Guest article by R. Elena Frâncu, Benjamin Lucas, and Martin Wetzels
Forecasts point to worldwide spending on wearable devices reaching $19 billion by 2018 (Feigenbaum, 2015) and reaching a staggering $34 billion by 2020 (Forbes, 2016). One explanation for these figures in the B2C market could be consumers’ escalating disenchantment with smartphones (Ben Wood, chief of research at CCS Insight) (Forbes, 2016) and a predicted trend towards consumer uptake of wearable devices instead (Acatech, 2015). Beyond the buzzword hype though, wearable technology is slowly disrupting a number of different commercial settings, often involving new data-driven services. In this article the authors briefly discuss this diverse landscape and also introduce research from Maastricht University focusing on the deployment of wearable devices for workforce analytics in a services context.
What is Wearable Technology?
The term wearable technology broadly encompasses any sensor or information presenting device worn on the body. The term is used in consumer electronics to refer to devices such as fitness trackers (e.g. Fitbit, Apple Watch, smart jewelry) and other health and well-being trackers (e.g. Spire for breathing and stress, Lumo Lift for posture correction), and in health to refer to devices such as medical and bioanalytical monitoring devices of various types, (Delahoz and Labrador, 2014; Heikenfeld, 2016). Wearable devices are also gaining traction in the B2B settings where they are deployed for workforce analytics (IBM, 2016a). In addition to facilitating certain sensing capabilities, certain wearable form factors also facilitate accessible real-time dashboarding of measurements, a good example being MIT’s conversation tone analyzing system, implemented on the Samsung Simband (MIT News, 2017).
As a research topic, wearable technology is also closely intertwined with the field of “quantified self” and overlaps with technical research fields including machine learning, signal processing, sensors and MEMS (micro-electrical mechanical systems), and sensor fusion. Via Internet of things (IoT), wearable tech links with other research fields, such as IT infrastructure (e.g. data handling and security), data analytics and visualization, and virtual user interface design. Computer science researchers pioneered the development, testing, and deployment of wearables in social science research contexts over a decade ago (Choudhury and Pentland, 2004), and this field has continued to evolve since (Montanari et al., 2016; Montanari et al., 2017). The goal of wearable tech deployment in these research contexts is advancing knowledge on social dynamics by collecting objective, high quality, real-time, longitudinal data (Kozlowski, 2015).
Wearable Technology and IoT Services
To increase the usefulness of wearable technology, such devices can be linked with cloud-based service platforms to collect and analyze user data. For example, in the B2C market, Microsoft offers such a service linked to its Microsoft Band wearable (Microsoft, 2017). In the B2B market, IBM has worked with North Star Bluescope Steel to develop wearable tech worker safety monitoring solutions powered by Watson (IBM, 2016a), and Honeywell (working with Intel) and Fujitsu are developing similar systems (Financial Times, 2016). This represents an interesting take on industrial IoT, which is currently centered on monitoring and optimizing physical assets and infrastructure.
In the wearable tech context, IoT platforms offer two main advantages. Firstly, IoT platforms allow more useful information to be extracted from the devices, as data can be stored over time in the cloud. Cloud computing also allows for more detailed and sophisticated analysis than what is facilitated by the onboard components in such small hardware (particularly when power management is a concern). Secondly, through IoT platforms, wearable device data can be easily linked with other relevant data sources to create extra value for consumers. MIT’s blockchain-based medical records system MedRec, has for example, the ability to incorporate fitness data from wearables alongside traditional health records from a patient’s different health service providers (Medium, 2016).
Wearable Technology to Augment and Replace Reality
Smart eyewear, based around head mounted displays, glasses or contact lenses (Rauch, 2014) is also often categorized under the broad wearable technology umbrella. Eyewear form factors facilitate additional sensor capabilities, but importantly also facilitate the presentation of information in an augmented reality format. This has given rise to a number of new devices in the B2C market, and new tools in the B2B market. For example, German company KNAPP has developed KiSoft vision, an augmented reality aid for warehouse picking (KNAPP, 2017), while Japan Airlines is developing software for Microsoft HoloLens to support engine mechanic training (Microsoft, 2016).
Wearable tech and IoT have also been recognized by Deloitte as a potential game-changers in human resources (Bersin et al., 2016). Wearable tech can be used, for example, to facilitate the measurement and monitoring of worker performance (Bloomberg, 2015) and well-being (HR Magazine, 2015). Humanyze, a spin-off from MIT Media Lab (Tech Crunch, 2015) offers workforce analytics solutions centered on teamwork, process improvement, and space planning (Humanyze, 2017) using data from digital communications and a proprietary wearable device, linked to a cloud analytics and dashboard service.
Workforce Analytics in Service Research
Torchbearer managers describe treating present and potential employees like customers (IBM, 2016b). Moreover, they point out the importance of building and constantly improving the employee experience, fostering the appropriate connections for employees to form communities (thus promoting loyalty), and drawing on employee analytics to predict future workforce trends. Taking this as a starting point, researchers at Maastricht University in the Netherlands are using the sociometric badges developed by MIT Media Lab (Olguín et. al 2009) to study collaboration between employees and customers in the context of new B2B service development. The research focuses on the role of collaborative creativity in generating new and useful ideas for service innovation (Frâncu et al., 2017). The researchers are empirically assessing social interactions between B2B actors (an online service design agency and a headhunting firm). The research involves social signal processing (SSP) to analyze the nonverbal aspects of the actors’ social behavior (Vinciarelli and Pentland, 2015) and linking this objective behavior to mutual dyadic evaluations of creativity.
The data collected from the sociometric badges is granular (measurements per second), allowing the researchers to directly pinpoint the convergences of nonverbal social behavior leading to improved innovation outcomes. This research also has important implications for management and human resources, on the particular subject of team collaboration. The researchers are also advancing social science research methodologically, applying advanced social network analysis and machine learning in this setting.
Figure notes: Nodes are sized by multiplex degree centrality and edges are weighted by the value of the underlying variable. Red edges represent body movement mirroring, green edges represent face-to-face interactions, dark blue edges represent posture mirroring, light blue edges represent turn taking (self-loops represent self-turns), and yellow edges represent volume mirroring (audio).
So… ‘Wear’ to Next?
As the B2C and B2B markets for wearable technology grow, so to will the corresponding service research agenda in this area. Currently, logical starting points are wearable enabled IoT platforms, workforce analytics for service enhancement, and understanding how service employee roles change as technology such as augmented reality wearables are deployed (for example in logistics service contexts). Other interesting avenues for research include servitization opportunities (e.g. for wearable work equipment where sensors can be added and linked to cloud services), and cross-integration between cloud services (e.g. AI engines) to improve wearable technology IoT service offerings. Future research will also consider the division and sharing of analytical tasks between hardware and cloud-based software as miniature computing components continue to improve, allowing for increasingly sophisticated sensing and analysis to take place inside wearable devices (Bourzac, 2017).
Martin Wetzels is a Professor and Chair in Marketing and Supply Chain Research at Maastricht University, School of Business and Economics, Netherlands.