The Internet-of-Things: Reflections on the past, present and future from a user-centered and smart environment perspective

2.1.The Internet-of-Things as a multi-faceted movement

The Internet-of-Things, the Embedded-Internet, Ubiquitous Computing, Pervasive Computing, and Ambient Intelligence are terms which, in the eyes of many ordinary people, seem to describe the same thing. However, in academic circles the nuances in the perceived meanings can be important and sometimes argued over. From the authors review of the literature these sometime subtle differences can be better understood by tracing the roots of each community. For example, the Pervasive Computer community have historically had a strong interest in communications and networking issues while the Ubiquitous Computing community have had a greater interest in HCI issues. Likewise the Ambient Intelligence and Intelligent Environments community have, as their names imply, a keen interest in the use of AI. The Internet-of-Things grew out of sensor networks and monitoring which, developed quickly into a broader interest for networked devices and infrastructures. Networking and infrastructure aspects of IoT are covered in depth in another chapter of this edited book by Gomeza et-al. [51]. Of course all communities cover all aspects of such systems, so it’s hardly surprising that, to the ordinary people, these terms seem to be synonymous with each other (and increasingly so, as the market introduces products that combine all these ideas). Given that the terminology of the Internet-of-Things arose from industry, and industry is bringing these technologies to the market, it’s hardly surprising that the Internet-of-Things is now the dominant term in the public arena. That being the case we now trace the history of the term, the Internet-of-Things.

The starting point for the term “Internet-of-Things” finding popular recognition in the public domain can be traced back to the 2005 World Summit in Tunis where the International Telecommunication Union (ITU), a body of the United Nations (UN), published a report entitled “The Internet of Things” [1]. It would seem that this was a pivotal moment in both publicising the term and creating an awareness of the enormous business opportunities arising from the connection of embedded computers, along with sensors and/or actuators, to the Internet. These embedded computers (things, in IoT terminology) can be made to function autonomously, with or without human intervention, communicating with other devices or people, via the Internet. With the addition of AI the ‘things’ can become smart, using pre-programmed rules or those learnt dynamically through machine-learning to make decisions. The sensors embedded into IoT devices can produce big-data for higher level analytical engines. The 2005 ITU report [1] described this concept in great detail together with the potential benefit that the technology could bring to industry and society. The report highlighted three important initial functions: tracking, sensing, and decision-making being the fundamental part of future Internet-of-Things eco-systems. Of course, this report was written over 10 years ago and since then, technology and ideas have advanced, creating bigger visions and possibilities, some of which we will touch on later in this paper.

2.2.Internet-of-Things phases of development

Having introduced the Internet-of-Things, we will now investigate how the historical development of the Internet of-Things might be characterised into phases, each with their own characteristics. Our analysis is based on a study of over forty definitions and narratives from published literature during in the period 2005–2017 (a 12-year period). In order to to complete this task we analysed data using common keywords (Table 2) based on the nature, characteristics, functionalities, and capabilities of the Internet-of-Things. From our analysis we deduced it is possible to characterise its development into five distinct phases. The first phase, before 2005 was when the Internet-of-Things was in its infancy and work was largely exploratory and ad-hoc in nature; the remaining four phases, all post-2005, each comprise a 3-year period which are described in the following sections.

2.3.Internet-of-Things characteristics and classifications

Just as the scope of the Internet-of-Things has changed down the years, so too do the features that characterise it. In its early days the Internet-of-Things was characterised, in general terms, by what was referred to as the five “C”s:

Later, these general characteristics evolved to include details to reflect the logical functions of IoT, in particular [1]:

While a view of the logical functions of the Internet-of-Things characteristics provides a useful summary, it does not reflect well the impact and benefits that the concept offers. For example, it does not capture the ability of Internet-of-Things systems to process large quantities of data and to infer high value information or knowledge which enable smartness, by supporting effective decision-making. Today’s view of the Internet-of-Things, especially from an industry perspective, is very much one of a network of ‘systems of systems’. In this context, the characteristics of a modern Internet-of-Things system can be summarised better as comprising:

4.1.PDorm (plant-dorimtory)

The pDorm (aka TESA – Towards Embedded-Internet System Applications), shown in Fig. 2, was one of the earliest examples of an Internet-of-Things application. [29] Developed in 2003, it took the form of a novel “botanical plant care” appliance, which explored the feasibility of applying the then, newly emerging low-cost Embedded-Internet devices to create a novel generation of products that could be accessed and controlled from anywhere, anytime, via a web-based interface. The principal challenges addressed by TESA were how to design an Internet-of-Things computing architecture that supported appliance control, a multimode heterogeneous client interface, and mixed wired and wireless communication (including access via mobile phone, before the era of smart phones). The system was presented in a custom-made box consisting of various lighting (top and bottom), a heater, a fan, a temperature and moisture sensor, attached to an embedded-internet board called TINI, manufactured by Dallas Semiconductor (Fig. 3). TESA supported wired (Ethernet) and wireless (Bluetooth and WIFI) communications over an IP network and could be accessed via 3 different interfaces, all with different resolutions which auto-triggered according to the client device’s screen resolution.

Fig. 3.

TINI board.

Programming Internet-of-Things systems at that time was the biggest challenge, due to a lack of out-of-the-box tools as technologies were constantly being refined, improved and updated. Developers and users had little choice but to work round various constraints. The major design issues faced in completing this project were:

4.2.The smart alarm clock

This project, ‘The Smart Alarm Clock’ (Fig. 4), was undertaken in 2013, some 10 years after the development of pDorm, and provides a good insight into how technology had changed, and the trends that were emerging as the Internet-of-Things moved forward. The Smart Alarm Clock was developed by Scott [83] who had identified that there wasn’t a commercially available smart alarm clock, with the functionality to dynamically and autonomously adjust alarm times based on weather and traffic conditions. Examples of the more advanced Internet clock products at the time included the La Crosse WE-8115U-S Atomic Digital Clock, which featured indoor/outdoor temperature and humidity readings, and the Dynamically Programmable Alarm Clock (DPAC), designed by students at Northeastern University in Boston, MA, which was a self-setting alarm clock, that used Google Calendar appointments to set alarm times and automatically adjusted them based on current traffic and/or weather conditions. However, while many of these products sought to use external data, none had fully exploited the potential for real-time Web services that ranged from conventional gathering of data from web-feeds through to accessing Internet-of-Things environment sensors that may be part of private or public spaces.

Fig. 4.

The smart alarm clock prototype.

Fig. 5.

Connection diagrams of the smart alarm clock.

Thus the concept of the Smart Alarm Clock (Fig. 5) was developed with distinguishing features that included rule processing, local sensor readings and integration with web services which was integrated into a single unit, that harnessed the full power of the Internet (including the Internet-of-Things) to determine the optimal alarm time for its owner to be awakened in order to reach their predetermined location at the right time. The alarm time adjustment was, for example, dependent on the severity of traffic conditions, weather forecast and actual local sensing.

Fig. 6.

The BReal set up with an ImmersaVU station being used with a set of Raspberry Pi based Internet-of-Things smart objects.

For instance, readings from the local temperature sensor were used to further adjust the alarm time to allow time for motorists to de-ice their vehicles, if necessary. Since some 10 years had passed from the development of the pDorm, many of the issues faced back then, such as a lack of standard low-cost platforms had been overcome with the advent of hardware such as the Arduino and Raspberry Pi, which had a substantial crowd of users and off-the-shelf peripherals.

In this case the project was built using a Raspberry Pi and was based on XBEE wireless radios networks (low-powered data transmission with a well-documented API). In the 10 years since the pDorm, programming support had also improved with, for example, developers’ forums dedicated to the particular platform being available. These forums allowed groups of similar-minded individuals to form their own communities, where they shared their expertise, ideas and experiences. The major design challenges faced in this project were:

4.3.BReal (a blended reality approach to the Internet-of-Things)

The Internet-of-Things does not stand alone as an innovation but, rather co-exists with other emerging technologies, one being virtual or mixed reality. Virtual reality shares many similarities with the Internet-of-Things in that both provide network components that are used as the building blocks of inhabitable worlds. Moreover, Internet-of-Things devices can have virtual representations, allowing them to exist in both the real and virtual world. Further it is possible to build worlds where some of the Internet-of-Things components are real, and some are virtual. Such environments are called Mixed Reality. Such a hybrid Internet-of-Things environment was built in the University of Essex during the phase four of the historical development of the Internet described earlier (i.e. 2015–2017, the smart period).

The project was called BReal which was an amalgamation of letters from ‘Blended Reality’ [76]. The environment consisted of 3 main parts: (i) the physical world, where the user and the xReality22 objects are situated; (ii) the virtual world, where the real world data will be reflected using the virtual object; and (iii) a human-computer interface (HCI) which captures the data obtained in real-time via the xReality object, processing it so it can be mirrored by its virtual object and thereby linking both worlds. Figure 6 shows the BReal set up which consisted of an ImmersaVU station running Unity (the VR environment), a set of Raspberry Pi based Internet-Of-Things smart objects.

To mirror and synchronize virtual representations the system used a Smart Fox Server X2, a middleware that is more often used to create large scale multiplayer games and virtual communities.

The major design challenges faced in this project were:

Maintaining real-time synchronisation between the real and virtual Internet-of-Things objects (test were conducted between countries separated by many thousands of miles).

While the technical challenges facing this project were considerable, the potential benefits were also enormous. For example, using this approach it is possible to develop and experiment with innovative Internet-of-Things designs ahead of any expenditure on manufacturing and deploying real devices. Also, for developing new Internet-of-Things systems, the collaborating developers can be geographically separated, which is particularly useful for large multinational companies where team members may be distributed around the world. In addition, with the current trend towards centralising Internet-of-Things services on cloud-based architectures (e.g., data analytics, management etc.) the approach is highly compatible with such schemes. Finally, it is worth noting that the core of the BReal innovative vision arose from the Science-Fiction Prototyping methodology described in the introduction of this paper. A Science Fiction prototype called “Tales from a Pod” was written that described students in a future time using Virtual-Reality and the Internet-of-Things in a futuristic learning environment that became the inspiration for this work [19]. The sheer diversity of Internet-of-Things devices and functionalities makes innovation both challenging and exciting since the possibilities are almost endless. Thus, marrying the Internet-of-Things with a powerful innovation tool, such as Science Fiction Prototyping makes a powerful combination. One outcome of this project is that one of the members of the BReal team is now introducing related techniques as a means of supporting BT field engineers to maintain the vast UK telecommunication infrastructure.

5.1.Customising IoT environments: A user-centred approach

While it is good achievement to present society with transformative technologies, such as the Internet-of-Things, it is also necessary to provide support for people so they can harness these technologies to their benefit. A particularly difficult, but important challenge concerns the development of mechanisms to enable users to customise their Internet-of-Things spaces and services. Currently there are three principal approaches for users: (a) let others do it for you (e.g. commercial companies), (b) customise the product oneself through suitable end-user tools or, finally (c) employ some form of Artificial Intelligence and let the systems do it for you. In this section we will discuss these approaches, illustrating them through examples of research projects.

5.2.User centric dimensions of the Internet-of-Things

User-centric approaches, as the name suggests, puts matters relating to the user at the heart of the process under consideration, in this case the design of Internet-of-Things products. Behavioural research has shown that the underlying motives driving human behaviour change little over time, despite the rapid advances in enabling technologies and the modes of provision. As DiDuca explained, “people will live as they have always lived in an (Internet-of-Things) environment, therefore the technology will have to adapt to them rather than designers relying on users’ having to become familiar with the technology in order to fulfil a need that they have” [40]. For example, people always want to communicate, whether it is in-person, via phone, SMS, email, social media or using some yet to be invented technology. This is a very helpful observation since it allows for the creation of innovative propositions based on core human desires and to ensure technology delivers what people truly need. This principle of putting people’s likes, desires and behaviours at the focal point of product research is the core principle in user-centric design which emerged in early 1990’s with work such as Jordan’s [63] Pleasures Framework, and Sanders’ [82] Experience Design approach. With regards to the Internet-of-Things, these ideas led to Chin’s Pervasive-Interactive-Programming paradigm (the first example of programming-by-example being applied to Internet-of-Things in a physical environment) which transformed users from passive into active designers of innovative “products”. Placing users at the core of the design process goes beyond simply allowing users to create highly personalised services (the products of their creation) but, to some extent, removes some of the ‘black-box’ mystic of technology and much of the technology-phobia (e.g. lack of understanding, loss of control, and compromising privacy) by making users stakeholders in Internet-of-Things product design. Given the pervasive nature of the Internet-of-Things, with billions of devices in the world and potentially hundreds in our own living space, these are important considerations for those who would like to see technology deliver its full potential to society whilst preserving the rights and freedoms of individuals [24]. Inevitably this raises issues relating to the balance of autonomy and control enjoyed by people and technology; for example the extent of control allowed to Artificial Intelligence versus the individual. These issues are discussed in the following sections.

5.3.Pervasive End-User Programming

Programming is an essential activity in creating Internet-of-Things applications. While hardware can often be purchased off-the-shelf, programming is difficult to avoid. One of the techniques that can come to the aid of would-be programmers of the Internet-of-Things, especially people with weak programming skills, is End-User Programming. The technique is characterised by the use of a combination of methods that allow end-users of an application to create “programs” without needing to write any code [23].

Examples of such approaches include using a jigsaw, a metaphor [54] that enabled novice programmers to snap together puzzle-like graphical representations of program constructs presented to users on a range of devices including smartphones [9]. Another example is Media Cubes [53] which creates a tangible interface in which users manipulate iconic physical objects (representations) to build context-aware Internet-of-Things-based applications. A technique that dispensed with any kind of representation in favour of demonstrating the required behaviour by directly interacting with Internet-of-Things gadgets, has emerged which is called by various names including ‘Programming-by-Example’ or ‘Programming-by-Demonstration’ [33]. It functions by reducing the gap between the user requirements and the delivered program functionality by merging the two tasks. These ideas are closely related to visual programming languages such as Scratch and Alice which have become popular simplified programming tools for children.

Another technique: ‘Pervasive-Interactive-Programming’ (PiP), derived from ‘End-User Programming’, aimed to create an intuitive programming platform that utilised the user’s target physical environment, with appropriate GUI support, to empower end-users to create programs that customised collections of Internet-of-Things devices (e.g. to behave in ways their owners wanted, without requiring any detailed technical knowledge or writing any code). In comparison to the case studies presented in the previous (Section 4.1, the pDorm), this project also addressed the programming of the functionality of a box, in this case a much large one, a building or more specifically a smart home.

Fig. 7.

PiP being used to configure an Internet-of-Things enabled dometic environment.

Figure 7 shows a picture of a person using PiP to configure an Internet-of-Things enabled domestic environment. In this instance the person is creating a set of rules that govern the behaviours that occur when the phone rings while they are sitting on the settee watching a streamed movie, possibly in the evening with low lighting. In this case the user is trying to set environment actions which respond to an incoming telephone call by raising the light level, pausing the video stream, and thereby allowing the occupant to deal with the incoming call. The difference to the earlier cases is that this project is dealing with a orchestrating the functionality of a collection of Internet-of-Things devices (a distributed set of embedded-computing devices), rather than that of a single device. The result of this programming is a rule-based object called a MAp (meta-application) that can be shared or traded with the wider crowd of PiP users. This is an example of the emerging areas of smart-homes and smart-cities. Programming distributed computers has been traditionally seen as more difficult than programming a single computer, so this project is a good illustration of how programming the Internet-of-Things can be simplified to the level that non-technical users can generate creative designs. With the aid of AI and machine learning techniques, the approach can be enhanced with respect to learning the users’ behaviour while reducing the cognitive load, and personalising the environment.

5.4.Harnessing artificial intelligence

We know from our own experience of life that intelligence is a continuum ranging from dumb to smart. The same is true for populations of Internet-of-Things devices where some are more capable than others. In life, we all want to be the smartest but in the world of technology, people can have strong views about how intelligent they want their technology to be. In the extreme, advocates of a technological singularity warn of super-intelligent robots emerging that dispense with their human creators [27] versus more positive voices which see artificial intelligence as enhancing the quality of our lives by removing the cognitive loads required to deal with technology (e.g. simplifying interaction with technology) or enhancing our reasoning and decision-making capabilities [21].

In the Internet-of-Things world, Artificial Intelligence is applied at two levels; one is concerned with controlling individual devices (e.g. embedded-agents) while the other harnesses the data accumulated from populations of devices (e.g. big-data). In the big data world, Artificial Intelligence is applied in a form of machine learning to harnessing data generated by individual devices, to learn users’ behaviours so as to provide a personalised experience to them. An example of such work is the recent Anglia Ruskin’s Hyperlocal Rainfall Project, funded by UK government (and partnered with industry), which sought to harness environmental sensor information combined with users’ cycling data to provide highly personalised route recommendations to the users. The focus of the project was to encourage more users to take up greener modes of transport by providing accurate localised (and personalised) weather and route recommendations, via a mobile app. The project expanded from its initial target of one city to cover the whole of the UK.

Concerning the use of Artificial Intelligence within individual devices, they use an approach called embedded-agents. This is a concept proposed in the late 90’s by one of the authors, Callaghan, who devised an approach that allowed meaningful amounts of intelligence to be integrated into computationally small devices. Essentially, he observed that both robots and seemingly static Internet-of-Things devices were both moving within a similar sensory space and the techniques, behaviour based Artificial Intelligence, that endowed mobile robots with robust real-time performance compact enough to work in Internet-of-Things devices (as against using the massive computational resources of cloud servers) [26].

5.5.Intelligent agents and adjustable autonomy

Given the potential for ‘AI-Phobia’, and its effect on commercialising Internet-of-Things applications, some years ago British Telecom (UK) commissioned research to understand people’s attitude to the role of intelligent devices in their customer’s lives. The study involved creating special smart (intelligent) Internet-of-Things devices that, in effect, had a knob on them which allowed the level of device intelligence or smartness to be set, much like you might set the volume of a hi-fi system or the temperature of a home. Typically, intelligence (in machines) is seen as comprising elements of reasoning, planning and learning.

Learning is an especially powerful element of artificial intelligence, since it enables a system to learn and improve its own performance, without human assistance (ultimately, enabling autonomous self-programming systems). The BT study, chose to investigate this topic through the concept of machine-autonomy which broadly concerned how independently of users, the technology might operate [8]. They hypothesized that there were various reasons that people may want to vary the intelligence or the amount of autonomy of their Internet-of-Things systems. For example, the amount of control a person wanted to seed to Artificial Intelligence might depend on a person’s mental or physical state (which may vary according to context, mood, age, health, ability etc.). For example, as the previous section on End-User Programming argued, since people are intrinsically creative beings, there is a possibility that too much computerisation might undermine this pleasurable aspect of life. Other reasons they hypnotised on included the shortcomings of Artificial Intelligence to accurately predict a person’s intentions (people may not always want to do what they did previously) and, of course, when predictive Artificial Intelligence makes mistakes, it can be very annoying! Finally, they posited on various surveys which suggested that people were fearful of too much intelligence and have a strong desire to remain in control [7]. The work sought to explore these hypotheses by conducting a study in the University of Essex iSpace, a purpose built experimental IoT environment that has been built in the form of a two bed-roomed apartment, see Fig. 8.

Fig. 8.

The essex iSpace.

The aim of the study was to gain an understanding of people’s opinions relating to how smart Internet-of-Things devices should be. The results produced findings which, at first glance were intuitive in that, the more “personal” an Internet-of-Things function was, the more the participants needed direct control over it whereas the more “shared” an Internet-of-Things function was, the less control they required. Thus, for example, participants wanted explicit control of their entertainment system but were happy to delegate climate control to Artificial Intelligence. When the results were explored in greater depth it was clear that people’s reasoning was more complex with some of the participants displaying a mental risk-versus-benefits calculation of their decisions to use any particular function. As explained earlier Artificial Intelligence is not perfect and is error prone. The cost of errors can vary from being just a mild irritation (e.g. in the case of the temperature being slightly wrong), to severely annoying (e.g. where the agent made a wrong choice of music). These findings were consistent with those of other researchers and offered an important lesson to Internet-of-Things system designers that, if Artificial Intelligence technology is to be utilised in Internet-of-Things applications, it should not undermine the users control or compromise their privacy. While the initial aim of the ‘Adjustable Autonomy’ work was to provide a mechanism to study the use of Artificial Intelligence in the Internet-of-Things, the ability to adjust the amount of intelligence an Internet-of-Things gadget or system offers was considered by users to be a desirable feature, and therefore a commercial asset to companies. However, this paradigm has yet to surface in the commercial marketplace which is showing a marked tendency to move away from distributed and localised control, to centralised systems and control. Clearly this is a complex topic and such a short section cannot adequately discuss the issues; thus, interested readers are referred to other papers from the authors and other that describe the methodology and studies in much greater detail [6,8,62].

5.6.Trust, privacy and security

The recent (2018) revelation that the UK’s Cambridge Analytica was able to harvest and exploit 50 million Facebook profiles, together with the earlier 2013 disclosure that the USA’s National Security Agency were running a programme of global surveillance of foreign and U.S. citizens, made the public and politicians aware of the risks that Internet-based technologies posed to society. Even the inventor of the World-Wide-Web, Sir Tim Berners-Lee, has joined the voices of concern saying “Humanity connected by technology on the web is functioning in a dystopian way” advocating the need to “continue fighting to keep the Internet open and free” which he believes can be addressed by stakeholders signing up to a “Contract for the Web” which he hopes will be available in 2019 [12], Furthermore he makes a plea to “decentralise the web” explaining “It was designed as a decentralised system, but now everyone is on platforms like Facebook” which can have a polarising effect that threatens democracy itself. These concerns are, of course, not new as many years earlier, there were reports from the European Parliament Technology Assessment unit [42] and the UK Information Commissioner’s Office [55] which highlighted these susceptibilities and the consequent need for debate on how society should balance the convenience that new technology affords with the need to preserve privacy. Indeed, from the outset, the Internet-of-Things community had raised such concerns themselves, taking these issues to the United Nations Habitat, World Urban Forum, explaining the risks to privacy that networked technologies such as the Internet-of-Things, pervasive computing, ubiquitous computing, and intelligent environments posed to the citizen or government, advocating the need for international regulation [34]. Sadly, no significant debate occurred (non that lead to regulatory changes) until the highly published transgressions of people’s privacy reported above surfaced. Before the Facebook Cambridge-Analytica debacle, most of the debate addressed the more visible aspects of technology and privacy such as surveillance cameras, identity or loyalty cards, Internet search engines and RFID tags. However, since then the debate has advanced, driven by the rising commercial interest in technologies like artificial intelligence and Big-Data. While the Internet-of-Things is not centre stage in this debate, given Internet-of-Things device deployment is in the order of billions, including our own homes and stretching out to critical services (e.g. hospitals, utility companies, defence), they are key players in any future privacy and security considerations. The risks to Internet-of-Things systems are many-folds, ranging from unauthorised access (and malicious activity) to privacy abuse of the Internet-of-Things generated data (e.g. monitoring and disclosure of private behaviours). Beyond this there are issues relating to Artificial Intelligence which is both embedded into Internet-of-Things devices and used within centralised analytical engines. Beyond the ‘here and now’ there are somewhat futuristic (and controversial) discussions about a potential technological singularity (that Artificial Intelligence developments may lead to machines be smarter than humans) through the massive distribution of embedded Artificial Intelligence into Internet-of-Things devices. In addressing these issues, many researchers argue we are caught in the paradox that in order to be useful, the Internet-of-Things sensors have to collect data, but once ‘the system’ knows, others can know too i.e., there is a direct threat to our privacy. The obvious solution is to introduce careful planning, design and regulation of the Internet-of-Things market which, due to its highly dynamic nature, is very challenging to governments, meaning that legislation inevitably trails technology, leaving the public at risk to having their trust, privacy and security compromised from time to time. Sir Tim Berners-Lee ‘Contract for the Web’ [12] would seem like an excellent start on the path to addressing these issues that aim to protect people’s rights and freedoms on the internet. This is particularly pertinent to this discussion as the web, in the form of web-appliances and embedded-web servers, is another mechanism that is used to create Internet-of-Things architectures [45]. In addition, many of Tim Berners-Lee’s concerns also relate directly to the management of the Internet and hence the Internet-of-Things, since the two technologies are interdependent. Clearly, not addressing these issues is unthinkable as, with unfettered commercial development of the Internet-of-Things, society risks creating a modern equivalent of Bentham’s Panopticon [88] exposing people to a form of “Big Brother” society [24] where some parties can monitor our every move which is probably not the kind of society most ordinary people would like to see IoT developments lead to. Thus, while the Internet-of-Things promises great benefits to society, without prudent oversight it raises significant new dangers for individuals and society as a whole. As researchers, we have an important role to play in ensuring technology in a morally and ethically responsible way, as work by Augusto et-al. [6] and Jones et-al. [62] most effectively illustrates.

5.7.Adoption, acceptance and appropriation of new technology

The relationship between human behaviour and technology can be viewed from different perspectives. For instance, from the sociological perspective, one looks at the use of technology and its effects on society [50,71], from the social-psychological perspective, one mainly looks at explanatory factors of technology use at the individual level [38,93], in the socio-cultural perspective, social constructivism plays a major role [13,72] and people and technology co-construct, and from the philosophical perspective, human-technology relationships are examined [56]. All these perspectives provide a specific and valuable contribution to our understanding of the relationship between human behaviour and technology.