MASTER’S THESIS IN INNOVATION MANAGEMENT
MEASURING REAL
-
TIME USER EXPERIENCE OF EMPLOYEES IN A WORKPLACE Case study
Victoria Saravas
Innovation Management Master’s Program June 11th, 2019
ABSTRACT Faculty
Faculty of Social Sciences and Business Studies
Department Business School Author
Victoria Saravas
Supervisor Päivi Eriksson Title
MEASURING REAL-TIME USER EXPERIENCE OF EMPLOYEES IN A WORK- PLACE
Main subject
Innovation Management
Level
Master’s thesis
Date 11/6/2019
Number of pages 83
Abstract
This thesis is a case study about one smart building, the Tieto Empathic Building, hereafter referred to as EB. Currently it does not have any AI systems integrated in it, but its owners consider AI as a viable future possibility to improve the current features and bring more value to the building users at work. The study is aimed at understanding the current situation and ex- ploring how a working environment can be improved for people using the facility. Investigation will be done based on users’ frustrations and positive feedback on the service. Before any as- sumptions could be made on what type of AI can be introduced to make the improvements, users’ experiences and behaviors will be studied first. Thus, the focus of this study will stay on the users and their experiences.
The purpose of the research is to explore, understand and analyze the user experience (UX) of EB users and to find a new way to measure the UX with the help of AI. To get these results, both positive and negative feedback from users will be collected. Both feedback types will be used as opportunities and sources for generating new ideas and scenarios on improving current UX.
Based on the identified needs from the users, further inputs from the experts working in EB will be gathered. Drawing from the inputs of both users and experts, appropriate AI sub-fields will be considered and scenarios for improving the current features will be suggested. The research aims at understanding the needs of users within the facility and their overall experiences as users.
The study setting takes place in the office environment. Words such as users, office workers, employees and building tenants will be used interchangeably in this research and all will describe the same participants in this field study providing inputs on their UX. The practical implications will be based upon disclosing and scrutinizing the experiences of users and proposing different scenarios in which AI can be potentially utilized to solve some of the identified problems and/or to improve currently working solutions.
The result of the study is the new insight about measuring UX in a smart building with the help of users’ feedback based on real-time data. In comparison to existing research, this study sug- gests a different view of measuring UX in the content of smart building environment. The pro- posed solution is the end-user index as a tool to enable a valuable end-user experience.
Key words: user experience, employees’ well-being, smart building, real-time experience
TABLE OF CONTENTS
ABSTRACT ... 2
1. INTRODUCTION ... 5
1.1 Topic of the research and background of the study ... 5
1.2 Research gap ... 5
1.3 The purpose of the study and research questions ... 6
1.4 Key concepts of the study ... 7
1.5 Delimitations ... 8
1.6 The structure of the thesis ... 9
2 THEORETICAL BACKGROUND ... 10
2.1 Research on UX ... 10
2.1.1 Interaction design ... 11
2.1.2 User-centered design ... 12
2.1.3 Ten usability heuristics by Nielsen ... 13
2.2 Research on smart buildings ... 15
2.2.1 The journey from origins until today ... 15
2.2.2 Benefits for users ... 16
2.2.3 Corporate benefits ... 17
2.3 Research on AI ... 18
2.3.1 Defining AI and comparing it to human intelligence ... 19
2.3.2 Economics of AI and technologies in general ... 21
2.3.3 AI’s place in organizations and day-to day work life ... 25
3 DATA AND METHODS ... 29
3.1 Case study as a methodological approach in this research... 29
3.2 Research design and data collection ... 35
3.3 Reliability and validity ... 40
4 FINDINGS ... 41
4.1 Results on experiences of EB users ... 41
4.2 Recommendations for change and/or improvement ... 43
4.3 Proposed scenarios by users and experts in which new technologies could be applied ... 50
5 CONCLUSION AND DISCUSSION... 56
REFERENCES ... 64
APPENDICES ... 78
Appendix 1. Form for the briefing with the users ... 78
Appendix 2. Template form for online log “Week in the life of users” ... 80
LIST OF FIGURES
Figure 1. Visualization of the structure of the thesis
Figure 2. Example of an error notification. Adapted from Cooper et al. (2004)
Figure 3. Distribution of building costs over its life and added value of smart building systems.
Adapted from Sinopoli (2009)
Figure 4. Unequal distribution of AI benefits between front-runners and non-adapters companies.
Adapted from McKinsey Global Institute analysis (2018)
Figure 5. The four stages of Industrial revolution. Adapted from DFKI (2011) Figure 6. EB packages (Tieto 2019)
Figure 7. Data collection process
Figure 8. Consolidated answers from the users describing what in their understanding EB does or trying to do
Figure 9. Consolidated statistics on how often and what features the users utilize in EB Figure 10. Recommendations for change and/or improvement
Figure 11: Some examples of current loading page design in EB (Tieto 2019)
Figure 12. Human as a sensor–profound understanding of user through different feedback (Tieto 2019) Figure 13. End-user index as a tool to enable a valuable end-user experience (Tieto 2019)
Figure 14. End-user experience as a correlation of three categories: physical space, technology and culture (Tieto 2019)
LIST OF TABLES:
Table 1: Some definitions of AI organized into four categories. Adapted from Russel & Norvig (2016).
Table 2: Users’ demographic characteristics.
1. INTRODUCTION
This chapter provides a brief overview of the study by outlining the initial setting of the research, importance of the topic and the need for studying it.
1.1 Topic of the research and background of the study
Today technologies have been adopted in most of the sectors and fields to support peoples’
work. They effect and influence various activities carried out within numerous contexts (Srinivasan, 2018). Society is moving towards being more digitalized and information centered. Inevitably behav- iors tend to change and as a result companies adjust and re-define their strategies and modernize their products and services. Some businesses recognize the need for change right away and act proactively, while others take reactive measures. Both approaches benefit businesses if changes are done promptly enough (Kliem et al., 1997).
Artificial intelligence (AI) is an essential area in business transformation that has significantly impacted the work environment. AI has gained overwhelming popularity due to its importance across various fields and industries (Poola, 2017). Some of the benefits associated with AI include time- saving and, as a result, increased business output from routine human activities. Additionally, AI has resulted to automated transport systems, computerized methods, reduced human effort, computerized jobs and reduced human involvement in perilous tasks (Poola, 2017).
This thesis is a case study about one smart building, the Tieto Empathic Building, hereafter referred to as EB. Currently it does not have any AI systems integrated in it, but its owners consider AI as a viable future possibility to improve the current features and bring more value to the building users at work. The study is aimed at understanding the current situation and exploring how working environment can be improved for people using the facility. Investigation will be done based on users’
frustrations and positive feedback on the service. Before any assumptions could be made on what type of AI can be introduced to make the improvements, users’ experiences and behaviors will be studied first. Thus, the focus of this study will stay on the users and their experiences.
1.2 Research gap
User experience (UX) is a commonly used term among business practitioners and is studied widely within the human-centered interaction (HCI) field especially when it comes to finding ways to achieve positive UX in digital artefacts such as web-pages and virtual tools (Law et al., 2014; Law et al., 2010; Hassenzahl & Tractinsky, 2006). From the very beginning, HCI researchers have been the pioneers in the field of usability research and leading the studies in academia as well. However,
placing the person in the center while designing systems and services only started in the late 1990s, when Don Norman introduced the term UX (Rosenzweig, 2015). Today users’ feedback is considered as one of the most valuable insights for service and product design development, while before it was perceived as an absolute luxury and meaningless redundancy (Kuniavsky, 2003). UX is still consid- ered to be quite new as a concept and when referring to it practitioners in the area usually talk about innovation, vision, satisfaction of the clients and growth as its key focus areas (Klein, 2013). Meas- uring emotions is one area in UX that has been studied widely (Benyon, 2014; Law et al., 2014;
Saariluoma & Jokinen, 2014; Klein, 2013; Desmet & Hekkert, 2007; Hassenzahl & Tractinsky, 2006).
There is limited academic research assessing the type of AI that is effective in improving UX.
The reason for the little research could be attributed to the fact that AI is an immensely broad field (Russel & Norvig, 2016; Nilsson, 2014). Of the few researchers who focus on UX involving AI, a majority direct their studies to a specific sub-field of AI, not a comprehensive study covering all aspects of it at once. Some examples of recent studies include integrating machine learning (ML) in UX practices (Dove et al., 2017) and using natural language processing (NLP) for conversational UX, such as virtual assistants or chatbots, which as of now are known to be lacking the conversational competences and remain disappointing within the current UX (Moore et al., 2017). However, UX in designing for AI is gaining popularity in blogs outside academia.
1.3 The purpose of the study and research questions
The purpose of the research is to explore, understand and analyze the UX of EB users and to find a new way to measure the UX with the help of AI. To get these results, both positive and negative feedback from users will be collected. Both feedback types will be used as opportunities and sources for generating new ideas and scenarios on improving current UX. Based on the identified needs from the users, further inputs from the experts working in EB will be gathered. Drawing from the inputs of both users and experts, appropriate AI sub-fields will be considered and scenarios for improving the current features will be suggested. The research aims at understanding the needs of users within the facility and their overall experiences as users. The study setting takes place in the office environment.
Words such as users, office workers, employees and building tenants will be used interchangeably in this research and all will describe the same participants in this field study providing inputs on their UX. The practical implications will be based upon disclosing and scrutinizing the experiences of users and proposing different scenarios in which AI can be potentially utilized to solve some of the identi- fied problems and/or to improve currently working solutions.
Research Questions
The goal of this research is twofold; firstly, to better understand the user needs, and secondly, to discover how to better measure real-time UX by introducing AI. The research will be addressed by answering one major research question (RQ) which is:
RQ. How can AI be applied in addressing the work-related needs of users at EB?
Sub-questions supporting the main research question are the following:
• What are the user experiences of EB users?
• How can AI be applied to improve the user experiences of EB users?
The first sub question aims at understanding the experiences that office employees have in EB.
The second sub question seeks to explore possible scenarios for improving UX with AI based on collected feedback. It also seeks to find out how UX can be measured in a better way in comparison to the current situation with the help of AI.
1.4 Key concepts of the study
The study seeks to find how AI can potentially improve UX in a smart building. Before going directly into the case, key terms and concepts will be presented.
The thesis is split across three main subjects. First one is UX as the core of the research, focus- ing on how users interact with the smart building, how well they understand its purpose and function- alities and what is the overall experience that they have when using it. Second subject is the smart building as an environment where field study is taking place. The third and last topic is AI and it is looked at as a future possibility to improve the current UX. The study defines each of these three topics much broader within the theoretical background and offers a brief overview in this section together with other important definitions supporting the study.
UX is a wide term and there is no single definition available that is commonly accepted by experts in the field and researchers (Law et al., 2009). There is a diversity in existing definitions and it is based on the industry in which the UX takes place. Currently most of the discussions around UX happen in the context of digital solutions and online.
Smart building combines set up and use of integrated and innovative building and technology systems (Sinopoli, 2009). It connects and adjusts its four main elements, which are structure, systems, services and interrelationship based on which it delivers an effective and cost-efficient environment (Fantana & Oae 2013, 223-224).
AI has been recognized as the most disruptive class of technologies (Gartner, 2017), is a vital tech layer for creation of competitive products and services (Nordicaiinstitute, 2019) and is as a major driving force behind the fourth industrial revolution (Schwaab, 2015).
IoT or Internet of Things, also referred to as Internet of Everything (Lee & Lee, 2015) allows sensors and actuators to blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP) (Gubbi et al., 2013).
Simply put it means that it takes all the things available and connects them to the internet. IoT is shaped by the rapid growth of both real and virtual worlds (Shrouf et al., 2014). IoT offers number of different types of services to improve everyday life and that also covers the workplaces, where IoT is used as basis to analyze and process data within the smart buildings (Plageras et al., 2018). The case study smart building is based on IoT technology without any AI features.
Some of AI’s related fields or sub-fields are explained below. They are explained here because they came up through data collection process and were suggested for scenarios for improving current solution of the building.
Machine Learning (ML) is a science that helps computer to automatically distinguish meaning- ful patterns and relationships of data as an alternative to being programmed manually (Shalev- Shwartz & Shai, 2014). ML can be referred to as a sub-field or related field of AI, which is itself a sub-field of computer science.
Deep Learning (DL) is a subfield of machine learning. DL allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction, it discovers intricate structure in large data sets by using the backpropagation algo- rithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer (LeChun et al., 2015).
Natural Language Processing (NLP) uses computational techniques to learn, understand and produce human language content (Hirschberg & Manning, 2015). It is related to number of fields, including linguistics, AI and computer science and primarily focuses on interaction between natural languages (those spoken by people) and computers (Chopra et al., 2013).
1.5 Delimitations
Although this study covers the topic of AI and introducing it to smart buildings, there won’t be any technical content shared in this study, as the focus will primarily stay on profound understanding of the UX of users and aligning with their needs. The study excludes any details on AI solutions and any technical architecture that could be used for building them. Moreover, AI will only be presented
as a possibility and potential solutions that may or may not be used by the technical team later when releasing further versions of the EB.
In terms of users’ selection, some limitations are recognized due to big variety of user types.
Only employees of Tieto from Keilaniemi office were taking part in the field study. External users from the same building, such as facility management personnel from catering and security services did not take part in the study. Other groups of users such as customers of EB who purchased the service to their offices were not taking part in the study either, nor were any other tenants of EB from other Tieto offices outside Finland where EB has been implemented. However, having feedback on UX of the members mentioned above would be beneficial if research will be extended further.
1.6 The structure of the thesis
To better understand the structure and how to navigate through this paper, there is a visualiza- tion on all the content details presented in this paper.
Figure 1. Visualization of the thesis structure
2 THEORETICAL BACKGROUND
This chapter presents a theoretical background to the study and is a literature review that aims at providing the setting for empirical part discussion. The content available in this review concentrates on exploring three main concepts: UX, smart buildings and AI.
2.1 Research on UX
Today, if the companies want to be successful, the first thing they need to acknowledge is the fact that they are in the business of customer experience, since competing based on existing products and services is no longer enough (Verhoef et al., 2009). What matters is how the companies deliver those products and services. Therefore, the focus is not just on what the companies deliver but also on how they do it (Berry et al., 2002). The experience could be good, bad or indifferent, but it’s always there and the focus must remain of how effectively it is managed by the provider. One crucial point to remember is that regardless of how complex the whole journey is, the customers are not usually aware or interested to know all the touchpoints involved such as separate processes or indi- vidual experiences within the whole journey. They want to receive an end-to-end experience with a well-defined beginning and end (Meyer & Schwager 2007, 116-126).
When we think about good design in the offices, it’s not just about getting aesthetics right, but also about how to create a working space that would make people comfortable and allow them to be more productive (Becker & Steel, 1995). How can one know whether design is good or bad? Just like with many other things, we know it through comparison and identifying weaknesses and strengths.
The two most vital features of a good design are discoverability and understanding. Within the dis- coverability it’s crucial to come up with the actions that are possible and figure out how and in which circumstances they can be fulfilled. As for understanding, the importance lays in comprehension of the meaning, functions, support and ease of usability (Norman 2013, 3). Users have significant inputs when it comes to providing feedback on usability, yet it is important for designers to know when to listen to users and when not to. The reason behind this is simple. Users may not always know what is possible to implement, but if they are really listened and guided and sometimes coached in a careful way, their opinions, knowledge and suggestions could be enormously valuable (Lowdermilk 2013, 17-18).
Usability is a quality feature evaluating the ease of navigation through user interface and it also refers to measures for refining ease-of-use during the design phase (Nielson, 2003). It is the study of how people relate to any product and it also refers to human factors (Lowdermilk 2013, 5). According to Nielson, usability is defined by five key elements:
1. Learnability - when faced with the design for the first time, how easy it is to complete and learn the basic tasks?
2. Efficiency - once users are familiar with the design, how long will it take for them to ac- complish the tasks?
3. Memorability - if design hasn’t been used for a while, how easily the users can restore their competences when they return to the same design again?
4. Errors - what is the number of the errors made by the user? How serious are those errors and how the user recovers from them?
5. Satisfaction - does the user find the design pleasant? (Nielson, 2003).
By focusing on usability, designers and developers can meet the needs of users and conse- quently save a lot of time in own work and use it for identifying possible problems. Because of careful evaluation, they can avoid the re-designing process of products and services and as a result can save a lot of money (Lowdermilk 2013, 8-10).
2.1.1 Interaction design
All modern devices and different types of artificial things have been designed and some re- quired certain complexity with different aspects in mind, such as layout, operation, mechanisms and others. There are different areas of focus in the process of design depending on the purpose of the final product. For example, interaction design concentrates on how people interact with technology.
It aims at increasing people’s understanding of what can be done, what is happening right now and what may happen later. Interaction design is based on the principles of psychology, art and emotion to ultimately provide users with positive and pleasant experience. There is also experience design that has its efforts on quality and overall enjoyment of the experience. So, if interactive designers under- line usability and understandability, experience designers emphasize the emotional impact (Norman 2013, 4-9).
When focusing on interaction in the design, the key principle should be to make sure that when interaction happens it would feel natural. The tone of interaction should remind an interaction with a helpful, considerate and polite person. Digital products may sometimes give the impression of being
“rude”. For example, the common error messages and notifications that pop-up whenever the user does something “wrong”. The content of the message clearly shows that the user failed and is also blamed for a mistake that isn’t his or her fault in the first place or at least it shouldn’t be. The action
required from the user is to accept the blame by clicking OK and to confirm the mistake as we see in the example below.
Figure 2. Example of an error notification. Adapted from Cooper et al. (2004)
The real problem here is the poor product design behavior that wasn’t considered when design- ing the product. If the user needs weren’t well anticipated in the product design creation, in the end users may end up with unfriendly products (Cooper et al., 2004).
2.1.2 User-centered design
User-centered design (UCD) is a methodology that designers and developers use to make prod- ucts which will meet the needs of the users (Lowdermilk 2013, 13). If the goal is to put users’ needs and capabilities first and analyze their behaviors as a priority, then we are dealing with human-cen- tered design. The designing process should then match those needs and capabilities. The design will be considered as good if it goes along with understanding of both technology and psychology. Design can be considered as implemented successfully if it shows good communication, especially from ma- chine to person, specifying what features are possible, what can happen and what will happen (Nor- man 2013, 4-9). UX is the vital element of interaction design and one cannot design a UX, only design for a user experience (Rogers et al., 2007, 15). If a product is used by someone, it automatically has a UX, covering how it is being used by someone and identifies the product behavior (Garret 2003, 10). To understand how people certain products will be used, it’s crucial to have a clear understanding what is the objective of developing the product in the first place. Usability goals should meet specific usability criteria, which could be improving the speed, efficiency, increasing safety, introducing new features that were missing to improve effectiveness, simplifying the usability or any others. These goals could be identified by asking the questions regarding the interactive product and the UX itself.
The questions must be very detailed to help identify potential problems that weren’t considered before.
The design work progress should involve a constant UX process (Garret, 2010). When interacting with users, some of them may feel a bit intimidated by for example programmers or other technical experts. However, since users play a vital role in providing insights about possible flows and are the ones who give ideas for potential improvements, it is important to acknowledge that those potentially intimidating technical professionals are the ones needing help from users. It helps users to feel more at ease and gives them a sense of confidence knowing that they are the ones with the answers, as they are helping to create better products. Thus, when asking for help, it’s crucial to ask questions contin- uously and get to the point of what the users are asking for, even if they aren’t making themselves very clear in explaining what they need. Follow-up questions and guidance should help users to be- come more explicit about their struggles and needs (Lowdermilk 2013, 20; 25).
2.1.3 Ten usability heuristics by Nielsen
In 1995 Jakob Nielsen introduced 10 usability heuristics for user interface (UI) design, in which the name "heuristics" is used because these rules are based on practice rather than representing con- crete theoretical guiding principles (Nielsen, 1995). Initially the work started on heuristic evaluation together with Rolf Molich, where both researchers focused on interface design and improving human- computer dialogue. They concluded that due to complexity of heuristic evaluation one single persons’
overview and feedback isn’t enough and couldn’t be considered reliable (Nielsen & Molich, 1990;
Molich & Nielsen, 1990). Subsequently Nielsen continued the research and through analyzing 249 usability problems based on factor analysis he introduced nine heuristics for UI design (Nielsen, 1994).
1. Visibility of system status - the user should always be well-oriented and fully understand what is happening in the system, whereas interaction between the user and the system should be as logical and quick as possible.
2. Match between system and the real world - system should be interacting with the user by using the language that he or she understands. using words, phrases and terms that are com- mon for the user in his or her daily lives is preferable rather than using specific terms
3. User control and freedom - users often make mistakes, so the system should always provide an obvious chance for the client to “return, as it was,” with a minimum of effort, as well as give a chance to “redo”.
4. Consistency and standards - don’t confuse the users by defining the somethings with differ- ent words and terms. Be consistent and follow the standards
5. Error prevention - minimize the number of conditions in which errors can be made. Ideally design carefully to avoid possible error messages.
6. Recognition rather than recall - do not force the user to memorize a large number of objects, actions and options. The visitor should not keep in mind the information, moving from one part of the system to another.
7. Flexibility and efficiency of use - do not overload experienced users with unnecessary in- formation, give them an opportunity to perform frequently repeated actions as quickly and easily as possible.
8. Aesthetic and minimalist design - discussions should not be irrelevant or outdated. Each extra wording creates a more difficult perception of the environment and makes it impossi- ble for the visitor to find what he came to the site for in the first place.
9. Help users recognize, diagnose, and recover from errors - error messages should be ex- pressed in a language that is understandable to the user, describing the problem as accurately as possible and providing possible solutions to it.
10. Help and documentation - even if the system can be used without documentation, reference information may still be required in the process of working with it. Such documents should be drafted in such a way, so it will be easy to find them and the focus should remain on the user’s task, it should be brief and should list the steps that user needs to take.
Heuristic evaluation helps to identify the usability problems and as a result saves the money by avoiding those mistakes that can potentially cost companies a lot of money. In one of his own projects Nilsen provided a financial breakdown from heuristic evaluation that he conducted. The team spent
$6,400 for their heuristic evaluation which involved four evaluators and discovered usability prob- lems worth of $395,000 (Nielsen 1992, 8).
2.2 Research on smart buildings
Working spaces just like many other things are also influenced by technologies and undergo through changes (Beirne & Ramsey, 2018). One of the trends within the buildings’ evolution is in- troducing smart building technologies to automate the working space premises and to positively affect peoples’ work life. By using smart buildings, the building owners can make them more commercially viable and the users can have a more functional experience inside the premises. When a workplace is designed with employees’ well-being in mind, it focuses on providing an enriching experience to its workers and engaged workers are known to be more successful in providing better experiences to their clients (Reijseger et al., 2017; Bakker et al., 2012; Bakker et al., 2008).
Smart spaces have been recognized as eight out of ten strategic technology trends for 2019. It is highlighted that “Smart spaces are a journey, not a destination”1, the journey that is developing from isolated and autonomous systems towards inclusive and intelligent environments. Smart spaces unite trends and technologies with a goal of creating an experience for industry scenarios or targeted personas. Smart buildings, especially in the content of a workplace shouldn’t be looked at as a set of business strategies or technologies, but instead should be perceived as an invitation or framework to collaborate with the purpose of employees’ self-development and achieving better engagement among each other. Such surroundings allow collaboration between people and technology-enabled systems letting them to have a connected, increasingly open and coordinated interaction (Gartner, 2018).
2.2.1 The journey from origins until today
The first research works about buildings automation without directly referring to it as such started in the 17th century. Dutch scientist Cornelis Drebbel, who is mainly known for his invention of the first navigational submarine, also invented the thermostat. With its system he could control the temperature in the building. It was based on U-shaped flask with mercury measuring the temperature, and on the lever, which, depending on the indicators, had an effect on the furnace, cooling or heating it (Tierie 1932, 43; 59-64).
The concept of the smart building per se, at least the discussion on the topic started to be formed in 1980s. In 1984 the New York Times published an article about the new generation of the buildings
1 https://www.gartner.com/doc/3891569?refval=&pcp=mpe#a-125065708
that are capable of independent thinking and started to be referred to as intelligent buildings. They were called as a matrimonial of two technologies: old-fashioned building management and telecom- munications. The beginning of 1980th introduced several technological trends. One of them was the change in telecommunication industry in the United States of America that was going through liber- alization and the telecom market started expanding with the appearance of new companies, services and various innovations. Another important trend that at that time wouldn’t be seen related to smart buildings was in the foundation of personal computers. Two of those trends formed the strong con- nection between the real estate and the technologies and created a new business model called “shared tenant services”. Since telecommunications as an industry was established recently, it allowed the real estate owners to enrich their businesses by reselling services within own premises. Already in 1990s the buildings started to have highly technological infrastructure – cable and audio systems, video surveillance, closed-circuit television (CCTV), access control systems and others (Sinopoli 2009, 2-5).
Today, with the progressively strict regulation and realization on climate change, energy reduc- tion became to be a recognizable driver in designing modern buildings (Ghaffarianhoseini et al., 2013, 1-17; Buckmann et al,, 2014). The market of smart buildings is predicted to reach $10,2 billion by 2026 from $3,6 billion in 20172.
2.2.2 Benefits for users
One of the benefits of smart buildings is the space optimization and costs savings that it brings along (Zhou & Haghighat, 2009). Already in 1980s there was a research about comfort in the build- ings that was conducted about the temperatures in the buildings. The results showed that peoples’
perception of control over the environment in which they worked directly affected their comfort and satisfaction (Leaman & Bordass 1999, 160). It’s no news to anyone that consumers get smarter every day. In our daily personal lives, we are used to number of convenient services and apps providing us with number of different capabilities and making our lives easier, helping us to faster find things that are beneficial to us. Now people expect similar consumer-grade experience when they are showing up for work. Smart buildings help companies to achieve better employee engagement, collaboration and productivity. The goal is to create a compelling workplace to engage the workforce and really focus on productivity gain in terms of business. The reality we are facing today is the shift in many organizations towards activity-based work. The trend will continue to drive the change in the work- place and especially in how the workplaces are being designed and allocated.
2 https://www.navigantresearch.com/reports/smart-buildings-and-smart-cities
Most of the organizations are mobilized, people don’t have assigned desks any more, they choose their places for work freely in the office by following flexible seating and moving around the office premises. They are in the meeting rooms, changing floors to work with different teams, if not in the office they can also be at customers’ or partners’ premises or they can be working distantly.
Colleagues are scattered, spaces are variable as with the laptop and Wi-Fi people can work anywhere and many meetings are virtual. This is the business culture at workplace that we are living today.
Smart buildings can help their tenants and visitors to have real-life interactions and get long- term space insights. Users can save time by finding colleagues fast and easily when they need them, can automate the usage of the work environment by finding the meeting rooms and workstations immediately and on the spot. At the same time they can immediately report about any possible issues in the facilities and overall have a better experience in the environment by having a control over personal preferences selection when it comes to temperature, lighting, shades etc.
2.2.3 Corporate benefits
There are tremendous corporate benefits that smart buildings can offer to organizations. First one is saving costs due to a space optimization (Minoli et al., 2017). Providing a space for each employee is very expensive (Voordt, 2003). The costs usually combine expenses within three main categories, such as building, operations and technology (Brill & Weidermann, 2011). They may be split into more details such as rent, insurance, tax, operating costs, lightning, power, keeping the building cooled or heated and many other add-on costs. It is very costly to maintain the place for professionals, especially if those spaces aren’t fully utilized. Real estate expenses are usually the second or third biggest expense area for majority of organizations (Stoy & Kytzia, 2006). If compa- nies pay attention to the space utilization, they can get rid of underperforming real estate and can potentially save a lot of money by eliminating unused square meters (Riratanaphong, 2013). Compa- nies can gain a good understanding of space utilization through reports done inside the smart build- ings. With available data about occupants and their behaviors companies can analyze employees’
collaboration and improve productivity statistics3. When employees interact with each other, building owners can see the touchpoints between different units through smart buildings reports. This infor- mation can help finding out whether people from different units spend enough time together collab- orating, as well as whether and how the productivity gain is happening based on the space layout.
The use of site amenities can also be measured by finding out if employees use and take advantage
3 https://mediacenter.ibm.com/media/t/0_8j8clerd
of for example phone booths, shared spaces in the kitchen and different collaboration areas. If it is evident that the spaces are not being utilized, they can be redesigned to the most preferable ones. If unnecessary space can be eliminated and matched to employees’ needs, ROI (return on investment) on those places can be reached on space optimization. With that ROI companies can utilize the money on employees’ engagement within the preferred spaces (IBM 2017).
The life cycle of a smart building combines the preliminary costs (concept, design, planning and construction) and the long-term operational costs of the facilities. Smart building can cut expenses on construction of technology systems and overall operations costs of the building. It doesn’t matter if construction costs should be considered as installed separately or integrated later. Either way they will include technology systems such as building automation, telecommunications and security sys- tems which will help reducing costs in the long run as shown in the figure 3 below (Sinopoli 2009, 159-161).
Figure 3. Distribution of building costs over its life and added value of smart building systems.
Adapted from Sinopoli (2009)
2.3 Research on AI
To get a general understanding of the subject, this section will present various definitions of the term throughout many years after its encounter, explain the reasons why AI means different things to different people and compare AI with a human. In addition, this section will also present AI’s place in today’s economy and forth industrial revolution, AI connection to psychology and its role in day- to-day working life for people in organizations.
2.3.1 Defining AI and comparing it to human intelligence
Defining AI
Today the topic of AI receives a lot of attention as media exposure and public discussions are almost impossible to avoid (Bloomfield, 2018). As a field AI has experienced rapid development in diversification and practicality and its’ methods have been growing and expanding over many years of its presence (Munakata 1998, 2). Due to all the changes in AI methods and its constant development, the term has been given a big number of definitions, some of which will be shared here. Sometimes AI is perceived as a combination of other areas and it is relevant to any intellectual task, however it is indeed a very strong self-discipline (Russel & Norvig 2016, 1).
The following definitions listed below (see Table 1) were consolidated from eight textbooks and categorized according to two different dimensions. Definitions in the top boxes are focusing on thought on process and reasoning, whereas two bottom boxes describe definitions from behavioral point of view. All the definitions are also split and systematized against human performance on the left and against rationality on the right.
Table 1: Some definitions of AI organized across four categories.
Source: Russel & Norvig 2016, 3.
Human-centered approach belongs to empirical science, whereas rational approach involves a combination of mathematics and engineering with each approach providing valuable insights. All four AI method groups that are illustrated in the table have been followed in different time by different
people. Thus, AI can be interpreted from number of different perspectives and due to variety of def- initions can be perceived from various angles (Russel and Norvig 2016, 1-3).
Comparing AI with a human
People in comparison with computers are quite fast at intuition but slower in calculations. AI enabled machines are good in both calculations and intuition when it comes to solving the problems (Kahneman 2011, 17). If a person is faced with a complex mathematical task, an average individual will go through a slow thinking process based on arranged sequence of steps. Solving the task would require mental work with certain effort and self-reasoning and due to some strain from the mental work when concentrating, would partly include the body. The machine on the contrary would solve the task by using pre-set algorithms answering the requirements of the task. Solving the problems quickly without losing the speed in calculation can be achieved through smart programming and building problem solving AI algorithms (Kahneman, 2011).
The fundamental difference between a human and a computer could be seen in memory capac- ity, ability to concentrate upon work and getting distracted, which sometimes could be problematic for a person, but is not an issue for a computer (Norman, 2014). Processing emotions and feelings is also another aspect where we can see the main difference between a human and a computer. Com- puter cannot wish for anything independently and therefore, regardless of how smart, strong and fast it is, it doesn’t have what a human has, which is a desire. Desire is an engine that creates life and is a real perpetuum-mobile (Kurpatov 2018, 18). Computer can be disassembled into constituent pieces and we will not find anything inside it beside the parts that it is made of. Yet it is very clear to us that we still have the exact same computer in front of us. When it comes to a human, we cannot dissemble a person to elements such as memory, focus, emotions, will, intellect etc. In neither of these elements we can find a human. A human can wish, and, in own wishing, the human has the pledge of own motion, which can be referred to as incomprehensive pertuum-mobile (Kurpatov 2018, 19).
Reasons for vague perception of AI by the public
AI has been identified as a conceptual breakthrough in a fledging field of computer science (Waterman 1986, 3) and as a new science with old heritage in philosophy and automata (Skinner, 2012). Many people often assume certain things to be AI when in fact they are not. Some may think it is about the artificial lifeforms that exceed the human intelligence, others can categorize it as pretty much any data processing technology. The first and most upfront reason for these confusions is quite simple - as of today there is no single officially agreed definition of AI (Jennings et al., 1998, 2;
Schalkoff 1990, 1; Russel & Norvig 1995, 5; Russel & Norvig 2003, 1; Russel & Norvig 2016, 2).
Another reason bringing misperception is the legacy of science fiction (Barrat 2015, 18). Number of media exposure brings certain level of disorder when it comes to the meaning of AI. Mainly it is the word artificial that may bring different associations and can arise fear of intelligent cyborgs (Hauge- land 1989, 3).
The last reason why perception of AI is quite nebulous is its complexity (Ertel, 2018). From the first glance it’s not always easy to understand which of the tasks are simple and which ones are dif- ficult for AI to perform. For example, a task such as picking up an object may seem rather simple to a person, it requires a combination of sub-tasks for AI, which makes it challenging in the end (Saxena et al., 2008). Simultaneously, what seems hard for people could be easy for AI. If we take chess, a game that takes people many years to master, turned out to be learnable by computer to the extend where it was able to outperform a human. In 1996 Gary Kasparov, one of the greatest chess players of all times (Newborn, 2012) had a competition with supercomputer Deep Blue created by IBM. That year they had 6 rounds and Gary won the man against computer battle with the score 4-2. A year later, IBM improved its computer with better decision-making tactics and invited Garry for a rematch. This time for the first time in history machine has conquered human in playing chess (Brynjolfsson &
McAfee 2014, 50). When Garry was using his intuition, Deep Blue relied on raw calculations which gave a supercomputer an edge and allowed to beat Garry (Pandolfini 1997, 7-8).
When comparing the skills between people and AI, it’s important to remember that only weak AI is available today, meaning that it is programmed to perform specific task, and, in that task, it may or may not outperform a human. Thus, current AI technologies remain limited to specific intellectual areas (Lu et al., 2018).
2.3.2 Economics of AI and technologies in general
Although there are certain causes that evidently show the economic potential of AI, there is a problem in viewing AI economically since it should involve three variables, which are fundamentally regarded as challenging. They are:
1. Solving the resource allocation (which attends to the uncertainty in preferences) 2. Rationality abstraction (which is useful for modeling behaviors of interactions)
3. Authority and activity decentralization (which identifies the nature of decision-making) (Wellman 1995, 360-362).
Technological innovation is neither a new phenomenon on its own (Teece 1986, 285), nor it’s a new phenomenon in terms of the impact on the Worlds’ economic growth (Malecki 1997, 1). Today technologies play an extremely important role in peoples’ lives and are entangled around humans’
existence (MacKenzie & Wajcman 1999, 1). After 1950s Information and Communications Technol- ogies (ICT) has shown a substantial progress and throughout its rapid development it was proposed that computing might eventually become the fifth utility in peoples’ daily lives after water, electricity, gas and telephony (Buyya et al., 2009, 599). In today’s modern World contribution to science, inno- vation and new technologies is a decisive factor of social and economic development (Rosenberg &
Nathan, 1982).
AI adoption in the future
It is predicted that by 2030 about 70% of organizations might have absorbed at least one type of AI out of the following five, which are NLP, robotic process automation, computer vision, virtual assistants and advanced ML and less than 50% will adapt all five categories. As a result, we can expect a certain gap in performance between the organizations that adapted full range of AI technol- ogies (front-runners) and those remaining ones that refused to adapt. This might result in front-run- ners gaining unbalanced profits. By 2030 they may potentially multiply their cash flow by two times and this will mean an extra yearly net cash flow increase of approximately 6% for more than next ten years. It has been noted that front-runners are usually more likely to invest in AI, look more positively on business cases for AI and tend to have a solid starting IT base. Meanwhile non-adapters could be faced with 20% decline in the cash flows compared to today’s figures, taking into assumption that the revenue model and profit pressure stay as they are today (Bughin et al., 2018).
Figure 4. Unequal distribution of AI benefits between front-runners and non-adapters companies.
Adapted from McKinsey Global Institute analysis (2018)
Fourth Industrial Revolution
Our lives have been fundamentally transformed within the past fifteen years with the rapid development of ICT and Internet, combining most of the communications World-wide (Chen 2012, 6). Industrial production also found its way to profit from the improvements in computer science and ICT (Weyer et al., 2015, 571). We can see it in the automation trend called Fourth Industrial Revolu- tion or industry 4.0 (Oesterreich & Teuteberg 2016, 1). The term has its origins from Germany, when in 2011 German government held a trade fair in Hannover (Kagermann, 2015). During the event it was suggested to start using the information technologies (IT) widely in production lines and it quickly became the countries’ national high-tech strategy for 2020 (Zhou et al., 2015). With the es- tablished strategy German enterprises started transforming themselves from being industrial to “smart”
ones and other countries which were actively developing the new technologies followed the trend.
With this movement, the term industry 4.0 became a synonym of the forth industrial revolution. Its essence is in the digital ecosystem that originates from cyber-physical systems (CPS) which came from a combination of material and virtual worlds (Hermann et al., 2016, 5).
The image below (see Figure 5) adapted from the German Research Centre for Artificial In- telligence (DFKI) displays all four industrial revolutions with the timelines, innovations and break- throughs within each of the periods. The first industrial revolution happened in the end of 18th century during which the main raw materials were coal and metal and the main technology used were steam and the conversion of thermal energy into mechanical energy. Second industrial revolution that hap- pened between second half of 19th century and the beginning of 20th was about the invention of elec- trical energy, the subsequent mass production and the division of labor. The third industrial revolution starting from 1970s was about the use of electronic and information systems in production that have provided intensive automation and robotization of production processes. The forth industrial revolu- tion which we are experiencing today is based on cyber-physical systems. The changes that are hap- pening based on the influence of information technology are helping to significantly increase the quality of products and services. As a result, the customer satisfaction and loyalty increase. In the meantime, manufacturers use new approaches and business models, which allows them to optimize the production and earn more (Bloem et al. 2014, 11-15).
Figure 5. The four stages of Industrial Revolution. Adapted from DFKI (2011)
Industry 4.0 is built on compatibility and transparency, allowing machines, devices, sensors, and people to interact and communicate with each other via the IoT. Simply put, IoT is a bridge between the physical and digital applications and could be compared to a relationship between things, such as products and services and people who initially made it a possibility to connect different plat- form and technologies (Schwab 2017, 22).
Links between psychology and AI
AI is not a psychological discipline; however psychology is strongly and irretrievably con- nected to AI. AI and computer science can live and prosper without psychology, but not the other way around. Psychology cannot prosper without AI. Their fates do worse than comingle, they coter- minate (Nilsson 2014, 1). Nilsson argues that this imbalance is caused by the fact that psychology doesn’t focus on creation. The asymmetry arises because psychology does not stand between Al and its proper goal, which is how to make computer do neat things. It can be discovered without resource to psychology simply by directly experimenting with computers.
Psychology is a behavioral study that focuses on mental processes of individuals, whereas artificial psychology is a discipline that examines mental processes of AI system (AIS) which is close to humans and if people create AIS, then we should have a good understanding on how such system
will be perceived and acknowledged by people (Crowder & Shello, 2012). Without doubts, psychol- ogy offers number of different standpoints and methodologies for studying AI. However, the theories proposed in psychology as a field are inadequate and unclearly defined to be realized in computational terms (Forbus & Kleer, 1993). Majority of work in AI has begun from psychological concerns and is related to the psychology of the problem-solving (Newell & Simon, 1972). There are few key elements to the theory of psychology of problem solving, one is in having a profound task analysis, the other is an inventory of possible problem-solving mechanism from which one can conclude what actual mechanisms are being used by humans. If we look at AI from psychological point of view in problem-solving at workplace, we can examine how it can ease people’s lives at work, as well as how people adapt towards change that AI brings to peoples’ lives at workplace (Neuhausser et al., 2000).
2.3.3 AI’s place in organizations and day-to day work life
From the big number of great and exciting challenges that the modern society is facing today, one of the most important and impressive one is the creation and shaping of the new technological revolution, which foresees the transformation of human kind (Schwab 2017, 7). According to Schwab, this transformation will change our lives, our work and our ways of communication. The future of work is changing already, and we can expect certain implications (Burke & Ng, 2006). If we take a step back and think of the origins of automation in the work environment, in the past it mainly took place in factories predicting to suppress repetitive jobs and revalue/re-evaluate and upgrade the jobs that cannot be simply replaced by automation (Friedmann 1961, 114-115). Already in 1980s upcom- ing revolution in the office by computer technologies was predicted to improve the productivity of white-collar labor force (Zisman 1978, 1).
Speed and precision became few of the most appreciative assets at work in the labor force and the term “speed as a skill” started to be used as a very valuable quality (Friedmann 1992, 9-10).
Besides improving the speed, automation has also been implemented to benefit predictability and efficiency (Dodgson et al., 2008, 251). Automation that was based on computer-integrated manufac- ture (CIM) sought solutions to number of problems. Among others, the key identified problems were:
how to produce high-quality standards, deal with high and rising overhead costs, manage poor sales forecasts, introduce new products on schedule and deliver them on time, finally, cut long production lead times (Dodgson et al., 2008, 248-251).
Technologies are often introduced to organizations to boost productivity and it happens based on three principles dependable on the perception of the technological world (Murphy & Pardeck, 1986). First one is the fact that people are primarily motivated by material rewards. Since technology
can improve work efficiency, this improvement affects the attitude of employees and can be enhanced in terms of how they feel and relate to their jobs. In other words, motivation can depend on working conditions (Gällstedt 2003, 449-455). Second principle is the productivity being compared with the generation of material items. Material gain is given a lot of attention in viewing of technological world. Gathering tangible assets turn out to be the measure of worth both personally and profession- ally. On the other hand, this principle is viewed as abstract since it doesn’t consider social factors related to productivity. Thus, productivity is entirely based on logical thinking without considering that economic growth has very little value as a concept if separated from social relations. The third principle is technology is believed to cut down systematic barriers to productivity. When a company is conceived in a mechanistic way, it can achieve integration between different elements of the work process. When every part of the labor division is aimed at fulfilling a need which is essential for organizational survival, a fundamental condition has been created to make sure that any action is methodized in the most explicit and logical matter. If this principle is followed, company is com- pletely rationalized and can be controlled without major difficulties (Weber 2009, 123; 329-341).
These three principles are presented as conditions that are essential to sustain long-term economic growth within the productivity of organizations (Murphy & Pardeck, 1986).
It is also noted that regardless of this rapid development, jobs won’t be replaced by AI any time soon, however people using AI at work will start replacing those who don’t and that applies to almost every industry (Ransbotham et al., 2017, 14). Business success is dependent on diversity and inclu- sion and the future most effective teams are predicted to have nonhumans (robots and algorithms) as a new dimension into diversity at workplace (Brown, 2017).
Alvin Toffler, a futurist, writer and a businessman tells a story in his book “The Third wave”
about the three waves revolutionizing peoples’ lives throughout many decades in the past and years to come (Toffler 1987, 2). The third wave focuses on the information and technology revolution fol- lowing the two revolutionary waves: agricultural and industrial. He argues that civilizations don’t do anything: people do (Toffler 1980, 21) and points out that it’s not limited to technology and econom- ics. It’s a combination of interrelated institutions and principles, such as technological, economic, organizational, social and political that are combined into one precisely fitting mechanism or form a single ecosystem.
According to Toffler, first wave started to lose its strengths between 1650-1750s with the raise of the second wave that created industrial society and conquered the World. In the mid of 1950s after expanding for 200 years the third wave went into decline in industrialized countries. Toffler talked about 1950s as a breaking point of the second wave as that was the time when the number of intel- lectual workers and workers in the service area exceeded the number of industrial workers in the
United States for the first time. If during the second industrial wave when people had to consider creating some products, their only focus was around making profit and making themselves more powerful, the period of third wave introduced ecological and social restrictions in addition to eco- nomic and strategic interests. The real contradiction of the third wave arises from the second one linearly. There is only one possibility: either we will control the technologies, or we will be controlled by them. “We” in this content stands for general public and not only for scientists, economists and politicians.
Today for the first-time humanity can and should learn how to choose only those innovations that will bring the most positive social and economic impact. Author also stresses that by entering the third wave people should think of lesser educated and their future because they will have difficulties later. He encourages to stop looking for profits alone but start thinking about the change in the work- ing environments and the impact it brings to society and people. He claims that every business idea is threatened by what is coming next. One trend that Toffler also predicted in his book was the shift towards working from home and it is clearly seen today. As we see today working remotely is be- coming a new normal in the modern workplaces (Brotherton 2012; Johns & Gratton 2013, Michaud 2018; Snyder 2012).
Going back to the Tofflers’ book, in the chapter “Electronic Cottage” he claimed that the de- velopment of computers and other network devices would create prerequisites of moving some of the working place from offices and factories to home. He predicted the process to be long and most likely painful and would require certain changes in management systems and motivation. However, he pointed out even back then in 1980s that the trends are strongly moving towards working from digital home. It is caused by the increasing time for commuting to work and money spent travelling, as well as by the increasing quality of communications. Social factors also facilitate the movement. The shorter the working day becomes, the relatively longer it will take to travel to the place of work and the harder it will be to justify the day. Moving a significant part of the work at home will have a profound impact on human lives. The population of residential areas will become more stable because people will move less often due to a change of job. This will mean closer ties between neighbors and greater involvement of people in solving long-term issues of communal life. The use of energy will decrease and the need for decentralization of its sources will increase. This will lead towards the increase in demand for small alternative energy, for example, solar panels. This will help reduce the burden on the environment. The new economic sectors will win, and the old industrial ones will lose.
Workers at home will more likely to become entrepreneurs possessing their own means of production.
Distance work will make communication at work between colleagues more impersonal, but the face- to-face relationships at home and with neighbors will become emotionally richer and closer.
To sum up, the trends in the workplaces are shifting and new options for business entrepreneur- ships are being introduced, such as:
• Community impact
• Environmental impact
• Economic impact
• Psychological impact (Toffler 1980, 210-222).
3 DATA AND METHODS
This chapter provides a brief overview of the case and as the methodology applied to it present- ing the qualitative research that has been conducted in this thesis with a single case study.
3.1 Case study as a methodological approach in this research
This study focuses on UX and qualitative research was a natural fit as a methodological method as it aims at capturing human behavior and peoples’ own words and opinions (Taylor et al., 2015).
The objective of a case study is to comprehend the phenomenon in its natural content and answer questions such as “Why” and “How” (Yin, 2003). Case-study is directed to intensive, deep and de- tailed study of a single case. The case may involve concrete, limited in time and place systems – persons, their actions, processes, events, incidents, social practice and programs (Creswell, 1998).
The results of the study have a form of learning lessons that represent analyzed case (Lincoln, 1985).
The case study in question is exploratory single-case research that aims at exploring UX. The study is conducted together with the users and experts of EB. The case takes place in the office envi- ronment of Tieto headquarters at Keilaniemi, Espoo in Finland, where the roles are split as following:
Tieto employees being the end-users of EB and the office itself is the field study setting.
Case company – Tieto
Tieto is a Finnish IT and software company established in 1968 in Espoo. Currently company has about 15 000 employees in close to 20 countries. Tieto’s turnover is approximately EUR 1.6 billion and its’ shares are listed on NASDAQ in Helsinki and Stockholm. Company aims at being customers first choice in digital and business transformation through offering innovative solutions and bringing value to its customers. According to the recently announced strategy in February 2019 Tieto focuses on accelerating customers’ design and data-led innovation and renewal in the Nordics.
Additionally, the company will renew its ways of working and leadership model, enabling faster time to market for customers 4.
Case study: Tieto’s EB
Tieto’s EB is one of the latest data-centric services inside the Data-Driven Unit at Tieto. It was delivered to the customers for the first time in autumn 2016 after it was show-cased at SLUSH, the
4 https://www.tieto.com/en/newsroom/all-news-and-releases/stock-exchange-releases/2019/02/tieto-embarks-on-a-
new-strategy-to-create-great-everyday-experiences-in-the-data-rich-world--accelerated-value-creation/
world’s’ leading start-up event5 by the name of Intelligent Building. Less than a year later it was re- named to Empathic Building. The goal of Tieto’s EB is to create user-friendly buildings and thereby improve the well-being and work motivation of building users. The service utilizes intelligent tech- nology solutions and provides a browser-based application to facilitate the everyday life of modern work environments and multifunctional offices. The application visualizes the work environment in digital form and helps the client's employees, for example, to find free workstations and meeting rooms and to see their colleagues in real time. With the application, employees can gather feedback on the work environment and make it even more user-friendly. The solution is based on sensor tech- nologies and system integrations on building data and its visualization. For example, the use of work- stations is monitored by motion sensors and with the indoor positioning technology, employees have the option of sharing their location in the office with their colleagues. The solution also provides analytics related to the use and occupancy of the premises (Tieto, 2019).
Tieto’s EB is a human-centric digital service that solves end-user problems and is based on a building digitalization platform. Within this platform different types of tools for buildings users can be created. Many smart building solutions focus on the building’s performance. Tieto’s EB aims to improve well-being and happiness and increase individual performance. By automating time-con- suming and non-productive tasks of communication and administration, it enables and encourages employees to interact, collaborate and co-innovate. The building combines sensor technology with industrial Internet solutions and data-driven analytics. It collects, combines and analyzes data from installed sensors and Building Automation Systems, and provides simple and understandable infor- mation that helps building owners, building operators and building end-users. Building owners can maximize occupancy and accelerate return on investment and in addition to that they can also offer better service for their existing tenants. Meanwhile building operators can minimize facility operating and maintenance costs, achieve automated and remote management maintenance and eliminate un- necessary manual tasks and presence.
As already mentioned, the building reacts and adjust accordingly in real time, it identifies threats and possibilities and can continuously evolve and reform itself. In addition to being smart, the building is intelligent, and it is empathic.
The creation of the concept
Tomi Teikko, the heart of the empathic building and the developer of the concept refers to Tieto’s EB as an ER (empathic reality) which means that the use of data in focused on end-user
5 https://www.slush.org/
