512 research outputs found

    Safety-centric and Smart Outdoor Workplace: A New Research Direction and Its Technical Challenges

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    Despite the fact that outside is becoming the frontier of indoor workplaces, a large amount of real-world work like road construction has to be done by outdoor human activities in open areas. Given the promise of the smart workplace in various aspects including productivity and safety, we decided to employ smart workplace technologies for a collaborative outdoor project both to improve the work efficiency and to reduce the worker injuries. Nevertheless, our trials on smart workplace implementation have encountered a few problems ranging from the theoretical confusion among different stakeholders, to the technical difficulties in extending underground devices' lifespan. This triggers our rethinking of and discussions about "smart workplace". Eventually, considering the unique characteristics of outdoor work (e.g., more sophisticated workflows and more safety-related situations than office work), we argue that "safety-centric and smart outdoor workplace" deserves dedicated research attentions and efforts under the umbrella discipline of smart environment. In addition, the identified technical challenges can in turn drive different research dimensions of such a distinguishing topic.Comment: 14 page

    MAPIT - A Mapping Application for Freshwater Invertebrate Taxa

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    With the increasing popularity of the World Wide Web among internet users across the world, the need for building web based applications is increasing with time. The Western Center for Monitoring and Assessment of Freshwater Ecosystems (WMC) and the National Aquatic Monitoring Center (NAMC) jointly host a central database containing biological data that is used to assess the condition of aquatic ecosystems. The information stored in the database contains biological and, - geographical data. This information is made available easily through a simple but effective tool called MAPIT. MAPIT is a search engine which can be used to search through the environmental and biological data related to aquatic invertebrates and their locations. MAPIT also produces a map of the location of where the individual taxa were collected. Users can also download the data in a standard format for further analysis

    When the Sea meets City: Transformation towards a Smart Sea in Finland

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    The Baltic Sea is increasingly becoming a living laboratory for rapid prototyping and testing solutions from cleaner and safer shipping to remote and autonomous navigation. The maritime industry in Finland is rapidly undergoing digital transformation to make activities at sea smarter. A Smart Sea can be understood as an ecosystem across city and sea interface in which businesses, knowledge institutions, citizens, municipal agencies and government collaborate towards shared situational awareness and create value in multiple dimensions – economic, social and environmental. This article presents Smart Sea implementation journey in Finnish public sector through notable improvements and setbacks, and identifies larger transformation effects for the society

    Digital Innovation and Incubators: A Comparative Interview Study from the Perspective of the Automotive Industry

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    As non-corporate (herewith referred to as “independent”) incubators gain in popularity for propelling digital innovation, traditional automotive firms have set up in-house incubators (herewith referred to as “corporate”) to accelerate innovation without disrupt-ing too much the inherent organizational structures and corporate cultures. The overarching objective is to establish the expected benefits for automotive firms from independent incubators when organizing corporate incubators. Using a comparative interview study, ten successful independent incubators in North America are discussed in terms of their ability to provide support in the digital domains. Our work has resulted in novel operating models for categorizing incubators to describe variations in focus areas and support for digital innovation. The results sheds light on how corporate incubators (internal to automotive firms) have the potential to shield digital ventures from the complexities of large and traditional establishments, and to promote interactions with other business units within the firm when performing digital innovation

    Influence of Enterprise Systems on Business Process Agility

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    Business process agility (a combination of speed and flexibility) is increasingly becoming an important weapon for achieving a competitive advantage in today’s growing competition and dynamic business environment. Based on literature review and past research by the author, this paper will present the development of a research framework to investigate the influence of enterprise systems on business process agility. Using exploratory qualitative case studies, this study will identify the major drivers and inhibitors for enhancing the process agility in business organizations that have implemented enterprise systems and investigate the possibility of attaining both process efficiency and agility simultaneously while automating and/or integrating business processes

    From Ad-Hoc Data Analytics to DataOps

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    The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage

    Institutional Reinforcement in a Newly Emerged Digital Industry

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    There is a growing number of newly emerging digital sectors where firms have potential for a rapid scalable expansion. One of them is mobile gaming, being triggered by development of smart devices, where Finland-based entrepreneurial firms have captured global markets in an extremely fast pace. How and why have these Finnish firms achieved a global market positioning, particularly in the light of Finland’s small home market, relative cultural isolation, and peripheral location? By building on the institutional polycentrism approach, this paper sheds light on how global industry-specific and informal socio-cultural institutional arrangements have been reinforced by local formal, government-sponsored ones to create the conditions for global leadership in a digital industry. This paper makes important contribution to the better understanding of the role of institutions in driving emergence of digital industries and shaping global competitiveness of home country firms in these digital sectors

    Ownership of co-creation assets: driving B2B value propositions in the service economy

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    The benefits of specialization have been driving the rise of the service economy and pushing capability frontiers and economic growth. In service economies, almost any activity, asset, and skill can be bought on competitive markets, making it harder to build competitive advantage on any of those inputs. Against that background, the question emerges what constitutes sustainable value propositions of service providers. Drawing on an emerging stream of research on the non-ownership value of services, we argue that service providers create value by taking on ownership of service assets and thereby transform uncertainty of value creation into economic opportunities. In our view, service providers offer the essential value proposition of transforming their clients’ uncertainty downsides into opportunities related to assets such as vehicles, real estate, equipment and computing platforms. Clients benefit by delegating ownership of assets to the domain of a service provider. In turn, clients can focus their investment on their most promising assets. Service providers create sustainable competitive advantage by assuming ownership and excelling at the management of (a) unique physical assets, (b) unique intangible assets and (c) maintaining an appropriate architecture of social capital through customer relationships and business ecosystems

    Machine learning and deep learning

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    Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.Comment: Published online first in Electronic Market
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