Internet of Things technology applications in the workplace environment: a critical review

Ingrid Nappi (Workplace Management Chair, ESSEC Business School, Cergy, France)
Gisele de Campos Ribeiro (Workplace Management Chair, ESSEC Business School, Cergy, France)

Journal of Corporate Real Estate

ISSN: 1463-001X

Article publication date: 30 January 2020

Issue publication date: 19 March 2020

4042

Abstract

Purpose

The purpose of this study is to examine the use of IoT technology (RFID technology, sensor networks, wearable devices and other smart items) in office settings and its respective impact on the optimization of employees’ productivity and workspace effectiveness.

Design/methodology/approach

The paper reviews 41 relevant publications reporting IoT use in office settings to identify how this technology has been applied in office settings and what topics are mostly addressed in the literature; how IoT technology improves employees’ productivity; and what the benefits and risks associated with IoT use in the workplace environment are.

Findings

Two main areas of application of IoT technology in the workplace environment were identified. The first one concerns the influence of the physical characteristics of workplaces on aspects related to workspace effectiveness. The second one is employee-centered and concerns the use of IoT data to identify employees’ social behavior, physiological data and emotional estates associated with productivity. IoT technology provides real-time data with speedy information retrieval. However, its deployment in office settings is not exempt from risks. Employee workplace surveillance, re-individualization of the IoT data and employee refusal of IoT technology in office settings are the main risks associated with this technology.

Originality/value

This literature review categorizes IoT application in office settings according to two perspectives and highlights employees' attitudes, user-experience of IoT technology and the risks associated with this technology. These results will help researchers and workplace managers interested in the deployment of this technology in the workplace environment.

Keywords

Citation

Nappi, I. and de Campos Ribeiro, G. (2020), "Internet of Things technology applications in the workplace environment: a critical review", Journal of Corporate Real Estate, Vol. 22 No. 1, pp. 71-90. https://doi.org/10.1108/JCRE-06-2019-0028

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited


Introduction

In recent years, we have witnessed the advent of new smart devices that can input autonomously into the internet all sorts of data regarding almost all aspects of people’s daily lives. These intelligent everyday objects (smartphones, coffee-machines, printers, etc.) use embedded systems (RFID, wireless sensors, etc.) to collect, analyze and share data via the internet without any human involvement. They represent the evolution of the internet into a network of interconnected objects that not only harvests information from the environment (sensing) and interacts with the physical world but also uses existing internet technological standards to provide services for information transfer, analytics, applications and communication (Gubbi et al., 2013). Since 1999, when the expression Internet of Things (IoT) was used for the first time to refer to these smart objects, it has received more and more attention from academics, IT experts, the popular press and companies in general.

The IoT is a dynamic global network infrastructure where physical and virtual “things” have identities, physical attributes, intelligent interfaces, and are seamlessly integrated into the information network (Sundmaeker et al., 2010). In the workplace, the advantages attributed to this technology are an increase in work productivity, time-saving activities and improvement of work satisfaction through attentive and reactive environments (Röcker, 2009a, 2009b). Through numerous applications, IoT technology has played a part in establishing new workplace experiences. Intelligent buildings that incorporate sensors and other technological devices are helping managers and users of buildings to improve operational efficiency and occupants’ enjoyment of space (Saiz and Salazar, 2017; Steiner, 2005).

Corporate Real Estate Management (CREM) and office design are strategic resources for business development. Real estate decisions must consider aspects such as employees’ well-being, productivity, innovation, workplace flexibility and design (Ianeva et al., 2015). As impending real estate costs weigh heavily on the operating business, it is essential to measure the value and utilization of office space (Ward, 2016). The use of IoT technology in buildings reduces energy consumption, maintenance costs and administrative expenses while enhancing building occupants’ satisfaction. Therefore, IoT technology has the potential to change the major patterns of corporate real estate and its deployment in the workplace environment can create value through supporting companies’ strategic decisions, investments and the optimization of operational efficiency (Shabha, 2006; Shabha, 2007).

Research objective and supporting questions

The purpose of this study is to review the literature dedicated to IoT technology in office settings and to identify relevant issues and knowledge concerning its utilization and respective impacts on the optimization of employees’ productivity and workspace effectiveness. To fulfill these objectives, the following research questions guided this study:

RQ1.

How has IoT technology been applied in workplace settings, which topics are most frequently addressed and how much do they contribute to the workplace research field?

RQ2.

What is the evidence that IoT technology improves office workers’ productivity?

RQ3.

What are the benefits and risks associated with the utilization of IoT technology in the workplace?

This article is organized as follows; first, a description of the concepts of workspace effectiveness and employees’ productivity are presented, followed by the literature review methodology and a description of the selected studies. There follows a presentation of the findings from the literature. Finally, the study discusses the perspectives and challenges for the development and popularization of IoT technology in the workplace environment and draws a conclusion.

Workspace effectiveness and employee productivity

A workplace is essentially made up of a number of planned zones and workspaces. A carefully planned and implemented workplace benefits its occupants’ health and well-being, and also increases the performance of the organization (Steiner, 2005; Ghaffarianhoseini et al., 2016). Workspace effectiveness is the measure of the extent to which the physical environment supports employees when they perform their activities in terms of quality and quantity of work done, time spent and global performance. Research concerning the impact of office design, office ergonomics and building facilities on employees’ productivity is not new and has received special attention from corporate real estate researchers and workplace managers (Appel-Meulenbroek and Feijts, 2007; Robertson et al., 2013; Seddigh et al., 2015). In addition, enhancing workplace efficiency and effectiveness to improve employees’ productivity has become one of the main activities of corporate real estate managers (Appel-Meulenbroek et al., 2011).

Productivity is the ability of people to enhance their work output through increases in the quantity and/or quality of the product or service they deliver (Leaman and Bordass, 2006). Even though researchers agree on the importance of measuring employee productivity, they do not agree on how to measure it. Neither do they agree on the best variable to measure productivity. The most common measures used by researchers are ratings of perceived productivity, cognitive performance tests (e.g. working memory, processing speed and concentration), monitoring computer activity (e.g. keystrokes and mouse clicks), absenteeism, presenteeism, reported frequency of health issues, mood, sleepiness, job satisfaction, job engagement, intention to quit, and turnover (Haynes et al., 2017; Bortoluzzi et al., 2018). Indeed, small increases in employees’ productivity can greatly outweigh significant reductions in real estate costs (Haynes, 2007). Employees’ satisfaction with a building (measured through the interior use of space and physical conditions) has been used as a predictor of their perceived productivity level. Air quality, thermal comfort, lighting, noise and office layout are examples of environmental conditions associated with employees’ health, well-being, and productivity (Haynes et al., 2017). However, there is a paucity of research on productivity measurement in the knowledge worker context (Bortoluzzi et al., 2018).

The inception of the internet and connected objects added new perspectives for researchers and practitioners in the workplace research field. Therefore, the workplace environment provides many opportunities for the deployment of IoT technology, and the analysis of the literature concerning the application of IoT technology in office settings can be a valuable contribution to improve the knowledge of employees’ productivity.

Literature review methodology

To evaluate the use of IoT technology in the workplace environment and answer these study research questions, a literature review was carried out. This methodology provides the best approach to the appropriation of a subject once it allows the identification of relevant issues developed in the literature; shows appropriate approaches to a topic, its relevance, possible contributions and helps the identification of methods for analysis (Hart, 1998). To identify research providing answers to the questions set out in this study, the following steps were established: choice of the key-word search terms, publication type and year of publication of the relevant studies.

Concerning the key-word search terms: to identify the relevant literature more accurately and assemble it, the present study used keywords formed by “specific IoT technology terms” associated with the workplace environment combined with Boolean logic terms OR and AND. The expression used as a baseline search criteria was: (“IoT” OR “smart items” OR “connected objects” OR “Internet of Things” OR “wearable” OR “sensor” OR “RFI technology” OR “intelligent building” OR “smart office”) AND (“workplace” OR “employee productivity” OR “employee performance” OR “workplace productivity” OR “workplace performance”). These keywords were used as search criteria in five academic databases: WEB of Science, EBSCO, SAGE, Spring Link and Emerald.

Regarding the type of publication: the initial intention was to consider in this literature review only research published in peer-reviewed journals. However, when incorporating peer-reviewed journals in the search criteria, only 11 articles reporting the application of IoT technology in office settings were identified.

The IoT and artificial intelligence (AI) are themes debated mostly by practitioners and research is published in informal media (Whitmore et al., 2015; Atkin and Bildsten, 2017). As recent quality empirical studies can also be reported at conferences, the authors of this study decided also to consider studies published in conference proceedings in this literature review.

Regarding the year of publication of the studies: At the beginning of this study, the intention was to review only studies published during the past 5 years. However, as the number of studies identified was still limited, the authors of this study extended the search time period. No limit for the year of publication was entered as one of the search criteria.

After adjusting the search criteria so that research published in conference proceedings was included and the limit for the year of publication was excluded, an initial number of 449 articles was obtained through the keywords presented previously.

Next, to identify and select the final list of studies to be included in this literature review, the following set of inclusion and exclusion criteria were applied by this study’s authors.

Inclusion criteria: the study must report the use of IoT technology; the study must include answers for at least one of the research questions; the study had to be performed in an office setting.

Exclusion criteria: technological paper; studies conducted in areas other than office settings (industry, consumer behavior, retail, etc.); professional/company reports; literature review articles.

The 449 studies identified by keyword search were screened using the inclusion and exclusion criteria. By its very nature, a literature review is a research method that is based on secondary data analysis. However, in this research, only research that reported IoT technology in office settings based on primary data were selected. The studies that reported IoT literature review (which are referred to as secondary studies) were excluded.

Thus, a total of 35 articles were identified and their respective lists of references were scanned to identify if any significant work had been overlooked by the keyword search criteria (backward literature search).

When a new reference was identified, it was included in this literature review list, and through Google Scholar its author’s list of publications was examined (forward author's search) to identify new relevant literature. Finally, Google Scholar was used to find articles that have mentioned the studies included in our reading list in their own references (forward references search). If these studies were relevant to this research, they were also included.

Through these procedures, six additional studies were identified and screened by the inclusion and exclusion criteria. Therefore, in all, 41 articles published between 2003 and 2019 were included in this literature review (Table I). Among them, 23 studies were published in peer-reviewed journals (56 per cent) and 18 were published in conference proceedings (44 per cent).

Regarding the year of publication of these studies, between 2003 and 2010, only 8 studies were identified (50 per cent published in peer-reviewed journals, and 50 per cent in conference proceedings). From 2012 to 2019, the number of publications identified was multiplied by around 4 to reach 33 studies (58 per cent published in peer-reviewed journals, and 42 per cent in conference proceedings).

Among the studies published in peer-reviewed journals, only 7 studies (17 per cent) were published in peer-reviewed management journals. The remaining studies were published in computer-related journals. Regarding the studies published in conference proceedings, all of them were published in technical and engineering conferences.

The potential for IoT applications is enormous and seems boundless. However, IoT applications are not adequately represented in the field of management research (Whitmore et al., 2015). Even popular IoT literature review articles do not present examples of IoT application in the workplace environment (Atzori et al., 2010; Bandyopadhyay and Sen, 2011; Miorandi et al., 2012; Whitmore et al., 2015; Mishra et al., 2016). A recent IoT literature review illustrates this situation aptly. It analyzed 102 studies concerning smartphone sensor data applications, and among them, only one study was about issues related to IoT technology applied in the workplace environment (Kamilaris and Pitsillides, 2016).

About 31 out of 41 studies identified by the literature search report research related to the use of IoT devices in the workplace environment. The remaining 10 studies report surveys (questionnaires) related to user-experience and perspectives for the application of IoT technology in office settings (Table AI in Appendix).

In conclusion, even though there is a consensus about the benefits attributed to IoT technology deployment in office settings, research related to this topic is still very limited. In addition, a significant proportion of the research concerning the use of IoT technology in the workplace environment has been published in conference proceedings and technology-related journals (Table II).

Findings from literature

RQ1: How has Internet of Things technology been applied in workplace settings, which topics are most frequently addressed and how much do they contribute to the workplace research field?

The 31 studies reporting IoT application in office settings can be divided into two groups. The first group includes research using IoT technology to study the relationship between the characteristics of the physical environment and measures of workspace effectiveness. It represents 19 per cent of the studies.

The second group is “employee-centered” and includes mainly research reporting the use of IoT technology to identify employees’ social behavior, health and emotional states associated with productivity measures. Employees’ experience with the use of IoT devices in the workplace environment and its contribution to improving productivity levels also figure in this group of studies. About 81 per cent of the studies reporting IoT use in office settings are employee-centered.

How can the overwhelming number of studies adopting this “employee-centered” perspective and corresponding focus on individual characteristics be explained? Three aspects seem to contribute to this situation. Firstly, most of the studies adopting this perspective use wearable devices and people are attracted by self-tracking especially if this action relates to wellness and health (Neff and Nafus, 2016). Secondly, smart objects like smartphones and other wearable devices (accelerometers, pedometers, sociometric badges, etc.) are simple to use/wear, and most of the time office employees do not need to do anything to collect the data (no questionnaire to answer, etc.). The last aspect is related to the type and the volume of data collected through IoT wearable devices (body temperature, steps, etc.). They are less vulnerable to subjectivity (not based on self-report measures) and enlarge the options of methodologies for data analysis (algorithms, big data, etc.) while disclosing new relationships (for example, face-to-face interaction and employee productivity). All these aspects contribute to the deployment of IoT technology in office settings, and the corresponding number of studies adopting this “employee-centered” perspective.

The physical environment and workspace effectiveness.

Through devices like sensor networks and sociometric badges, IoT technology was used to evaluate how office design and the characteristics of the physical environment contribute to better use of the space.

Employee location and office occupancy rates.

A promising application of IoT technology is related to the measurement of office occupation rates and the evaluation of the occupants’ real use of the workspace. RFID technology (radio frequency identification) and Bluetooth proximity sensing have been used to monitor employees’ location and building occupancy rates of activity-based offices (Clark et al., 2018; Ianeva et al., 2015). Through RFID tags inserted into employees’ badges, a study collected real-time data about the occupation of different workspaces (meeting rooms, bubbles, workspaces, etc.) and user category (employee, visitor, or trainee). The results suggested that there is a gap between the planned and the real use of the shared workspaces, and problems with employees’ acceptance of the new IoT devices (Ianeva et al., 2015).

Workplace design and employee face-to-face interaction.

Data collected by wearable devices are being used to evaluate the impact of workplace design on employees’ face-to-face communication and collaboration (Berstein and Turban, 2018). Wireless tags were used to collect face-to-face interaction and location data of office employees before and after their moving to a new building and also to identify the workplace areas that facilitate face-to-face communication between people of different subgroups (Brown et al., 2014a). Another important research finding is the fact that the building layout and office design are two aspects that can be used to facilitate employees’ face-to-face communication. Office serendipitous interactions are influenced by individuals’ cultural differences and workplace layout. Common areas such as cafeterias and printing areas are important places to facilitate face-to-face communication between employees (Brown et al., 2014a; Brown et al., 2014b).

Employee comfort.

Research has correlated workplace environmental (air temperature, humidity, illuminance, noise and motion) and physiological data (heart data, steps taken, calories burned, activity data, galvanic skin temperature, air temperature and sleep pattern data) with office workers’ self-reported productivity measures (Van der Valk et al., 2015). These results seem to support the expected correlation between comfort and employee productivity.

The studies here analyzed showed that IoT technology can be used to monitor office environmental characteristics and help workplace managers improve building occupiers’ experience through the evaluation of occupancy rates. This technology also contributes to improving workspace effectiveness through the identification of workplace design and environmental characteristics that facilitate employees’ face-to-face communication and comfort.

The “employee-centered” perspective studies.

IoT devices allow collecting and analyze the kind of data that was almost impossible to consider before; employees’ physiological and behavioral data are being collected and used to estimate employees’ emotional states associated with productivity measures.

Employees’ social behavior.

Informal meetings and social interaction are important occasions to enhance employees’ collaboration. In the workplace environment, the most valuable form of communication is face-to-face (Pentland, 2012). Employees’ social behavior can be measured and better understood through the use of IoT technology. Sociometric badges (wearable sensors containing microphones, accelerometers and infrared transmitters) are being used to capture employees’ interaction and communication patterns (Pentland, 2012; Kim et al., 2012; Waber et al., 2010). Several studies using sociometric badges showed that the total time spent by employees’ in face-to-face communication is positively correlated with self-reported measures of productivity and job satisfaction (Ara et al., 2008; Wu et al., 2008; Olguín-Olguín et al., 2009). This correlation was also verified when employees’ productivity was evaluated through objective measures (Waber et al., 2010; Dong et al., 2012; Ara et al., 2012; Pentland, 2012). These studies showed that in the workplace environment, employees with high levels of face-to-face communication perform better and present higher levels of job satisfaction.

Employee health.

Employees’ health and wellbeing are associated with productivity measures. Through health programs, companies are investing in wearable devices and IoT applications to improve employees’ health and also to prevent behaviors that could cause health problems (Glance et al., 2016). Based on data extracted from a sensor network attached to the office furnishing, an IoT application was designed to encourage adequate work-sitting posture and prevent employees' postural injuries and ensuing productivity losses (Roossien et al., 2017).

Employee emotional states.

Stress deteriorates employee physical and mental health and decreases productivity. IoT technology can be a valuable tool to identify and manage job stress. Research using smartphone data obtained good accuracy rates on detecting employees’ stress, and these rates rise when smartphone data is combined with wearable device data (Maxhuni et al., 2016; Muaremi et al., 2013). Research using accelerometer data obtained an accuracy rate of 71 per cent when detecting employee stress (Garcia-Ceja et al., 2015). Using wearable devices to collect employees’ physiological data, another study obtained accuracy rates varying from 84 to 94 per cent in their estimates of employees' stress levels (Han et al., 2017). Combining wearable sensor devices data with employee MS Outlook calendar information, a study found a correlation between physiological stress, events on employees work diaries and the results of a short online survey (Bakker et al., 2012).

Mood and happiness are two important contributors to employees’ productivity that can be estimated and monitored through IoT technology. Using employees’ physiological data (heart rate, body temperature, acceleration features, pulse rate, etc.) and physical activity levels obtained from wearable devices makes it possible to predict mood and happiness at quite accurate rates. For instance, compared with self-reported measures the Zenonos et al. (2016) study obtained a 70.6 per cent rate of accuracy in mood identification. The study by Yano et al. (2015) found a strong correlation (r = 0.92) between patterns of employees’ physical activity and employee self-reported measures of happiness. According to the authors of the study, employees with above-average happiness are 34 per cent more productive than those with below-average happiness. In addition, the happiness level (estimated through physical activity patterns) and employees’ productivity were both higher on the days when employees’ face-to-face communication was higher (Yano et al., 2012, 2015).

The following section presents the findings that show how IoT technology improves employees’ productivity.

RQ2: What is the evidence that Internet of Things technology improves office workers’ productivity?

Internet of Things technology support for employee productivity.

The studies mentioned previously showed that sensor networks and wearable devices are largely used to study the patterns of workers’ physical movements and social behavior (face-to-face interactions) and their respective relationship with employees’ productivity. However, this literature review found only two studies focusing specifically on IoT technology as a tool to improve employees’ productivity in office settings.

Based on a Wi-Fi sensor network, an assistive technology platform (composed of a smart editor, smart communication, smart help, and a behavior and analysis tool) was specially developed for supporting office workers with disabilities. Through self-reported measures, 88 per cent of the platform users agreed that the assistive technology made them more productive (Kabar et al., 2016).

Sensor devices data were used to better distribute desks in a commercial building. Based on the premise that productivity seems to be greater when people working on the same project are in close proximity, an algorithm decided where each employee should sit. The study results showed that compared with random and single desk locations, the algorithm improved seating locations by approximately 2.8 with a conservative estimation of increase in workers’ productivity level between 0.1 per cent and 5 per cent (manager evaluation) (Cooper et al., 2017).

Even though only two examples were identified, the potential of IoT technology to improve employees’ productivity seems very promising. Indeed, this literature review showed that IoT technology is being used to help workplace managers identify variables and behaviors that correlate with employees’ productivity, disclosing situations and/or behaviors conducive to productivity improvement. However, some studies found that this action is not without problems, especially those related to employees’ privacy concerns.

The next section presents the studies reporting employees’ privacy concerns with the use of IoT technology in office settings and a discussion of the benefits and risks associated with the utilization of IoT data.

RQ3: What are the benefits and risks associated with the utilization of the Internet of Things technology in the workplace?

User experience and risks associated with Internet of Things data.

Through employees’ interaction with smart objects (tablets, smartphones) and sensor network location tracking, IoT applications can report employees’ presence or absence in the workplace and also provide information about their activities (coffee, be right back, lunch, do not disturb, in a meeting, gone for the day). In general, office employees found it very useful to have as much information as possible about colleagues’ location and status; however, they are less comfortable with sharing their own data (Lai et al., 2003; Efstratiou et al., 2012). Besides that office employees feel discomfort with the possibility of other people watching them interacting with IoT devices in the workplace environment (Röcker, 2009a) and they show a large preference for inputting data anonymously (Mathur et al., 2015a, 2015b).

A study evaluating office employees’ attitude toward smart office applications found that they would not expect these applications to help them with their productivity levels, nor to provide them valuable support in their daily tasks (Röcker, 2009a). Furthermore, workplace safety and building operational efficiency are the main reasons for the acceptance of wearable devices in the office environment (Jacobs et al., 2019; Shabha, 2007). Employees’ privacy concerns appear when they feel compelled to share their own data (Lai et al., 2003; Efstratiou et al., 2012). In addition, when their data are captured automatically by the IoT applications, office workers show reluctance to provide context information (different types of information considered as personal or those that could be misinterpreted). The willingness to provide context information differs according to employees’ cultural backgrounds and the degree of their informatics knowledge (Röcker, 2009a). However, employees’ attitudes toward IoT technology is positive in specific situations.

Companies’ health programs that provide self-tracking wearable devices are well perceived by office employees (Gorn and Shklovski, 2016; Chung et al., 2017) and only a small number of them express privacy concerns about sharing their information with their employer and/or other colleagues. Employees express privacy concerns only when they feel monitoring encroaches upon their private life (Gorn and Shklovski, 2016). However, the fact that companies have access to employees physiological data could motivate the emergence of a “wellness syndrome” since it could open the door to employee biometric surveillance or penalize those employees that decide to opt out of such wellness programs (Moore and Piwek, 2017). Some companies even embed radio-frequency identification into workers themselves (for instance, Citywatcher.com in 2006), a practice that raises questions about workplace surveillance (Moore and Robinson, 2016; Moore and Piwek, 2017).

Taken together, these studies showed that employees’ acceptance of IoT technology is nuanced by privacy concerns and perceived value. They find the information they can get about other colleagues useful and valuable. However, they do not feel comfortable with the idea of sharing their personal information or other kinds of information that could be perceived negatively by others. Collecting data anonymously is very important to generalize employees’ acceptance of IoT technology in the workplace environment.

In addition to privacy concerns, the use of IoT wearable devices in office settings raises questions about employees access to the data they have generated themselves. In most cases, the owner of IoT data is neither the employee who generates it, nor the employer, but the company that produces the IoT device (Neff and Nafus, 2016). Most of the time, these companies give only limited access to the data. In general, employees participating in a company health program can see the number of steps they have taken; however, they are not allowed to download this data. The reason for this lies in the economic value of the data. For instance, with the use of a specific algorithm, accelerometer data can reveal the possible presence of Parkinson’s disease (Neff and Nafus, 2016). This kind of information could improve the quality of people’s lives (Lee and Lee, 2015). Supposing that the IoT device were a smartphone, it would not be difficult to re-individualize the accelerometer data. Besides being of interest for the person who produced the data, this information could also interest the employer (interested in preventing employee sick leave), insurance companies (health insurance costs could rise), and even the pharmaceutical industry (marketing and selling drugs to people with Parkinson’s disease). Therefore, it will be an enormous challenge to protect employees’ data since it is possible to infer more personal information from seemingly innocuous data like the number of steps an employee takes (Neff and Nafus, 2016). Table III synthesizes the benefits and the risks associated with the use of IoT technology in office settings.

Perspectives and limits for Internet of Things application in workplace environment

Advances in IoT technology have accelerated changes in the workplace environment. Through better-quality information and speedy information retrieval, IoT technology has a promising potential to improve employees’ productivity (Shabha, 2006; Attaran, 2017) and even become an assistive agent in the decision making process of employees (Fort et al., 2016).

To increase companies’ interest and application of IoT technologies in office settings some aspects require further research development. Sensor devices generate a very large volume of multidimensional data and making sense of these sets of information is a major challenge. The use of sophisticated algorithms, data mining and machine learning techniques capable of analyzing unstructured images, voice and video data must be developed to popularize IoT technology in the context of workplace management (Cook et al., 2009; Lee and Lee, 2015; Guo et al., 2013).

The majority of the studies analyzed here are based on correlation. The IoT data reveals that employees’ face-to-face interactions and respective physical movement patterns are significantly correlated with productivity measures (self-reported and/or objective). However, understanding and explaining why these correlations occur in the workplace environment depend on the availability of a greater number of theoretical foundations. More research on how IoT devices can improve office workers productivity is needed to fill this literature gap.

Another important challenge to overcome is the limited number of studies in workplace management that examines the impact of IoT technology on employees’ productivity and business performance. The majority of the studies analyzed here, come mainly from technological areas. In addition, most of the studies concerning IoT technology in the workplace are based on employees’ self-reported measures of productivity. The development of more objective measures would help to estimate the real contribution of IoT technology and also foster its deployment in more companies.

The use of wearable sensor devices like sociometric badges to measure interaction patterns among office employees could be a convenient start. Despite the fact that this related body of research is somewhat limited, the Chaffin et al. (2017) study evaluated the value of wearable sensors use in social behavior research. In two out of four studies, they found strong convergence in the co-location networks generated by Bluetooth data and those obtained by survey measures. Even though this convergence was not perfect, it seems that it offers some potential to identify employees’ communication networks in the workplace environment. Moreover, the authors highlight the need for organizational researchers to take an active role in the development of wearable sensor systems to ensure that the measures derived from IoT devices lead to the improvement of the workplace management field (Chaffin et al., 2017).

The studies analyzed here show that employees’ awareness of the benefits and risks associated with IoT technology are only partial. Even though office employees’ judge that anonymous data is important, no study mentioned employees’ awareness of the economic value of the data generated by them nor raised questions about data ownership.

Thus, employees’ attitudes and acceptance of IoT devices, their respective privacy concerns, and the question of data ownership are important aspects that workplace managers must consider. Greater transparency about the use of employees’ data is the key to their acceptance of IoT technology in office settings (Khakurel et al., 2018). Workplace managers must be clear about the objectives and the time of use of employees’ data, and most importantly, they should make sure that all employees can control the information they share with the company. If employees do not accept it, the future of IoT technology application in the workplace environment seems to be limited and uncertain.

Another aspect that adds complexity to the management of employees’ privacy concerns is the length of time IoT technology remains in use in the office setting. A one-time IoT experiment requires less complex data protection policies than ongoing IoT monitoring tools. Among the studies analyzed here, only one mentioned IoT utilization on a long-term basis (Ianeva et al., 2015). To ensure its continuity, the authors of the study took measures to improve employees’ acceptance of sociometric badges. Regarding the studies mentioning one-time IoT experiments (Brown et al., 2014a, 2014b; Cooper et al., 2017; Berstein and Turban, 2018), the period of IoT use varied from one-single day to 4 weeks.

All these challenges should be addressed for IoT technology to spread and become popular in the workplace environment.

Conclusion

This literature review study identified only 41 studies and a significant proportion of them were published in conference procedures. About 31 studies reported the application of IoT technology in office settings. Among those, only two studies showed how IoT technology can be deployed to improve productivity. The remaining studies were used to identify and measure variables correlated with productivity (for instance, face-to-face interaction, mood and happiness). In those studies, while IoT technology was not shown to improve employee productivity levels, it did disclose situations and/or behaviors conducive to increasing employee productivity.

Previous IoT literature review studies made an important contribution to the knowledge of smart building, smart office and environmental monitoring areas (Atzori et al., 2010; Miorandi et al., 2012; Gubbi et al., 2013). However, the impact of IoT technology on office employees’ productivity was addressed less frequently in these studies. In this sense, the present study complements these studies with a novel perspective by adding employees' perceptions and attitudes toward IoT technology in office settings, and the discussion of the risks associated with the use of employees’ data, and questions about data ownership.

Even though few studies broach the subject of IoT technology application in workplace management, this literature review shows that there are promising results that can be used to improve employee performance and productivity. The most important findings to emerge from this literature review are the relationship between face-to-face communications and workers’ productivity and the fact that IoT technology can be used to gain deeper insights into employees’ social behaviors.

The growing importance of real estate costs and the need for better use of company spaces have made office design a strategic resource for business development. Different office types have emerged and all of them are supposed to provide adequate work conditions and promote workplace productivity. Workplace research has already focused on this topic (De Been and Beijer, 2014; Lee, 2006). However, the inception of IoT technology opens up new perspectives for the improvement of workspace effectiveness. Future studies could use IoT wearable devices data to identify optimal physical environment conditions (temperature, lighting, indoor air, etc.) favoring employees’ concentration and performance. Which combination of temperature and lighting leads to high concentration levels? This is the kind of question that could be answered by adopting this technology.

Job stress is a serious problem and has important consequences for employees’ health and productivity. Workplace researchers could evaluate the use of IoT wearable devices to monitor office workers stress-response (increase in the heart rate, quickening of breathing, tightening of muscles, and rises in blood pressure). Through IoT data, when this chemical reaction starts, it would be possible to alert the employee to take a small break, or start another task, etc.

However, the absence of methodology to analyze the volume of data produced by IoT devices can also reduce its popularization in the Corporate Real Estate Management area. Technical aspects related to the development and deployment of IoT sensor networks in the workplace, the need for expertise in large data settings and the complexity of the data being gathered can limit its adoption by a large number of companies.

This literature review has identified primordial aspects that must be considered by companies desiring to deploy IoT technology in office settings: employees’ attitudes toward IoT technology, employees’ awareness of the benefits and the risks associated with the use of this technology in office settings, and employees’ control of the data they generate.

Finally, employees’ privacy concerns, data ownership and risks associated with IoT devices in the workplace environment must be considered by researchers and workplace managers. This is a question that remains contentious and must be addressed before IoT technology becomes a mainstream feature in office settings.

List of the 41 studies included in the literature review

Publication type Journal/conference name Author(s)
Peer-reviewed journal (23 studies; 56%) Applied Ergonomics Roossien et al. (2017)
Applied Ergonomics Jacobs et al. (2019)
BioNanoSci Muaremi et al. (2013)
Business Horizons* Lee and Lee (2015)
Computers in industry Han et al. (2017)
Employee Relations* Moore and Piwek (2017)
Facilities* Cooper et al. (2017)
Facilities* Shabha (2006)
Havard Business Review* Pentland (2012)
IEEE Journal of Biomedical and Health Informatics Garcia-Ceja et al. (2015)
IEEE SPECTRUM Yano et al. (2012)
IEEE Transactions on Systems, Man, And Cybernectics Olguín-Olguín et al. (2009)
International Journal Distributed Sensor Networks Kabar et al. (2016)
Journal of Biomedical Informatics Maxhuni et al. (2016)
Journal of Facilities Managment* Shabha (2007)
Journal of Information Processing Ara et al. (2008)
Journal of Information Processing Ara et al. (2012)
Journal of Organizational Behavior** Kim et al. (2012)
New Media and Society Moore and Robinson (2016)
Northwestern Journal of Technology and Intellectual Property Fort et al. (2016)
Phil. Trans. R. Soc Berstein and Turban (2018)
PLOS ONE Clark et al. (2018)
World Wide Web Guo et al. (2013)
Conference proceedings (18 studies; 44%) ACM Conference on Computer Supported Cooperative Brown et al. (2014a)
ACM Conference on Computer Supported Cooperative Brown et al. (2014b)
ACM International Joint Conference on Pervasive and Ubiquitous Computing Mathur et al. (2015b)
ACM SIGHIT International Health Informatics Symposium Bakker et al. (2012)
CHI Conference on Human Factors in Computer Systems Gorn and Shklovski (2016)
IEEE Tenth International Conference Van der Valk (2015)
IFIP Conference on Human-Computer Interaction (INTERACT) Lai et al. (2003)
Int. Conf. Pervasive Computing Efstratiou et al. (2012)
International Conference on Digital Health Conference - DH'16 Glance et al. (2016)
International Conference on Information Systems 2008 Proceedings, 127 Wu et al. (2008)
International Conference on Mobile Systems, Applications and Services Mathur et al. (2015a)
International Conference on Pervasive Computing and Communication Workshops Zenonos et al. (2016)
International Conference on Work with Computer Systems Röcker (2009a)
Ninth International Conference on Wearable and Implantable Body Sensor Networks Dong et al. (2012)
Proceedings of the 30th International Sunbelt Social Network Conference (SSNC'10) Waber et al. (2010)
Proceedings of the HWID conference Ianeva et al. (2015)
SIGCHI Conference on Human Factors in Computing Systems (CHI'17) Chung et al. (2017)
Symposium on Very Large Scale Integration, Digest of Technical Papers Yano et al. (2015)
Note:

*Peer-reviewed management journal

The 31 studies reporting IoT technology use in the workplace environment

Group Topic Study Measure/estimate IoT device
Workspace effectiveness (6 studies; 19%) Physical environment (6 studies; 19%) Brown et al. (2014a) Workplace areas and face-to-face interaction Sociometric badge
Brown et al. (2014b) Workplace design and face-to-face interaction Sociometric badge
Ianeva et al. (2015) Activity-based office occupation rates Sociometric badge
Van der Valk et al. (2015) Correlate comfort and productivity Sensor data and wearable
Berstein and Turban (2018) Workplace design and face-to-face interaction Sociometric badge
Clark et al. (2018) Employee location in office settings Sensor network
Employee-centered (25 studies; 81%) Social behavior (8 studies; 26%) Ara et al. (2008) Face-to-face interaction Sociometric badge
Wu et al. (2008) Face-to-face interaction Sociometric badge
Olguín-Olguín et al. (2009) Face-to-face interaction Sociometric badge
Waber et al. (2010) Face-to-face interaction Sociometric badge
Ara et al. (2012) Face-to-face interaction Sociometric badge
Dong et al. (2012) Face-to-face interaction Sociometric badge
Kim et al. (2012) Face-to-face interaction Sociometric badge
Pentland (2012) Face-to-face interaction Sociometric badge
Health (2 studies; 6.5%) Glance et al. (2016) Health measures (blood glucose, renal function, etc.) Wearable activity tracker
Roossien et al. (2017) Sitting behavior Smart chair
Emotional states (8 studies; 26%) Bakker et al. (2012) Stress GSR and accelerometer
Yano et al. (2012) Happiness Sociometric badge
Muaremi et al. (2013) Stress Smartphone and wearable
Yano et al. (2015) Happiness Sociometric badge
Garcia-Ceja et al. (2015) Stress Smartphone
Maxhuni et al. (2016) Stress Smartphone
Zenonos et al. (2016) Mood Smartphone and wearable
Han et al. (2017) Stress Wearable device
Support for productivity (2 studies; 6.5%) Kabar et al. (2016) Smart interface to assist people with disabilities Sensor network
Cooper et al. (2017) Hot desking allocation Sensor network
User experience (5 studies; 16%) Lai et al. (2003) IoT user experience and privacy Sensor network
Efstratiou et al. (2012) IoT user experience and privacy Sensor network
Mathur et al. (2015a) IoT user experience and privacy Sensor data and wearable
Mathur et al. (2015b) IoT user experience and privacy Sensor data and wearable
Gorn and Shklovski (2016) IoT user experience and privacy Pedometers

Benefits and the risks associated with the use of IoT technology in office settings

Benefits Risks
Real-time data with speedy information retrieval Employee discomfort in sharing his/her data, threats to employee private life
Access to a new range of employees' data Employee limited control of the information shared with the company and coworkers
Can help to improve employees' health Employee biometric surveillance
Allows the identification of unexpected associations (for example, face-to-face interaction and employee productivity) The economic value of the data can motivate the re-individualization of the employee (IoT) data
Employee low perceived value and privacy concerns can lead to refusal of IoT applications in office settings

Additional studies concerning the IoT application in office settings

Topic Sub-topic Study
User experience IoT user experience Shabha (2007)
IoT user acceptance Röcker (2009a)
IoT user experience and privacy Lee and Lee (2015)
Risks and ethical issues Moore and Robinson (2016)
Risks and ethical issues Moore and Piwek (2017)
IoT user experience and privacy Chung et al. (2017)
IoT user experience and privacy Jacobs et al. (2019)
IoT perspectives Perspectives for IoT application Shabha (2006)
Perspectives for IoT application Fort et al. (2016)
Perspectives for IoT application Guo et al. (2013)

Appendix

Table AI

References

Appel-Meulenbroek, R. and Feijts, B. (2007), “CRE effects on organizational performance: measurement tools for management”, Journal of Corporate Real Estate, Vol. 9 No. 4, pp. 218-238.

Appel-Meulenbroek, R., Groenen, P. and Janssen, I. (2011), “An end-user’s perspective on activity-based office concepts”, Journal of Corporate Real Estate, Vol. 13 No. 2, pp. 122-135.

Ara, K., Akitomi, T., Sato, N., Takahashi, K., Maeda, H., Yano, K. and Yanagisawa, M. (2012), “Integrating wearable sensor technology into project-management process”, Journal of Information Processing, Vol. 20 No. 2, pp. 416-418.

Ara, K., Kanehira, N., Olguín-Olguín, D., Waber, B.N., Kim, T., Mohan, A., Gloor, P., Laubacher, R., Oster, D., Pentland, A.S. and Yano, K. (2008), “Sensible organizations: changing our businesses and work styles through sensor data”, Journal of Information Processing, Vol. 16, pp. 1-12.

Atkin, B. and Bildsten, L. (2017), “A future of facility management”, Construction Innovation, Vol. 17 No. 2, pp. 116-124.

Attaran, M. (2017), “The Internet of Things: limitless opportunities for business and society”, Journal of Strategic Innovation and Sustainability, Vol. 12 No. 1, pp. 10-29.

Atzori, L., Iera, A. and Morabito, G. (2010), “The Internet of Things: a survey”, Computer Networks, Vol. 54 No. 15, pp. 287-2805.

Bakker, J., Holenderski, L., Kocielnik, R., Pechenizkiy, M. and Sidorova, N. (2012), “Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching”, Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI ‘12), ACM, New York, NY, pp. 673-678.

Bandyopadhyay, D. and Sen, J. (2011), “Internet of Things: applications and challenges in technology and standardization”, Wireless Personal Communications, Vol. 58 No. 1, pp. 49-59.

Berstein, E. and Turban, S. (2018), “The impact of the ‘open’ workspace on human collaboration”, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 373 No. 1753, pp. 1-8.

Bortoluzzi, B., Carey, D., McArthur, J.J. and Menassa, C. (2018), “Measurements of workplace productivity in the office context: a systematic review and current industry insights”, Journal of Corporate Real Estate, Vol. 20 No. 4, pp. 281-301.

Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D., Mascolo, C., Scott, J. and Key, P. (2014a), “The architecture of innovations: tracking face-to-face interactions with Ubicomp technologies”, Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (CSCW ‘14), pp. 811-822.

Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D. and Mascolo, C. (2014b), “Tracking serendipitous interactions: how individual cultures shape the office”, Proceedings of the 17th ACM conference on Computer supported cooperative work and social computing (CSCW ‘14), pp. 1072-1081.

Chaffin, D., Heidl, R., Hollenbeck, J.R., Howe, M., Yu, A., Voorhees, C. and Calantone, R. (2017), “The promise and perils of wearable sensors”, Organizational Research Methods, Vol. 20 No. 1, pp. 3-31.

Chung, C.-F., Gorm, N., Shklovski, I. and Munson, S.A. (2017), “Finding the right fit: understanding health tracking in workplace wellness program”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’17), pp. 4875-4886.

Clark, B.K., Winkler, E.A., Brakenridge, C.L., Trost, S.G. and Healy, G.N. (2018), “Using Bluetooth proximity sensing to determine where office workers spend time at work”, PLOS One, Vol. 13 No. 3, pp. 1-15.

Cook, D.J., Augusto, J.C. and Jakkula, V.R. (2009), “Ambient intelligence: technologies, applications, and opportunities”, Pervasive and Mobile Computing, Vol. 5 No. 4, pp. 277-298.

Cooper, P.B., Maraslis, K., Tryfonas, T. and Oikonomou, G. (2017), “An intelligent hot-desking model harnessing the power of occupancy sensing data”, Facilities, Vol. 35 Nos 13/14, pp. 766-786.

De Been, I. and Beijer, M. (2014), “The influence of office type on satisfaction and perceived productivity support”, Journal of Facilities Management, Vol. 22 No. 2, pp. 142-157.

Dong, W., Olguin-Olguin, D., Waber, B., Kim, T. and Pentland, A.S. (2012), “Mapping organizational dynamics with body sensor networks”, Ninth International Conference on Wearable and Implantable Body Sensor Networks (BSN).

Efstratiou, C., Leontiadis, I., Picone, M., Rachuri, K.K., Mascolo, C. and Crowcroft, J. (2012), “Sense and sensibility in a pervasive world”, Proc. 10th International Conference Pervasive Computing, June, pp. 416-424.

Fort, T.L., Raymond, A.H. and Shackelford, S.J. (2016), “The angel on your shoulder: prompting employees to do the right thing through the use of wearables”, Northwestern Journal of Technology and Intellectual Property, Vol. 14 No. 2, pp. 139-170.

Garcia-Ceja, E., Osmani, V. and Mayora, O. (2015), “Automatic stress detection in working environments from smartphones’ accelerometer data: a first step”, IEEE Journal of Biomedical and Health Informatics, Vol. 20 No. 4, pp. 1053-1060, doi: 10.1109/JBHI.2015.2446195.

Ghaffarianhoseini, A., Berardi, H., Alwaer, H., Chang, S., Halawa, E., Ghaffarianhoseini, A. and Clements-Croome, D. (2016), “What is an intelligent building? Analysis of recent interpretations from an international perspective”, Architectural Science Review, Vol. 59 No. 5, pp. 338-357.

Glance, D.G., Ooi, E., Berman, Y., Glance, C.F. and Barrett, H.R. (2016), “Impact of a digital activity tracker-based workplace activity program on health and wellbeing”, Proceedings of the 6th International Conference on Digital Health Conference – DH ‘16, ACM Press, New York, NY, pp. 37-41.

Gorn, N. and Shklovski, I. (2016), “Sharing steps in the workplace: changing privacy concerns over Tim”, CHI Conference on Human Factors in Computer Systems, San José, CA, 7-12 May.

Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. (2013), “Internet of Things (IoT): a vision, architectural elements, and future directions”, Future Generation Computer Systems, Vol. 29 No. 7, pp. 1645-1660.

Guo, B., Zhang, D., Yu, Z., Liang, Y., Wang, Z. and Zhou, X. (2013), “From the internet to embedded intelligence”, World Wide Web, Vol. 16 No. 4, pp. 399-420.

Han, L., Zhang, Q., Chen, X., Zhan, Q., Yang, T. and Zhao, Z. (2017), “Detecting work-related stress with a wearable device”, Computers in Industry, Vol. 90, pp. 42-49.

Hart, C. (1998), Doing a Literature Review: Releasing the Social Science Research Imagination, SAGE, London.

Haynes, B. (2007), “Office productivity: a shift from cost reduction to human contribution”, Facilities, Vol. 25 Nos 11/12, pp. 452-462.

Haynes, B., Sucley, L. and Nunnington, N. (2017), “Workplace productivity and office type – an evaluation of office occupier differences based on age and gender”, Journal of Corporate Real Estate, Vol. 19 No. 2, pp. 111-138.

Ianeva, M., Faure, S., Theveniot, J., Ribeyron, F., Cordon, G. and Gartiser, C. (2015), “Pervasive technologies for smart workplaces: a workplace efficiency solution for office design and building management from an occupier’s perspective”, Proceedings of the HWID conference.

Jacobs, J.V., Hettinger, L.J., Huang, Y.-H., Jeffires, S., Lesch, M.F., Simmons, L.A., Verma, S.K. and Willetts, J.L. (2019), “Employee acceptance of wearable technology in the workplace”, Applied Ergonomics, Vol. 78, pp. 148-156.

Kabar, G., Al-Daraiseh, A., Mien, S.H. and Abidi, M.H. (2016), “Utilizing sensors networks to develop a smart and context-aware solution for people with disabilities at the workplace (design and implementation)”, International of Distributed Sensor Networks, Vol. 12 No. 9, pp. 1-25.

Kamilaris, A. and Pitsillides, A. (2016), “Mobile phone computing and the Internet of Things: a survey”, IEEE Internet of Things Journal, Vol. 3 No. 6, pp. 885-898.

Khakurel, J., Melkas, H. and Porras, J. (2018), “Tapping into the wearable device revolution in the work environment: a systematic review”, Information Technology and People, Vol. 31 No. 3, pp. 791-818.

Kim, T., Mcfee, E., Olguin, D.O., Waber, B. and Pentland, A.S. (2012), “Sociometric badges: using sensor technology to capture new forms of collaboration”, Journal of Organizational Behavior, Vol. 33 No. 3, pp. 412-427.

Lai, J., Yoshihama, S., Bridgman, T., Podlaseck, M., Chou, P. and Danny, W. (2003), “Myteam: availability awareness through the use of sensor data”, Proceedings of the IFIP Conference onHuman-Computer Interaction (INTERACT), pp. 503-510.

Leaman, A. and Bordass, B. (2006), “Productivity in buildings: the ‘killer’ variables”, in Clements-Croome, D. (Ed.), Creating the Productive Workplace, 2nd ed., Taylor and Francis, London.

Lee, I. and Lee, K. (2015), “The Internet of Things (IoT): applications, investments, and challenges for entreprises”, Business Horizons, Vol. 58 No. 4, pp. 431-441.

Lee, S.Y. (2006), “Expectations of employees toward the workplace and environmental satisfaction”, Facilities, Vol. 24 Nos 9/10, pp. 343-353.

Mathur, A., Van den Broeck, M., Vanderhulst, G., Mashhadi, A. and Kawsar, F. (2015a), “Quantified workplace: opportunities and challenges”, The 13th International Conference on Mobile Systems, Applications, and Services, Florence.

Mathur, A., Van Den Broeck, M., Vanderhulst, G., Mashhadi, A. and Kawsar, F. (2015b), “Tiny habits in the giant enterprise: understanding the dynamics of a quantified workplace”, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ‘15), pp. 577-588, doi: 10.1145/2750858.2807528.

Maxhuni, A., Hernandez-Leal, P., Sucar, L.E., Osmani, V., Morales, E.F. and Mayora, O. (2016), “Stress modelling and prediction in presence of scare data”, Journal of Biomedical Informatics, Vol. 63, pp. 344-356.

Miorandi, D., Sicari, S., Pellegrini, F.D. and Lamtac, I. (2012), “Internet of Things: vision, applications and research challenges”, Ad Hoc Networks, Vol. 10 No. 7, pp. 1497-1516.

Mishra, D., Gunasekaran, A., Childe, S., Papadopoulos, T. and Dubey, R. (2016), “Vision, applications and future challenges of Internet of Things: a bibliometric study of the recent literature”, Industrial Management and Data Systems, Vol. 116 No. 7, pp. 1331-1355.

Moore, P. and Robinson, A. (2016), “The quantified self: what counts in the neoliberal workplace”, New Media and Society, Vol. 18 No. 11, pp. 2774-2792.

Moore, P. and Piwek, L. (2017), “Regulating wellbeing in the brave new quantified workplace”, Employee Relations, Vol. 39 No. 3, pp. 308-316.

Muaremi, A., Arnrich, B. and Tröster, G. (2013), “Towards measuring stress with smartphones and wearable devices during workday and sleep”, BioNanoScience, Vol. 3 No. 2, pp. 172-183.

Neff, G. and Nafus, D. (2016), Self-Tracking, The MIT Press, Cambridge, MA, ISBN: 9780262529129.

Olguín-Olguín, D., Waber, B.N., Kim, T., Mohan, A., Ara, K. and Pentland, A. (2009), “Sensible organizations: technology and methodology for automatically measuring organizational behavior”, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol. 39 No. 1, pp. 43-55.

Pentland, A.S. (2012), The New Science of Building Great Teams, Harvard Business review, Brighton, April, pp. 3-11.

Robertson, M.M., Ciriello, V.M. and Garabet, A.M. (2013), “Office ergonomics training and a sit-stand workstation: effects on musculoskeletal and visual symptoms and performance of office workers”, Applied Ergonomics, Vol. 44 No. 1, pp. 73-85.

Röcker, C. (2009a), “Acceptance of future workplace systems: how the social situation influences the usage intention of ambient intelligence technologies in work environments”, Proceedings of the 2009 International Conference on Work with Computer Systems (WWWCV’09).

Röcker, C. (2009b), “Perceived usefulness and perceived ease-of-use of ambient intelligence applications in office environments”, in Kurosu, M. (Ed.), Human Centered Design. HCD 2009. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, Vol. 5619.

Roossien, C.C., Stegenga, J., Hodselmans, A.P., Spook, S.M., Koolhaas, W., Brouwer, S., Verkerke, G.J. and Reneman, M.F. (2017), “Can a smart chair improve the sitting behavior of office workers?”, Applied Ergonomics, Vol. 65, pp. 355-361.

Saiz, A. and Salazar, A. (2017), “Real trends: the future of real estate in the United States”, Capital One Banking/Massachusetts Institute of Technology, SSRN, available at: https://ssrn.com/abstract=3100934

Seddigh, A., Stenfors, C., Berntsson, E., Baath, R., Sikström, S. and Westerlum, H. (2015), “The association between office design and performance on demanding cognitive tasks”, Journal of Environmental Psychology, Vol. 42, pp. 171-181.

Shabha, G. (2006), “A critical review of the impact of embedded smart sensors on productivity in the workplace”, Facilities, Vol. 24 Nos 13/14, pp. 538-549.

Shabha, G. (2007), “An assessment of the effectiveness of embedded smart sensors on users’ performance in the workplace”, Journal of Facilities Management, Vol. 5 No. 3, pp. 179-187.

Steiner, J. (2005), “The art of space management: planning flexible workspaces for people”, Journal of Facilities Management, Vol. 4 No. 1, pp. 6-22.

Sundmaeker, H., Guillemin, P., Friess, P. and Woelfflé, S. (2010), “Vision and challenges for realizing the Internet of Things”, Cluster of European Research Projects on the Internet of Things, European Commision, Vol. 3 No. 3, pp. 34-36, doi: 10.2759/26127, ISBN 978-92-79-15088-3.

Van der Valk, S., Myers, T., Atkinson, I. and Mohring, K. (2015), “Sensor networks in workplaces: correlation comfort and productivity”, Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE Tenth International Conference on April 2015, pp. 1-6.

Waber, B., Olguín-Olguin, D., Kim, T. and Pentland, A. (2010), “Productivity through coffee breaks: changing social networks by changing break structures”, Proceedings of the 30th International Sunbelt Social Network Conference (SSNC’10).

Ward, D. (2016), “The smart way to use corporate real estate”, Corporate Real Estate Journal, Vol. 5 No. 3, pp. 205-212.

Whitmore, A., Agarwal, A. and Xu, L.D. (2015), “The Internet of Things – a survey of topics and trends”, Information Systems Frontiers, Vol. 17 No. 2, pp. 261-274.

Wu, L., Waber, B., Aral, S., Brynjolfsson, E. and Pentland, A.S. (2008), “Mining face-to-face interaction networks using sociometric badges: predicting productivity in an IT configuration task”, International Conference on Information Systems 2008 Proceedings, p. 127.

Yano, K., Lyubomirsky, S. and Chancellor, J. (2012), “Sensing happiness”, IEEE Spectrum, Vol. 49 No. 12, pp. 26-31.

Yano, K., Akitomi, T., Ara, K., Watanabe, J., Tsuji, S., Sato, N., Hayakawa, M. and Moriwaki, N. (2015), “Profiting from IoT: the key is very-large-scale happiness integration”, Proceedings of the Symposium on Very Large Scale Integration, Digest of Technical Papers, Kyoto, June, pp. 15-18, pp. C24-C27.

Zenonos, A., Han, A., Kalogridis, G., Vatsikas, S., Lewis, T. and Sooriyabandara, M. (2016), “HealthyOffice: mood recognition at work using smartphones and wearable sensors”, Proceedings of the International Conference on Pervasive Computing and Communication Workshops, IEEE, pp. 1-6.

Further reading

Cook, D.J. and Das, S.K. (2007), “How smart are our environments? An updated look at the state of the art”, Pervasive and Mobile Computing, Vol. 3 No. 2, pp. 53-77.

Corresponding author

Gisele de Campos Ribeiro can be contacted at: decamposribeiro@essec.edu

Related articles