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Wearable devices have evolved over the years and shown significant increase in popularity. With the advances in sensor technologies, data collection capabilities, and data analytics, wearable devices now enable interaction among users, devices, and their environment seamlessly. Multifunctional nature of this technology enables users to track their daily physical activities, engage with other users through social networking capabilities, and log their lifestyle habits. In this chapter, the authors discuss the types of sensor technologies embedded in wearable devices and how the data collected through such devices can be further interpreted by data analytics. In parallel with abundance of personal data that can be collected via wearable devices, they also discuss issues related to data privacy, suggestions for users, developers, and policymakers regarding how to protect data privacy are also discussed.

Previous research on information technology (IT) implementation and organizational change postulates that neither technology nor human agency determines the new structure of the organization, but rather the new structure emerges as a result of the interplay between technology and human agency. A majority of these studies assume a linear relationship between contingencies and outcome during the emergence process. However, during the implementation process, the characteristics of organizations become non-linear, almost chaotic. Therefore, we postulate that approaching to IT-enabled change from complexity theory would be better suited to explain the emergence process. We propose a framework based on dissipative structure theory and specify four stages that organizations undergo during the implementation process. While the emergence process is considered unpredictable, we argue that with the help of certain organizational practices (i.e., organizational learning/unlearning) and managerial interventions (i.e., use of rhetoric), organizations can condition the emergence of the new structure for the success of the implementation.

Wearable devices have evolved over the years and shown significant increase in popularity. With the advances in sensor technologies, data collection capabilities, and data analytics, wearable devices now enable interaction among users, devices, and their environment seamlessly. Multifunctional nature of this technology enables users to track their daily physical activities, engage with other users through social networking capabilities, and log their lifestyle habits. In this chapter, the authors discuss the types of sensor technologies embedded in wearable devices and how the data collected through such devices can be further interpreted by data analytics. In parallel with abundance of personal data that can be collected via wearable devices, they also discuss issues related to data privacy, suggestions for users, developers, and policymakers regarding how to protect data privacy are also discussed.