the mobile ecosystem is unlikely to become a monopoly. Since exclusive contracts between application developers and mobile application ecosystems are rare, multi-homing is a viable component of risk management and a publishing strategy. The study advances the theoretical understanding of the influence of multi-homing on competition in software ecosystems.

A bibliometric analysis of the development of next generation active nanotechnologies

AbstractDelineating the emergence of nanotechnologies that offer new functionalities is an important element in an anticipatory approach to the governance of nanotechnology and its potential impacts. This paper examines the transition to next generation active nanotechnologies which incorporate functions that respond to the environment or systems concepts that combine devices and structures that are dynamic and which may change their states in use. We develop an approach to identifying these active nanotechnologies and then use bibliometric analysis to examine the extent of research papers and patents involving these concepts. We also examine references to environmental, health, and safety concepts in these papers, given that these next generation nanotechnologies are likely to have risk profiles that are different from those of first-generation passive nanomaterials. Our results show a steady growth overall in focus on active nanotechnologies in the research literature and in patents over the study period of 1990–2010. We also find an increase in consideration given to environmental, health, and safety topics. While gaps are highlighted in our understanding of research and innovation in active nanotechnologies, the results suggest that there is beginning to be a shift to active nanotechnologies, with the implication that governance processes need to be conscious of this shift and to prepare for it.

Phases of growth in a green tech research network: a bibliometric evaluation of fuel cell technology from 1991 to 2010

This study uncovers the evolution of a fuel cell research network through a bibliometric study focusing on a period from 1991 to 2010. From a dataset of 37,435 research articles, the study focuses on the evolution of fuel cell research networks at a national level. Focusing solely on the expansion of the research networks, and the policies effecting collaboration, the paper poses three research questions (1) Is research into fuel cells more unconcentrated than in science overall and if so, (2) is there changes within time and (3) can we identify a cluster among certain countries. To answer the research questions, the data was compared to findings on the overall scientific output worldwide. In addition, an ego network analysis was performed and a modularity algorithm was used in order to identify clusters from the network data. The study showed that fuel cell research co-operation has had a distinct evolution within the time frame of the study. Research has increased in both volume and in co-operation, but research co-operation is more unconcentrated than in science overall. Non-TRIAD countries have a stronger role in fuel cell research than in science overall. Clusters in research co-operation have evolved into two modes of co-operation—one around Asia and North America and the second around European co-operation with US and Asia.

Busting myths of electronic word of mouth: the relationship between customer ratings and the sales of mobile applications

Business and academic research frequently highlights the power of electronic word of mouth, relying on the knowledge that online customer ratings and reviews influence consumer decision making. Numerous studies in different disciplines have been conducted to examine the effectiveness of electronic word of mouth communication. Previously, typically small sample studies suggest that positive electronic word of mouth increases sales and that the effects depend on the volume and valence of reviews and ratings. This studys contribution lies in testing the relationship between electronic word of mouth and the sales of applications in a mobile application ecosystem (Google Play) with an extensive dataset (over 260 million customer ratings; 18 months). The results show that higher values of valence of customer ratings correlate statistically significantly with higher sales. The volume of ratings correlates positively with sales in the long term but negatively in the short term. Furthermore, the relationship between electronic word of mouth and sales seems to be more important when the price of the application increases. The findings also underline the importance of the choice of a measurement period in studies.