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A SWOT Analysis Relating the Internet of Things to Designing Effective HR Performance Management Systems
Introduction
In the history of work and human resources (HR), the New Lanark textile mill community managed by the entrepreneur Robert Owen in nineteenth-century Scotland is frequently looked back on as a pioneering and progressive community form of organization, given its emphasis on valuing employee education and creating fair working conditions for all (Donkin, 2010). In this utopian integration of the industrial and the social, Owen introduced a colored wooden block suspended near each worker’s station called a ‘Silent Monitor’ to indicate their performance. On each of the four sides of the block was a different color (white, yellow, blue and black), and the color turned to the front reflected the assessed level of performance from the previous day, a record of which was also kept in a ‘book of character’ (Donkin, 2010).
Two centuries on from this historical example of New Lanark, organizations are still greatly invested in monitoring, managing and developing the performance of employees as successfully as possible. The purpose of this chapter therefore is to investigate how the emerging technological trend of the ‘Internet of Things’ (IoT) is likely to affect the HR processes and practices involved in employees’ performance management, where the wooden blocks and paper ledgers of the nineteenth century are replaced by the digital, wireless, interconnected sensors and devices of the twenty-first.
IoT at its simplest reflects “the possibility of connecting various physical objects (‘things’) to the Internet… [that] can exchange information and interact with each other… [and] become ‘smart things’ that can behave autonomously [in ways] appropriate to the context and the situation” (Strohmeier, Franca, Majstorovic, & Schreiner, 2016, p. 5). The IoT vision for the next generation of the Internet becomes grander as more physical objects worldwide become digitally connected to the Internet and each other, and are able to intelligently and autonomously control and configure themselves and their environments (Li, Xu, & Zhao, 2015).
In the workplaces where technological trends such as IoT will continue to have an impact on employees, behind any broad discussion of talent management or HR strategy, there needs to be some consideration of the performance management systems and processes in place within organizations. Performance management concerns the “continuous process of identifying, measuring, and developing the performance of individuals and teams and aligning performance with the strategic goals of the organization” (Aguinis & Pierce, 2008, pp. 139–140).
Performance management is often viewed skeptically and narrowly in terms of ‘performance appraisal’, which is only a relatively nonstrategic meeting, typically once a year, to describe an employee’s strengths and weaknesses (Aguinis & Pierce, 2008). Annual performance appraisal meetings with employees are now widely considered as outdated, inadequate and a bureaucratic waste of time (Ewenstein, Hancock, & Komm, 2016). The IoT can thus play a role in building more sophisticated, scientific, open, fair, continuous and inclusive performance systems with multiple raters, ratings, sources of data and feedback, with links to rewards at all levels, and adjustments to fit aspects of strategic and international business contexts (Ewenstein et al., 2016).
Some organizations are using more dynamic, continuous forms of performance coaching supported by technology in the form of apps and crowd-sourcing to collect performance data in real time, for example (Ewenstein et al., 2016). In terms of IoT, the question becomes how various forms of digitally connected, data-driven objects can contribute usefully and appropriately to these processes, with advantages and drawbacks being anticipated and navigated accordingly.
Thus far, however, there is almost no work addressing the IoT in relation to HR practices and strategies, despite the broader applications of the IoT and the use of smart technologies in the workplace (e.g. Kim, Nussbaum, & Gabbard, 2016; Da Xu, He, & Li, 2014). One recent exception is work by Stefan Strohmeier and colleagues (2016), who conducted a Delphi study with 37 academic and practitioner experts in HR or human resource information systems (HRIS), which confirmed a range of general expectations that the IoT will lead to major changes. These included greater collection of employee data through sensors, more technically integrated interactions between novel objects and existing HR software and the continuing automation of administrative HR work and positions.
Given the limited work to date, this chapter aims to understand the potential positive and negative relationships between IoT and performance management, as well as constructive actions that HR functions can take to engage and address such relationships. To some extent, this requires creative theory building, drawing together some relevant strands of existing work concerning the IoT, performance management, electronic-HR (e-HR), digital sociology and urban informatics to extrapolate and imagine how the IoT might affect future work environments and HR practices.
Many frameworks around the IoT are emerging, with some expanding the acronym into Internet of People, Things and Services (IoPTS), as a reminder that as well as the ‘things’ or objects themselves, there are people interacting through them and services being provided across them (Eloff, Eloff, Dlamini, & Zielinski, 2009). Such frameworks can fragment the field, but where there are common and complementary factors, they are useful for guiding inquiry.
This chapter uses three IoT frameworks to guide its SWOT (strengths, weaknesses, opportunities, threats) analysis of the key factors affecting how effectively the IoT and performance management can fit together as part of a ‘smart’ HR performance management system. First, Eloff and colleagues (2009) propose a three-dimensional model of IoPTS based around configurations of different aspects of privacy, trust and security in any given system. Second, Wilson, Shah, and Whipple (2015) break down emerging IoT usage into four areas of security, self-quantification, machine optimization and enhanced experiences. Finally, Miorandi, Sicari, De Pellegrini, and Chlamtac (2012) propose four main IoT research areas: security; computing, communication, identification; distributed systems; and distributed intelligence. They also note six critical domains of IoT application: smart homes/buildings, smart cities, environmental monitoring, health care, smart inventory/product management, and security and surveillance.
Following a SWOT analysis guided by these frameworks, the chapter concludes with several implications for future HR research and IoT-supported performance management practice. The aim of the chapter is to argue that the IoT builds on existing strengths and weaknesses of technological and HR systems in organizations, but also extends toward opportunities and threats for HR and performance management along a longer-term horizon.
SWOT Analysis
This chapter has chosen SWOT analysis, over and above other frameworks, to unpack the IoT in relation to HR strategy, given the tool’s exploratory, flexible and balanced nature in representing a current technological trend, as well as its heuristic value for guiding practical risk and value pursuits in organizations. SWOT analysis is a popular structured planning tool for assessing the strategic fit of an organization or other venture with its external environment (Chermack & Kasshanna, 2007). Surveys examining consultants’ use of SWOT have criticized the framework for being too general as to verge on being meaningless, creating excessively long lists of factors, lacking in prioritization and not connecting properly with latter stages of a strategic implementation process (Hill & Westbrook, 1997).
However, the use of SWOT in the current chapter is justified precisely because these criticisms indicate oversimplifications or forms of misuse of SWOT (Chermack & Kasshanna, 2007) and even suggest constructive suggestions about how to deploy it more effectively. Weihrich (1982) has argued that SWOT can be applied most fruitfully when factors are clearly prioritized, it is mapped to features of the wider context, used in conjunction with other tools, used repeatedly over time and the dynamic interrelationships between specific factors in the SWOT quadrants are considered more systematically. Similarly, Chermack and Kasshanna (2007) argue that the effectiveness of SWOT depends on whether it is implemented in an open, unbiased fashion as part of a broader developmental process (not entirely unlike performance management itself).
This chapter will therefore continue by using SWOT to proceed with its project of identifying major inherent strengths and weaknesses (SW) of IoT and performance management practice internal to organizations and relating them to opportunities and threats (OT) in broader external environments. In turn, this allows discrete potential actions and points of guidance for HR and managerial decision makers to be derived.
SWOT can help to dictate or inform IoT/IoPTS strategy in HR by outlining a holistic set of practices and domains, negative and positive, as well as how practitioners can ensure strengths are ‘matched’ to opportunities, and weaknesses and threats ‘converted’ to strengths and opportunities, respectively (Piercy & Giles, 1989). In line with Eloff and colleagues’ (2009) IoPTS framework, the SWOT here seeks to address all three dimensions of security, trust and privacy in relation to HR and performance management. Organizations need to match existing employee data security practices to opportunities to improve and convert them away from threats. Organizations should leverage employee trust as a strength where it already exists as a resource and build it up where it is weaker or lacking. Finally, existing privacy practices may serve as strengths, but could easily become threats if organizations do not anticipate technological change and manage risks and upscale proactively.
The following sections correspond to the four quadrants of the SWOT tool, and in each case three major factors are prioritized in relation to IoT and performance management. The SWOT analysis can thus help organizations avoid pitfalls in terms of understanding the current risks and limitations of IoT trends and developing the right capabilities and architecture for running IoT systems efficiently and effectively in the delivery of HR services. In particular, performance management HR practices can be enhanced via greater information processing to aid better-quality decision making, employee and line manager empowerment and more seamless interconnectivity across teams and distributed, diverse workforces. The SW aspect of the analysis helps to engage the current status of capabilities, whereas the OT aspect helps to trace possible evolutionary trajectories of change as HR strategy and IoT/IoPTS innovations become more entwined. In particular, this concerns the expansion of employee monitoring, the interconnectivity of employee performances on a larger scale and the increased interaction with digital objects and data to coordinate tasks more efficiently and effectively (Eloff et al., 2009). HR academics and practitioners should therefore be able to use the SWOT survey to make incremental adjustments to existing practices and systems, while proactively preparing for managing future risks and investing in future IoT-related opportunities.
Strengths
First, both IoT and performance management are embedded within a rich existing knowledge base and related trends, applications and paradigms that continue to affect workplaces. If they are considered entirely new or reduced to existing in a relatively isolated vacuum, then there is a risk that the strength that could be drawn from these existing connections might be overlooked. Performance management, for example, can and should be informed by related, long-standing areas of HR, organizational behavior (OB) and psychology literature on goal setting (Latham & Locke, 2007), feedback seeking (Ashford, Blatt, & Vande Walle, 2003) and conceptions of talent management (Dries, 2013). Indeed, as notions of performance and talent have evolved, a more comprehensive view of the wider system becomes important in terms of understanding and capitalizing upon the interplay between technologies, stakeholders and organizational practices in shaping forces of supply and demand in labor markets (Bersin by Deloitte, 2013).
Similarly, the IoT sits nested within a next-generation cluster of closely related technological developments or trends that are likely to mutually reinforce one another’s development through their synergies and histories, spurring growth and innovation forward until at least 2025 (Pew Research Center, 2014). These trends include big data analytics, social media, cloud computing, machine learning, artificial intelligence (AI), biomedical engineering, wearable technologies and virtual reality (VR). In terms of wearable technologies, for example, health, safety and productivity aspects of performance have been usefully tracked via armbands, belts, visors, watches and other sensory devices in health care, sports, the military and many other industrial and organizational settings going back 50 years or more (Wilson, 2013). Building on existing devices and equipment is a strong way to keep developing IoT applications. These developments date back to some of the earliest trends in trying to improve workforce efficiency and motivation, particularly in terms of Taylorism, scientific management and ‘time-and-motion’ studies (Kanigel, 2005).
Second, IoT and performance management can act as mutually enabling strategic drivers for one another. Performance management is a significant part of the HR profession’s platform for developing its strategic contributions to the performance of the organization as a whole (DeNisi & Smith, 2014). Although the precise nature of the strategic synergies between technology and HR strategies are still relatively elusive (Marler & Parry, 2016), the IoT and related digital, data-driven trends are likely to play a role in strengthening the capacity of HR in at least two ways—first, by helping HR to gather evidence more systematically to better support its decisions (Rousseau & Barends, 2011), and second, by more precisely accounting for how employees add value to an organization’s balance sheet in conjunction with more fixed, tangible assets (Fulmer & Ployhart, 2014). A fairly recent example of this strength in action comes from the work of Alex ‘Sandy’ Pentland, his Human Dynamics Lab at MIT and the company he co-founded, Sociometric Solutions (Pentland, 2012). By using sensors built into sociometric badges worn by employees, these researchers have been able to track workers’ commutes, financiers’ trading patterns, call center employees’ coffee break schedules and the conversational dynamics of team meetings to suggest interventions for improving productivity to the tune of millions of dollars in value added (Pentland, 2014).
A third and final strength factor concerns a renewed emphasis on the sociotechnical at work—the integration of the human and the physical or material in real time, with an emphasis on ergonomics and usability (Clegg & Walsh, 2004). A performance environment populated by various IoT devices and sensors would contain hardware, software and ‘liveware’ (employees, teams, managers), enabling customized, self-organizing opportunities for learning to occur in local, embodied and networked ways (Davis, Challenger, Jayewardene, & Clegg, 2014). For example, Italy’s biggest grocery cooperative, Coop Italia, worked with Microsoft and other partners to adopt a ‘supermarket of the future’ IoPTS concept, using motion sensors to offer customers a more seamless, interactive and responsive shopping experience. This also stands to enhance employee performance, enabling employees to gain more rapid, richer insights into customer preferences and make more efficient, dynamic use of spare shop space (Ray, 2016).
To give another example, John Lanchester, the British novelist and journalist, in his account of using the ‘Amazon Echo’ device in his home, reports being pleasantly surprised by the life-enhancing, user-friendly benefits of the voice-activated technology, noting how enabling these features could be for those whose sight or mobility are restricted (Lanchester, 2017). Often when technology is discussed in HR terms in the workplace, it is reduced to automation, decreased headcount and other cost-savings benefits. With regard to the IoT, however, responsive devices that can connect more dynamically to the Internet and other objects open up the possibility of more affordances that interact with human capabilities more directly to enrich them (Want, Schilit, & Jenson, 2015). This could help challenge views of talent and performance as a competitive war and promote more creative, collaborative and inclusive performance systems. Furthermore, IoT devices that communicate with their own anthropomorphic voices can encourage greater engagement through their perceived social presence (Kim, 2016), and these strengths might dovetail with a performance management system in the form of an IoT-supported 360-degree feedback program, for instance.
Weaknesses
First, the IoT and wearable technology has so far really only shown growth in some domains and markets—such as health care devices, industrial sensors, and household appliances—and relatively slow or uneven growth and adoption at that (Bradshaw, 2017; The Economist, 2016). The IoT still seems to require something of a technological leap of faith beyond the success of smartphones and tablets, where the dream of living and working in a brave new world of densely interconnected infrastructure, standardization and measurement may defy fuller expansion and aggregation for some time to come (Bell, 2015).
Performance management is also struggling to reach a tipping point in progressing beyond outdated annual appraisals, on the one hand, and brutally disruptive ‘forced ranking’ performance management that promotes, develops or fires employees based on categorized performance rankings (Pfeffer & Sutton, 2006). Amazon, for example, still reportedly uses the latter approach, sometimes termed ‘rank and yank’, despite reports of its destructive effects on individual employees and organizational performance (Spicer, 2015). In sum, the IoT and performance management are unlikely to work strongly in combination until ineffectual elements in their respective marketplaces are eliminated and more interactive, user-friendly products and practices are adopted more widely.
Second, there is a general lack of digital skills and literacy among current generations of employees and managers. A recent survey of 268 HR professionals in the UK found only 15% or less of them reported team expertise in various digital skills (Patmore, Somers, D’Souza, Welch, & Lawrence, 2017). HR practices like performance management seem to be moving slowly along the digital adoption curve, largely due to reasons of inadequate retraining and slow updating and integrating of legacy systems into improved decision making and return on investment (ROI) (Patmore et al., 2017). The best computer or data scientists and start-ups are still often described as ‘unicorns’ to signify their rareness (McNeill, 2016). It’s unclear as of yet how such rareness can shape or develop into connected workforces that collaborate more extensively on IoT-related innovations (Puthiyamadam, 2017). More broadly, uneven or weak digital skills and access could reinforce inequality-related issues arising from ‘digital divides’ along various socioeconomic and sociodemographic lines (van Dijk & Hacker, 2003).
Third, there are shortcomings inherent to the automation of IoT components of a digital ecosystem, particularly as they interact with any human and cultural shortcomings inherent to a performance management system, the two sets of shortcomings being likely to exacerbate one another to some extent. Since the first days of electronic computer terminals in organizations, for instance, there has been a sense that devices and automation present users with something of a confined and self-contained situation that can constrain their cognitive processes (Weick, 1985). So-called ‘ironies of automation’ can present themselves, where human operators are assisted by technology but face expanded challenges where the technology fails under more abnormal conditions and the operator is left with responsibility for diagnosing and recovering from the problem (Bainbridge, 1983). A related concept is that of ‘automation surprises’, where technology designers’ intentions lead to unintended consequences for users, prompting new kinds of error, confusion and questions along the lines of ‘what is this technology doing?’ and ‘why is this happening?’ (Sarter, Woods, & Billings, 1997). Regarding IoT technologies, the media have reported everyday gripes with Amazon and Google voice assistants, such as automatically ordering unwanted products and responding to a child’s misheard request by directing him to porn, much to the panic of his parents (Clark, 2016; Waters, 2017).
The same weaknesses can present in the workplace too, and at a more systemic level when they interact with human and cultural weaknesses of performance management systems. Common mistakes using talent analytics in performance management, for example, include systems overemphasizing certain metrics, ignoring nonquantitative aspects of performance and only holding lower-level employees accountable to the technology, not senior management (Davenport, Harris, & Shapiro, 2010). Examples of this might include Amazon’s ‘Anytime Feedback Tool’, an internal platform that office workers can use to anonymously share praise and critique/blame regarding their peers. The tool has been criticized for being used as a hotbed of political scheming and sabotage that can ultimately lead to employees being unfairly eliminated for reasons unknown to them and that they are powerless to challenge (Stone, 2015). IoT technology can also be used as an appropriate means to inappropriate performance ends in relation to leaders and executives embracing a cult of extreme physical ‘super’ endurance, supported by wearable biometric devices and mind-boosting drugs (The Economist, 2015).
Opportunities
First, one key vision is the ‘Industrial Internet’ or Industry 4.0, often associated with General Electric (GE) and their large investments into infusing their logistics, operations, manufacturing and product development processes with digital sensors and analytics that connect tasks and equipment that were previously analog in nature (Iansiti & Lakhani, 2014). The potential for greater connectivity across tasks and equipment implies devices for measuring performance more accurately, reliably and holistically. Blurring boundaries and integrating manufacturing, IT and service skills more tightly together can better coordinate employee and business model performance, rather than employees working in functional silos or outsourcing capabilities (Kleiner & Sviokla, 2017). For the employees performing tasks using the digitally connected equipment, it can be providing them with rapid, personalized feedback on their productivity, error rates, safety and so on. For example, ABB, the multinational technology corporation, outlines an IoPTS case study on its website of ‘Remote Support’ and ‘Remote Condition Monitoring’ services, as applied to an SSAB steel factory in Finland (ABB, 2017). By using data from drives inside pumps, motors and industrial components (‘things’), ABB and SSAB operations and maintenance planning teams (‘people’) were able to improve proactive problem resolution and prevention in disturbances and downtime of key processes (‘service’ and performance management).
Overall, an Industrial Internet means redefining employee production performance by linking it more closely to the coding and use of digital devices, data streams and platforms to cooperate and innovate in relation to diverse others (Kagermann, 2015). One image of this future employee—albeit a fanciful and provocative one—is as a sort of James Bond–type actor, whose performance is managed through the improvisational use of gadgets across a series of challenging projects or missions (Rose, 2014).
Second, there are possibilities inherent to improved machine learning and artificial intelligence (AI) capabilities of IoT devices. Networked objects can use algorithms and computational processing of large amounts of information from their environment to learn, adapt and make decisions more autonomously. This ‘machine intelligence’ can usefully “augment employee performance, automate increasingly complex workloads, and develop ‘cognitive agents’ that simulate both human thinking and engagement” (Briggs & Hodgetts, 2017, p. 35). This mirroring of performing employees by thinking, learning, performing devices could revolutionize performance management by putting humans and machines on a more equal and reciprocal footing in terms of how they mutually enhance and complement one another’s performances. Given that technology is improving in its abilities to process language and neural-type connections, it is not too hard to envision AI that coaches and supervises employees, and vice versa (O’Reilly Media, 2017). Algorithms and devices are already being deemed effective performers in terms of hiring employees and detecting criminals (Datafloq, 2017; Kuncel, Ones, & Klieger, 2014), where humans may be freed up to work more effectively and complementarily on more socially and emotionally involving tasks (Beck & Libert, 2017). The opportunity is to develop transhumanist performance management practices that jointly appraise and develop humans and machines in how they doubly add value, through divisions of labor and interdependent forms of assistance, learning, care and improvement (Lorenz, Rüßmann, Strack, Lueth, & Bolle, 2015).
Third, larger-scale (inter)connectivity can be achieved, in spatial and geographical terms, to boost performance in aggregate, coordinating outputs across higher levels of analysis. To the extent that the IoT is able to grow on a larger scale, there is an opportunity for larger, smarter environments to develop, exercising greater capabilities than single devices or subsets of devices. One obvious level in question here is the city, or ‘smart city’ vision, where the technological solutions of IoT are used to securely manage a city’s assets and the quality of life of its citizens and workers (Zanella, Bui, Castellani, & Vangelista, 2014). In terms of performance management, surveys of talented knowledge workers reveal that a desirable smart city location and community is key for attracting and developing employees (Thite, 2011).
Economic geographers and urban planners have long recognized this potential, but urban informatics and the IoT are bringing a digitalized version of the vision more sharply into view. Performance management systems can thus be improved by broadening their notions of performance beyond the internal environment of a single organization. Thus a wider IoT-supported architecture could help provide a useful emphasis on relatively neglected aspects of performance. These might include contributions to solving messy, high-level ‘wicked problems’ such as poverty and terrorism (Waddell, 2016), as well as interorganizational collaboration and boundary-spanning performance behaviors (Calvard, 2014). Employees have always been highly motivated by understanding how their performance has an impact on the bigger picture (Grant, 2007), and the IoT can only provide more data and transparency to helping employers and employees appreciate such impact. As well as cities, regional hubs, confederations, clusters and other centers of systemic, networked human and economic activity may well be able to take advantage of similar opportunities. Organizations and employees have a vested interest in understanding and acting upon IoT-type data generated on issues like parking, traffic, pollution, education, health care, crime, weather and utilities, all of which can affect their performance.
Threats
First, there is the sheer complexity of IoT objects (variety, dynamism) and the need to ensure their standardization and compatibility in informing performance standards and policies. Managing heterogeneous applications, environments and devices has been cited as a major IoT challenge, particularly in establishing interoperability standards and protocols at global or international levels, where consensus building and regulatory planning (e.g. for radio spectrum allocation) can be very slow, involving many stake-holders (Bandyopadhyay & Sen, 2011). The reality is that the IoT remains fragmented, with standards elusive across manufacturers, operating systems and levels of connectivity and programmability. Bigger firms remain relatively disinterested, with little immediate incentive to cooperate and surrender competitive advantages unique to their own products and services—in short, the IoT may fail to speak a common language (Newman, 2016).
In terms of HRIS and performance management, interoperability issues of the IoT will add to the typical implementation issues facing managers and employees of replacing existing legacy systems, customization across the organization, and training and support in new technological standards (Dery, Hall, Wailes, & Wiblen, 2013). Adoption and effectiveness of technologies can be highly uneven across employees in the organization, and at worst they may feel that the system is unfair or counterproductive (Stone, Stone-Romero, & Lukaszewski, 2003). There is a very real threat that IoT could expand and amplify the worse aspects of bureaucracy at work—the dehumanizing, absurd, frustrating and coercive webs of inflexible rules—as they are translated across great assemblages of objects and data (Stanley, 2015).
Second, employees might resist both the technological changes represented by the IoT and changes made to performance management processes. Internet technologies can lead to unhealthy patterns of human addiction and dependence that negatively affect workplace performance (Griffiths, 2010). Heightened awareness and concern could invite more political responses, and even ‘neo-Luddite’ acts of resistance, such as attacks on drones, people wearing Google Glass products and taxi drivers rioting against Uber cars and drivers in France (Dillet, 2015; Hill, 2014). Regardless of how extreme the response, employees are likely to be ambivalent in general about heightened surveillance, monitoring and invasions of privacy.
Electronic performance monitoring can run counter to popular management rhetoric on employee empowerment, trust, flexibility and ‘results only’ work environments. Survey evidence shows employees feel negatively towards being closely monitored or recorded through devices, and more so if the monitoring is focused on individuals and is unpredictable in nature (Jeske & Santuzzi, 2015). Clearly a respect for ethical and legal boundaries, as well as social support, is needed to frame monitoring more positively. A name has been coined for users who exhibit misconduct in relation to Google Glass wearables—‘Glassholes’. It serves as a reminder of the tensions and strains IoT could put on workplace relationships, as well as the risk of darker forms of counterproductive work behavior that show contempt for privacy and rights (Healey, 2015). A broader, critical perspective can recognize that power runs through both human employees and material objects in complex, interactive ways—leading to a ‘government of things’ presiding over their possible (inter)actions (Lemke, 2015).
Third, it’s unclear whether or not the overall cybersecurity and safety of an IoT performance management system can be effectively and sustainably upheld. Security researchers have demonstrated how easily they can hack into objects, including a 2014 Jeep Cherokee automobile, prompting Fiat Chrysler to recall 1.4 million vehicles. The company had to post out USB drives with patches to block any further attacks on the infotainment systems of the cars and the Sprint network connecting cars and trucks (Greenberg & Zetter, 2015). In terms of workplace performance, hacking vulnerabilities through IoT-enabled objects could threaten trust in sensitive objects and information, enable counterproductive behaviors like theft or sabotage and pose serious risks to safety and control while employees work.
As Roman, Zhou, & Lopez (2013, p. 2270) note, “the threats that can affect the IoT entities are numerous, such as attacks that target diverse communication channels, physical threats, denial of service, identity fabrication, and others”. Cybersecurity mechanisms therefore need to be correspondingly numerous and strong in their defenses against these attacks. Because of the dynamic and distributed nature of the IoT vision, traditional security methods are too static and generic. Flexible and improvisational countermeasures are needed, ones that take into account changing territories of trust and risk (Sicari, Rizzardi, Grieco, & Coen-Porisini, 2015). The main areas to attend to are access, authentication and identity management. Quick fixes are unlikely to be possible. Organizations will need to map out their performance systems by layers: how devices provide access to assets and which devices are vulnerable because of being left unattended or having low computing power. This means a systems approach, considering the types of vulnerabilities, threats, intruders and attacks that might be likely to occur in a given organizational context (Abomhara & Kien, 2015). Failure to do so is likely to invite an array of possible IoT abuses and threats, including blackouts, break-ins, lock-outs, thefts and other kinds of confusing and dangerous crisis (Dhanjani, 2015).
Regarding performance management, Dhanjani (2015) notes the threat posed from nosy or disgruntled employees, citing the example of the likely involvement of disgruntled Sony Pictures employees in leaking data (executive emails) in 2014 that was damaging to the company brand and reputation. Employees may be in a position to put colleagues and customers at risk, particularly if they have inside knowledge of IoT and performance systems, but also, depending on their role, they may themselves be vulnerable to ‘social engineering attacks’—where threat actors rely on human deception rather than attacking the technology directly (Dhanjani, 2015). Performance management architects should look carefully at the design of jobs and roles that involve IoT cybersecurity risks, and assess and reward competent cybersecurity policy development and compliance, as well as IoT attack detection and prevention, where appropriate.
Discussion
Having presented the SWOT analysis and each set of factors in turn, this chapter now concludes by offering further implications and recommendations: three for future research on the IoT and performance management and three concerning future practice by HR, managers and employees. These recommendations should go some way toward ‘joining up’ the four areas of the SWOT, providing ways forward in terms of exploiting positive opportunities and converting negative concerns into more neutral and positive forces (Piercy & Giles, 1989). Positive and negative areas identified as surrounding the IoT/IoPTS aid in the crafting of corresponding policy recommendations around how to improve the security, trust, privacy and digitally distributed intelligence of HR’s performance management, using research as evidence to inform practice.
Starting with future research, one recommendation is to give greater consideration to ‘sociomateriality’ in theoretically explaining and trying to account for sets of relationships and effects. Sociomateriality reflects a commitment to integrate, rather than separate, the technological and the social or organizational (Orlikowski & Scott, 2008). This move is given even greater urgency by the development of the IoT, where technological classes of material objects are fused even more richly and intimately with daily lives, relationships and practices. Research on components of the IoT and performance management (e.g. coaching, appraisals, leadership development) should therefore not overemphasize either technological determinism or unconstrained social construction at the expense of the other. Such research is likely to yield greater insights into how technological and social relationships are entangled and dynamically affect one another—understandings which will be important for organizations if they are to understand control, accountability and capacity (Boos, Guenter, Grote, & Kinder, 2013).
Second, theories of motivation and performance should be tested in conjunction with IoT technologies to see if traditional findings can be replicated with IoT devices and environments, or if they need to be modified. Goal-setting theory, for example, is starting to be tested and refined in relation to ‘gamification’ technologies, where performance-related objects and features like leaderboards and simulations are found to have positive motivational effects on task performance (Landers, Bauer, Callan, & Armstrong, 2015). Building on such research agendas will help ensure that performance management as a set of HR practices remains evidence-based in nature (Rousseau & Barends, 2011) and that decisions about incorporating IoT systems into the workplace are based on carefully formulated research.
Third, future research may benefit from focusing on transhumanism, in terms of the technological possibilities for boosting human performance by extending, transferring and improving various resources that go beyond the limits of the current physical and mental capabilities of individual employees. IoT environments might help employees to become ever smarter, fitter and healthier in various contexts (Bostrom, 2005). Future research might be able to further explore cases where the data and devices of the IoT present opportunities to transform performance in positive ways (e.g. in sports and medicine). In the music industry, the careers and performances of pop stars are now manufactured in highly digital, data-driven terms, going above and beyond the human pop artist themselves to ensure high levels of success (Colburn, 2017).
Turning to practice, perhaps the first and foremost priority for managers and HR to address is the digital (and statistical/analytical) skills gaps in their workforces. Future work skills, as predicted by panels, tend to prioritize a mixture of cognitive, social and technological capabilities (Davies, Fidler, & Gorbis, 2011). Managers and HR should think about how these three areas are integrated into their existing training needs analyses and programs to best adapt to trends like the IoT. Coding and programming devices have serious prospects for creating a new generation of blue-collar jobs (Thompson, 2017), so HR managers need to consider this in terms of recruitment and job design. Extending digital skills training to teams is also a good opportunity to integrate IT and technological functions with operations, HR and other areas of the organization in order to have more cohesive, value-adding discussions. Furthermore, the physical and material nature of IoT developments may lend itself well to more innovative training across space and objects that invokes design thinking, discovery and experiential learning methodologies, as opposed to more formal, traditional methods.
A second area for practice is to develop shared understandings around classifications of different objects or ‘things’ in the IoT. Some objects may be wearable, others not; some fixed in a local position, others more ‘ambient’ in their presence and sensory capacities; some more adaptive and programmable in their levels of machine learning, others more scripted and limited in functionality, and so on. Dodge and Kitchin (2009), for example, have categorized digital objects, by their permeability, reactivity and recording capacities. Their most sophisticated class are termed ‘logjects’ and are described as highly interoperable and have ‘awareness’ in terms of recording information from their environment for storage and future reuse (Dodge & Kitchin, 2009). If managers, HR and employees engage in this classification exercise as a practical change process, it will enable them to develop a common language around IoT-related performance management, developing clearer strategies about the status and use of such objects in existing task performance situations. The ‘endpoints’ or direct sensors in the proximal work environment can thus be traced back to functional hubs, and finally to more integrated and enhanced forms of performance management systems and services (e.g. dashboards, talent pipelines) in the cloud (Burkitt, 2014).
Finally, practice needs to address cybersecurity and ensure IoT performance management systems that are strong and resilient, avoiding the threat posed by various unwanted, invasive attacks. In terms of trust and fairness, this means having a system set up in ways acceptable to all users. In performance management, trust and confidence in the top management and the consistency of the system are intimately related (Mayer & Davis, 1999). On the IoT side, the reliability, dependability and trustworthiness of various technological layers are no less crucial in shaping employee perceptions of trustworthiness and risk. Thus managers are well advised to engage in ‘trust management’ (TM), taking a systematic and transparent approach to showing workforces how data are securely and robustly transmitted and fused across an IoT system according to clear, agreed-upon principles and goals (Yan, Zhang, & Vasilakos, 2014).
Conclusion
This chapter has presented a SWOT analysis outlining key factors with the potential to positively and negatively influence the success of an IoT-supported performance management system, also drawing implications for future research and practice at the junction of the two topics of the IoT and performance management. Almost no theory or research to date has explicitly linked the IoT as a technological trend with specific HR practices and strategies. Hopefully, as IoT products and services proliferate in households and industries, similar discussions on how to integrate them with various HR practices and workforce settings affecting employees will continue to be debated, explored and refined.
References
ABB. (2017). Internet of Things delivers innovative remote services for drives maintenance planning. (Webpage). Retrieved August 20, 2017, from http://new.abb.com/about/technology/iotsp/top-stories/customer-stories/innovative-remote-services-for-drives-maintenance-planning
Abomhara, M., & Kien, G. M. (2015). Cyber security and the Internet of Things: Vulnerabilities, threats, intruders and attacks. Journal of Cyber Security, 4, 65–88.
Aguinis, H., & Pierce, C. A. (2008). Enhancing the relevance of organizational behavior by embracing performance management research. Journal of Organizational Behavior, 29 (1), 139–145.
Ashford, S. J., Blatt, R., & Vande Walle, D. (2003). Reflections on the looking glass: A review of research on feedback-seeking behavior in organizations. Journal of Management, 29 (6), 773–799.
Bainbridge, L. (1983). Ironies of automation. Automatica, 19 (6), 775–779.
Bandyopadhyay, D., & Sen, J. (2011). Internet of Things: Applications and challenges in technology and standardization. Wireless Personal Communications, 58 (1), 49–69.
Beck, M., & Libert, B. (2017). The rise of AI makes emotional intelligence more important. Harvard Business Review. Retrieved February 20, 2017, from https://hbr.org/2017/02/the-rise-of-ai-makes-emotional-intelligence-more-important
Bell, L. (2015, August 5). Wearables in the workplace? Not yet: IoT is just too complex. The Inquirer. Retrieved February 1, 2017, from www.theinquirer.net/inquirer/feature/2420475/wearables-in-the-workplace-not-yet-iot-is-just-too-complex
Bersin by Deloitte. (2013, December). Predictions for 2014: Building a strong talent pipeline for the global economic recovery: Time for innovative and integrated talent and HR strategies. Retrieved January 20, 2017, from www2.deloitte.com/content/dam/Deloitte/au/Documents/human-capital/deloitte-au-con-predictions-bersin-2014-1213.pdf
Boos, D., Guenter, H., Grote, G., & Kinder, K. (2013). Controllable accountabilities: The Internet of Things and its challenges for organisations. Behaviour & Information Technology, 32 (5), 449–467.
Bostrom, N. (2005). A history of transhumanist thought. Journal of Evolution and Technology, 14 (1), 1–25.
Bradshaw, T. (2017, February 9). Wearable tech groups press on despite not living up to hype. Financial Times. Retrieved February 10, 2017, from www.ft.com/content/9005134c-ee66-11e6-930f-061b01e23655
Briggs, B., & Hodgetts, C. (2017). Tech trends 2017: The kinetic enterprise. Deloitte University Press. Retrieved February 20, 2017, from https://dupress.deloitte.com/content/dam/dup-us-en/articles/3468_TechTrends2017/DUP_TechTrends2017.pdf
Burkitt, F. (2014, November 10). A strategist’s guide to the Internet of Things. Strategy+Business. Retrieved February 20, 2017, from www.strategy-business.com/article/00294
Calvard, T. S. (2014). Difficulties in organizing boundary-spanning activities of inter-organizational teams. In J. Langan-Fox & C. Cooper (Eds.), Boundary-spanning in organizations: Network, influence, and conflict (pp. 116–142). New York: Taylor & Francis.
Chermack, T. J., & Kasshanna, B. K. (2007). The use and misuse of SWOT analysis and implications for HRD professionals. Human Resource Development International, 10 (4), 383–399.
Clark, P. A. (2016, December 30). Amazon Alexa helpfully tries to give porn to a child. Yahoo! News. Retrieved February 5, 2017, from https://uk.news.yahoo.com/amazon-alexa-helpfully-tries-porn-143018413.html
Clegg, C., & Walsh, S. (2004). Change management: Time for a change! European Journal of Work and Organizational Psychology, 13 (2), 217–239.
Colburn, R. (2017, February 23). Meet the 14-year-old pop star who’s literally being created with internet data. A.V. Club. Retrieved February 24, 2017, from www.avclub.com/article/meet-14-year-old-pop-star-s-literally-being-create-250918?utm_content=Main&utm_campaign=SF&utm_source=Facebook&utm_medium=SocialMarketing
Datafloq. (2017, February 17). How facial recognition plays a role in catching criminals. Retrieved February 20, 2017, from https://datafloq.com/read/facial-recognitions-role-in-catching-criminals/2679
Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88 (10), 52–58.
Davies, A., Fidler, D., & Gorbis, M. (2011). Future work skills 2020. Institute for the Future. Retrieved February 21, 2017, from www.iftf.org/futureworkskills/
Davis, M. C., Challenger, R., Jayewardene, D. N. W., & Clegg, C. W. (2014). Advancing socio-technical systems thinking: A call for bravery. Applied Ergonomics, 45 (2), 171–180.
Da Xu, L., He, W., & Li, S. (2014). Internet of Things in industries: A survey. IEEE Transactions on Industrial Informatics, 10 (4), 2233–2243.
DeNisi, A., & Smith, C. E. (2014). Performance appraisal, performance management, and firm-level performance: A review, a proposed model, and new directions for future research. Academy of Management Annals, 8 (1), 127–179.
Dery, K., Hall, R., Wailes, N., & Wiblen, S. (2013). Lost in translation? An actor-network approach to HRIS implementation. The Journal of Strategic Information Systems, 22 (3), 225–237.
Dhanjani, N. (2015). Abusing the Internet of Things: Blackouts, freakouts, and stakeouts. North Sebastopol, CA: O’Reilly Media, Inc.
Dillet, R. (2015, June 25). French anti-Uber protest turns to guerrilla warfare as cabbies burn cars, attack Uber drivers. Tech Crunch. Retrieved February 4, 2017, from https://techcrunch.com/2015/06/25/french-anti-uber-protest-turns-to-guerrilla-warfare-as-cabbies-burn-cars-attack-uber-drivers/
Dodge, M., & Kitchin, R. (2009). Software, objects, and home space. Environment and Planning A, 41 (6), 1344–1365.
Donkin, R. (2010). The Silent Monitor. In The History of Work (pp. 87–102). London: Palgrave Macmillan.
Dries, N. (2013). The psychology of talent management: A review and research agenda. Human Resource Management Review, 23 (4), 272–285.
The Economist. (2015, December 16). Here comes SuperBoss: A cult of extreme physical endurance is taking root among executives. Retrieved January 5, 2017, from www.economist.com/news/business/21684107-cult-extreme-physical-endurance-taking-root-among-executives-here-comes-superboss
The Economist. (2016, June 11). Where the smart is: Connected homes will take longer to materialise than expected. Retrieved February 1, 2017, www.economist.com/news/business/21700380-connected-homes-will-take-longer-materialise-expected-where-smart
Eloff, J. H. P., Eloff, M. M., Dlamini, M. T., & Zielinski, M. P. (2009). Internet of people, things and services: The convergence of security, trust and privacy. 3rd CompanionAble Workshop-IoPTS, Brussels, 2 December.
Ewenstein, B., Hancock, B., & Komm, A. (2016, May). Ahead of the curve: The future of performance management. McKinsey Quarterly. Retrieved January 5, 2017, from www.mckinsey.com/business-functions/organization/our-insights/ahead-of-the-curve-the-future-of-performance-management?cid=other-eml-ttn-mkq-mck-oth-1701
Fulmer, I. S., & Ployhart, R. E. (2014). ‘Our most important asset’ a multidisciplinary/multilevel review of human capital valuation for research and practice. Journal of Management, 40 (1), 161–192.
Grant, A. M. (2007). Relational job design and the motivation to make a prosocial difference. Academy of Management Review, 32 (2), 393–417.
Greenberg, A., & Zetter, K. (2015, December 28). How the Internet of Things got hacked. Wired. Retrieved August 15, 2017, from www.wired.com/2015/12/2015-the-year-the-internet-of-things-got-hacked/
Griffiths, M. (2010). Internet abuse and internet addiction in the workplace. Journal of Workplace Learning, 22 (7), 463–472.
Healey, K. (2015, November 8). We are all glassholes now: Privilege, Silicon Valley and our new wearable tech problem. Salon. Retrieved February 4, 2017, from www.salon.com/2015/11/08/we_are_all_glassholes_now_privilege_silicon_valley_and_our_new_wearable_tech_problem/
Hill, K. (2014, July 15). The violent opt-out: The neo-Luddites attacking drones and Google Glass. Forbes. Retrieved February 4, 2017, from www.forbes.com/sites/kashmirhill/2014/07/15/the-violent-opt-out-people-destroying-drones-and-google-glass/#6426f32b3bb7
Hill, T., & Westbrook, R. (1997). SWOT analysis: It’s time for a product recall. Long Range Planning, 30 (1), 46–52.
Iansiti, M., & Lakhani, K. R. (2014). Digital ubiquity: How connections, sensors, and data are revolutionizing business (digest summary). Harvard Business Review, 92 (11), 91–99.
Jeske, D., & Santuzzi, A. M. (2015). Monitoring what and how: Psychological implications of electronic performance monitoring. New Technology, Work and Employment, 30 (1), 62–78.
Kagermann, H. (2015). Change through digitization: Value creation in the age of Industry 4.0. In H. Albach et al. (Eds.), Management of permanent change (pp. 23–45). Wiesbaden: Springer Fachmedien.
Kanigel, R. (2005). The one best way: Frederick Winslow Taylor and the enigma of efficiency. New York: Viking Press.
Kim, K. J. (2016). Interacting socially with the Internet of Things (IoT): Effects of source attribution and specialization in human-IoT interaction. Journal of Computer-Mediated Communication, 21 (6), 420–435.
Kim, S., Nussbaum, M. A., & Gabbard, J. L. (2016). Augmented reality ‘smart glasses’ in the workplace: Industry perspectives and challenges for worker safety and health. IIE Transactions on Occupational Ergonomics and Human Factors, 4 (4), 253–258.
Kleiner, A., & Sviokla, J. (2017, February 1). The thought leader interview: GE’s Bill Ruh on the Industrial Internet Revolution. Strategy+Business. Retrieved February 10, 2017, from www.strategy-business.com/article/The-Thought-Leader-Interview-Bill-Ruh?gko=9ae51&utm_source=itw&utm_medium=20170214&utm_campaign=resp
Kuncel, N. R., Ones, D. S., & Klieger, D. M. (2014). In hiring, algorithms beat instinct. Harvard Business Review. Retrieved February 12, 2017, from https://hbr.org/2014/05/in-hiring-algorithms-beat-instinct
Lanchester, J. (2017). Short cuts. London Review of Books, 39 (3), 22.
Landers, R. N., Bauer, K. N., Callan, R. C., & Armstrong, M. B. (2015). Psychological theory and the gamification of learning. In Gamification in education and business (pp. 165–186). New York: Springer International Publishing.
Latham, G. P., & Locke, E. A. (2007). New developments in and directions for goal-setting research. European Psychologist, 12 (4), 290–300.
Lemke, T. (2015). New materialisms: Foucault and the ‘government of things’. Theory, Culture & Society, 32 (4), 3–25.
Li, S., Xu, L. D., & Zhao, S. (2015). The Internet of Things: A survey. Information Systems Frontiers, 17 (2), 243–259.
Lorenz, M., Rüßmann, M., Strack, R., Lueth, K. L., & Bolle, M. (2015, September). Man and machine in industry 4.0: How will technology transform the industrial workforce through 2025? Retrieved February 12, 2017, from www.bcgperspectives.com/content/articles/technology-business-transformation-engineered-products-infrastructure-man-machine-industry-4/?chapter=3
Marler, J. H., & Parry, E. (2016). Human resource management, strategic involvement and e-HRM technology. The International Journal of Human Resource Management, 27 (19), 2233–2253.
Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84 (1), 123–136.
McNeill, D. (2016). Governing a city of unicorns: Technology capital and the urban politics of San Francisco. Urban Geography, 37 (4), 494–513.
Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of Things: Vision, applications and research challenges. Ad Hoc Networks, 10 (7), 1497–1516.
Newman, J. (2016, March 18). Why the Internet of Things might never speak a common language. Fast Company. Retrieved February 20, 2017, from www.fastcompany.com/3057770/why-the-internet-of-things-might-never-speak-a-common-language
O’Reilly Media. (2017, February). Artificial intelligence now. Free Ebook. Retrieved February 20, 2017, from www.oreilly.com/data/free/artificial-intelligence-now.csp
Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2 (1), 433–474.
Patmore, B., Somers, J., D’Souza, D., Welch, D., & Lawrence, J. (2017, January 10). The state of digital HR in 2017. Retrieved February 1, 2017, from www.hrzone.com/resources/the-state-of-digital-hr-in-2017
Pentland, A. (2012). The new science of building great teams. Harvard Business Review, 90 (4), 60–69.
Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. New York: Penguin.
Pew Research Center. (2014, May). The Internet of Things will thrive by 2025. Retrieved January 20, 2017, from www.pewinternet.org/files/2014/05/PIP_Internet-of-things_0514142.pdf
Pfeffer, J., & Sutton, R. I. (2006). Evidence-based management. Harvard Business Review, 84 (1), 62–74.
Piercy, N., & Giles, W. (1989). Making SWOT analysis work. Marketing Intelligence & Planning, 7 (5–6), 5–7.
Puthiyamadam, T. (2017, February 8). How to build a connected workforce. Strategy+Business. Retrieved February 12, 2017, from www.strategy-business.com/blog/How-to-Build-a-Connected-Workforce?gko=71ef6&utm_source=itw&utm_medium=20170214&utm_campaign=resp
Ray, S. (2016, April 5). The supermarket of the future: Designing for humans. Microsoft [blog]. Retrieved August 15, 2017, from https://blogs.microsoft.com/transform/feature/the-supermarket-of-the-future-designing-for-humans/#sm.0008mvfaa9o4dl910ez2bluhrpe0u
Roman, R., Zhou, J., & Lopez, J. (2013). On the features and challenges of security and privacy in distributed Internet of Things. Computer Networks, 57 (10), 2266–2279.
Rose, D. (2014). Enchanted objects: Design, human desire, and the Internet of Things. New York: Simon and Schuster.
Rousseau, D. M., & Barends, E. G. R. (2011). Becoming an evidence-based HR practitioner. Human Resource Management Journal, 21 (3), 221–235.
Sarter, N. B., Woods, D. D., & Billings, C. E. (1997). Automation surprises. Handbook of Human Factors and Ergonomics, 2, 1926–1943.
Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks, 76, 146–164.
Spicer, A. (2015, August 17). The flaw in Amazon’s management fad. The Guardian. Retrieved January 28, 2017, from www.theguardian.com/commentisfree/2015/aug/17/amazon-management-fad-rank-yank-jeff-bezos
Stanley, J. (2015, April 7). The Internet of Kafkaesque things. ACLU. Retrieved February 15, 2017, from www.aclu.org/blog/free-future/internet-kafkaesque-things
Stone, D. L., Stone-Romero, E. F., & Lukaszewski, K. (2003). The functional and dysfunctional consequences of human resource information technology for organizations and their employees. In Advances in human performance and cognitive engineering research (pp. 37–68). New York: Emerald Group Publishing Limited.
Stone, M. (2015, August 15). Amazon employees reportedly slam each other through this internal review tool. Business Insider. Retrieved February 5, 2017, from http://uk.businessinsider.com/amazon-employees-reportedly-slam-each-other-through-this-internal-review-tool-2015–8?r=US&IR=T
Strohmeier, S., Franca, P., Majstorovic, D., & Schreiner, J. (2016, February). Smart HRM: A Delphi study on the future of digital human resource management (‘HRM 4.0’). Retrieved January 5, 2017, from www.uni-saarland.de/fileadmin/user_upload/Professoren/fr13_ProfStrohmeier/Aktuelles/Final_report_Smart_HRM_EN.pdf
Thite, M. (2011). Smart cities: Implications of urban planning for human resource development. Human Resource Development International, 14 (5), 623–631.
Thompson, C. (2017, February 2). The next big blue-collar job is coding. Wired. Retrieved February 21, 2017, from www.wired.com/2017/02/programming-isthe-new-blue-collar-job/
van Dijk, J.A.G.M. and Hacker, K. (2003). The digital divide as a complex and dynamic phenomenon. Information Society, 19, 315–326.
Waddell, S. (2016). Societal change systems: A framework to address wicked problems. The Journal of Applied Behavioral Science, 52 (4), 422–449.
Want, R., Schilit, B. N., & Jenson, S. (2015). Enabling the Internet of Things. Computer, 48 (1), 28–35.
Waters, R. (2017, February 10). My everyday grips with Google and Amazon voice assistants. Financial Times. Retrieved February 2, 2017, from www.ft.com/content/e6efb16c-eeb1-11e6-930f-061b01e23655
Weick, K. E. (1985). Cosmos vs. chaos: Sense and nonsense in electronic contexts. Organizational Dynamics, 14 (2), 51–64.
Weihrich, H. (1982). The TOWS matrix: A tool for situational analysis. Long Range Planning, 15 (2), 54–66.
Wilson, H. J. (2013). Wearables in the workplace. Harvard Business Review, 91 (9), 23–25.
Wilson, H. J., Shah, B., & Whipple, B. (2015). How people are actually using the Internet of Things. Harvard Business Review. Retrieved August 1, 2016, from https://hbr.org/2015/10/how-people-are-actually-using-the-internet-of-things
Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for Internet of Things. Journal of Network and Computer Applications, 42, 120–134.
Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for smart cities. IEEE Internet of Things Journal, 1 (1), 22–32.