Creating Products in the Absence of Markets: A Robust Design Approach

The purpose of this study is to examine how firms deal with a situation of true uncertainty about their potential markets and technologies. Specifically, we ask how firms can create products when the corresponding market does not exist. Design/methodology/approach : This paper is based on a longitudinal study of a high-tech firm, combined with analysis of existing theory in Product Design and Entrepreneurship. Findings – Markets and products are usually a defining choice made early on by firms in their strategic process. Such a choice guides their development by providing a ‘stable concept' to which decisions can be related to. When markets do not exist yet, however, this approach is not effective: Early choice of products and markets limits firms' flexibility by constraining their ability and willingness to adapt, while fundamental new technical and market information is likely to emerge during the project that will prove the initial assumptions wrong."New Product Development";"uncertainty";"high-technology venture"

Creating Products in the Absence of Markets: A Robust Design Approach

Purpose - The purpose of this study is to examine how firms deal with a situation of true uncertainty about their potential markets and technologies. Specifically, we ask how firms can create products when the corresponding market does not exist. Design/methodology/approach – This paper is based on a longitudinal study of a high-tech firm, combined with analysis of existing theory in Product Design and Entrepreneurship. Findings – Markets and products are usually a defining choice made early on by firms in their strategic process. Such a choice guides their development by providing a ‘stable concept' to which decisions can be related to. When markets do not exist yet, however, this approach is not effective: Early choice of products and markets limits firms' flexibility by constraining their ability and willingness to adapt, while fundamental new technical and market information is likely to emerge during the project that will prove the initial assumptions wrong. We show an alternative approach where products and markets actually result from a generic process of products and markets exploration driven by the firm. We suggest that this approach forms a robust design in that it allows the firm to deal with the uncertainty by simultaneously developing its products and exploring markets, while preserving the flexibility to adapt to the changing environment. Practical implications – The practical implication of this paper is to suggest an alternative approach to deliberate planning in high-tech ventures. With this approach, rather than markets and products, strategy defines a market and technology exploration process. Originality/value – The paper is original in three ways: 1) It links the product design and market exploration processes in high-tech firm development; 2) It is based on an in-depth longitudinal study; and 3) It results from an academic-practitioner collaborative work.New Product Development; uncertainty; high-technology venture.

The SECURE collaboration model

The SECURE project has shown how trust can be made computationally tractable while retaining a reasonable connection with human and social notions of trust. SECURE has produced a well-founded theory of trust that has been tested and refined through use in real software such as collaborative spam filtering and electronic purse. The software comprises the SECURE kernel with extensions for policy specification by application developers. It has yet to be applied to large-scale, multi-domain distributed systems taking different application contexts into account. The project has not considered privacy in evidence distribution, a crucial issue for many application domains, including public services such as healthcare and police. The SECURE collaboration model has similarities with the trust domain concept, embodying the interaction set of a principal, but SECURE is primarily concerned with pseudonymous entities rather than domain-structured systems

Carving out new business models in a small company through contextual ambidexterity: the case of a sustainable company

Business model innovation (BMI) and organizational ambidexterity have been pointed out as mechanisms for companies achieving sustainability. However, especially considering small and medium enterprises (SMEs), there is a lack of studies demonstrating how to combine these mechanisms. Tackling such a gap, this study seeks to understand how SMEs can ambidextrously manage BMI. Our aim is to provide a practical artifact, accessible to SMEs, to operationalize BMI through organizational ambidexterity. To this end, we conducted our study under the design science research to, first, build an artifact for operationalizing contextual ambidexterity for business model innovation. Then, we used an in-depth case study with a vegan fashion small e-commerce to evaluate the practical outcomes of the artifact. Our findings show that the company improves its business model while, at the same time, designs a new business model and monetizes it. Thus, our approach was able to take the first steps in the direction of operationalizing contextual ambidexterity for business model innovation in small and medium enterprises, democratizing the concept. We contribute to theory by connecting different literature strands and to practice by creating an artifact to assist managemen

Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems

Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a mature and remarkable state, inference latency and throughput are a major concern especially when targeting low-cost and low-power embedded platforms. CNNs' inference latency may become a bottleneck for Deep Learning adoption by industry, as it is a crucial specification for many real-time processes. Furthermore, deployment of CNNs across heterogeneous platforms presents major compatibility issues due to vendor-specific technology and acceleration libraries. In this work, we present QS-DNN, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of CNNs on heterogeneous embedded devices. We show that, an optimized combination can achieve 45x speedup in inference latency on CPU compared to a dependency-free baseline and 2x on average on GPGPU compared to the best vendor library. Further, we demonstrate that, the quality of results and time "to-solution" is much better than with Random Search and achieves up to 15x better results for a short-time search

Proceedings of International Workshop "Global Computing: Programming Environments, Languages, Security and Analysis of Systems"

According to the IST/ FET proactive initiative on GLOBAL COMPUTING, the goal is to obtain techniques (models, frameworks, methods, algorithms) for constructing systems that are flexible, dependable, secure, robust and efficient. The dominant concerns are not those of representing and manipulating data efficiently but rather those of handling the co-ordination and interaction, security, reliability, robustness, failure modes, and control of risk of the entities in the system and the overall design, description and performance of the system itself. Completely different paradigms of computer science may have to be developed to tackle these issues effectively. The research should concentrate on systems having the following characteristics: • The systems are composed of autonomous computational entities where activity is not centrally controlled, either because global control is impossible or impractical, or because the entities are created or controlled by different owners. • The computational entities are mobile, due to the movement of the physical platforms or by movement of the entity from one platform to another. • The configuration varies over time. For instance, the system is open to the introduction of new computational entities and likewise their deletion. The behaviour of the entities may vary over time. • The systems operate with incomplete information about the environment. For instance, information becomes rapidly out of date and mobility requires information about the environment to be discovered. The ultimate goal of the research action is to provide a solid scientific foundation for the design of such systems, and to lay the groundwork for achieving effective principles for building and analysing such systems. This workshop covers the aspects related to languages and programming environments as well as analysis of systems and resources involving 9 projects (AGILE , DART, DEGAS , MIKADO, MRG, MYTHS, PEPITO, PROFUNDIS, SECURE) out of the 13 founded under the initiative. After an year from the start of the projects, the goal of the workshop is to fix the state of the art on the topics covered by the two clusters related to programming environments and analysis of systems as well as to devise strategies and new ideas to profitably continue the research effort towards the overall objective of the initiative. We acknowledge the Dipartimento di Informatica and Tlc of the University of Trento, the Comune di Rovereto, the project DEGAS for partially funding the event and the Events and Meetings Office of the University of Trento for the valuable collaboration