Institution
Aalto University
Education•Espoo, Finland•
About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Carbon nanotube. The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.
Topics: Population, Carbon nanotube, Cellulose, Graphene, Thin film
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors investigate the transformation process towards services in more detail and find that manufacturers develop product related services through a dedicated service division designed to exploit the commercial opportunities of servicing an installed base of equipment, while the strategy of integrated solutions is utilized to enhance the competitiveness of their core product offering under industry conditions which make it difficult to maintain competitive advantage purely through technological leadership.
171 citations
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TL;DR: In this article, the importance of business models for value creation and company performance has been investigated, however, existing research on business models has not investigated the relationship between business models and value creation.
171 citations
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Technische Universität München1, University of Stuttgart2, Federal Fluminense University3, University of Innsbruck4, Polytechnic University of Turin5, Federal University of Amazonas6, Karlstad University7, Queen's University Belfast8, Aalto University9, University of Helsinki10, University of Calgary11, University of Oulu12, California State University, Long Beach13, University of Tartu14, Pontifícia Universidade Católica do Rio Grande do Sul15, Simula Research Laboratory16, Federal University of Bahia17, Charles III University of Madrid18, University of Twente19
TL;DR: The Naming the Pain in Requirements Engineering (NaPiRE) initiative as discussed by the authors is a family of surveys on the status quo and problems in practical requirements engineering (RE) in 10 countries in various domains.
Abstract: Requirements Engineering (RE) has received much attention in research and practice due to its importance to software project success. Its interdisciplinary nature, the dependency to the customer, and its inherent uncertainty still render the discipline difficult to investigate. This results in a lack of empirical data. These are necessary, however, to demonstrate which practically relevant RE problems exist and to what extent they matter. Motivated by this situation, we initiated the Naming the Pain in Requirements Engineering (NaPiRE) initiative which constitutes a globally distributed, bi-yearly replicated family of surveys on the status quo and problems in practical RE. In this article, we report on the qualitative analysis of data obtained from 228 companies working in 10 countries in various domains and we reveal which contemporary problems practitioners encounter. To this end, we analyse 21 problems derived from the literature with respect to their relevance and criticality in dependency to their context, and we complement this picture with a cause-effect analysis showing the causes and effects surrounding the most critical problems. Our results give us a better understanding of which problems exist and how they manifest themselves in practical environments. Thus, we provide a first step to ground contributions to RE on empirical observations which, until now, were dominated by conventional wisdom only.
170 citations
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TL;DR: In this article, a nonparametric variant of the corrected ordinary least-squares (COLS) method, referred to as corrected concave non-parametric least squares (C2NLS), is presented.
Abstract: Data envelopment analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis. In this paper, we show that DEA can be alternatively interpreted as nonparametric least-squares regression subject to shape constraints on the frontier and sign constraints on residuals. This reinterpretation reveals the classic parametric programming model by Aigner and Chu [Aigner, D., S. Chu. 1968. On estimating the industry production function. Amer. Econom. Rev.58 826--839] as a constrained special case of DEA. Applying these insights, we develop a nonparametric variant of the corrected ordinary least-squares (COLS) method. We show that this new method, referred to as corrected concave nonparametric least squares (C2NLS), is consistent and asymptotically unbiased. The linkages established in this paper contribute to further integration of the econometric and axiomatic approaches to efficiency analysis.
170 citations
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12 Sep 2016TL;DR: In this article, Petri Karjalainen, senior vice-president at OpusCapita Group, presented challenges faced by the RPA in financial process automation in a teaching case.
Abstract: OpusCapita Group is a Finnish company offering financial processes and outsourcing services to medium-sized companies and large corporations. OpusCapita particularly focuses on comprehensive Purchase-to-Pay and Order-to-Cash processes. In hopes to stay ahead of the curve in financial process automation, OpusCapita is betting on Robotic Process Automation (RPA). This teaching case presents challenges faced by Mr. Petri Karjalainen, Senior Vice President at OpusCapita Group, who is looking for ways to introduce RPA to the market, and provide added value to new and existing customers.
170 citations
Authors
Showing all 10135 results
Name | H-index | Papers | Citations |
---|---|---|---|
John B. Goodenough | 151 | 1064 | 113741 |
Ashok Kumar | 151 | 5654 | 164086 |
Anne Lähteenmäki | 116 | 485 | 81977 |
Kalyanmoy Deb | 112 | 713 | 122802 |
Riitta Hari | 111 | 491 | 43873 |
Robin I. M. Dunbar | 111 | 586 | 47498 |
Andreas Richter | 110 | 769 | 48262 |
Mika Sillanpää | 96 | 1019 | 44260 |
Muhammad Farooq | 92 | 1341 | 37533 |
Ivo Babuška | 90 | 376 | 41465 |
Merja Penttilä | 87 | 303 | 22351 |
Andries Meijerink | 87 | 426 | 29335 |
T. Poutanen | 86 | 120 | 33158 |
Sajal K. Das | 85 | 1124 | 29785 |
Kalle Lyytinen | 84 | 426 | 27708 |