Using Sensors in Organizational Research-Clarifying Rationales and Validation Challenges for Mixed Methods

Sensor-based data are becoming increasingly widespread in social, behavioral, and organizational sciences. Far from providing a neutral window on 'reality,' sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral-, and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors

Tracking serendipitous interactions: How individual cultures shape the office

In many work environments, serendipitous interactions between members of different groups may lead to enhanced productivity, collaboration and knowledge dissemination. Two factors that may have an influence on such interactions are cultural differences between individuals in highly multicultural workplaces, and the layout and physical spaces of the workplace itself. In this work, we investigate how these two factors may facilitate or hinder inter-group interactions in the workplace. We analyze traces collected using wearable electronic badges to capture face-to-face interactions and mobility patterns of employees in a research laboratory in the UK. We observe that those who interact with people of different roles tend to come from collectivist cultures that value relationships and where people tend to be comfortable with social hierarchies, and that some locations in particular are more likely to host serendipitous interactions, knowledge that could be used by organizations to enhance communication and productivity.This work was supported by the Google Europe Fellowship in Mobile Computing.This is the author accepted manuscript. The final version is published in the Proceedings of the ACM Conference on Computer Supported Cooperative Work and Social Computing and can be found in the ACM digital library here: http://dl.acm.org/citation.cfm?doid=2531602.2531641

Inferring Person-to-person Proximity Using WiFi Signals

Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches---including RFID badges or Bluetooth scanning---offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply

The architecture of innovation: Tracking face-to-face interactions with UbiComp technologies

The layouts of the buildings we live in shape our everyday lives. In office environments, building spaces affect employees' communication, which is crucial for productivity and innovation. However, accurate measurement of how spatial layouts affect interactions is a major challenge and traditional techniques may not give an objective view.We measure the impact of building spaces on social interactions using wearable sensing devices. We study a single organization that moved between two different buildings, affording a unique opportunity to examine how space alone can affect interactions. The analysis is based on two large scale deployments of wireless sensing technologies: short-range, lightweight RFID tags capable of detecting face-to-face interactions. We analyze the traces to study the impact of the building change on social behavior, which represents a first example of using ubiquitous sensing technology to study how the physical design of two workplaces combines with organizational structure to shape contact patterns.This is the author accepted manuscript. The final version is available at http://dl.acm.org/citation.cfm?id=2632056&CFID=528294814&CFTOKEN=36484024

The relationship between the quality of kindergartens’ outdoor physical environment and preschoolers’ social functioning.

The ability to initiate and engage in relationships is a critical landmark and predictor of children’s development and well-being. In kindergarten, children exhibit greater social participation outdoors rather than indoors. Indeed, the physical environment influences preschoolers’ social proximity. In this study, we examine the relationship between the quality of kindergartens’ outdoor physical environment and preschoolers’ social functioning. Two kindergartens in Gondomar, Portugal, were selected to participate according to different levels of their physical environment outdoors (poor and fair quality) and measured by a specific physical environment rating scale. Twenty-six children (aged 3–6, 10 boys) participated in this study. Children’s social proximity at the playground was measured through Radio Frequency Identification Devices (RFID). Mann–Whitney statistical tests were used to compare social proximity between groups. Our results showed that in the higher quality outdoor area, children spent less time alone and more time in social proximity with their peers in smaller groups (one or two children). More time was also spent in social proximity with different genders. Our study emphasizes the critical importance of reviewing kindergartens’ outdoor physical environments to support preschoolers’ social needs in a more challenging and diverse setting

Human versus machine - testing validity and insights of manual and automated data gathering methods in complex buildings

With the advancement of information technologies, automated methods of gathering data on space usage patterns in complex buildings using sensors are gaining popularity. At the same time, typical Space Syntax studies still rely on traditional social science methods and manual data gathering, for instance through direct observations and user surveys. How insights generated by each approach compare to each other is still poorly understood. Therefore this paper reports findings from an in-depth two week long study of space usage in a university building, where both manual methods (direct observations, user surveys) and automated data gathering methods (RFID sensors recording locations and interactions of users) were employed in parallel. The main hypotheses to be tested are that automated data captured by RFID sensors delivers comparable findings (1), complementary findings (2) or contradictory findings (3) to direct observations and self-reported surveys. The user behaviour under investigation includes movement flows, patterns of occupancy, interactivity and interaction networks. Results suggest that variable degrees of overlap can be established between the two approaches with rather few comparable findings. For certain space usage behaviours high levels of variance between the automated and manual datasets are found, pointing towards predominantly complementary and contradictory findings. It is shown that the goodness of the fit between automated and manual data depends on the way data is aggregated. This allows systematic reflections on the strengths and weaknesses of each of the approaches. In summary, evidence suggests that both human and machine based data gathering reveal crucial insights into behaviours of building users. Substituting manual methods with automated ones cannot be supported by the data of this study. Further suggestions for future studies of social life in complex buildings are made, thus contributing to the development of research methods in the field

Active buildings: modelling physical activity and movement in office buildings. An observational study protocol

Introduction: Health benefits of regular participation in physical activity are well documented but population levels are low. Office layout, and in particular the number and location of office building destinations (eg, print and meeting rooms), may influence both walking time and characteristics of sitting time. No research to date has focused on the role that the layout of the indoor office environment plays in facilitating or inhibiting step counts and characteristics of sitting time. The primary aim of this study was to investigate associations between office layout and physical activity, as well as sitting time using objective measures. Methods and analysis Active buildings is a unique collaboration between public health, built environment and computer science researchers. The study involves objective monitoring complemented by a larger questionnaire arm. UK office buildings will be selected based on a variety of features, including office floor area and number of occupants. Questionnaires will include items on standard demographics, well-being, physical activity behaviour and putative socioecological correlates of workplace physical activity. Based on survey responses, approximately 30 participants will be recruited from each building into the objective monitoring arm. Participants will wear accelerometers (to monitor physical activity and sitting inside and outside the office) and a novel tracking device will be placed in the office (to record participant location) for five consecutive days. Data will be analysed using regression analyses, as well as novel agent-based modelling techniques. Ethics and dissemination The results of this study will be disseminated through peer-reviewed publications and scientific presentations. Ethical approval was obtained through the University College London Research Ethics Committee (Reference number 4400/001)