Evaluation of Strategic Opportunities and Resulting Business Models for SMEs: Employing IoT in Their Data-Driven Ecosystems

Evaluation of Strategic Opportunities and Resulting Business Models for SMEs: Employing IoT in Their Data-Driven Ecosystems

Izabella V. Lokshina (The State University of New York College at Oneonta, USA), Cees J. M. Lanting (Independent Researcher, Belgium) and Barbara Durkin (The State University of New York College at Oneonta, USA)
DOI: 10.4018/978-1-7998-2355-1.ch007
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Abstract

This chapter focuses on ubiquitous sensing devices, enabled by Wireless Sensor Network (WSN) technologies, that cut across every area of modern day living, affecting individuals and businesses and offering the ability to measure and understand environmental indicators. The proliferation of these devices in a communicating-actuating network creates the internet of things (IoT). The IoT provides the tools to establish a major global data-driven ecosystem with its emphasis on Big Data. Currently, business models may focus on the provision of services, i.e., the internet of services (IoS). These models assume the presence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated. Different business models may support creating revenue and value for different types of customers. This chapter contributes to the literature by considering, innovatively, knowledge-based management practices, strategic opportunities and resulting business models for third-party data analysis services.
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Introduction

The much-discussed Internet of Things (IoT) provides a set of tools enabling a major, global data-driven ecosystem to develop devices (or Things) encompassing everything from pedometers to seismographs, collect data and produce unprecedented amounts of information about the parameters and items in the world around us. When put in the hands of people and businesses, this information can make every area of life, including business, more data driven, completer and more efficient.

Things are not really a new concept. We've been using sensors to collect scientific data for centuries. What's different now is the interconnection of all these devices, producing ever more granular data sets, all while that data is becoming more and more accessible, potentially to everyone. To put that data to work, we need to make sense of it. When massive amounts of data become accessible and understandable, the implications are enormous for civic life, personal health and business.

Until now, much attention and effort have gone in the development of business models for the provision of services in this data-driven ecosystem in the context of the IoT, sometimes referred to as the Internet of Services (IoS). These business models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. However, not every business model may support opportunities that generate revenue or value or are suitable for different types of customers. Other business models should also be considered.

In this chapter, the authors investigate knowledge-based management practices, business models, new ventures and potential business opportunities for third-party data analysis services. The goal is to give a reasonable, qualitative evaluation, from theoretical and practical viewpoints, of knowledge-based management practices and business models, strategic implications and new business opportunities for American and European small and medium enterprises (SMEs) that use IoT and Big Data techniques to support their innovative performance.

A discussion of the infrastructure is outside the scope of this chapter. The authors assume that a significant time will be needed for deployment. Regulatory clarity and appropriate permissions in addition to possible privacy and national security issues must be addressed.

This chapter makes several important contributions to the literature. First, the chapter considers knowledge-based management practices, business models, new ventures and potential business opportunities for third-party data analysis services. It complements other research about the positive effect of knowledge management on companies’ innovative performance. Second, the chapter discusses access to information generated by third parties in a new context of analysis, i.e. as a prerequisite to data analysis services and in relation to Big Data techniques and new business opportunities. It complements prior research done by the authors on access to data from third parties by Big Data analyzers. Third, the chapter evaluates strategic implications and business opportunities for American and European small and medium enterprises (SMEs) that use the IoT in their data-driven ecosystems. It complements previous research about the positive effects of information and communications technology (ICT) tools and competencies on business performance.

Finally, what is addressed by chapter also has some social implications since governments are interested in using the latest technologies to promote the best social climate for their citizens. The chapter assumes that an obvious consequence of using the latest technologies will be a broader scope of deliberative democracy. However, the legal framework of the latest technologies is still considered rather vague or absent to a certain extent. Such issues as standardization, service design architecture and models, as well as data privacy and security, create management and governance problems. These issues have not been solved sufficiently by the current service architectures. The latest technologies can also become subject to power politics with the attendant risks of cyber-criminality, cyberterrorism and even cyberwar.

Key Terms in this Chapter

Business model: A design for the successful operation of a business, identifying revenue sources, customer base, products, and details of financing.

Digital Society: A modern, progressive society that is formed as a result of the adoption and integration of Information and Communication Technologies (ICT) at home, work, education and recreation, and supported by advanced telecommunications and wireless connectivity systems and solutions.

Big Data: Extremely large volume of data - both structured and unstructured - that inundates a business on a day-to-day basis and may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Smart World: A concept of a digital world that evolves from the definition of ubiquitous computing and promotes the ideas of a physical world that is richly and invisibly interwoven with sensors, actuators, displays, and computational elements, embedded seamlessly in the everyday objects of our lives, and connected through a continuous network.

Ethics: The branch of knowledge that deals with moral principles.

Small and Medium-sized Enterprises (SMEs): Non-subsidiary, independent firms which employ fewer than a given number of employees. This number varies across countries. The most frequent upper limit designating an SME is 250 employees, as in the European Union.

Internet of Things (IoT): A system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

Data Analysis: A process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Big Data Analytics: The process of examining big data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

Knowledge Management: The process of creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieving organizational objectives by making the best use of knowledge.

Business Venture: A small business. Many ventures will be invested in by one or more individuals or groups with the expectation of the business bringing in a financial gain for all backers. Most business ventures are created based on demand of the market or a lack of supply in the market.

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