Linköping University | Department of Management and EngineeringMaster Thesis | 30hp

Spring Term 2016 | TQIE30

Implementation of augmented reality technologyfor computer supported collaborative work

THORSTEN REINHARD WIECHERT

Supervisor: Prof. Dr. Christian BerggrenExaminer: Dr. Nicolette Lakemond

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Abstract

Rapid developments in computer and wireless communication technology have ledto the proposition of a 4th industrial revolution. With the wireless communicationcapabilities of emerging technologies, cyber-physical-systems aim to connect people,objects, and services in the internet of things. A research field within this develop-ment is computer supported collaborative work. One particular technology used toachieve this is augmented reality. It allows the augmentation of additional digitalinformation into a perceived real environment.

Adopting an innovation or technology does not always produce the desired ben-efits for companies. In many cases a technology is adopted, but not implementedsufficiently. This dilemma creates an interesting starting point for research. Theemerging technology of augmented reality is therefore an adequate object to bestudied in the context of implementation of a new technology or innovation.

The aim of this study is to describe and analyse characteristics of the implemen-tation of an augmented reality remote collaboration technology in the adaptationstage. From existing theory, a model is developed that contains the four categoriesobstacles, incentives, support factors, and expected benefits. This model is appliedto empirical data to identify factors that fall into the corresponding categories andto analyse how they influence the implementation process of the studied technology.Data is gathered from empirical illustrations originating from different sources ofevidence as a bricolage. Empirical data is first classified with the developed modeland then interpreted with singular and comprehensive analysis to conjoin the in-sights.

The findings indicate that the two factors perceived usefulness and perceivedease of use play a central role in supporting the innovation implementation process,as well as in discrepancies of perception that occur between management and users.In the perception of ease of use, scalability and the characteristics of the conductedtask itself play a central role. Regarding the context of industry 4.0, availability ofinternet connectivity, a passive stance towards obstacles, and EU data secrecy legis-lation are the biggest obstacles. Another important relationship has been discoveredbetween financial resources availability, training, and the development of skills andknowledge for innovation use. Furthermore, it was found that a high learning curvesupports the effectiveness of training measures. Expected benefits are mainly ofeconomic character.

Table of contents

List of Figures iv

List of Tables v

List of acronyms vi

1 Introduction 11.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Market development of augmented reality applications . . . . . . . . . . . 31.4 Thesis company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.5 The studied technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.6 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.7 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Theoretical framework 62.1 Industry 4.0 and internet of things . . . . . . . . . . . . . . . . . . . . . . 62.2 Computer-supported cooperative work . . . . . . . . . . . . . . . . . . . . 72.3 Augmented reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4 AR technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.5 Video based AR display . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.6 Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.7 Innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.8 Innovation implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 142.9 Information technology innovation implementation . . . . . . . . . . . . . 192.10 Factors influencing innovation implementation . . . . . . . . . . . . . . . . 222.11 Research model development . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.11.1 Model categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.11.2 Factor classification . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3 Methodology 283.1 Research approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.3 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

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3.4 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.4.1 Sources of evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.4.2 Primary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.4.3 Secondary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.4.4 Complementary data . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.5 Method for data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.6 Validity and Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.7 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4 Empirical illustrations 424.1 Primary data illustrations . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.1.1 Remote support for control cabinetsIllustration 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.1.2 Remotely guided machine maintenanceIllustration 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.1.3 Chalmers University workshopIllustration 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1.4 Summary of findings . . . . . . . . . . . . . . . . . . . . . . . . . . 464.2 Secondary data illustrations . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.2.1 Task instruction modes and execution timeIllustration 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.2.2 Remote collaboration technology and organisational efficiencyIllustration 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.2.3 Remote maintenance for machining toolsIllustration 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.2.4 Evaluation of laboratory and industrial context remote collabora-tionIllustration 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.2.5 Summary of findings from secondary data . . . . . . . . . . . . . . 504.3 Complementary data illustrations . . . . . . . . . . . . . . . . . . . . . . . 51

4.3.1 Augmented Reality in the Aerospace IndustryIllustration 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.3.2 Shared Space in CSCWIllustration 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.3.3 Aspects of AR remote collaborationIllustration 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.3.4 Remote visual assembling guidanceIllustration 11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.3.5 Summary of findings from complementary data . . . . . . . . . . . 55

5 Analysis 565.1 Implementation process phase . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.1.1 Primary data process phase . . . . . . . . . . . . . . . . . . . . . . 575.1.2 Secondary data process phase . . . . . . . . . . . . . . . . . . . . . 57

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5.1.3 Studied process phase . . . . . . . . . . . . . . . . . . . . . . . . . 585.2 Obstacles and their influence . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.2.1 Financial resource availability, training, perceived usefulness . . . . 585.2.2 Protection of corporate and personal data . . . . . . . . . . . . . . 605.2.3 Availability of internet connectivity . . . . . . . . . . . . . . . . . . 605.2.4 System mobility, field of view, seasickness, mental load . . . . . . . 605.2.5 Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.2.6 Summary of insights about obstacles . . . . . . . . . . . . . . . . . 61

5.3 Incentives and support factors . . . . . . . . . . . . . . . . . . . . . . . . . 625.3.1 Compliance, training, learning curve . . . . . . . . . . . . . . . . . 625.3.2 Ease of use, scalability . . . . . . . . . . . . . . . . . . . . . . . . . 635.3.3 Quality of executed task, supporting studies . . . . . . . . . . . . . 645.3.4 Summary of insights about incentives and support factors . . . . . 64

5.4 Expected benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655.4.1 Reducing travelling time and cost, reducing system down-time . . 655.4.2 Improving communication quality . . . . . . . . . . . . . . . . . . 655.4.3 Documentation, Quality assurance . . . . . . . . . . . . . . . . . . 665.4.4 Task quality, error reduction, validity, environmental impact . . . . 665.4.5 Summary of insights about expected benefits . . . . . . . . . . . . 67

6 Comprehensive analysis 676.1 Perceived usefulness and perceived ease of use . . . . . . . . . . . . . . . . 676.2 Discrepancy of perceptions between organisational groups . . . . . . . . . 686.3 Training and education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686.4 Data protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696.5 Environmental benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

7 Conclusion 70

8 Expanded implications 73

9 Future research 74

10 References 75

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List of Figures

1 The industrial revolutions Reference: Kagermann et al. (2012) . . . . . . 62 Milgram’s reality-virtuality continuum Reference: Milgram and Kishino

(1994) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Mediality/Virtuality Continuum Reference: Mann (2002) . . . . . . . . . 94 Video based AR display Reference: Billinghurst et al. (2015b) . . . . . . . 115 Unitary sequence model (as described in Pichlak (2015)) . . . . . . . . . . 136 Extended unitary sequence model (Deducted from Cooper and Zmud

(1990)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Basic research model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Research model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Research process flow chart . . . . . . . . . . . . . . . . . . . . . . . . . . 3010 Studied process phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

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List of Tables1 Summary of primary data illustrations . . . . . . . . . . . . . . . . . . . . 352 Summary of secondary data illustrations . . . . . . . . . . . . . . . . . . . 373 Summary of complementary data illustrations . . . . . . . . . . . . . . . . 384 Summary of primary data findings . . . . . . . . . . . . . . . . . . . . . . 465 Summary of secondary data findings . . . . . . . . . . . . . . . . . . . . . 506 Summary of complementary data findings . . . . . . . . . . . . . . . . . . 557 Research model factors proposed in theory . . . . . . . . . . . . . . . . . . 568 Model based obstacles and additional insights . . . . . . . . . . . . . . . . 619 Model based incentives and support factors, and additional insights . . . . 6410 Model based expected benefits, and additional insights . . . . . . . . . . . 67

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List of acronyms

AR Augmented realityAREA Augmented Reality for Enterprise AllianceAV Augmented virtualityCAGR Compound annual growth rateCSCW Computer supported collaborative workCPS Cyber Physical SystemHMD Head mounted displayHWD Head worn dispayIT Information technologyROV Remotely operated vehicleTAM Technology acceptance modelTRA Theory of reasoned actionVCC Volvo Car CorporationVR Virtual reality

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1 IntroductionRapid advances in computer technology, broad availability of wireless connectivity withan abundance of devices and the possibility to send, receive, evaluate and use digital data(Bauer et al., 2013; Burmeister et al., 2015) create the need for companies to react toenvironmental changes and improve competitiveness of their business. It requires themto adopt, implement and communicate new technology to reduce the uncertainty aboutits expected impact (Rogers, 1983). Since there are existing structures, processes, andbehaviour within a company, a new technology cannot immediately be used (Daman-pour, 1991; Sung and Choi, 2014). The use of a new product or innovation is precededby innovation adoption, which includes recognising the need for an innovation and thestrategic decision to adopt it (Pichlak, 2015). The final step in this process then is theimplementation. In this phase members of an organisation become skilled and commit-ted in the use of an innovation (Klein and Sorra, 1996).

Literature has identified that in most cases where an adopted innovation is not pro-ducing the expected benefit, it is the innovation implementation that fails, not the inno-vation itself (Klein and Sorra, 1996). The success of this process is crucial for companiesin regard to losses that arise due to insufficient utilisation of a product or system at thestage of implementation (Dong et al., 2008). Important in this regard is the quality andconsistency of the targeted organisational members use of a technology that has beenadopted (Klein and Sorra, 1996). Dong et al. (2008) name two examples of the problemsrelated to innovation implementation ineffectiveness in industry. A study in the UnitedStates, targeting 23000 information system implementations over the time of 5 yearsshows that only 58% of the successful implementations ultimately produced the desiredoutcome. A study in Canada showed that only 39% of information system implementa-tions produced the expected benefits. These statistics represent the apparent problemsduring technology innovation implementation.

To understand the implementation process and give possible starting-points for theindustry to improve, researchers have studied the determinants, antecedents, and facili-tating factors for innovation implementation (Klein and Sorra, 1996; Klein et al., 2001;Johnson, 2001; Ensminger and Surry, 2008; Dong et al., 2008), as well as obstacles forinnovation implementation and how to overcome them (Klein and Knight, 2005). Schol-ars agree that although research about innovation implementation has been conductedin recent years, there are still relatively few studies about the innovation implementationprocess and knowledge is limited (Klein et al., 2001; Klein and Knight, 2005; Sung andChoi, 2014; Dong et al., 2008; Holahan et al., 2004).

In the industry movement of industry 4.0 that deals with developing strategies onhow to approach these changes, one promising technology is frequently mentioned - aug-mented reality (Billinghurst et al., 1998; Tiefenbacher et al., 2014) With the nowadayswidespread coverage of wireless and mobile internet connectivity, augmented reality and

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its applications have been applied inter alia in field service applications, production linesand surgical procedures over the last decade (Billinghurst et al., 2015b; Tiefenbacheret al., 2014). This technology enables the addition of digital content to a perceivedreal world environment. With a display mounted on a persons head, this content canbe experienced while being able to freely move and interact with the real environment(Billinghurst et al., 2015b). The use of augmented reality in remote collaboration isa special case, where a person is remotely guided to complete a task with the help ofinformation that is digitally added into that persons field of view. This way problemscan be solved collaboratively or with the help of added information over distance despitephysical absence of the supporting person (Fussell et al., 2004; Billinghurst et al., 2015b).

A common problem that can be seen in the process of innovation adoption is thata technology is often adopted, but then not sufficiently implemented; meaning that theexpected benefits of its use are not utilised (Klein and Knight, 2005). This dilemmamakes the implementation process an important object to study. The literature showsthat much research has been done focusing on implementation effectiveness during thewhole process of innovation implementation (Klein and Sorra, 1996; Klein et al., 2001;Johnson, 2001; Ensminger and Surry, 2008; Dong et al., 2008), instead of mappingimportant influences at a certain point in time. The literature research process forthis thesis has further documented that the use of augmented reality technology incomputer supported collaborative work (CSCW) has not been object of thorough studiesyet. Linking these two together, this thesis focuses on studying a particular CSCWtechnology in the early stages of innovation implementation, more particularly the stageof adaptation. It fills the research gap by bringing together empirical illustrations fromstudies about this particular technology’s implementation and describing its generalimplementation characteristics.

1.1 Aim

The aim of this thesis is to qualitatively describe and analyse the implementation charac-teristics of an augmented reality technology for computer supported collaborative workon the company level. Factors in the categories obstacles, incentives, support factors,and expected benefits, significant for the phase of adaptation in the implementation pro-cess will be identified and derived from existing theory. Combining them with findingsfrom empirical illustrations will then create insights in the research field and show theunique characteristics of innovation implementation of augmented reality technology forcomputer supported collaborative work.

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1.2 Research questions

• Which are the obstacles for the implementation of augmented reality in CSCWand how do they hinder this process?

• Which are incentives and other support factors for the implementation ofaugmented reality in CSCW and how do they facilitate implementation?

• Which are the expected benefits of using augmented reality in CSCW?

1.3 Market development of augmented reality applications

Technology in the areas of augmented, virtual, or mixed reality applications have beengrowing in the industrial market segment over the last decades(Azuma et al., 2001).Digitalisation in industrial production and development is growing fast, as well as theinterest in movements like industry 4.0, which increases world-wide The sector of aug-mented reality is a part of the development that follows this trend (Billinghurst et al.,2015a).

The international market of augmented reality applications has been estimated togrow by a CAGR of 89% between the years 2016 and 2024 (Research and Markets,2016). Especially high is the anticipated market growth of head mounted displays, witha CAGR of 70% between 2016 and 2024, and industry experts further expect that theaugmented reality market size as a whole will reach a total of $100 billions by 2024 (PRNewswire, 2016). The market for mobile augmented reality applications will reach nearly$80 billion by 2022 (Research and Markets, 2015). According to Research and Markets(2015) in 2014 the biggest players in the industry were situated in North America. Thismarket is also the biggest in national size. The European augmented reality market isestimated to represent 33% of the global market. The fastest growing national marketis the Asian-Pacific market, with a CAGR of 91.06% (Research and Markets, 2015).Enterprise applications of augmented reality only represent a small part of the market,while augmented reality hardware has the biggest share.

The above shows the relevance of studying implementation of augmented reality tech-nology, now during the rapid development of market sizes in this area. Looking at theseestimations, an increasing amount of companies can be expected to adopt and imple-ment such technologies. This clearly shows the need to understand its implementationprocess and the process characteristics.

1.4 Thesis company

This thesis was conducted in collaboration with XMReality AB. The company providesan augmented reality remote collaboration technology as a system solution to interna-tionally renowned customers, mainly in the area of field service and remote maintenanceoperations. It currently has 11 employees and was founded in 2007 by two Swedish

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researchers and has since accumulated and created knowledge in the fields of mixed re-ality, virtual reality, and augmented reality. In 2015, XMReality produced a turnover ofSEK 3.5 million, which translates into a growth rate of 350% in 2014, compared to theprevious year.

The company actively participated in research programmes in the medical sector, aswell as human-computer interaction. It also is an active member of the AREA (Aug-mented Reality for Enterprise Alliance), which is an international non-profit organisationthat has the goal to globally promote and advance augmented reality applications in in-dustrial enterprise settings by providing information, guidance, and research. Recentstudies, like bbc Research (2016) have named XMReality among the most importantplayers in the wearable technology industry.

1.5 The studied technology

The studied technology in the illustrations collected as primary data is developed by thecompany XMReality. It is a system solution for remote collaboration using augmentedreality and is intended to be used in field service applications, where at least one in-dividual is conducting physical field work operations (worker), while the other person(helper) is remotely connected and giving advise.

On the side of the worker, the used technology can consist of two main parts. Acomputing device with the communication software installed, and a head word display(HWD) (video based) with an integrated video camera that points to the worker’s fieldof view. The used computing unit can be a tablet with included camera, which canbe worn on the body with a carrying belt. The camera included in the tablet can beused to film a desired object or scene by holding it. Instead of the tablet, however, theHWD can be used to film the worker’s field of view. In the case of using the tablet,the filmed image is directly seen on the tablets screen itself. When using the HWD, theHWD’s captured video is shown on the HWD’s display. Instead of a tablet, the workercan also use a smartphone as a computing device. In that case however no HWD canbe connected. With the smartphone’s integrated camera and video screen it is used asa handheld device instead.

The helper is equipped with a computing unit that can be a regular personal com-puter, tablet, or other device running an operating system suitable for the communica-tion software. The helper is thought to sit at an office desk with the computer screenin front of him/her. Additionally, the helper places a pad with a camera on a rack infront of the computer in a way that the persons hands, or other physical objects likespecial tools needed for the collaborative task, are in sight of the cameras focus area.The camera is connected to the personal computer and the helper can see a video pictureof their own hands on the screen.

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Using the software, a helper and a worker can remotely connect to each other viathe internet. They can talk to each other, using microphones. The helper can see thevideo filmed by the worker on his/her computer screen and directly point and gestureunder the camera with their hands. The video of the hands is overlaid onto the helper’sand worker’s video screen in real-time. This means that the worker’s field of view isaugmented with the filmed hands of the helper and the helper can see the same imagein the computer screen as the worker.

The software also allows both parties to either take photos or record videos of theactions taken together. Furthermore, the helper can use markers to highlight importantareas within the live video or share documents or desktop content that contains neededinformation. For short information messages there exists a chat-bar, which displays theentered content in the corner of the video feed.

1.6 Delimitations

This thesis studies only one particular augmented reality remote collaboration technol-ogy and investigated is only one phase in the innovation implementation process, theadaptation phase. This means that all other phases in the process (initiation, adoption,acceptance, routinisation) are not included and changes over time cannot be accountedfor. In the analysis approach, the two organisational groups managers and users areconsidered, but primary data is only collected from managers. The main aim is to anal-yse the described excerpt of the implementation process along four distinct categories- incentives, support factors, obstacles, and expected benefits. Furthermore, qualifyinginnovation implementation effectiveness is not part of this research.

1.7 Structure

After this introduction, the thesis continues with section 2, theoretical framework, whichsummarises theory about the topics industry 4.0, computer supported collaborativework, augmented reality and technology, innovation, and innovation adoption and imple-mentation. At the end of the same section, factors influencing innovation implementationare derived and a research model is created in accordance with these factors, classify-ing them into categories. The next section, section 3 (methodology), first explains theresearch approach, followed by subsections about the literature review, sampling, datacollection, and the method for data analysis. It closes with reflecting on validity andreliability, as well as limitation applying to this thesis.

Section 4, empirical illustrations, presents the collected primary, secondary, and com-plementary data in a structured way, summarising the corresponding findings in tablesrespectively. This is followed by the analysis in section 5. It includes a subsection for theanalysis of the implementation process phase that has been studied, and a subsectionfor each of the three research questions. These are analysed by using the proposed cat-egories in the research model and findings from each subsections are summarised with a

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table. The next section, section 6 (Comprehensive analysis), further relates the insightsof the analysis between the different research questions.

The conclusion in section 7 shortly summarises the research process and approach andcloses with outlining the produced insights from analysis and comprehensive analysis.The second last section, expanded implications (Section 8 hypothesises implications ofthe insights gained in this thesis in a broader sense, not strictly relating to the definedboundaries of this research. Finally, the last section, includes propositions for futureresearch.

2 Theoretical framework

2.1 Industry 4.0 and internet of things

Kagermann et al. (2012) and Bauer et al. (2013) introduce an update to the classicalmodel of step-wise industrial revolutions. It is summarised that the world’s economy asa more and more globally connected network of industries as it exists today has beencharacterised by three major industrial revolutions (see Figure 1). In the end of the18th century, steam engines revolutionised the production. In the beginning of the 20thcentury the first production lines with division of labour were introduced, followed bya third revolution in the 1970’s, where electronics and information technology furtherautomated production processes.

End of 18th century Beginning of 20th century Beginning of the 1970’s20th century

Today

Levelo

fcom

plexity

First weaving loom1784

1st Industrial RevolutionIntroduction of mechanicalproduction manufacturingplants using water andsteam power

First assembly line1870

2nd Industrial RevolutionDivision of labour andmassproduction usingelectrical energy

First stored programcontrol (SPC)1969

3rd Industrial RevolutionApplication ofelectronics, informationtechnology, and furtherautomation

4th Industrial RevolutionDevelopment ofcyber-physical systems

Figure 1: The industrial revolutionsReference: Kagermann et al. (2012)

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It is proposed that in the present time the industry faces a new kind of revolution,which is mostly referred to as industry 4.0 in the literature. It builds on the entirelynew possibility for digital information to be transferred wireless and online betweendevices, machines, within productions systems or whole economies, creating an entirelynovel set of possibilities to change the way people will work in the future (Kagermannet al., 2012; Bauer et al., 2013; Burmeister et al., 2015; Dean et al., 2012). Up tothree billion people are estimated to have internet access by 2016 (1.9 billion in 2010)(Billinghurst et al., 2015a). It is also expected that 4\5th of all broadband connectionsare accounted for by mobile devices like smartphones or tablets (Dean et al., 2012). Thisunderlines the development of higher use and availability of connectivity. Industry 4.0 ispart of the general digital transformation trend and substitutes priorly manual businessprocesses with digital computer structures, also called cyber-physical-systems (CPS)(Burmeister et al., 2015). In a broader sense, the future vision of the internet of people(privat\corporate users), the internet of objects (machines\buildings\vehicles\electricitygrids etc.) and the internet of services (web-based services and offers) being connectedby CPS’s is called the internet of things (Kagermann et al., 2012; Bauer et al., 2013;Burmeister et al., 2015).

2.2 Computer-supported cooperative work

The term computer-supported cooperative (CSCW) work was, according to Grudin(1994), first introduced in 1984. This research field is also frequently referred to asgroupware or collaborative systems (Jørnø et al., 2013). It generally focuses on thedevelopment of software for commonly available platforms intended to be used in end-to-end support of communication, collaboration, or coordination of tasks (Jonathan andSteven, 2014). The basic typology of this concept was compiled in a matrix by Johansen(1988), which proposes that CSCW can be categorised along two main dimensions, whichare time and place. According to this typology, cooperative actions at the same time inthe same place are called face to face interactions (e.g. decision rooms or wall displays).Tasks happening at the same place but at different times are named continuous tasks(e.g. shift work or project management). If cooperation happens in different places atdifferent times, it can be considered communication or coordination (e.g. email, groupcalendars or work flow). And finally, collaboration at the same time but in differentplaces is characterised as remote interaction (e.g. video conferencing or instant messag-ing).

One particular example of remote interaction is also called remote collaboration(Jørnø et al., 2013). It describes the process of conducting a collaborative physical task(Fussell et al., 2004). This means that two or more individuals that are remotely con-nected perform actions to achieve an objective in the real world (Fussell et al., 2004; Taitet al., 2013). Fussell et al. (2004) further define that at least one person, the helper, thatis remotely connected to a worker, does during this process not physically manipulate

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objects. This is then called an instructional collaborative task. It is also emphasisedthat the way the helper gives instructions has to be comparable to a situation wherethe involved individuals would be at the same physical location. Another term for thisis remote guidance, where a remotely connected person communicates and supervises atask, often conducted by a person not trained for the scenario (Tiefenbacher et al., 2015;Fussell et al., 2004).

This kind of remote interaction can be achieved by different technologies, as wasshown by Johansen (1988). One technology that creates possible use case scenarios inthe area of remote collaboration is augmented reality (Billinghurst et al., 1998; Tiefen-bacher et al., 2014). This proposition correlates with the proposal of solution by scholarsin the area of industry 4.0. They see similar potential in future requirements of com-munication and user-interfaces. According to Bauer et al. (2013), it is believed that apromising technology allowing new ways for the user to interact with the environmentis augmented reality. This technology is seen to facilitate partial and collaborative tele-operative problem solving of highly complex tasks. Especially when taking into accountthat the increasingly accelerated complexity of machines and equipment excels the rateat which companies can train users in the required skills. (Scavo and Perey, 2016)

2.3 Augmented reality

Historically, there are different levels of combining content from the virtual and the realenvironment, shown in Milgram’s reality-virtuality continuum in Figure 2. Following thisclassification, in general all variations of combining the real and the virtual environmentare called mixed reality. The term augmented reality (AR) describes the process ofvirtually adding objects to the naturally perceived environment. Augmented virtuality(AV) on the other hand is characterised by adding information from the real environmentto a virtual environment (Azuma, 1997; Azuma et al., 2001; Billinghurst et al., 2015b).

Mixed Reality

RealEnvironment

AugmentedReality

VirtualReality

VirtualEnvironment

Figure 2: Milgram’s reality-virtuality continuumReference: Milgram and Kishino (1994)

This one-dimensional continuum has however been refined by Mann (1994) and Mann(2002). Mann (1994) introduces the concept of mediated reality as the process of filteringor modifying the view of the real environment in contrast to AR, where information isonly added. Mann (2002) introduces the Mediality/Virtuality continuum, shown in

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Figure 3. The x-axis represents Milgram’s reality-virtuality continuum, while the y-axisis used to show the amount of mediation or filtering that is performed (Billinghurst et al.,2015b). The matrix shows that one can speak of AR if the environment is altered witha low level of mediation. If the the mediation is increased, the continuum introduces theterms of mediated reality and mediated virtuality instead.

RealEnvironment

VirtualEnvironment

MediatedEnvironment

Severely MediatedVirtuality

Aug.R.

Med.R.

Aug.V.

Med.V.

Figure 3: Mediality/Virtuality ContinuumReference: Mann (2002)

An essentially different approach compared to AR is virtual reality (VR). As statedby Billinghurst et al. (2015b), other authors like Rekimoto and Nagao (1995), describethat the main difference in these approaches is that AR is used to enhance the realenvironment with additional digital information. VR on the other hand is used to createa completely virtual environment. The goal is here to reduce all classical user interfacesthat are perceived as a connection between reality and virtuality. This means VR isstriving for total immersion of the user into the virtual environment (Rekimoto and Na-gao, 1995; Billinghurst et al., 2015b; Azuma et al., 2001).

2.4 AR technology

Azuma (1997) derives three basic requirements for an augmented reality technology fromMilgram’s continuum. It has to combine real and virtual content, has to be interactivein real-time, and needs to be registered in 3D. Billinghurst et al. (2015b) translate theseinto technical requirements for an AR-system, which are the presence of a trackingsystem that can translate the users view and position it in the real world, a display thatcombines virtual and real images, and a computer system that can interpret input inreal-time and generate interactive graphics.

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The process of tracking, as defined by Billinghurst et al. (2015b), combines twophases - a registration phase and a tracking phase. During registration, the viewer’sposition relative to the real-world as follows. In the tracking phase, the last known poseof the viewer is updated relative to the previously known phase. There are differenttechnologies, used for tracking. Billinghurst et al. (2015b) describe two main technolo-gies that can be used to achieve positional tracking as the following. One solution is theuse of magnetic fields that allow definition of the relative position of a viewer. Anotherpopular approach is vision based tracking, which uses sensors to track light either withinthe visable spectrum or infrared. While the basic function of these tracking systems isthe analysis of reflected or emitted light by optical sensors, Billinghurst et al. (2015b)further define different tracking techniques using visible light. This includes tracking ofspecially designed markers, natural features or 3D-generated models are searched for inthe real environment. Other technologies are the use of GPS, accelerometers or gyro-scopes, as well as a combination of the different techniques (Billinghurst et al., 2015b).

Moreover, have advances in AR technology historically included the developmentof head worn displays (HWD), also called head mounted displays (HMD). These allowthe user to move freely, without being bound to a stationary screen (Billinghurst et al.,2015b; Azuma et al., 2001). After the first development of a stationary bound AR dis-play in 1968 by Ivan Sutherland and Bob Sproull (Sutherland, 1968), researchers strivedto develop wearable AR interfaces that could theoretically be worn all day (Billinghurstet al., 2015b). Head mounted displays are designed to show virtual images directly infront of the user’s eyes. The line of sight is not disturbed by any other physical object(Billinghurst et al., 2015b). The general design is in most cases goggle-like and enablesthe display to be worn on the head. With the availability of mobile phones with inte-grated camera, research also started to more thoroughly focus on hand-held AR devicesand wireless data transmission. A turning point could be seen in the year 2009, whereAR technology became widely and commercially available (Billinghurst et al., 2015b).Another important aspect the authors mention was the rapid improvement of smart-phones’ camera hardware and computing power.

2.5 Video based AR display

In video based AR displays, the real environment is digitalised before it is combinedwith the virtual content, using a digital process (Billinghurst et al., 2015b). A schematicdescription of such displays can be seen in Figure 4. It shows that both the real en-vironment, as well as the virtual scene are combined into digital data, before they arepresented to the user on a video display. To create the illusion of seeing the real environ-ment on the video display and not a digitalised version, a video camera can be mountedat the front of the display e.g. integrating a camera in a HWD. By digitalising a picturealgorithms can accurately control the process of combining the real environment andvirtually added information (Billinghurst et al., 2015b).

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RealEnvironment

VideoCamera

DigitalCombiner

Rendered imageof virtual scene

VideoDisplay

User

Figure 4: Video based AR displayReference: Billinghurst et al. (2015b)

As opposed to video based AR displays, optical see-through displays do not digitaliseboth of the visual components. These kinds of displays instead use beam splitters likehalf mirrors or combined prisms that allow seeing through the display to observe thereal environment, while seeing the reflection of an image from a video screen at the sametime (Billinghurst et al., 2015b). A third technique is to project digital content directlyinto the real environment (Billinghurst et al., 2015b).

2.6 Innovation

In innovation theory there exist many definitions and classifications of innovation. Aclassic view embossed by Tushman and Anderson (1986) and common in the researcharea of technological change is that technology innovations can have an incremental orradical character. Incremental innovations bring relatively small changes to existingproducts or processes and further exploit the potential of an established design. Radicalinnovations on the other hand introduce a new set of ideas to technology and processdesign (Tushman and Anderson, 1986). This often opens up new markets and forcescompanies to adapt to these environmental changes (Henderson and Clark, 1990). Thiskind of definition holds on a level of analysis that includes markets and their development.

For innovation on the organisational level, Damanpour (1991) and Damanpour andSchneider (2006) give a broad definition of the topic, describing innovation as the "adop-tion of an internally generated or purchased device, system, policy, program, process,product, or service that is new to the adopting organization". This definition is alsoaligned with the classic definition by Rogers (1983): "An innovation is an idea, prac-tice, or object that is perceived as new by an individual or other unit of adoption."Rogers (1983) further concludes that innovation reflects the response to changes in theinternal or external environment, while improving company performance (Damanpour,1991; Damanpour and Schneider, 2006). This results in a competitive advantage of theadopting company (Damanpour, 1991) and also in terms of economics (Greenhalgh and

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Rogers, 2010; Baumol, 2014). This definition can include many types of innovations. Re-ferring to Evan (1966), Daft (1978) distinguishes between technical and administrativeinnovation. While a technical innovation here describes an idea for a new product, pro-cess or service, an administrative innovation includes policies of recruitment, allocationof resources, and structuring of tasks, authority and reward. Daft (1978) further addsthat with this definition technical innovations are related to technology and administra-tive innovations to the social structure of an organisation. Technical innovation doesnot merely address the use of a new technology, but the appearance and implementationof the idea of a new product, service or element in the service operations of a company(Damanpour and Evan, 1984).

Based on the definition of technology by Thompson (1965), Rogers (1983) also givesa more elaborate meaning to technological innovation, which does not only focus on thetechnology itself. Instead, technological innovation is a design for instrumental actionsto reduce the uncertainty in achieving the desired outcome. In more detail, it can be seento have a hardware and a software aspect. Hardware in this regard is the physical toolor object that the technology embodies. Software on the other hand is the informationbase that is needed for using the technology as a tool (Rogers, 1983).

This thesis does not analyse the particular kind of technology from a market per-spective, as in the classic view of Tushman and Anderson (1986). It rather orients itselfon the definitions of innovation by Rogers (1983) and Damanpour (1991). Innovationin the general context is therefore defined as the adoption of an internally generatedor externally purchased device, program, product or service that is new to the unit ofadoption. The studied innovation will be related to the underlying technology, like Evan(1966) and Daft (1978) propose. Therefore the two terms technology and innovationcan be used interchangeably. Hence innovation is not treated as the process of designinginstrumental actions like Rogers (1983) describes. To prevent confusion, the terminologyof Rogers (1983) concerning hardware and software part of a technology is not used inthis thesis. Instead, the two terms are used describing the computer related physicaland the programmed appearance of an information technology.

2.7 Innovation adoption

The stages in the innovation adoption process have been defined differently in literatureconcerning the level of generalisation, reaching from two to three or more stages (Pich-lak, 2015). Pierce and Delbecq (1977) identify a congruence of the model developed byWilson (1966): conception, proposing, and adoption, the findings of Thompson (1965):generation, acceptance, and implementation, and the model developed by Knight (1967)that in its two stages "creation and development of an idea", and "its introduction andadoption" includes the steps stated in the other models. This view is also shared bySlappendel (1996). The research area of innovation diffusion describes this process asthe innovation-decision process. According to Rogers (1983), this process includes thestages of knowledge, persuasion, decision, implementation, and confirmation. In this

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regard, the analysis of Pichlak (2015) is used, where these different levels of generalisa-tion are summarised in three consecutive steps that all together represent the generalapproach of a unitary sequence model consisting of initiation, adoption decision, andimplementation (Figure 5). The first step includes awareness of the need for innovation,gathering of knowledge and the selection of an innovation. In the second step financial,strategic and technological evaluation of the innovation takes place. Finally, the inno-vation needs to be implemented, which leads to the use of an innovation after sufficienttrials.

Pichlak (2015) concludes that initiation is characterised by realising the need for aninnovation, searching for a solution (as in Damanpour and Schneider (2006)), formingan initial attitude towards an innovation, and promoting the adoption (compare alsoRogers (1983)). Adoption decision includes accepting the proposed solution by evalu-ating it practically, financially, strategically and technologically (see Damanpour andSchneider (2006)), as well as allocating resources for the acquisition. The final step,implementation, includes preparing the innovation for its use in the company, and per-forming trials to confirm the technology’s acceptance by the future users.

Initiation Adoption Implementation

Figure 5: Unitary sequence model(as described in Pichlak (2015))

King (1990) defines three levels of analysis that are relevant for innovation adoption- individual level, group level, and organisational level. Gopalakrishnan and Daman-pour (1997) named four levels - industry level, organisational level, subunit level, andinnovation level. As can be seen, a shared level of analysis in these approaches is the or-ganisational level. At this particular level of abstraction, research on innovation adoptioncan generally be seen to have two different approaches that coexist in scientific litera-ture - the process approach and the factor approach (Pichlak, 2015; Gopalakrishnanand Damanpour, 1997; King, 1990). The process approach analyses important eventsin the implementation process, aligned with stage-like models and information feedbackflows (Pichlak, 2015; Damanpour and Schneider, 2006). Broad classes of events duringthis process are considered that describe an organisations behaviour when it comes toadoption and implementing new solutions (Pichlak, 2015).

The factor approach treats the implementation process as multidimensional (Daman-pour, 1991; Pierce and Delbecq, 1977). Examples for factors that have been part ofstudies are innovation characteristics, organisational characteristics, user acceptance at-tributes or environmental characteristics (Pichlak, 2015). Pierce and Delbecq (1977) and

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Slappendel (1996) further present three main perspectives within the factor approachto innovation adoption. The first approach states that context and structure are mostinfluential on innovation adoption. The second concentrates on the attitude of organ-isational members. The third approach uses an interrelation of the two factors namedabove by using a holistic approach to innovation adoption as a dynamic process.

2.8 Innovation implementation

Innovation implementation is the last step in the innovation adoption process, as ear-lier described in the process approach on the organisational level. There is an essentialdifference between adoption and implementation, since innovations are often adoptedbut not implemented successfully (Klein and Knight, 2005). Klein and Sorra (1996)define innovation adoption as a presupposition to innovation implementation; it is thedecision that employees within a company should use an innovation in their work. Kleinet al. (2001) refine the meaning of adoption as a decision point, plan or purchase, whileimplementation is the actual use of a new technology. It is a time period in that organ-isational members that are affected by an innovation become more skilful, consistent,and committed in their use of the technology (Klein and Sorra, 1996; Klein et al., 2001;Klein and Knight, 2005) and ranges from the avoidance of an innovation to enthusiasticuse (Klein and Sorra, 1996).

Klein and Sorra (1996) state and explain two general types of stage models that areused to describe the innovation implementation process. Source based stage models focuson the the innovation developer and trace the process from the creation of the productuntil the final product. User-based stage models however are based on the final users’perspective. The process starts with a first awareness of the need for change and endswith embodiment of an innovation in a users’ preferred behaviour Klein and Sorra (1996).

The cited works about innovation adoption and the subordinated phase in this pro-cess, innovation implementation, have shown that different approaches to research inthe area exist. As could be seen in innovation adoption literature, a common level ofabstraction is the organisational level (King, 1990; Gopalakrishnan and Damanpour,1997). Pierce and Delbecq (1977) refine research approaches on this level to be a mix-ture between structural and individual factors, both of which influence the outcome ofthe innovation adoption process. Klein and Sorra (1996) add that research in the area ofinnovation implementation can have two main views, source-based (following the processat a developer of an innovation) and user-based (looking at the user-side of the innova-tion use).

Studying the two lines of research, adoption and implementation of innovations, itcan be seen that there is no clear definition of when exactly adoption goes over to imple-mentation. Innovation adoption scholars see it as the point where a proposed solutionhas been evaluated (financially/strategically/technologically) and resources have beenallocated for acquisition of the technology (Damanpour and Schneider, 2006; Pichlak,

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2015). Innovation implementation scholars propose that adoption is completed with thedecision that a technology will be used in a company (Klein and Sorra, 1996; Klein et al.,2001). It can however be assumed that the decision for a company to use a technology isconnection to financial commitment enabling the acquisition of the targeted technology.Therefore this allocation of financial resources to acquire a technology marks the turningpoint between the second phase in the unitary sequence model of innovation adoptionand the final phase of innovation implementation. Innovation adoption research thenstates that implementation includes preparing an innovation for its use in the companyand performing trials to confirm the technology’s use and acceptance of targeted users(Damanpour and Schneider, 2006). In their innovation implementation research Kleinand Sorra (1996) and Klein et al. (2001) define it as targeted organisational membersbecoming skilled and consistent in their use and quality in using the innovation. Itcan therefore be concluded that actions during the innovation implementation phaseare aimed to create user acceptance, developing skills, conducting trials, and finally, tosucceed having a consistent and qualitatively optimal use of the technology.

In their work Klein and Sorra (1996) develop a model that depicts the innovationimplementation process as a function of an organisation’s climate towards implementa-tion of a certain innovation and organisational members’ perception of the innovationsfit to their values. The model presumes that, while the use of an innovation might varybetween organisational members or groups, implementation effectiveness is a constructthat holistically describes the quality and consistency of an innovations use on the or-ganisational level (Klein and Sorra, 1996; Holahan et al., 2004). Therefore the termsinnovation use and innovation effectiveness can be used interchangeably in this regard(Klein et al., 2001). Following the conceptualisation of climate by Schneider (1975), it isthe employees’ perception of events, practices, procedures, and behaviours that are ex-pected and supported. More generally speaking, it is the employees’ shared perceptionsof the importance of an innovation implementation in an organisation, since all actionstowards implementing an innovation as a whole build a picture of how well the processis encouraged by the company (Klein et al., 2001).

This concept focuses on three perceptions - individual perceptions of the work en-vironment, shared perceptions, and the shared perception of the fit of work practicesand rewards with the strategic interest. The climate for implementation of an innova-tion therefore is the summary of employees shared perceptions to whether the use ofan innovation is rewarded and supported in an organisation (Klein and Sorra, 1996).This means that implementation policies and practices that are aligned with the wayemployees perceive encouragement and reward of using a targeted innovation are morelikely to produce the desired outcome (Klein and Sorra, 1996; Klein et al., 2001).

Klein and Sorra (1996) summarise policies and practices that can influence the out-come of an innovation implementation. The first factor within these two, of which thepositive influence has been proven by Compeau and Higgins (1995), is training in in-

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novation use. The study found that training positively influences the self-efficacy of atrained user, which builds confidence in using the technology and reduces knowledgebarriers. Ensminger and Surry (2008), and Choi and Moon (2013) refer to presence anddevelopment of skills and knowledge as a facilitating factors for innovation implementa-tion. Also, the compliance of an innovation to fit the users most valued requirementsdoes improve the quality of innovation use (Venkatesh and Brown, 2001). This researchhas shown that the self-efficacy has three particular barriers - knowledge, difficulty of use(Klein and Sorra (1996) call this user-friendliness) and cost. The terms describe havingthe relevant knowledge to use an innovation, the perceived ease of use by the user, andthe monetary cost associated with acquisition of the technology. In conclusion, offeringappropriate resources to reduce barriers through costs and providing technical supportto reduce knowledge barriers support the implementation of an innovation. These can becalled user support services (Venkatesh and Brown, 2001; Compeau and Higgins, 1995).

Klein and Sorra (1996) add that the user needs to have time to experiment with thetechnology and that the use of it should be promoted within the company to support itsapplication. In contrary, it can happen that the non-usage of an innovation is penalisedby job-reassignment or elimination. Relevant is also the allocation of financial resourcesto the cause of implementing and using the technology. Adequate financial resourceavailability helps to nurture the implementation, but the absence of it has a negativeimpact on an innovation’s use (Klein and Sorra, 1996).

In summary, a strong implementation climate improves the use of an innovation byproviding and developing the employees’ skills to use the technology (training), providingincentives for innovation use and disincentives for avoiding the innovation (supportingresources), as well as removing obstacles for the use of an innovation (sufficient resourceand training access) (Klein and Sorra, 1996; Klein et al., 2001; Holahan et al., 2004) Thestated conceptualisation of the implementation process does not create a specific set ofpractices and policies that lead to an outcome, but an infinite number of combinationsthat can create a desired result (Klein and Sorra, 1996; Holahan et al., 2004).

In their study including over 1200 interviews in 39 companies implementing a techni-cal innovation Klein et al. (2001) quantitatively confirm the significant positive influenceof policies and practices, and climate on innovation implementation promoted by Kleinand Sorra (1996). The research adds to the model by demonstrating the positive rela-tionship of financial resource availability with policies and practices. It is concluded thathigh-quality implementation policies and practices like training, support and rewards areexpensive indeed (Klein et al., 2001; Klein and Knight, 2005). It is also stressed thatfinancial resource availability is a significant predictor for overall quality of a company’simplementation policies and practices (Klein et al., 2001; Klein and Knight, 2005; Pich-lak, 2015; Ensminger and Surry, 2008).

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Klein and Sorra (1996) further conclude that the climate for implementation is lim-ited by social influences on worker motivation, referring to the framework developed bySussmann and Vecchio (1982). This framework shows two particular stances that a usercan take towards influence by superiors - compliance and internalisation. The formermeans accepting influence to gain rewards and avoid punishment. The latter, the accep-tance of influence because it is conform with the employees own values. A similar conceptcan be found in the research area of innovation diffusion. Rogers (1983) distinguishesbetween centralised and decentralised diffusion systems, where diffusion describes theprocess of communicating an innovation through certain channels over time among themembers of a social system. In a centralised diffusion system decisions about evaluationand the way of communicating are made by a small and defined group of individuals. Ina decentralised diffusion system on the other hand, these kinds of decisions are spreadout to the users of the innovation, creating a more horizontal communication of infor-mation and spreading an innovation (Rogers, 1983). In both of the approaches, Rogers(1983) and Sussmann and Vecchio (1982), a distinction is made between a horizontallypowered system of promoting an innovation, and giving more decision power to a groupof horizontally spread technology users. Ensminger and Surry (2008) add another di-mension by proposing that the stance towards an innovation can come from the level ofparticipation that a user experiences in the process of innovation implementation. Thiscan improve the interest in successful implementation of a new technology.

As an addition to these stances towards an innovation, Klein and Sorra (1996) pro-pose that if different groups, that have different perceptions of an innovation are targetedby an adopted innovation, the horizontal and vertical relationship between them is im-portant. If two groups are horizontally related in a way that they have the same powerover each other, a strong innovation implementation climate supports the group that isin favour for the innovation. All targeted users are likely to use the innovation properly.If the climate is weak the group opposing the innovation is supported in their opinion(Klein and Sorra, 1996). If one group has vertically more power than the other and itsees positive towards the innovation, a strong implementation climate will support itsopinion and it can use its power to create additional incentives for the innovation imple-mentation (Klein and Sorra, 1996). Is the innovation climate weak on the other hand,the higher authority group is likely to undermine the implementation process negatively,even if the other group would like to implement the innovation (Klein and Sorra, 1996).

Klein and Sorra (1996) conclude that a strong climate for implementation does needa combination of different things: Skilful use of an innovation by employees, support viaincentives and implementation practices, an internalised and committed perception ofthe innovation use by the employees, and a fit of the innovation to users’ values. Kleinand Sorra (1996) more precisely describe the last of the listed elements as the extent towhich a targeted user perceives the use of an innovation to encourage the fulfilment oftheir values. A good fit of the innovations values with the organisation’s or group’s val-ues is therefore needed to support implementation effectiveness (Klein and Sorra, 1996).

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Dong et al. (2008) have reviewed and applied the framework by Klein and Sorra(1996) in their study, and describe qualifiable measures for the framework. A main em-phasis in climate for implementation is whether employees have been informed aboutnew working procedures and the changes of working procedures after implementing thesystem. Additionally, employees should be taught what they can accomplish with thetechnology and what kind of outcomes are expected from the use. Dong et al. (2008)have a way of qualifying measures that is consistent with the description of policies andpractices that have been summarised. The strong focus on building the users knowledgeand skills about an innovation is here equally represented.

Another point Dong et al. (2008) raise is that users of an innovation need to be pro-vided with the necessary hardware, manuals and documentation, time to learn using thesystem, and the financial support the use needs to be ensured. Clear definition of rewardemphasis is needed for the users to understand how their performance is measured whenusing the system. This also includes acknowledgement of their development in expertiseby using the system. More explicitly addressed than in the works of Ensminger andSurry (2008), Choi and Moon (2013) and Klein and Sorra (1996) is the topic of howreward should be communicated and applied during innovation implementation.

For evaluating innovation use, the combined effect of implementation climate andinnovation-values fit is considered. The ideal scenario is a strong implementation cli-mate with good innovation-values fit. Here employees experience enough training andsupport, obstacles to innovation use are removed, which leads to commitment in usingthe innovation. When a good innovation-values fit is paired with weak implementationclimate, employees lack proper training, are therefore not skilled in the use of an inno-vation. Obstacles are not removed, while employees are actually motivated to use theinnovation. This leads to frustration and inadequate innovation effectiveness.

Resistance is likely to appear among employees when a strong implementation cli-mate meets poor innovation-values fit. In the best case this leads to compliant use ofthe innovation. With poor innovation-values fit and weak implementation climate, useof the technology is unlikely to happen at all, because employees do not need to use itand do not see it fit with their values. The neutral combinations lead to adequate usein a strong implementation climate and no use in a weak implementation climate.

A study on administrative innovations by Dong et al. (2008) empirically tested an-tecedents for innovation use underlying the model proposed by Klein and Sorra (1996).It tested five measurement items - climate, skills, incentives, absence of obstacles, andaffective commitment towards their influence on implementation effectiveness. In thecase of the study 63.4% of the variance in innovation use could be accounted to skills,incentives, obstacles, and affective commitment. All of these in turn were significantlyinfluenced by implementation climate and innovation-values fit.

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It was shown that for the analysis on the organisational level, an approach to de-scribe implementation climate can use the combined or shared perceptions of employees,which gives a more holistic view on the construct. Further it was stated that the use ofthe construct innovation-values fit presumes that an innovation is critically inspected byemployees to align with their values. Consequently, a passive picture of the change inemployees values towards an innovation is drawn. There exists however another view,as proposed by Choi and Moon (2013). The article describes different forms of imple-mentation and assumes that an innovation can be re-interpreted by its users during theimplementation process, combined with a change in their original values towards the in-novation during the process. This has an effect on the form of innovation implementation.

Klein et al. (2001) on the other hand propose that not the process itself, but the finaloutcome of the implementation induces changes in the user’s perception towards futureinnovations. It is concluded that the outcome of one innovation implementation can haveinfluence to a company’s future innovation climate and its members innovation-values.Three possible outcomes are presented - an effective implementation that improves thecompanies performance, a successful implementation that does not improve the organ-isation’s performance, and implementation failure. The first outcome strengthens theorganisations future implementation climate. In the second case the support for inno-vations declines. The third outcome induces that the companies members rethink theapproach towards innovation and may improve future incentives and practices facilitat-ing innovation implementation. If the innovation was mainly aligned with the values ofthe targeted users, they might in the future resist innovation implementation attempts,because they do not see their values fit changes. If an unsuccessful innovation wasnot aligned with the targeted users’ values, their attitude towards future innovationsmight change to the positive, because they saw an innovation fail that did not suit theirbelieves.

2.9 Information technology innovation implementation

Parallel to the more general models of innovation implementation, several models ad-dressing the innovation implementation of information technology (IT) have been devel-oped. As opposed to the priorly stated stage based models of innovation implementation,in classic IT implementation theory there exist a different terminology. The process thatwas earlier in this thesis referred to as innovation adoption is here called innovation im-plementation, as can be seen in the model by Cooper and Zmud (1990) that was priorlydeveloped in an unpublished paper (Zmud and Apple, 1989). The development oft thismodel is said to originate from the classical Lewins change model (Cooper and Zmud,1990). The model includes the following six stages: Initiation, adoption, adaptation,acceptance, routinisation, and infusion. Initiation here is the realisation of the need forproblems and opportunities using new IT technology, adoption is the rational negotia-tion for allocating resources for the use of a new IT technology. Adaptation as the thirdstep in the process refers to installation and maintaining the new application, as well

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as training users. Then, the acceptance stage contains actions that induce commitmentto use the new technology and to employ it in organisational working routines. Finally,infusion or routinisation is the effect of improving organisational effectiveness by usingthe technology.

Referring to earlier theory comparison, one can identify that adoption in the contextof information technology innovation implementation is described as the allocation ofresources, which has been defined as the turning point between adoption and implemen-tation. Therefore the three following steps (adaptation, acceptance, and routinisation)in the model of Cooper and Zmud (1990) are seen as being part of the innovation imple-mentation phase. Figure 6 shows, how these insights add to the earlier proposed unitarysequence model by adding three additional phases that earlier were combined in thephase of innovation implementation.

InitiationAdoption(Decision) Adapdation Acceptance Routinisation

Figure 6: Extended unitary sequence model(Deducted from Cooper and Zmud (1990))

Kwon and Zmud (1987), as also summarised by Cooper and Zmud (1990), identifycontextual factors that influence the processes and outcomes of each of the phases. Onefactor is the user community. It includes the final users resistance to change, as wellas the education level regarding the use of a technology. It is here further mentionedthat resistance as well as problems with understanding the technology can be overcomeby education activities. Cooper and Zmud (1990), list technology complexity as a sep-arate factor, but it can be seen that it is closely related to the first one. Earlier, thiskind of contextual factor has been called perceived ease of use, while it can be assumedthat the perception of ease of use changes with the users training level in using thetechnology. Factors that could not be defined in detail, because of a lack of supportingliterature are organisational characteristics (specialisation, centralisation, formalisation),characteristics of the task that the technology is applied to (task uncertainty, autonomyand responsibility of the person performing the task, task variety), and characteristics ofthe organisational environment (uncertainty, inter-organisational dependencies) (Cooperand Zmud, 1990).

Kwon and Zmud (1987) further give a more diffusion oriented, but significant evalu-ation of the adopter characteristics in the horizontal dimension, which can be applied toa person, a group or an organisation. Technologies are here sorted into two types. Type1 are technologies that are primarily intended for for single-users. This would includefor example micro computers, laptops or other portable devices. Type 2 are technologies

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with high knowledge-barriers like structured systems analysis or a CAD program (Kwonand Zmud, 1987). These two dimensions - horizontal dimension and type give a broadoverview of the a studies context.

A fundamental model originating in social psychology is called the theory of reasonedaction (TRA). It is concerned with the determinates of consciously intended behaviours(Ajzen and Fishbein, 1980). The behaviour in this case is the intention to use a cer-tain IT. Davis et al. (1989) summarise that TRA specifies that a person’s individualperformance of a special behaviour is determined by behavioural intentions; this on theother hand is influenced by that individuals attitude towards such behaviour. Hence itcan be said that this theoretical approach analyses a persons’s behaviour in a relativelysubjective way (Ajzen and Fishbein, 1980).

Another well-established model is the technology acceptance model (TAM), intro-duced by Davis (1985). According to Davis et al. (1989), this model is a modificationof the TRA for modelling user acceptance of information systems. It suggests that twomain beliefs are the underlying principle behind IT acceptance: perceived ease of useand perceived usefulness. The former of the two is defined as "the degree to which anindividual believes that using a particular system would be free of physical and mentaleffort" (Davis, 1985). The latter describes if and to which extend an individual believesthat an IT system would enhance job performance (Davis, 1985). Davis et al. (1989) addthat an underlying hypothesis in organisational settings is that people prefer behaviourthat in their believe is going to increase their job performance. Therefore the perceivedusefulness is the superior of the two dimensions. On the contrary this also means that anindividual’s personal feeling are secondary when a clear performance benefit in workingroutine can be identified (Davis et al., 1989). Davis (1985) additionally concludes thatperceived ease of use has a direct positive influence on perceived usefulness, since aninformation technology that is easy to use will evidently improve the job performanceof a person when used as intended.

Innovation implementation theory discusses the same two beliefs that are mentionedby Davis et al. (1989) - perceived ease of use and perceived usefulness. The former wasalso called difficulty to use (Venkatesh and Brown, 2001) or user-friendliness (Klein andSorra, 1996) and was linked to knowledge barriers that arise when implementing a newtechnology. Taking the proposed policies and practices by Klein and Sorra (1996), pro-viding technical support and placing sufficient financial funds on implementation reducesknowledge barriers, as well as costs that hinder innovation use are attested. In returnusers should be able to have a better understanding of technology’s functionality andtherefore perceive it easier to use. Consequently then, the perceived usefulness shouldalso rise. More abstractly, perceived usefulness can be related to innovation values-fit.While this tries to describe the personal perceptions of users towards a technology, moreabstractly it could be seen as how an individual sees the use of a technology in relationto which tasks are to be done.

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2.10 Factors influencing innovation implementation

Innovation can be seen as a technology that is internally or externally developed and newto the adopting organisation or individual (Damanpour and Schneider, 2006; Rogers,1983) Using this definition, the terms technology and innovation can be used inter-changeably. Innovation that is associated directly with the underlying technology iscalled technical or technological innovation (Damanpour and Evan, 1984; Daft, 1978).

The process of adopting an innovation includes distinctive phases. The last phasein this process is innovation implementation. Literature shows that there is an exactmoment in time, where adoption is completed and the implementation of an innovationin a company takes place. This can be defined as the point in time, where a finan-cial commitment to acquire and use a new technology has been made (Damanpour andSchneider, 2006; Pichlak, 2015; Klein and Sorra, 1996; Klein et al., 2001). It is a periodof time where an innovation is prepared for its use in the company. This includes per-forming trials to confirm the technology’s use and the innovation acceptance of targetedusers (Damanpour and Schneider, 2006) Following the stage-based approaches towardsinnovation implementation, one can identify three steps - adaptation, acceptance, androutinisation (Cooper and Zmud, 1990). These most accurately describe the ongoingcircumstances that a company implementing a new technology can be facing. However,to exhaustively describe the characteristics of a technology’s innovation implementation,a more holistic approach also including relevant factors needs to be considered (Pierceand Delbecq, 1977; Slappendel, 1996).

Models from different research areas describing the phenomenon innovation imple-mentation have shown important aspects to consider, when analysing innovation imple-mentation. Research uses different descriptive terms to specify factors that influence theinnovation implementation process. It can also be seen that different models analysealong different dimensions. However, they all seem to include efforts in the same areas.These are creating user acceptance, developing the user’s skills, and conducting trials.After successful implementation, this creates consistent and qualitatively high use of thenew technology. In other words, a user makes using the technology his/her preferredbehaviour for associated work tasks (Damanpour and Schneider, 2006; Klein et al., 2001;Klein and Sorra, 1996).

Qualitatively measuring factors within these areas requires a unified model. Schol-ars show different ways to cover the area of user acceptance. Venkatesh and Brown(2001) single out the need of the technology to fit the user’s most valued requirementsas a factor. Supporting this is achieving internalisation of the technology during theimplementation by including future users in the evaluation process. Matching user re-quirements also refers to the concept of perceived usefulness, which is a user’s belief thata technology will improve job performance. According to Davis (1985) and Venkateshand Brown (2001), this believe gives a clear incentive to use a technology and thereforeto its implementation. A factor that is closely related to the former is perceived ease of

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use, as also supported by Davis et al. (1989). These scholars describe that a technologythat can be understood without high mental effort is more likely to be implemented. Itis therefore a factor supporting the implementation of a technology. Of the two lastlyproposed factors, perceived usefulness excels in influence on the innovation implementa-tion. The incomprehension of a technologies functionality by the users otherwise leadsto barriers because of the perceived difficulty of how to use an innovation (Venkateshand Brown, 2001).

Understanding how a technology works is also important in terms of avoiding resis-tance that can occur. If this is the case, use of the innovation is not likely not to beadequate or minimal (Klein and Sorra, 1996). Another important part plays communi-cation Rogers (1983), more particularly the information about reward and punishmentwhen using or not using the system (Sussmann and Vecchio, 1982). This is also relatedto how the innovation is introduced in the company. If users are compliant, it meansthat an innovation is only used, because it is assigned to be used.

The area of developing skills dominated by the proposal of training future operatorsin innovation use. Klein and Sorra (1996) list training in innovation use, as well asthe development of skills and knowledge about the technology as factors in this regard.Closely related to these measures is financial support of these actions. Venkatesh andBrown (2001), and Compeau and Higgins (1995) confirm that the allocation of sufficientphysical and monetary resources reduce barriers that otherwise arise by the lack of fund-ing and consequently the absence of skills and knowledge of how to use a technology.Appropriate financial support has been proven to enhance the overall quality and effectof training and other activities that support the development of skills and knowledge forinnovation use (Klein et al., 2001; Klein and Knight, 2005).

A more detailed description of the obstacles for CSCW technologies in particular isgiven in Bauer et al. (2013). The article stresses that for the increased exchange of datain the internet of things, the availability of comprehensive, area wide connectivity is notyet developed. Therefore obstacles can arise in this area when such technologies are used.This view is backed up by Scavo and Perey (2016). Both Bauer et al. (2013) and Scavoand Perey (2016) add that there is an increased need for protecting the corporate datathat is transmitted in these networks. Not being able to assure data safety can hindercompanies to implement and use new technologies. Additionally, the authors mentionthat not only corporate, but to a high degree also personal data is transmitted, whichneeds to be handled confidentially.

The earlier mentioned area of conducting trials has not yet been mentioned as con-taining relevant factors for the analysis. This is, because there has not yet been anydata collected about trials. Such data will be collected by analysing empirical illustra-tions, thus filling the last of the three areas, conducting trials, with information. Tocomplete the research model, an additional category is added, namely expected benefits.

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It is used to collect the common expectations that companies in the collected empiricalillustrations have towards the particular studied innovation. This way the picture of theprocess is rounded to allow comparison between expectations and the facilitating andrestricting conditions.

The following list summarises the factors collected from theory:

• Perceived ease of use• Perceived usefulness• Compliance\Internalisation• Training• Skills and Knowledge

• Allocating financial resources• Availability of internet connectivity• Protection of corporate data• Protection of personal data

2.11 Research model development

2.11.1 Model categories

The literature has revealed several factors that have shown to be influential during theprocess of innovation implementation. For the structured analysis of empirical data,these are summarised in a model, which classifies the different factors into four cate-gories. The first two categories incentives and support factors are related to the firstresearch question. This research question is separated into two different categories toallow a more detailed analysis of differences between incentives and support factors andtheir influence on the implementation process. The third category is described with theterm obstacles, which could also be called barriers or hindering factors. For a more com-plete description of innovation implementation of the targeted technology, an additionalcategory, expected benefits, is added. This category corresponds to the third researchquestion. The model is shown in Figure 7 and will be further described in followingSection.

ImplementationProcessIncentives Expected

Benefits

Obstacles

SupportFactors

Figure 7: Basic research model

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The categories incentives and obstacles, aim to collect the clear positive and negativeinfluences on innovation implementation. The term incentives, as the linguistic meaningsuggests, can be applied to describe factors that have a profound affect on the perceptionof an innovation in an organisation. It means that they create a sense of positive attrac-tion towards using a technology, as supported by the method Dong et al. (2008) use tocharacterise incentives. Assuming that a factor is proposed to act like an incentive, thetechnology has to appear unattractive to use otherwise. Dong et al. (2008) reduce theidentification of an incentive to whether it creates motivation or discouragement to usean innovation.

The category of obstacles on the other hand is applied to factors that restrict theimplementation of a technology, whether it is strongly or marginal. When thinkingabout the two, it becomes clear that a factor can only be assigned to either of those, ifit is given a certain value. Theoretically, this means that if empirical data shows thata factor is perceived low (or non-existent), then the consequence would be that it is anobstacle for the implementation of a technology; if it is high (or existent) it facilitatesimplementation. High and low values could of course mean the opposite thing, depend-ing on the context. Obstacles do not necessary have to be fed from these seeminglybilateral factors. Instead it can be very practical issues that arise from the context of atechnology’s use.

Practically speaking however, the polar definition of factors falling into either thecategories of obstacles or incentives does no necessarily mean that a factor not perceivedas an incentive automatically acts as an obstacle. The mere implication of this polardefinition is that it becomes quite unlikely for a factor to act as an incentive and anobstacle at the same time. This clear distinction is supposed to support the analysisin its clarity. During the classification, there will always be a context involved, whichallows a contextual codification of the targeted factor.

When considering the contextual integration of the factor in the analysed data, anadditional category becomes necessary. Factors that do not elicit a full incentive char-acter, while not hindering the implementation process have to be gathered in a moremoderate collection. Therefore the category support factors is introduced, which repre-sents the second part of the second research question. These factors do however play amentionable role for the implementation, as the literature has shown.

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Finally, the category expected benefits is added. This category is intended to capturethe positive, or beneficial outcomes of a successful innovation implementation. It depictsthe prospect that a company has, when implementing a technology. The factors collectedin this category do not have an active impact on the process itself. However, it is expectedthat the aim a company has when implementing a technology can give interesting insightsin the process characteristics. This category is therefore, relative to the others, anoutgoing node of from the implementation process depicted in the research approach.

2.11.2 Factor classification

In the following, the collected factors are further discussed as of how to use them andto qualify them in the collected empirical data in the proposed model. For this purpose,they are arranged in the proposed categories. Starting with the first of the factors listedin the end of Section 2.10, perceived ease of use; Davis (1989) use a scale with sevensteps to measure how easy it is for a user to learn operating a technology; if it was clearand understandable, if it was easy to be come skilful using it, and if it generally wasperceived as easy to use. As earlier concluded from the work of Davis et al. (1989), itcan be seen as a supporting factor.

The qualification characteristics for perceived usefulness, as used by Davis (1989),include questioning whether the technology helps to increase performance, productivity,and effectiveness, makes a job easier, or if the innovation is generally perceived as usefulfor the targeted task. As opposed to perceived ease of use, usefulness is needed for anindividual or organisation to even consider using a technology in the first place. The-oretically, using a technology can still solve an apparent problem sufficiently when noteasy to use. Perceived usefulness is therefore seen as potential incentive.

The general stance of compliance or internalisation is deducted from the contextthat an empirical observation gives. For qualifying the information from the empiricalillustrations, a rather polarised stance is taken. Looking at the basic idea of Ensmingerand Surry (2008), an improved interest in implementation of an innovation is assumedif people throughout a company are involved in the process. This leads to the definitionthat a high degree of involvement, associated with internalisation, leads to an incentivefor innovation implementation. If compliance can be attested, it only supports the pro-cess, as long as no resistance occurs. That instead would obviously create an obstaclefor implementation.

Training in innovation use are events and actions that support implementation byimproving the user’s self-efficacy, as Compeau and Higgins (1995) concludes. The confi-dence built up this way has a positive effect on other factors like improved perceived easeof use or building skills and knowledge. Training, or user support services, are tightlyconnected to developing skills and knowledge (Venkatesh and Brown, 2001; Compeauand Higgins, 1995). It is assumed that sufficient skills and knowledge for using a technol-ogy do support implementation. However, when data shows that these are not available,

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they are expected to turn into obstacles. Obviously, a technology cannot be used, ifthere is no knowledge about how to use it. Just as the knowledge about how to dosomething does not give an incentive for doing so, if no benefit is seen.

Finally, as has been shown by many authors like Venkatesh and Brown (2001); Com-peau and Higgins (1995); Klein et al. (2001); Klein and Knight (2005), an abundance offinancial resources is the most frequently proposed support factor in theory. It is shownto influence the overall quality of all policies and practices within the implementationprocess. In turn this indicates that the absence of sufficient financial resources is an ob-stacle for innovation implementation, since policies and practices cannot be supported,which in turn means that skills and knowledge of the users cannot be developed.

As an addition, the following expected benefits concretely mentioned in the theory,have to be verified to hold for the specific case of augmented reality in CSCW. Theywill be confirmed or invalidated by the information from the empirical illustrations. Theonly study with this focus so far by Scavo and Perey (2016) summarises benefits that areexpected, when using AR remote collaboration technologies. Considering the case thatinstead of an expert travelling to a location, support is given remotely, travelling timeand costs are expected to be reduced. Another proposed benefit is that communicationitself is accelerated, as visual information is added. This simplifies the communicationprocess between a worker and a remotely connected helper, as opposed to pure vocalcommunication. In turn, operational down-times of machinery are reduced and supportservices can be outsourced.

Scavo and Perey (2016) specify another set of expected benefits for companies, re-ferred to as experience capturing. Instead of manually compiling information aboutprocedures and practices, video and photo documentation can be used to create thisinformation without additional effort. Such procedures enable capturing, sharing anddisseminating of information within the company. A related benefit according to Scavoand Perey (2016) is here also the increased documentation quality when it comes to qual-ity, safety, and certification requirements for that specific actions and task sequences needto be attested. Obstacles specific for the case of AR remote guidance, as proposed byScavo and Perey (2016) and Bauer et al. (2013) are connected to the safety and con-fidentiality of corporate, as well as personal data that is transmitted or recorded in amore connected world and the internet of things.

After assigning the collected information to the four categories in the earlier definedbasic research model, Figure 8 depicts the full set of factors that need to be qualifiedwith empirical data. The arrangement of the factors in the categories is based on theproposition in existing literature. For some of the factors, it was already possible tosee from the literature, that the context of the analysed data could possibly change, inwhich category they are situated. The analysis will show, how exactly the factors arearranged in the categories and how they impact the innovation implementation process.

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ImplementationProcess

Obstacles:• Resistance• Availability of internetconnectivity

• Protection of corporate andpersonal data

Incentives:• Perceived usefulness• Internalisation

Support factors:• Perceived ease of use• Compliance• Training• Sufficient financial resources

Expected Benefits:• Reducing travelling time and costs• Improving communication quality• Reducing system down-times• Experience capturing

Figure 8: Research model

3 Methodology

3.1 Research approach

The main purpose of this thesis is to identify, classify, and discuss factors and theircollaborated impact on the innovation implementation process of augmented reality re-mote collaboration technology. The main questions to answer are of the "what" and"how" character. Meredith (1998), as summarised by Voss et al. (2011), propose qual-itative research aiming at cases to be most suitable to tackle these kinds of questions.It is a situated activity that locates the observer in the world and makes sense of itby using different interpretative methods and practices and transforming it into docu-mented empirical representations (Denzin and Lincoln, 2000). They are said to allowcreating a relatively thorough understanding of the nature and complexity of a stud-ied phenomenon. The first and second research question in this thesis represent thetwo proposed question themes as of "Which are the obstacles for the implementation ofaugmented reality in CSCW and how do they hinder this process?" and "Which are in-centives and other support factors for the implementation of augmented reality in CSCWand how do they facilitate implementation?". The third research question ("Which arethe expected benefits of using augmented reality in CSCW?") however does not includethe "how" character. This is due to the characteristics of the collected data and thestudied time frame, as will be explained in the following.

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This research was initially planned as an in depth case study. It was planned thatthe researcher would study the implementation of the company’s technology that thestudy was conducted in, at one of its customers. The studied technology is a systemfor augmented reality remote collaboration. In addition to the main case-study, one orseveral smaller case studies regarding the same technology were expected to give furtherinsight and allow cross-case analysis. A concession for collaboration in the main casestudy was made by the company of interest early on in the pre-study phase for thisresearch. However, after several weeks into the literature research phase, the concessionwas withdrawn, creating the necessity for reorientation. Communicating and negotiat-ing with the companies that had been contacted to participate in smaller case studiesproceeded in a similar manner. The reasoning for this was in all cases related to internalorganisational processes of the implementation project. Finally, the promise of collabo-ration was withdrawn by all priorly interested companies.

This rather unexpedient development created the need to change the focus of theresearch approach during an advanced stage of the research process, since access to suf-ficient information for a deep study was not longer given. Instead of focusing on sourcesexternal to the company that the thesis was conducted at, it had to be resorted to aninternalised approach. In practice this meant that distributed pieces of information thatwere accessible internally were collected as empirical illustrations instead. This approachprohibited using interviews or surveys, the historically most common source of qualita-tive data, as well as it complicated the selection of information sources, as Voss et al.(2011) states. Another characteristic of case studies is the possibility to directly observecause and effect relationships over a period of time (Voss et al., 2011; Yin, 2014). Butsince the time of observation is reduced to insights at particular points in time, changesover time are not possible to be accounted for. This is also the reason why the thirdresearch question does lack the "how" character. Additionally, all illustrations that couldbe gathered internally corresponded to a very early stage of the implementation process,the adaptation phase, which means only the characteristics of this phase in the processare object to study in this thesis. Some of the gained insights refer to even earlier phasesin the innovation adoption process. These circumstances left the researcher with thesettings of a rather superficial study.

In summary, the flow chart in Figure 9, shows the important incidents of reorientationand change of focus in the centre, which consequently created the need to adjust theresearch questions and supporting literature during several phases of the research process.Collection of the definite illustrations and refinement during the early stages of theanalysis finalised the literature review. After that, further analysis, discussion, andconclusion proved to be a rather linear process.

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Pre-studyImmature research questions

Main case studyconfirmed

Preliminary literature reviewPreliminary research

questions

Refined literature reviewRefined research questions

Final literature reviewFinal research questions

Withdrawal main case studyNegotiations with companies

of smaller case studies

Withdrawal all case studiesBricolage approach

Collecting illustrations

Reorientation

Change of focus

Finalisation

Data analysis

Discussion

Conclusion

Refinement

Figure 9: Research process flow chart

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3.2 Literature review

Irrespective of the detail specific adjustments that had to be adopted during the courseof the literature review, the information core is described in the following. The gatheredliterature contains the main theories concerning innovation implementation from scien-tific literature. First, the search for literature was guided by the keyword "technologyimplementation". Corresponding literature has however found to be mostly related tothe personal understanding and behaviour of individuals towards new technology. Thisdoes not match the organisational level focus of this thesis.

Further literature search has then been focusing on the keyword "innovation adop-tion", which itself leads to the main focus area - innovation implementation. It was soonunderstood that the implementation of technology innovation is one particular part ofthe underlying process, which led to the final keyword search. To describe the studiedtechnology and support general information, other keywords were added. Most impor-tant in the search for articles and other literature were the following:

• Innovation implementation• Innovation adoption• Information technology innovationimplementation

• Augmented reality• Computer supported collaborativework

• Remote Collaboration

During the course and development of the literature adjustments had to be made incore areas like industry description, research depth or how precisely the theory is tailoredto the collected data. As a result, the collected theory is not specifically tailored for acertain industry or company. Instead it gives a broad overview of theories with generalapplicability. It starts by giving a general introduction of the areas of industry 4.0, com-puter supported collaborative work, and augmented reality; then proceeds with a basicdescription of innovation, leading to the topics of innovation adoption and innovationimplementation. Finally, more focus is laid on the deduction and collection of factorsthat are described to have an impact on the innovation implementation process.

3.3 Sampling

As opposed to random sampling, the theoretical optimum in collecting data (Etikanet al., 2016), this thesis does build on what is called convenience sampling. It is a typeof non-random sampling, where members of the chosen population have to fulfil certaincriteria. This can be accessibility, geographical proximity, accessibility during a certaintime or the willingness to participate in the first place (Etikan et al., 2016; Emerson,2015).

The individuals and companies that have been studied during the process of gath-ering empirical data in this thesis are characterised by accessibility in the time periodassigned for this thesis, but most importantly by the willingness to share data about

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internal processes and events. Furthermore, for the study of implementing a certaintechnology, samples are characterised by including a particular kind of technology. Thisfurther reduces the possible width of the population and makes it one main factor ofsampling for convenience. Cases that are used as empirical illustrations have been chosento fit into the topic area of augmented reality and, more specifically, to give informationabout the early phases of a technology’s implementation in a broader sense.

This sampling approach is connected to possible result bias, since the studied popula-tion is likely to not represent the theoretically possible population. Statistically speaking,the population cannot be tested for outliers (Etikan et al., 2016). On the other hand,this thesis presents a study that focuses on the connection of insights from many differ-ent sources. Such studies are known to, themselves, reduce the generalisability of theproposed findings. This thesis aims to hold internal validity for the studied cases. Andby connecting the different cases studied this way, external validity is consolidated.

3.4 Data collection

3.4.1 Sources of evidence

The process of bringing together data from a variety of different sources and approachescan be described as a montage, as Denzin and Lincoln (2000) state. It is further illus-trated that a montage brings together different pieces that represent a certain evidenceto, together, create a new interpretation of these in a specific context. A researcher thatresorts to a collection of methods for gathering data and interpreting it is accordinglycalled a bricoleur; someone that fits individual representations into a specific context.Such a methodology can hence be called a bricolage. (Denzin and Lincoln, 2000)

To achieve data triangulation and validity, the bricolage of this research uses multiplesources of evidence. This means that the converged evidence of multiple sources isused to provide different measures of the same phenomenon in one analysis approach.Empirical data is gathered following the descriptions of the methodology of bricolageby Denzin and Lincoln (2000). The main sources of evidence for qualitative researchare adequately summarised in the work of Yin (2014). Although Yin (2014) appliesthese methods for case study research in particular, they do concur with the descriptionof the most commonly used sources of evidence in qualitative research as proposed byDenzin and Lincoln (2000). These are documentation, archival records, interviews, directobservations, participant-observation, and physical artefacts. The application of thedifferent sources of evidence in this thesis is described in the following.

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Documentation

Yin (2014) bring forward that the most important use of documentation is to reinforceevidence that is found in other sources. It can verify the conformity of date or showcontradictory statements. One type of documentation that this thesis utilises are meetingminutes. Several meeting minutes originate from meetings with a particular purpose.An important aspect here is if the researcher was present and which role was takenduring the observed actions. Regarding the context therefore this can be seen as mix ofdocumentation and observation. For this purpose, the researcher uses a research diarywhich includes observed and witnessed discussions and events, as well as unstructuredinterviews. According to Yin (2014) documentation like this can be seen as stable andexact information that is reproducible, however it can be biased because of the selectivecharacter of the documented information from the company side.Archival records

From the specified kinds of archival records, only survey data from other scientific re-search about augmented reality in CSCW is re-evaluated. In addition to the mentionedevidence criticism, Yin (2014) brings forward that archival records have proven to becase-dependent, precise and generally more quantitative than other sources of evidence.Further, however, it is said that accessibility is often restricted and the reflexivity of thestudy has to be critically observed, before setting the data into perspective.Direct and participant observations

Direct and participant observations are gathered in different events. It includes technicaldemonstrations of the remote collaboration technology that the researcher himself holdsin front of an audience. In this context, informal discussions and debates are takingplaces. Another kind of event are installation, demonstration and education sessionsthat the researcher conducts at a company’s site. At these occasions, also informaldiscussions and debates can occur. The role of the researcher in both of these eventsalternates between being a participant in the occurring curse of action and being apassive observer. Referring to Yin (2014), this approach to collecting evidence gives agood insight in real behaviour of people and their interpersonal behaviour. But againproblems can arise from reflexivity, where an observed process could be altered by thepure fact that participants know that they are observed. Furthermore, if the researcheracts as a participant, bias because of situational manipulation, selective documentationof data, and subjective perception can arise.

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3.4.2 Primary data

The studied technology

The studied technology in the illustrations collected as primary data is developed by thecompany XMReality. It is a system solution for remote collaboration using augmentedreality and is intended to be used in field service applications, where at least one in-dividual is conducting physical field work operations (worker), while the other person(helper) is remotely connected and giving advise.

The data

Primary data was collected in three occasions, comprised in the first three illustrations,which all relate to the described technology. Information in illustration 1 results fromdirect observations and was gathered from an informal discussion at the end of a businessmeeting at a company producing and maintaining control cabinets for railroad systems.The meeting was held as a company representative of XMReality, but the following dis-cussion was aimed solely at research purposes. The dialogue developed from a generalinformal discussion with the present service operations manager focusing on the imple-mentation of XMReality’s technology at the company. Observations during the courseof the conversation were documented in a research diary and then transcribed into rel-evant illustration data. The discussion was unstructured, but could be guided alongthe four dimensions obstacles, incentives, support factors, and expected benefits by theresearcher.

Illustration 2 consists of two independent observations. In observation 1, data froman implementation project status meeting was collected in a research diary and latertranscribed into illustration data. The meeting was held at an international companyfrom the heavy machinery industry aiming to conduct remotely guided machine main-tenance using XMReality’s technology. Company internal trials had at this point beenongoing since 6 months in an internal evaluation project. The researcher, together withthe CEO of XMReality discussed the customer’s project status and characteristics withthe technology implementation manager and the corresponding responsible line man-ager. The researcher was not able to guide the discussion. Therefore only passivelyobserved information could be collected in notes for later transcription.

The second observation at the same company and documented in the research diaryis comprised of information collected at a cross functional implementation status andplanning meeting. Purpose of the meeting was to discuss and clear the path for con-tinuing internal trials and evaluation of the technology. The researcher together withXMReality’s CEO and two programming consultants was present at the meeting. Onthe side of the visited company three persons were physically present, and two connectedvia phone. The implementation project manager and two IT-infrastructure and projectmanagers were present. The two persons connected via phone had the positions of server

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and IT infrastructure specialists. It was not possible for the researcher to guide the dis-cussed topics in any way. Therefore notes were taken passively.

Illustration 3 is comprised of transcribed information gathered at a workshop eventat the Chalmers University. The workshop focused on demonstrating and debating onXMReality’s AR remote collaboration technology. The researcher was present togetherwith the CEO of the company XMReality and was responsible to demonstrate the tech-nology. Attendants were the following: A professor and three PhD candidates fromChalmers University, three representatives from the defence industry sector, one rep-resentative with higher authority from the aviation industry, two representatives fromanother research institution and several university members, including professors, PhDcandidates and students. After the technology was demonstrated and every workshopattendee had solved an assembly task using it, a discussion was held to give insight inthe opinion towards the technology of the different representatives. The researcher tooka passive, observing role in the ongoing discussion, taking notes. Table 1 summarisesthe collected primary illustrations.

Table 1: Summary of primary data illustrations

Illustration Research Date Duration Position of Role ofSetting attendee(s) researcher

1 Unstructured 14.03.2016 20 min Service operations manager InterviewerInterview

2.1 Business meeting 21.03.2016 2 h Implementation manager ObserverLine manager

2.2 Business meeting 11.04.2016 2 h

Implementation manager

ObserverIT infrastructure manager

IT project managerServer specialist

IT infrastructure specialist

3 Workshop 05.04.2016 4 h

Testing managerHead of production systems

Test engineerIT manager

R& D manager PresenterPhD Candidates Observer

Technology managerAssociate Professor Production Systems

Communications managersStudents

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3.4.3 Secondary data

The studied technology

The technology that is subject to study in illustrations categorised as secondary datais the same as described in Section 3.4.2. The technology developed by the companyXMReality. It is a system solution for remote collaboration using augmented realityand is intended to be used in field service applications, where at least one individual isconducting physical field work operations (worker), while the other person (helper) isremotely connected and giving advise.

The data

Four empirical illustrations that are classified as secondary data are utilised in this the-sis. They are listed as illustrations 4 to 7. Illustration 4, a paper by Li et al. (2016)includes a quantitative study focusing on task execution time and quality of a remotelyguided task when using XMReality’s remote collaboration technology. This thesis usesthe qualitative and quantitative findings from the paper.

Illustration 5 comprises qualitative aspects of the master thesis by Svensson (2016),which focuses on whether the AR remote collaboration technology of XMReality en-hances organisational efficiency. This thesis uses the findings from the evaluated ques-tionnaire, interviews, and observations from a workshop.

Illustration 6 summarises the relevant aspects of a technical report that was aimedto qualitatively evaluate the technology’s aspects of use and its performance for remotemaintenance of machining tools solutions. The report was provided by an external com-pany, name and identity need to be kept confidential.

Finally, Illustration 7 compiles aspects relevant to this thesis’ research focus fromanother technical report, which includes two cases studies, where the technology is used.It includes two case studies, where the technology has been used. One of them in alaboratory setting and one of them on a shop floor. The report gives qualitative insightsof relevant aspects concerning positive and negative aspects of the studied technology.Corporate author and origin of this report are confidential. Table 2 summarises thecollected primary illustrations.

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Table 2: Summary of secondary data illustrations

Illustration Research Research Research Authorsetting character focus

4 Case study Quantitative Task execution time Li et al. (2016)Execution quality

5 Case study Qualitative Organisational efficiency Svensson (2016)

6 Technical report Qualitative Technology evaluation Corporate(Confidential)

7 Case study Qualitative Technology evaluation Corporate(Confidential)

3.4.4 Complementary data

The illustrations in this section are characterised as complementary data, because theyfocus on technologies different from the one described in 3.4.2. Therefore each of therespective technologies are introduced individually, in connection with elaborating onthe corresponding illustration’s characteristics.The data and corresponding technology

Four illustrations including relevant complementary data have been collected, which areillustrations 8 to 11. The referenced technology in illustration 8, is a collection of dif-ferent technologies for remote collaboration that are not thoroughly described in theirfunctionality. The source is a webinar (Perey, 2016), i.e. an online seminar that washeld in on behalf of AREA, an augmented reality business alliance. Four experts inaugmented reality in the aviation industry participated, besides the host. A technologyexpert of the Boeing Company, the director of industry and manufacturing at AERTECsolutions, the CEO of Talent Swarm and a technical and quality manager of the EropeanSpace Agency. The illustration summarises the experiences, propositions and lessonslearned of these four experts from the online seminar discussion. The main focus of thementioned studies is task execution time and quality. Most of the gained insights areof qualitative character. However one study is described that presents quantitative data.

In the case study analysed in illustration 9, researchers use a remote collaborationsystem that includes smart glasses with two different operation modes - see through andcovered, creating the possibility for either augmenting virtual content in the real environ-ment or creating a fully virtual environment. The corresponding paper by Billinghurstet al. (1998) investigates different modes of augmentation in connection instructionmodes in a case study. The analysis concentrates on time difference between the opera-tion modes, as well as task performance. The study contains qualitative and quantitativecontent that is extracted in the illustration.

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The technology studied in the underlying case study to illustration 10 by Gurevichet al. (2015) consists of a robotic arm with connected cameras and pico-projector, whichallows projection of augmented context onto surfaces in the real environment. The robotcan be operated by a remotely connected helper. This person can transmit and projectannotations that are done on a computer screen into the real environment, aiding aworker on site executing a physical task. The case study provides qualitative insightsand improvement propositions for future AR remote collaboration technology, which arecollected in the illustration.

The data gathered from Syberfeldt et al. (2015) that is the basis for illustration 11builds on a video based AR-display that creates a full enclosure around a user’s eyes. Twoweb-cameras mounted at the front facing side of the HMD record the real environment,which is then shown on the HMD’s video display. These cameras are adjustable, whichallows fine-tunig for each users individual eye-distance. By using a specially programmedsoftware, the two cameras work together, transmitting the recorded real environment intothe video display. One of the cameras is further used to recognise particular patterns,while the other camera is used for positional tracking. Virtual objects can then virtuallyrendered in a correct relative to the recognised pattern and the relative position of theusers viewpoint.

The study included 12 participants. None of them priorly had experience with ARtechnology. The task was to assemble a three dimensional puzzle in a specific orderand position. The needed pieces, existed physically, but were virtually marked withcolour. Those had then to be placed in an exact position that was virtually highlighted.Half of the test subjects used paper-based instructions, while the other half used the ARsystem. Measured were the task performance and time in a two sides approach, includingsubjective and objective analysis of the collected data. The findings were transcribed ininto the illustration. This being the final illustration enclosed as complementary data,a summary of this section is given in Table 3.

Table 3: Summary of complementary data illustrations

Illustration Research Research Research Related Authorsetting character focus technology

8 Multiple Qualitative Task execution time Remote collaboration Perey (2016)case studies Quantitative Execution quality Miscellaneous

9 Case Qualitative Task execution time Augmented reality Billinghurst et al. (1998)study Quantitative Task performance Shared space

10 Case study Qualitative Technology evaluation Robotic arm Gurevich et al. (2015)Improvement propositions AR projection

11 Case study Qualitative Task execution time Video-based Syberfeldt et al. (2015)Task performance AR display

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3.5 Method for data analysis

For analysing the different empirical illustrations it is important to consider the casesettings, as well as the stage in the implementation process that most accurately de-scribes it. Understanding each illustrations individual circumstances is crucial, whengoing over into detailed analysis. There exist different possibilities to analyse and con-nect data from different cases. One popular approach is called cross-case analysis. Itfacilitates the comparison of differences and conformities of events, activities, and pro-cesses between different cases. Besides pure comparison, is is also possible to find deriveexplanations from factor combinations between cases that might lead to a certain out-come. (Khan and VanWynsberghe, 2008) Elsbach and Kramer (2016) add that the useof cross-case analysis enables the researcher to explain why one case might be differentfrom another and gain a more holistic understanding of the subject of interest.

Khan and VanWynsberghe (2008) suggest to distinguish between two main tech-niques of how to approach cross case analysis - variable and case-oriented research. Forthis thesis, a case-oriented approach has been chosen, which is called typologies (Khanand VanWynsberghe, 2008). Denzin (2001) promote to use this technique to deductpriorly existent perceptions of a phenomenon and then scout for essential elements orcomponents across multiple cases. The combination of derived and newly discoveredinformation is then re-constructed into an ordered whole and fitted into the originalcontext. Following this approach, the factors that have earlier been arranged in the fourcategories incentives, support factors, obstacles and expected benefits to represent thedeconstructed perceptions. Consequently, they build the basis for cross-case comparingthe collected empirical illustrations. Note here, that this is closely related to factorbased analysis approaches from innovation implementation theory. Even though theempirical data consists only of illustrations rather than full cases, the same approachis used to compare their relationship and achieve comprehensive understanding. It isobviously questionable if an illustration can live up to the quality of information in a fullcase. It is necessary to treat findings in terms of attesting generalisability and causal-ity with caution. However, the structure and logic behind this approach are not affected.

Innovation implementation is merely investigated as an excerpt of the whole pro-cess, meaning that a specific point in time is studied, not potential changes during theprocess. There exist different analysis levels in innovation adoption and innovation im-plementation theory. A level of analysis that has however shown to hold through allresearch fields is the organisational level. On this level it is possible to abstract findingsin a way that cumulated individual opinion can be sufficiently prescinded to representgeneral statements (Klein and Sorra, 1996). Thus it is possible to combine observationsabout individuals together with findings on a corporate level.

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3.6 Validity and Reliability

To ensure high validity for this thesis, the work of Yin (2014) has been used as mainguidance in the following. The authors suggest four main tests that should be conductedduring the research process. These aim for the verification of construct validity, internalvalidity, external validity, and reliability. The first test is applied during the data collec-tion phase and supports the researcher in identifying the correct operational measuresfor the studied concepts. This requires the studied phenomenon to be defined in termsof specific concepts, which allow the identification of operational measures. This thesisutilises the main tactics for increasing construct validity described by (Yin, 2014). Mul-tiple sources of evidence are used, as described in Section 3.4.Triangulating data fromdifferent sources improves validity as Voss et al. (2011) states. And furthermore, thereader is guided from initial data collection to final proposition by a chain of evidence,testifying how the conclusions are produced.

The second test, internal validity, is most frequently experienced as an issue in ex-planatory and causal studies Yin (2014). Studies, like this one that do address questionsof descriptive character are not likely to suffer from internal validity issues. Howeverthough, it is still important to show a sound way of creating arguments from analysedinformation. To reduce the possibility of losing internal validity, the proposition of causalrelationships is handled with caution. Moreover, the two tactics pattern matching andaddressing contradictory data, as proposed by Yin (2014), are used. This is done usingtypologies for deducting and re-matching information (see Section 3.5).

External validity, as the third of the tests proposed by Yin (2014) deals with whethera study is generalisable and applicable to other cases outside of the studied specific set-tings. In contrast to quantitative research, a qualitative study can only be generalisedanalytically, which means generalising a collection of results to a broader topic. This isonly possible as long as similar hypothesised circumstances do occur. For this researchthis means that generalisability is only given towards studies that focus on an AR remotecollaboration technology, which is implemented at a company. Results are furthermoreonly valid for the very early phases of the implementation process - more precisely theadaptation phase. Another characteristic is that direct observations were only gatheredfrom managers.

The final test that Yin (2014) proposes is reliability. It is concerned with minimisingerrors and bias of a study. An important requirements here is clear and insightfuldocumentation of the research process. Two strategies for creating reliable insights inthe raw data of a case study are the creation of a case study protocol and a case studydatabase. This thesis does not build on a case study protocol, mainly because thefrequent changes that had to be adapted to during the research process (compare Figure9). The final primary data was characterised by accessibility and convenience sampling(described in Section 3.3). Therefore instead, it was resorted to creating a case studydatabase in the form of a research diary, as a structured way of comprising observations.

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3.7 Limitations

Results from this research are particularly valid in the context of augmented realitywithin CSCW, more specifically remote collaboration. The used empirical data howevermostly gives insights in the adaptation phase of the innovation implementation process.Therefore it is not possible to account for developments or changes that occur duringthe process. The previous sections have shown that the original research approach hadto be revised several times. In the end, a deep case study was not possible and the gath-ered primary data was mainly characterised by its accessibility. This restricted accessto data resulted in sampling for convenience and obtaining data solely representing amanagement perspective of the studied subject. To fill the gap of lacking user’s opinion,secondary data that elaborating on this perspective was collected. Since it was furthernot possible to actively influence the mode of data collection, common data collectionplanning including questionnaires or planned interviews were not feasible.

Critically speaking, can the fact that this thesis uses a bricolage of empirical datato create triangulation in connection with a certain topic area, be a point of discussion.By utilising a variety of different data gathering methods from different sources, theresearch process can, on the one hand, be said to reduce internal validity. This wouldmean that the causal conclusion based on the study becomes non-attestable, because ofthe many facettes that are related to each other. On the other hand, this adds valueto the research by providing and relating findings from different areas to the desiredtopic that otherwise would not have been possible to grasp. This allows triangulation ofmore diverse insights and to reflect on the findings in more comprehensive way, addingvarious perspectives. Another noteworthy circumstance is that the sources of evidenceproposed by (Yin, 2014) are created as a framework to complement each other if alldata is related to the same case. The fact that different sources are referenced for thedifferent kinds of data could reduce the compensation the different sources of evidencehave on each other. Contrariwise, can the different aspects of the same technology indifferent companies add another level of insights that would not be possible otherwise.

Finally, another influential limitation is the available time to conduct the thesiswork. The author is given 5 months to gather theory, collect empirical data, analyseand conclude. Because of the recurring need to restructure the research approach tofind accessible data, the quantity of primary empirical data is restricted to an amountfeasible in the given time settings.

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4 Empirical illustrations

4.1 Primary data illustrations

4.1.1 Remote support for control cabinetsIllustration 1

Context

The studied company uses the augmented remote collaboration technology to fix prob-lems with control cabinets that regulate certain functions and switches during railwayoperations. The control cabinets are situated with a very short distance to the overheadcontact line that the trains use. When a train passes, there are remarkable voltagedifferences that can also effect the control modules that are situated in the control cabi-nets. This can lead to malfunctions and other problems. If a technician is on-site fixingthe problem, there are many possibilities, what could be the problem and how it canbe fixed. The company wants to use the remote collaboration technology to supervisemaintenance tasks, as well as doing after-work quality assurance.

Obstacles

An exemplary problem that the company experienced is that railway networks that thecompany works with can be partly underground, which makes it virtually impossible tohave a wireless network connection to use XMReality’s technology. The manager ex-plains that the obstacle towards using and implementing the technology lies not withinthe technology. Instead, the project organisation itself is the problem. To be able towork, at least in European countries, the company needs a lot of certificates and permitsbefore work actions in the field can be done. This slows down the process and planningand leads to an interrupted working procedure, which makes it difficult to include imple-mentation events of new technology. Furthermore, the company in this area often workswith delicate data. This fact makes it difficult to use video transmitting equipment on-site, since regulations of information secrecy might not allow recording devices to be used.

The manager continues that the company does in its operations not focus on theintroduction of new technology. There are too many things that need attention andthere are no resources to actively support technology implementation along the regularbusiness operations. Furthermore, there has been no preparation time before using thetechnology. Also, "people are not mature enough for using the technology". This doesnot mean that they do not like what benefits the system bring or that they do not wantto change how they work. But technicians do already carry a lot of equipment with themand one more case that is used to transport the remote collaboration hardware, becomesan issue that keeps them from using the solution. In this connection the managers alsouses the term "resistance" without further specifying the exact meaning of it.

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The manager adds that the decision to adopt the technology was made on the highestlevel. The technology was encountered during the screening of other technology (ROV’s)on a technology fare. Contacts in the area then led to the remote collaboration technol-ogy. The high-level manager did like the technology and made the decision to use it.

Expected Benefits

Referring to the manager, the company sees the values that come with the technologyaligned with the company’s strategic operation. He expects that using it will generate abetter operations planning, improve experience and technology sharing, as well as betterdocumentation of working procedures during installations and during follow up services.

4.1.2 Remotely guided machine maintenanceIllustration 2

Context

The studied company uses the technology to provide the possibility for remotely guidedservice at the site of machinery that can be located all around the world. The equipmentis used by technicians that travel to plants that have reported a failure or are in theneed for maintenance operations. Technicians are there guided by helpers, located at afixed central location.

Implementation project status meetingObservation 1

Obstacles

A big bottleneck at the company at the moment is the definition, scope and future ofuse as well as finding and defining use-cases. A goal is to implement the solution in thecompany’s internal network; have it up, running and proven, before using it externally.The company is trying to get started with the use of the equipment, but it seems like theend-users are not positively affiliated to the use of the technology. Even if the solutionis available. They need to be taught when and for what the solution is useful and thatit is helpful for their daily operations. On the management side of the implementationproject, the company has only few resources, which means that the idea is interestingbut the actual implementation difficult.

The company has cleared the budget for a 3 min. internal marketing film whereit uses XMReality, conduct interviews and develop a story, but it is lacking internalinformation and documentation for the film at the moment. It is planned to start thefilming of both sides, worker and helper. One of the problems to tackle is to make peo-ple understand the positive effect of reducing man-hours and resources that are neededto fix a problem. People need to use the technology to understand how it works and

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what benefit it brings. A small risk-analysis at the company has further shown thathaving sufficient internet connection can be a problem in certain working environments.Further, there could be problems with not seeing enough in the HMD during a work task.

Another problem seems to be the fact that, even if the bag that is used to transportthe HMD is small, an app can be more interesting since there is no physical object tocarry around besides the phone/tablet that is probably already part of a technician’sequipment. One hinderer to use the technology via an app is at the moment that thecompany uses the operating system iOS within the whole company and the app is notavailable for such an operating system.

Incentives and support factors

Especially for external use, but also as a scalable solution, mobile applications that runon iOS, secondary on Android are interesting. The use of these devices gives differentpossibilities for differently scaled applications. A non-hands-free application of the tech-nology might be suitable in cases, where problems occur. HMD’s might not be on-site atall occasions, because they are one extra bag to carry. HMD’s might be more interestingin planned service scenarios, because of the organisational factors that are connected todistributing the glasses.

An app opens new ways to use the technology. While for planned service it mightbe feasible to plan taking an HMD on-site, for unplanned upcoming problems the appmight be a more suitable solution. The HMD might not be needed and a phone with acamera on the back might be sufficient. This will also increase the possibility to spreadout the technology within the company and the company’s customers if needed.

There is a big conference coming up, where the company meets with its new partnerin Abu Dhabi. The company has a collaboration with them in the service engineeringpart of the business. XMReality will be shown to the new partner at this conferenceand people are expected to be interested in the technology. The company also has sendequipment to Libya for one service case. The company there is working on maintenance,fixing and changing machines. Eight people will be coming to Sweden and learn solvingproblems at the machines and learn using XMReality’s system.

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Cross-functional implementation status and planning meetingObservation 2

Obstacles

The managers explain that one whole system, including equipment for a worker and ahelper has been sent to a customer in an Arabic country. A delegation of local repre-sentatives from that country have been invited to be education in technology use. Theuse-case in that Arabic country has also shown that the size of the equipment is a burdenfor local technicians to bear. It includes carrying one more bag, which is seen as imprac-tical and is said to also induce organisational problems. Especially regarding organisingthe correct location and re-location of the equipment.

The implementation manager further stresses that it is crucial to be able to establisha safe internet connection. This means that measures need to be taken to prohibit dataaccessibility for external connections. At the same time mobile access to the internetneeds to provided to the workers that use the technology.

Incentives and support factors

Demonstrations of XMReality’s technology have been done by the company internallyin different European and Arabic countries. At all occasions the functionality of thesystem was characterised as "very good", according to the implementation manager. Hefurther states that in that context, other international divisions of the company haveshown interest in the use of the technology.

He also stresses that an internet connection is a vital part of the remote collabora-tion system to work. The company itself created several scenarios for providing internetaccess when using the technology. One scenario is that a service technician could usea wireless network connection on site, if available. Another solution is the use of bear-able mobile routers or using a mobile phone as a hot spot. A third solution works byusing wireless network technology that is delivered with all machines that the companydesigns. They allow a direct connection to the company’s internal network, providing asecure connection.

Expected benefits

Benchmarking has shown that no other available solution offers the same functionalityand the connected benefits. These are identified as economic savings because of reducedtravel amount and time and a reduction of system down time. A problem that is solvedby using the remote collaboration technology is that the scarcely available experts atthe company are relieved by the introduction of the technology. There are not enoughspecially educated experts available to satisfy the demand.

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4.1.3 Chalmers University workshopIllustration 3

Context

During the workshop, participants had the possibility to test the AR remote collab-oration technology provided by XMReality. It was the same physical assembly taskthat described in more detail in Li et al. (2016), which consists of a LEGO engine as-sembly. Following this was an open discussion about system application and restrictions.

Obstacles

An industry representative from the aviation industry mentions that it can be seen crit-ically to continuously film actions of an employee. An individuals personal freedom isreduced by this kind of supervision.

Expected benefits

Other industry representatives however emphasised the quality assurance possibility ofsuch operations. This would allow post-operational verification of a tasks correct exe-cution. It is added that video material however may only be reviewed if a justified casearises. This way the personal freedom of an individual is protected.

4.1.4 Summary of findings

Table 4: Summary of primary data findings

Illustration Obstacles Incentives Support factors Expected benefits

1

•No availability of •Experience sharinginternet connectivity •Knowledge sharing

•No training •Operations planning•Low perceived usefulness •Documentation•No management resources•EU legislation•Protection of corporateand personal data

•Resistance•System mobility

2

•Financial resources •Training •Reducing travelling time•Internet connectivity •Compliance •Reducing system down time•System mobility •Scalability•Field of view •Ease of use•Delicate data•Perceived usefulness

3 •Information confidentiality •Quality assurance

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4.2 Secondary data illustrations

4.2.1 Task instruction modes and execution timeIllustration 4

Context

This empirical illustration includes the study of Li et al. (2016). It focuses on theinfluence of different instruction modes on the task execution time, the product qual-ity and the operators self-assessed emotions. Compared are face-to-face instructions,text-picture based instructions, remote collaboration with augmented reality, and moviebased instructions. Two rounds of experiments were carried out at the Chalmers Uni-versity of Technology. Test subjects had to assemble a Lego gear box in the correct way,using the different instruction modes.

Obstacles

The results of Li et al. (2016) show that the time to fulfil the task decreases with consec-utive repetitions. The task being guided with augmented reality remote collaborationcan be seen to always take more time, when compared to all other instruction modes.The study further found that using such a technology is physically more demanding thanpaper based instructions.

Incentives and support factors

On the other hand, the findings also show that using this mode of instruction alwaysresulted in a 100% correct outcome of the task. Only movie-based instructions resultedin an almost equally, but slightly lower task correctness.

An additional small statistical survey conducted by the Li et al. (2016) with 95participants found that 73% of the respondents testing the technology for the first timefound it very easy to understand and use. 14% found it to be moderately easy, while 6%answered with difficult and 7% with very difficult. No particular reasons were collected.

4.2.2 Remote collaboration technology and organisational efficiencyIllustration 5

Context

The following illustration is part of the empirical study in the master thesis of Svensson(2016). The study focuses on whether using XMReality’s remote collaboration technol-ogy can enhance organisational efficiency. The targeted company was the Volvo CarCorporation (VCC). Besides a survey and questionnaire, an event during Svensson’s re-search was a workshop that has been held at the company.

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The workshop consisted of two main activities. The first one was deducted fromthe experiment by Li et al. (2016) and included the same Lego model. One participant(worker) was guided to assemble the engine by a helper. Then they switched positions.The second activity was the task of changing a front light bulb of a certain Volvo carmodel. After the two activities were completed, all participants gathered for a discussionsession, including the following topics: benefits/opportunities, disadvantages/obstacles,function, incentives, and scenarios.

Obstacles

The respondents identified several problems - responsible decision-making leaders notrealising the potential of the technology, as well as absence of general knowledge of ex-istence and functionality of the technology. Findings from the workshop conducted inSvensson (2016) show that users describe problems with the latency between the pictureshown in the HMD and the changed physical location of the head when moved. Problemscould however be overcome within a short time span of about 20 minutes after first use.Negatively noted was further the possible feeling of seasickness due to the video screenslatency, as well as the ergonomics of the equipment.

In Svensson (2016) it is further stated that an internal expert at VCC describedprior use cases for the technology that the company had. This includes application ofthe technology to do line-walks at assembly lines in other countries. In connection withthis, the size of the systems hardware and operating it in conditions with high temper-ature is criticised, referring to the weight of the equipment.

Incentives and support factors

According to Svensson (2016), none of the individuals included in the questionnairehave had training or experience with augmented reality remote collaboration before. Itis further summarised that two thirds of the respondents agreed to the statement thatthe solution would make their job easier and result in a higher quality. Furthermore,communication effectiveness would be increased, costs reduced and environmental sus-tainability improved.

Expected benefits

Respondents in Svensson (2016) also noted that the promoted technology would helpvisualising, as well as remotely and collaboratively solving a problem in a remote lo-cation. At the same travel time and cost could be reduced. The discussion in theseminar showed that the biggest functional benefit of the technology was the connectionof voice communication and gesture instruction. According to the participants of theworkshop, the biggest benefit of implementing the technology is the economic benefit it

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brings, such as time savings, support cost reduction and therefore lower environmentalpollution. Another incentive the participants described is faster support, which leads toshorter system down times, faster verification of a problem or solution, or lightening thedecision whether the company really needs to send someone to fix a problem or not.

4.2.3 Remote maintenance for machining toolsIllustration 6

Context

The following is transcribed from a technical report at a company that tested and evalu-ated the remote collaboration technology provided by XMReality. The company screenedthe technology in connection with the possibility to do remote maintenance in the areaof machining tools and solutions. The technology is here intended to be used for plannedmaintenance, post-installation support, or unexpected inquiries.

Obstacles

However, the report brings forward that the value gained for an individual is not note-worthy enough to invest in such a system. The technology would need to be more mobileand readily available on smartphones. Another important aspect according to the reportis that the potential users are not ready for such a technology at the moment, since it isnothing that they are accustomed to. They are said to not see a benefit towards othervideo communication software without augmented reality features. Other negativelystated points are that the use of the HMD was limited by the achieved resolution onthe integrated screen and the two dimensional representation of the filmed environmenthindered the perception of depth.

Expected benefits

In the report it is analysed that the use of the system would bring benefits for thecompany by reducing the downtime of machinery at a customer location, while at thesame time saving travelling costs. Furthermore, it is said to help spreading knowledgethat is held by a small amount of people that are situated in one physical location.

4.2.4 Evaluation of laboratory and industrial context remote collaborationIllustration 7

Context

This illustration is compiled from a technical report from a research agency that eval-uated XMReality’s technology in trials with two companies. The first takes place ina laboratory environment. Participants were remotely guided through instructions tofollow a typical maintenance procedure at a control cabinet. The second test run was

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situated in a production floor for circular cement plates. The operators need to handlecranes, move and mix specific amounts of liquid components in an industrial size blender.

Obstacles

The first experiment shows that using a video based HMD can create problems, whensmall details are the point of interest. The resolution of the integrated video display isdescribed as too low. Furthermore, users complained about a restricted field of view thatmade it difficult to orientate within the real world. For comparison, the same task wasalso conducted with only voice instructions on a mobile phone. This mode of operationmade it more difficult for the worker to identify the correct component with the helpersinstructions.

The second trial showed that the reduction of depth perception, when wearing anHMD with a 2D video display can cause problems, when navigating in a shop floor withmoving objects and high safety requirements. Paired with the reduced field of view of anoperator, the use of such an AR remote collaboration technology is therefore concludedas not feasible.

Incentives and support factors

The participants agreed that the AR operation mode made it possible for the helper totransmit a certain kind of emotion with the instructions, making it more natural for thehelper to recognise precarious actions or situations.

4.2.5 Summary of findings from secondary data

Table 5: Summary of secondary data findings

Illustration Obstacles Incentives Support factors Expected benefits

4 •Seasickness •Learning curve •Reducing travelling time and cost•Task execution time •Task correctness •Faster problem identification

5

•System mobility •Higher work quality •Learning curve •Reducing travelling time and cost•No management resources •Communication effectiveness •Faster problem identification•Different perceived usefulness •Reduced system down-time

•Lower environmental impact•Lighten decision-making

6

•Low perceived usefulness •Scalability •Spreading knowledge•Users not ready •Reducing travelling time and cost•Depth perception •Reducing system down-time•System mobility

7•Depth perception •Static/dynamic use •Quality assurance•Field of view•User safety

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4.3 Complementary data illustrations

4.3.1 Augmented Reality in the Aerospace IndustryIllustration 8

Context

This illustration is a summary of the webinar by Perey (2016). It was on behalf of AREA(Augmented Reality for Enterprise Alliances). This is an alliance of companies that areactive in the field of augmented reality. Several participants representing the aviationindustry participated. All of the corresponding companies have conducted research andtrials with AR remote collaboration technology internally. The seminar’s aim was to ex-change knowledge about adoption, driver, barriers and implementation of the technology.

Obstacles

In the following course of Perey (2016), several barriers to implementing such technologyare discussed. One of them is that offered AR software solutions do often not provideinteroperability with existing IT, which makes expensive adjustments necessary. Thenalso, there are not enough studies that verify the business benefits sufficiently and quan-titatively. Additionally, when doing trials with CSCW AR technology, the attendantcompanies have experienced user resistance to the new technology. Mainly because peo-ple are used to be provided with instructional information in paper format. A side notehere is also the eventually occurring feeling of sea sickness when using an HMD.

Two of the participants raise concern over strict labour laws and regulations in theEuropean Union, when it comes to recording data that can be traced back to a partic-ular person. And on the other hand the targeted users themselves are cautious aboutthem being recorded. All of this is summarised in problems regarding integrity and datasecurity.

Incentives and support factors

As a summarising proposition, one of the participants suggests that the industry shouldstart by using AR for the pure purpose of remote collaboration, without adding advancedamounts of 3D content. This would allow for remote problem solving in a tele-presencemanner. In the future then, more functions could be added to AR systems, when thetechnology is capable of sufficiently integrating data from external systems.

In Perey (2016), a study is mentioned that had a comparable setup to Li et al. (2016),with an augmented reality remote collaboration technology where a remote helper anda worker using an HMD solved a physical, manual task. The task included assemblingelectrical harnesses, while using a projection based augmented reality system, whereinformation was directly projected onto a hardboard. Test subjects had to correctly as-

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semble elements like connectors and wires. The findings indicate that the time to finishthe task is reduced by 30%, when using the technology. Comparative instruction modehere was a text-based, pdf instructional paper. First time quality of the work is shownto increase by 90% referring to the mistakes that were made during the process, againcompared to paper based instructions.

Expected benefits

The industry representatives together summarise the following expected outcomes forimplementing an augmented reality for CSCW. First of all, reduced time for remote tasksusing AR as opposed to instructions via phone are unanimously mentioned as one of themain goal. Furthermore, the quality of collaboratively executed manual tasks is expectedto increase, as mentioned above. This simultaneously includes the reduction of errors,when conducting these tasks. More drastically speaking, one of the representatives callserror to be not an option.

4.3.2 Shared Space in CSCWIllustration 9

Context

In their study, Billinghurst et al. (1998) use smart glasses that can be operated in twomodes - see-through and covered. The latter means that only augmented context isshown in the glasses, creating an immersive viewing mode. These glasses use augmentedreality technology, which means that virtual content can be augmented into the realenvironment, but full virtual context is also possible. Different modes of instructions arecoupled with the different operation modes of the glasses during the study.

The study uses the concept of shared space in the context of CSCW. In this casethis means that two users of the technology work collaboratively in a partly artificiallycreated, partly virtually generated environment. Two participants stand side by sideand are free to move around in the real world. Their task is to collaboratively spot andsort virtual objects. But only one person can identify them, while the other person canmove and interact with them. In the experiment, different equipment and environmentsetups are used:

• Real world - Real bodyParticipants see each other and the real world

• Reald world - No bodyA sheet is dropped between the participants but they can see the real word

• Virtual world - No bodyThe glasses are used in immersive mode without representation of the participantsbodies

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• Virtual world - Virtual bodyThe glasses are used in immersive mode with representation of the participantsbodies

• Reduced virtual world - Virtual bodiesThe glasses are used in immerseive mode with representation of the participantsbodies but the virtual world is reduced to only a ground plane

Incentives and support factors

The results of Billinghurst et al. (1998) show that in general the individuals performedbetter, when they could see each others bodies, whether in reality or virtually. It wasfurther found that individuals that use body, as well as non-body cues to communicateshowed a higher performance during the studied task. Learning effects did also positivelyinfluence the test subjects performance. The users own perception supported the findingsthat seeing each other improved task performance, as well as using body cues.

4.3.3 Aspects of AR remote collaborationIllustration 10

Context

Gurevich et al. (2015) developed and tested an augmented reality system that consistsof a robotic arm with attached cameras and pico-projector. This construction is placedat the location, where remote support is needed. Then, a remotely connected helpercan see the filmed workplace. Using a software on a personal computer, the helper isable to make annotations with different graphical tools in the transmitted video. Theseannotations are directly projected onto the filmed physical workplace. The reality of aworker in this workplace is therefore augmented with projected digital information thatis added by the helper.

Obstacles

The study of Gurevich et al. (2015) shows that an augmented reality remote collaborationtechnology must fulfil the need of mobility. This means that the necessary hardwareneeds to be portable to different places of operation. In connection with this, it is alsoimportant that the helper can be given different angles of the scene instead of one fixedposition relative to the filmed working environment. While the use of an HMD is seento be a suitable solution, it is criticised that head movement of the worker influences thenecessary position of the annotations in the video stream. Therefore the workers headmovement is limited for effective use of the technology.

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In the conclusion it is argued that alternatively, the use of a tablet brings advantagesin acquisition costs and technology distribution. The solution however creates problemswith mixed attention for the worker and therefore increased mental load. During theprocess, the tablet and the instructed task in reality needs to be paid attention to. If adevice is held, a worker might be able to work with the other hand. This is a problemwith more complex tasks and bigger parts that are used.

4.3.4 Remote visual assembling guidanceIllustration 11

Context

For their study Syberfeldt et al. (2015) use a video base AR-display. Users fully relyon the video based output inside the display, because it builds a full enclosure aroundthe eyes. Two web-cameras mounted at the front facing side of the HMD record thereal environment, which is then shown on the HMD’s video display. These cameras areadjustable, which allows fine-tunig for each users individual eye-distance. By using a spe-cially programmed software, the two cameras work together, transmitting the recordedreal environment into the video display. Virtual objects can then virtually rendered in acorrect relative to the recognised pattern and the relative position of the users viewpoint.

The study included 12 participants. None of them priorly had experience with ARtechnology. The task was to assemble a three dimensional puzzle in a specific order andposition. The needed pieces, existed physically, but were virtually marked with colour.Those had then to be placed in an exact position that was virtually highlighted. Half ofthe test subjects used paper-based instructions, while the other half used the AR system.

Obstacles

The objective evaluation by Syberfeldt et al. (2015) shows that it took significantlylonger to execute the different steps in the task’s process. Negatively stated was alsothat, in comparison to paper based instructions, the AR systems was physically moretiring.

Incentives and support factors

The results consist of a subjective survey, taken by the participants and and objectiveevaluation by Syberfeldt et al. (2015). The survey showed that AR instructions for thephysical task were almost equally easy to understand, even though it had never beenused before. Additionally, it was often stated that more training with the system wouldimprove task performance. Although task execution time was significantly longer usingAR remote collaboration, after placing two of the puzzle pieces, the participants didalready show a high learning curve and became better in operating while using system.

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As opposed to the participants using paper based instructions, all AR users finalisedthe task without any errors. Other remarks are that the user needs to feel that theassembly task is complex enough to gain a benefit from using AR technology. This isconnected to expected gains in efficiency that cannot be achieved, when time for taskexecution is the most important measurement for the tasks execution quality.

Expected benefits

The most important benefit with such a technology is what the authors call a built-incontrol against humans mistakes, which is expected to have a high value in the industry,where the maximal possible quality is required.

4.3.5 Summary of findings from complementary data

Table 6: Summary of complementary data findings

Illustration Obstacles Incentives Support factors Expected benefits

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•IT integration costs •First time quality increase •Supporting studies •Support time reduction•Resistance •Task time reduction •Increase quality•Seasickness •Error reduction•EU legislation•Protection of corporateand personal data

•User resistance

9 •Seeing each other •Body cues•Learning curve

10 •System mobility •Providing different angles•High mental load •Scalability

11 •Task execution time •Ease of use •Human error control•High mental load •Learning curve

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5 AnalysisThe developed research model has shown that there are four main categories along whichthe analysis of the collected empirical data is oriented. These are obstacles, incentives,support factors, and expected benefits. Innovation implementation literature proposesfactors that have an influence on the innovation implementation process. These havebeen identified, collected and assigned to the categories proposed in the research model.Table 7 presents the factors proposed in theory in each of the categories.

Table 7: Research model factors proposed in theory

Obstacles Incentives Support factors Expected benefits

•Availability of internet •High perceived usefulness •Perceived ease of use •Reducing travelling timeconnectivity •Internalisation •Compliance and costs

•Protection of corporate •Training •Improving communicationand personal data •Sufficient financial qualityresources resources •Reducing system

down-times•Experience capturing

The categories in the research model are aligned with the three research questionsthat this thesis aims to answer. The first one is "Which are the obstacles for the imple-mentation of augmented reality in CSCW and how do they hinder this process?". Thisresearch question is discussed in Section 5.2, ’Obstacles and their influence’. Researchquestion two, "Which are incentives and other support factors for the implementationof augmented reality in CSCW and how do they facilitate implementation?", is con-centrated on in Section 5.3, under the headline ’Incentives, support factors, and theirinfluence’. In the research model, the two categories incentives and support factors havebeen separated (see Section 2.11), mainly for more precise differentiation between thetwo. In the research question however the two are combined for a more insightful discus-sion. The last research question "Which are the expected benefits of using augmentedreality in CSCW?", is then discussed in Section 5.4 - ’Expected benefits’.

5.1 Implementation process phase

Besides the four main dimensions of analysis, there is one additional circumstance thathas a fundamental impact on how the findings are analysed and related into the contextof the innovation implementation process. The primary data illustrations that couldbe analysed in connection with the model only describe an excerpt of the innovationimplementation process. Only the first two primary data illustrations, where a singlecompany was involved, could be characterised in this regard. In the secondary dataillustrations, two illustrations could be analysed in regard of process phase.

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5.1.1 Primary data process phase

In Illustration 1 (Remote support for control cabinets), it is shown how the technologythat has bee acquired by the company has not been used in a case, where it wouldhave been needed. From the literature review, three distinct phases within innovationimplementation have been described. An important characteristic for the stage of accep-tance is that commitment for using a technology, as well as integration in organisationalstructures need to be present. The fact that cases are missed out in that the technologycould have solved a problem indicates that this stage has not yet been reached in theimplementation process.

Furthermore, a lot of paperwork is needed to gain access to the working area, whichincludes permits for delicate data. This includes prohibiting the recording of video dataand therefore blocking the regular use of the technology. From the interview it could beseen that there is a big focus on these formalities, before actions on site can be taken. Asdocumented in illustration 1, this focus restricts the resources available to drive forwardimplementation processes of a new technology that is not already an integrated part ofthis elaborate process. This lack of process integration of the innovation clearly showsthat the technology is situated in the phase of adaptation.

Looking at the given information in l lustration 2 (Remotely guided machine mainte-nance), it is not obvious to define, in which of the phases in the innovation implementa-tion process the technology is situated at the moment. The innovation is integrated inorganisational structures in a way that it has been made available for technicians to use.Furthermore, internal and external partners have been introduced to the technology. Apositive feature is that even foreign technicians are invited to be educated in technologyuse. A full commitment can however not be attested. Since the users in general are notfully supporting the use of the technology, it has not yet reached a full acceptance stage.

Illustration 3 (Chalmers University workshop) shows some statements that raisedconcern. From an implementation point of view, this kind of data could be useful asa reference, since none of the participants during the seminar had used this kind ofinnovation before. It reflects considerations that are initiated during early phases of theadoption, before the adoption decision is made. The fact that industry representativeswere present at the seminar however shows that the need for such a technology has beenidentified and that early phases of evaluation within innovation adoption are ongoing.

5.1.2 Secondary data process phase

Considering the fact that the studied company in l lustration 5 (Remote collaborationtechnology and organisational efficiency) had already done at least one reported casestudy, proves that the technology has already been acquired. This identifies the case tobe in the process of innovation implementation. But even though the technology hasbeen applied in a use-case, it cannot be seen that it is employed in the organisational

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working routines. Setting up a workshop and evaluating the innovation internally withtrials falls in the area of education and training. The company has hence not reached ahigh commitment towards the innovation, which suggests that it still is situated in theadaptation phase.

l lustration 6 (Remote maintenance for machining tools) presents another specialcase. The company had adopted the technology, but during the phase of adaptation adecision was made to not further proceed implementing the technology. Insights hereare therefore also gained in an early stage between adoption and adaptation.

5.1.3 Studied process phase

In most of the discussed illustrations, the technology has been acquired by the companyand is indeed used. The only exception builds illustration 3, where companies wereinvolved that still actively evaluate the innovation. This case therefore provides insightsin the phase of adoption, before a decision for acquisition is made. Illustration 6 showsa company terminating the implementation process after further evaluation during theadaptation. The three other illustrations indicate an integration of the technology intothe working routines of the users has yet to happen. The phase of the innovationimplementation process that is focus in the analysis is therefore the phase of adaptation,marked in Figure 10.

InitiationAdoption(Decision) Adapdation Acceptance Routinisation

Figure 10: Studied process phase

5.2 Obstacles and their influence

5.2.1 Financial resource availability, training, perceived usefulness

Primary data indicates that management is not provided with sufficient financial re-sources to support implementation of new technology. Illustration 1 (Remote supportfor control cabinets) shows that there is a big focus on formalities, before the work sitecan be accessed. These resource demanding processes dominate the regular workingroutines and restrict the possibility of resource allocation on system implementation.This makes it difficult to allow technology use. Illustration 2 (Remotely guided machinemaintenance) stresses the difficulties that the company has internally to define actionsthat need to be taken during the implementation project. These problems are reflectedin the statement that the management side of the implementation project does onlyhave few resources available for their cause. Furthermore, users do communicate a lowperception of the technology’s usefulness.

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Adding insights from secondary data, Illustration 6 (Remote maintenance for ma-chining tools) confirms the perception of users that the technology is not useful. Resultsfrom the workshop in Illustration 5 (Remote collaboration technology and organisationalefficiency) also show that perceived usefulness is low on the side of management. Work-shop participants claimed that people belonging to that organisational group would notunderstand the functionality and use of the technology. This low support of the tech-nology at the crucial part of the company makes it also rather unlikely that sufficientfinancial support and resources are provided.

Illustration 6 (Remote maintenance for machining tools) also broaches the issue ofusers not being ready to use such a technology. The only stated reason here is that theyare not accustomed to such a technology. And, more importantly, they do not identifysignificant differences this innovation brings in comparison to video communication ser-vices that already exist. All of this together clearly shows a low perceived usefulness,which in turn leads to a low willingness to use the technology.

Furthermore, the absence of training and education has been attested and identifiedas a problem that reduces the understanding of users about how the technology canimprove their performance. Hence the perceived usefulness. As Klein et al. (2001) andKlein and Knight (2005) proposed, it is expensive to provide effective measure for educa-tion to increase users’ skills and knowledge. The absence of sufficient financial resourcesand the lack of training together indicate that skills and knowledge of the employeesregarding the innovation cannot be developed, which in turn leads to a low perceivedusefulness, like the work by Klein and Sorra (1996) indicates. Both primary and sec-ondary illustrations show the difference in perceived usefulness between managementand users.

From the literature it was expected that sufficient financial resources and trainingwould support, and high perceived usefulness give an incentive for technology use andimplementation. However the data has shown that in reality, the circumstances makethat these factors have an obstructive character. It is striking that the perception ofusefulness does differ appreciably between the two organisational groups users and man-agement. The evaluation process of the technology in the companies shows that onlyone of the groups is included in the evaluation respectively. It could create a group phe-nomenon that was described by Klein and Sorra (1996), where certain groups of peoplein the company have different opinions about an innovation. It is proposed that in a sit-uation like this, the group that has more decision making power succeeds in building thecompany’s opinion towards a technology in general. This directly reflects the observedsituations, where it is always the management that either deduces the technology to beused even if users do not see its usefulness, or blocks the implementation because of itslow perception of usefulness on the management side.

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5.2.2 Protection of corporate and personal data

The research model is confirmed in proposing the factors protection of corporate andpersonal data and availability of internet connectivity as obstacles. The primary datashows the importance of company intern data safety regulations. The descriptions inIllustration 1 (Remote support for control cabinets) go deeper into companies’ depen-dencies on obeying laws and regulations regarding data transmission and recording. Theamount of paperwork included, as well as the delicate quality of the data that is trans-ferred during the process of gaining access to the working site shows, how important datasafety is when using AR remote collaboration technology within data safety regulationsin the European Union. There exist strict regulations that prohibit recording of videodata which creates an obstacle for using such a technology.

Another angle on this elaboration is given in Illustration 3 (Chalmers Universityworkshop) and is supported by Illustration 8 (Augmented Reality in the AerospaceIndustry) from the complementary data. Instead of focusing on the corporate aspect,individual freedom is target with these concerns. Regulations are strict about recording,but more importantly the users are cautious about data that is recorded, which can bepersonally connected to them. These concerns add a personal dimension to the identifiedproblem of data safety, which can be related to an individuals personal freedom.

5.2.3 Availability of internet connectivity

Concerning the availability of internet connectivity, different stances could be observedthat play an important role in the context of industry 4.0. In the majority of the cases,where connectivity was mentioned as an issue, no preventative steps had been taken.Only the company in Illustration 2 (Remotely guided machine maintenance) had plannedfor different connection scenarios and developed solutions to eliminate problems. Withthe expectations of Kagermann et al. (2012) and Bauer et al. (2013) regarding the futuredevelopments of the industry, including the proposed 4th industrial revolution, the ubiq-uity and necessity of a constant and safe internet connectivity becomes a necessary andbasic requirement to compete in these industry settings. Consequently, it is importantto have a proactive stance towards this, at the moment still, challenging requirement.The company in Illustration 1 (Remote support for control cabinets) for example onlystated the circumstances in that the company’s work partly takes place underground,but did not take any steps to overcome this problem.

5.2.4 System mobility, field of view, seasickness, mental load

Besides the mentioned technological infrastructure problems, the studied data has how-ever also shown more mundane obstacles that had not been proposed by literature earlier.The frequency in that the following factors are mentioned in the empirical illustrations,allows reasoning about them having a significant impact. They represent a reoccurringpattern in the literature.

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Illustration 2 (Remotely guided machine maintenance) reflects on the negative in-fluence of the low system mobility and reduced field of view that users are experiencing,when using the HMD. Additionally, users do complain about the size of the equipmentand the additional burden that they have to carry. Both of these facts are supported bythe findings of secondary and complementary data respectively.

Furthermore, have the reduced field of view and the loss of depth perception whenusing the HMD, commonly been linked to feeling of seasickness. Both of these, reduc-tions in the human perception and well-being can lead to unacceptable safety risks inindustrial shop floor environments, like Illustration 7 (Evaluation of laboratory and in-dustrial context remote collaboration) shows. Complementary studies have in additionshown that the mental load for a user increases, when using AR remote collaborationtechnology. Both tablet and HMD screens force the user to concentrate more than com-pared to executing the same task without remote support or with paper or phone basedinstructions. This in turn leads to faster mental exhaustion.

5.2.5 Resistance

In Illustration 1 (Remote support for control cabinets) the description about the men-tioned disparity between management and user perception of usefulness gives significantinsights. Management perceives the usefulness of the technology as high, but users per-ceive usefulness as low, which results in a resistant behaviour of the users. They do notexpect the innovation to improve their job performance. A possible relation to a causeis found in Illustration 6 (Remote maintenance for machining tools) from the secondarydata. Together with two of the complementary illustrations it indicates that the exis-tence of resistance among potential users can be based on the instruction mode itself.The studies show that users, who are used to apply established modes of instructions likepaper, video, or phone based, showed a low interest in incurring an additional mentalload to solve a problem.

5.2.6 Summary of insights about obstacles

Table 8: Model based obstacles and additional insights

Model based obstacles Additional insights into obstacles

•Resistance •Restriction of personal freedom•Two stances towards assuring •Insufficient financial resourcesinternet connectivity •Insufficient training

•Internal requirements and •Group discrepancy of perceivedEU legislation for data protection usefulness

•Feeling of seasickness•Increased mental load•Compliance with safetyrequirements

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5.3 Incentives and support factors

5.3.1 Compliance, training, learning curve

First of all it has to be stated that none of the two proposed factors from theory thatare expected to give an incentive for technology use could be attested in the studiedcontext. The difference in perception of usefulness has already been discussed as an ob-stacle. Moreover, none of the studied illustrations showed internalisation to be presentas a stance towards the technology. Regarding the support factors, sufficient financialresource availability was not present in any of the studied illustrations either.

Regarding the support factors proposed from theory it can be observed that in Il-lustration 2 (Remotely guided machine maintenance), innovation use is compliant andsupports the innovation implementation. Training has here also been identified as animportant means for educating users which includes promotional and educational events,aligned with the proposed model. As opposed to the other illustrations from primaryand secondary illustrations however, it is unique that the need for training and its im-pact has been identified and actions are taken.

A support factor, additional to the model that is positively affiliated with train-ing is the high learning curve that the technology incorporates. Secondary data fromIllustration 4 (Task instruction modes and execution time) testifies this. Workshop par-ticipants said that initial problems that they had operating the system could be overcomein 20 minutes, so that they felt confident to use the system. Additional implications onthe topic of training are given in the complementary data. The study in Illustration 9(Shared Space in CSCW) for example states that over the cause of the short experiment,improvements in the task performance could already be attested. Illustration 11 (Re-mote visual assembling guidance) further strengthens the proposition of positive effectsthat using the technology created as a training influence on the participants during thestudy. The learning curve in this experiment was visible already after two consecutivesteps that were executed using the technology.

Bringing together the evidence of primary, secondary, and complementary data, theproposition that structured training can increase skills and knowledge of Klein and Sorra(1996), and therefore perception of ease of use and consequently task performance isconfirmed. A presupposition can therefore be that specially tailored training sessions forthe use of the innovation would be effective. Because of the high learning curve not alot of time and resources would have to be invested to provide the development of skillsand knowledge.

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5.3.2 Ease of use, scalability

The perceived ease of use from the proposed factors is shown in an evaluation of thetechnology in Illustration 2 (Remotely guided machine maintenance). Functionalityand understandability of the technology are described as very good. Secondary datafindings from Illustration 4 (Task instruction modes and execution time) support thisproposition. 73% of the study’s participants felt that the technology was very easy tounderstand. It has to be put into perspective that the outcome of the studied assemblytask always was 100% correct. This means that even if the technology was for someof the participants not easy to understand, they were still able to perform effectively.Improvements in task correctness are also attested in complementary data, however upto maximal 90%. These findings definitely confirm a high ease of use of the technology’sfunctionality.

From a hardware perspective on the other hand, the needed equipment is describedas too bulky to carry it during technicians’ regular business operations. Primary, sec-ondary, and complementary data all propose the need for a less complex solution, basedon a mobile application, meaning the use of the software on a smartphone. The addi-tional factor mentioned here is related to scalability. The findings indicate that perceivedease of use and usefulness can depend on the work that is conducted. For example Illus-tration 6 (Remote maintenance for machining tools) suggests that actions including theneed for orientation in the real world in a broader sense created problems for the userswearing an HMD. Stationary tasks on the other hand showed a higher degree of usability.

Illustration 7 (Evaluation of laboratory and industrial context remote collaboration)indicates that when performing a task using AR remote collaboration technology in-cluding an HMD, it was easier to identify certain objects of interest, as compared toguidance via phone. Using AR was described as a more natural way of communicating,even including a sense of emotion that could be transmitted. Tasks that included adynamic environment however showed that users felt unsafe moving around.

Illustration 9 (Shared Space in CSCW) concludes that task performance is positivelyinfluenced by the fact that people that remotely collaborate with each other can see eachother. This means that performance in remote collaboration is improved if the body ofthe helping person is augmented with its correct relative position into the worker’s fieldof view and vice versa. Test subjects scored better results in executing a collaborativetask if this was the case. The ease of use of a system that besides task related informationalso shows physical information of the remotely connected person can therefore expectedto be higher. At the same time, a higher task correctness can be achieved. Accordingto Davis (1985) and Venkatesh and Brown (2001) improving the quality of the executedwork clearly translates into an incentive.

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As a conclusion it can be said that the perception of AR technology for remotecollaboration differs between different task modes and induces the need for differentoperation modes. Generally however, is the use of AR body cues connected to a bettercommunication quality, which is expected to be a driver for demanding a scalable solutioninstead of offsetting with other communication modes.

5.3.3 Quality of executed task, supporting studies

The survey included in Illustration 5 (Remote collaboration technology and organisa-tional efficiency) showed that two thirds of the participants believed that the technologywould make their job easier, as well as it would improve the quality of work. This wasalso referred to as increased effectiveness. This can be seen as a clear incentive. Com-plementary studies show that the quality of a task executed for the first time was upto 90% higher compared to text based instructions, while a study from secondary datashowed a task correctness of 100%.

However before these insights can be used and applied in the industry, aviation rep-resentatives call for verifiability of a technology in connection with existing quantitativestudies about AR remote collaboration technology. Apparently, the need for externalvalidation instead of internal tests and trials is a much needed information source forconsiderations in the implementation process. It adds to the dimension of support factorsas an important insight about understanding how to drive innovation implementation.

5.3.4 Summary of insights about incentives and support factors

Table 9: Model based incentives and support factors, and additional insights

Model based incentives Additional insights into incentivesand support factors and support factors•Compliance •High learning curve•Effective training possible •System mobility•High ease of use of software •Scalability

•Ease of use is task dependent•High communication quality•Body cues and physical representation of users•Increased task quality•Scientific validation of technology

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5.4 Expected benefits

5.4.1 Reducing travelling time and cost, reducing system down-time

Illustration 2 (Remotely guided machine maintenance) shows that the main benefits thatare expected by the company are embodied in the proposed factors. Economic benefitslike reducing travelling time and costs, as well as reducing system down-time are themain drivers behind the implementation. This finding is backed up by all secondarydata. Illustration 4 (Task instruction modes and execution time) gives a broader insightinto the relation of these two terms, where reduced system down-time as an expectedbenefit, can be deviated from reducing travelling time. Here of course it becomes amatter of time estimation. A task that takes significantly longer using AR technologycould make it more desirable to send a technician instead. But assuming that companiesshowing interest in AR technology are already growing into the area of industry 4.0, itis not a wild guess that such companies are already part of a national or internationalnetwork of companies and industries. This would mean that travel distances could spanover countries or continents.

5.4.2 Improving communication quality

Looking at the benefits that have been collected in Illustration 1 (Remote support forcontrol cabinets), it can be seen that the improvement of communication quality is iden-tified as an expected benefit. In particular, expectations are concerned with experienceand knowledge sharing. The improvement of this in turn leads to a rising quality ofcommunicated context. Secondary data, and most clearly the technical report in Illus-tration 6 (Remote maintenance for machining tools) adds to this view by proposing thatthe technology enables the company to communicate knowledge that is concentrated atone physical location and spread it to other individuals.

This last point is not found in the list of promoted benefits as such. But it canbe sorted into the factor of improving communication quality. This is a matter ofinterpretation. On the one hand, if specialised experts are able to communicate theirknowledge more easily, this improves the communication quality. At the same time then,this adds to the ability of these experts to spread their knowledge. Communicationquality could be seen as the pure improvement of communication channels. In thiscontext it does not have to do anything with the fact that knowledge is spread. In amore abstract sense therefore, it can be concluded that improving communication qualitydoes include improving spreading of knowledge. It was analysed that communicationwould be less difficult. With easier communication, the information that is transmittedwould have a higher quality, because it is easier to interpret. An expectation fromthis, deducted in the collected data is a possibly faster decision-making and problemidentification ability through collaboration of remotely connected helpers and workers.Illustration 8 (Augmented Reality in the Aerospace Industry) explains this to be leadingto a general support time reduction.

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5.4.3 Documentation, Quality assurance

A new insight could be gained from primary data. Especially the experts in Illustration 3(Chalmers University workshop) who have the opinion that video data could be used forquality assurance purposes. The process of reviewing such data is however connected tolegal requirements. This aspect would mean that recorded data could only be reviewed ifproven to be necessary, assuring a recoded employees personal freedom. Applied wouldbe an ethical way of capturing and reviewing data that affects a person and its behaviour- a justifiable experience capturing system, although not meant for distribution.

5.4.4 Task quality, error reduction, validity, environmental impact

Benefits that are expected throughout experts cited in complementary Illustration 9(Shared Space in CSCW) are time reduction of remotely guided tasks in comparison tosupport via phone, quality improvement of remotely guided manual tasks, and at thesame time reduction of errors that occur during these tasks. In some industries there is aneed for no errors at all. As opposed to the promoted reducing travelling time and costsin the theory, this illustration shows that AR technology in CSCW is not intended to beused to offset the need for travelling. Instead, the technology is used as an alternative totasks that are at the moment already remotely guided, but via phone. The study showsthat the quality of work is improved by using such a technology. This hence correspondsto the need of reducing or eliminating errors and indicates an improved communicationquality.

The authors in Illustration 11 (Remote visual assembling guidance) propose anotherfactor that has not yet been collected in the theory - the built in control against humanmistakes. Especially, when the most important evaluation factor is quality, an AR sys-tem can significantly improve task performance. Adding this to the expected benefits,rounds up the characteristics of the studied technology’s implementation process.

However before these insights can be used and applied in the industry, aviation rep-resentatives call for verifiability of a technology in connection with existing quantitativestudies about AR remote collaboration technology. Apparently, the need for externalvalidation instead of internal tests and trials is a much needed information source forconsiderations in the implementation process. It adds to the dimension of support factorsas an important insight about understanding how to drive innovation implementation.

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5.4.5 Summary of insights about expected benefits

Table 10: Model based expected benefits, and additional insights

Model based expected benefits Additional insights intoexpected benefits

•Reduction of travelling time and costs •Faster decision making and•Reduction of system down-times problem identification•Improving communication quality •Improved quality of executed work•Experience capturing •Human error reduction

•Environmental impact

6 Comprehensive analysis

6.1 Perceived usefulness and perceived ease of use

During the analysis of the data in regard of the individual research questions, perceivedusefulness and perceived ease of use have been the factors most frequently referencedin explanation building. These two factors that have been proposed in the researchmodel could be seen to have significant impact on the innovation implementation pro-cess. Studying the work of Davis (1985), Venkatesh and Brown (2001) and Davis et al.(1989) shows that there is a close relation of the two factors. The three works show thatperceived usefulness surpasses ease of use in its effect on overall technology perception.If perceived usefulness is low, high perceived ease of use does not create willingness touse a technology. If both of the factors are rated low, the technology hence is not worthyto implement.

As a deductive proposition from Davis (1985) and Venkatesh and Brown (2001)it has been shown that a positive perception of usefulness elicits an incentive effect ontechnology use. In this context it is interesting to see that, as opposed to the illustrationsfrom real industrial settings, the studies that were conducted in a laboratory settingshared the unitary proposition that the perceived usefulness was high. The studiesevaluated usefulness more objectively, oriented along the two main dimensions time andquality. They indicate that the quality of the conducted work increases, when AR remotecollaboration technology is used. Reasoning from the described superiority of perceivedusefulness over perceived ease of use, the testified increase of task execution time isinferior to the produced quality in industrial settings. In conclusion this means that thequality of a completed task is decisive for the evaluation of quality. Even if it is likelyto be connected with a higher time needed to fulfil a certain task.

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The data shows that ease of use in the specific case of AR remote collaborationtechnology is high, when targeting the software that is included in the system solution.But when looking at the connected hardware, a conflictive picture is created. The lowsystem mobility, together with different perceptions of the hardware aspect dependingthe conducted task and the call for a scalable solution opposes the positive perceptionof ease of use from the software part of the technology. Since only the hardware partsare continuously mentioned in the studied illustrations, one can say that this aspect ofthe technology is dominant in the technology evaluation process.

6.2 Discrepancy of perceptions between organisational groups

As the data has shown, the two organisational groups management and users do often nothave the same perception of usefulness and ease of use respectively. In illustrations, whereparticipation in the evaluation has been on the side of management, it is attested that thisgroup gains a positive perception of usefulness, as opposed to the users. Where users wereinvolved in the evaluation, the perceptions were the other way around. This supportsthe proposition from the proposed factors that internalisation during the implementationprocess acts like an incentive for innovation use. It can however not be seen as a factorthat automatically creates the same opinion throughout the company. Instead it showsthat groups of people that are actively involved in the implementation process of atechnology recognise the usefulness of the technology. Groups of people that are notinvolved have no knowledge about it, i.e. they do not see the usefulness.

6.3 Training and education

Looking at the mentioned discrepancy in a broader sense, in the analysed data it hasalready been identified as a problem of education, and communication. The companyfrom Illustration 2 (Remotely guided machine maintenance) that put effort into educat-ing users and allocated resources on training measures is the only company, where nodegree compliance instead of resistance could be attested. In the context of the studiedtheory, it can be referred to Ensminger and Surry (2008), Choi and Moon (2013) andKlein and Sorra (1996). The apparent consequence of not providing training and educa-tion has the consequence that skills and knowledge are not established among the users.Companies not utilising such measures risk, as could be seen in the illustrations, thatcompliant innovation use or even internalisation following the clarification of Sussmannand Vecchio (1982) cannot be achieved.

The theory clearly states that policies and practices supporting the education ofinnovation use are expensive (Klein et al., 2001; Klein and Knight, 2005). This theoret-ically means that without financial resources, measures to develop skills and knowledgecannot be provided in the studied cases. Interestingly, all laboratory studies about thetechnology confirm that there is a steep learning curve. Test subjects experienced an ap-preciable improvement in their ability to operate the technology within a short amountof time. Improvements in time to conduct a task are already seen after the first steps of

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subsequent tasks. This indicates that educating users in using the innovation is effective,because it quickly produces noticeable improvements. This could oppose the applicabil-ity of Klein et al. (2001) and Klein and Knight (2005), who sate that education is anexpensive endeavour. Even though it is expensive, education is worthwhile because ofthe high learning curve and low amount of training needed.

6.4 Data protection

Besides this complex relationship of different groups and stances towards innovation,there are more mundane factors restricting the implementation of remote collaborationtechnology. European laws and regulations restrict and determine the way companiesneed to bureaucratically approach tasks that appear in the area of industry 4.0. In thiscase the expectations an ever more connected world, where internet accessibility is oneof the main prerequisites for the internet of things (Kagermann et al., 2012; Bauer et al.,2013; Burmeister et al., 2015; Dean et al., 2012). Companies are already aware of thisin the phase of adoption, before the implementation itself starts. This tells that theseobstacles are not removed before implementation starts, even though they are identifiedearly on. This gives an insight in the problematic that companies do on the one handstrive to enter these new industry developments, but on the other hand attitude, regu-lations, and the willingness to assure that a technology’s requirements are met, are notyet incorporated in organisational planning.

Three different views on data safety further complicate the processes occurring whentransmitting video data. The legislative side protects the freedom of individuals beingfilmed. At the same time, companies have their own internal requirements for data safety.While both parties want to secure the recorded and transmitted data, the reasons behindit are fundamentally different. Companies want to safe data from being intercepted byunauthorised parties. European legislation on the other hand pursuits protecting thedata from being connected to a particular individual. Additionally, of course, there isthe opinion of the user itself as an individual that nowadays includes the awareness ofpersonal data and its use. Obviously, there exist different opinions and goals that clashin regard to this topic, which overcomplicates a seemingly simple technology requirement.

Furthermore, two approaches towards these problems could be observed. Eitherproactive or reactive, or even more fittingly, passive. Relating this back to the generaltopic of the developments in the movement of industry 4.0, a hypothesis is that one reasonfor companies to fail in achieving their expected outcomes is the way that they approachthe problems that arise within the field the corresponding technologies. Without activelystriving to solve problems that exist in this early phase of the industry development,chances for a successful technology implementation are low.

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6.5 Environmental benefits

Almost all of the companies that are part of the illustrations in this thesis comply to theexpectations, which are developed with the research model. Benefits in connection withthe adopted technology are consistently economically motivated. Time and cost are thedominant measurements of benefits that are expected from using the technology. Onlyonce could the reduction of environmental impacts be observed as a relevant considera-tion. It is apparent that even in the present time, the majority of companies does nothave a big interest in reducing their environmental impact. Just as a side remark in thisthesis, one could consider the thought of possibly high savings in emissions when car,train, or plain trips over national and international distances could be substituted byremote collaboration session. Pairing this with the expected improvement in quality ofthe executed work and the reduction of human error or faster decision making abilitiescreates a very positive image of considering the technology as a benefit for a company.

7 ConclusionLiterature has shown that innovations are often adopted, but fail to produce the ex-pected benefits. It is known that in the studied cases, the reasons for this are reportedlynot related to the innovation or its comprised technology. Instead it is the process ofinnovation implementation that fails.

The possibility to study an augmented reality technology for remote collaborationthat is emerging in the industry and therefore perceived as new by the adopting entity,has created an interesting research setting that has followed the aim to qualitatively de-scribe and analyse the implementation characteristics of an augmented reality technologyfor computer supported collaborative work in the adaptation phase of the innovation im-plementation process. The approach was built around the four categories of obstacles,incentives, support factors, and expected benefits to create insights about this centralprocess phase on a corporate level.

The research process was guided by answering three research questions:

• Which are the obstacles for the implementation of augmented reality in CSCWand how do they hinder this process?

• Which are incentives and other support factors for the implementation ofaugmented reality in CSCW and how do they facilitate implementation?

• Which are the expected benefits of using augmented reality in CSCW?

Researchers have earlier focused on antecedents, determinants and facilitating fac-tors for innovation implementation (Klein and Sorra, 1996; Klein et al., 2001; Johnson,2001; Ensminger and Surry, 2008; Dong et al., 2008). Insights about obstacles and howto overcome them were proposed by (Klein and Knight, 2005). Although, as can be

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seen, there have been studies focusing on this research topic, scholars agree that knowl-edge is still limited and complementary research is needed on innovation implementation.Especially in the area of the emerging technology of augmented reality, studies are scarce.

To fill this knowledge gap, the four categories have been developed and incorporatedinto a research model. Applying these on the existing literature allowed filling themwith a total of 13 factors that created the basis for a research approach applicable to thecollected empirical data. The research model with the filled categories is depicted in inFigure 8 in Section 2.11.2.

Analysing the data in regard to the first research question showed that the threeobstacles proposed in the research model are indeed represented in the studied illustra-tions. Especially in connection with the availability of internet connectivity, two stancestowards ensuring the prerequisites technology use can be deducted from primary andsecondary illustrations - proactive and passive. For companies to succeed in these newdevelopments and in implementing augmented reality remote collaboration technology,it is important to actively remove problems that occur with the growing relevance of theinternet of things (Kagermann et al., 2012; Bauer et al., 2013). A significant obstaclethat is created is also the influence of legislative regulations regarding data security andsecrecy, also in the area of assuring a persons individual freedom. Furthermore, resis-tance to innovation use can occur, when different perceptions of an innovations usefulnessoccur, because of educational differences between management and users in regard oftechnology related skills and knowledge. Different decision making power then deductsthe opinion of the more powerful group onto the company as a whole (Klein and Sorra,1996).

The primary data, together with secondary data has given further insights into fac-tors, important in the mentioned context. The most important is insufficient financialresource availability that leads to few or low resources to be allocated on training tobuild skills and knowledge for innovation use. Additional obstacles arise from the hard-ware incorporated in the technology, including a feeling of seasickness, increased mentalload, and non-compliance with industrial safety standards.

Analysing the gathered data for the second research question did not confirm any ofthe two proposed incentives, perceived usefulness and internalisation, to be present inthe collected illustrations. Instead a high communication quality and increased qualityof the remotely guided executed task were found to create an incentive. Moving to thesupport factors, compliant innovation use and the importance of training as a measurefor developing it was confirmed. The high ease of use of the software, included in thetechnology as such also confirms the proposed supportive character of this factor. Thethird of the proposed support factors, training, is confirmed to be highly effective inthe studied context for developing skills and knowledge, as proposed by Klein and Sorra(1996) and Klein et al. (2001).

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Additionally, the findings from secondary illustrations embellish the effectiveness oftraining as a support factor for implementing augmented reality remote collaborationtechnology with attesting a high learning curve. Other support factors derived fromanalysing secondary illustrations are an increased system mobility, as well as a scalablesolution, where different operation modes can be adapted to different tasks. At thesame time, a task related perception of ease of use could be attested, which is positivelyinfluenced by the possibility to communicate with body cues.

For the third research question, all proposed economical benefits were confirmed byprimary data. Namely, the four factors reduction of travelling time and cost, reduc-tion of system-down times, improving communication quality, and experience capturing.Secondary and complementary data indicated faster decision making and problem iden-tification, as well as improved quality of the conducted task, and reduction of humanerror to be relevant expected benefits. Only one illustration from the primary datamentioned reducing environmental impacts.

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8 Expanded implicationsScholars from the movement of industry 4.0 predict that the new developments in theindustry, including the expansion of internet enabled devices and their communicationcapabilities. The ability to compete in an inter-connected and information sharing in-dustrial society is significantly influenced by a company’s ability to adopt and implementnew technologies and systems that share information with each other.

The thesis has shown that companies at the moment experience problems with pro-viding the prerequisites for technologies that originate on the basis of these assumptions,for example by not actively pursuing solutions for internet connectivity problems. Evenmore striking is the fact that there are already technologies that require such an ap-proach for their basis to be operable. But is it not only the attitude of companies thatplays a role in this process. It could also be seen that legislative entities do not providethe possibilities for a smooth implementation of new technologies with such requirements.

Additionally, it could be seen that users are reluctant to let go of their implementedand proven way of solving certain problems. In regard to this thesis, this relates to usersnot seeing benefits in using an AR remote collaboration technology in comparison to forexample paper based instructions. Of course there are also problems with the hardwarethat produces a negative stance towards the use of the technology.

Interestingly, it was proposed that 4\5th of all mobile broadband connections areaccounted for by mobile devices. At the same time, the empirical data has shown thatcompanies and users anticipate augmented reality systems to become more portable andscalable. The idea of a broadband connection enabled device such as a smartphone seemsalready to be a part of peoples natural network technology understanding.

The industry, as well as the developments of technologies utilising the internet ofthings consequently stand in the beginning of a process that has to include changes inthe mindset of people and how companies approach data and networking capabilitiesof technology, as well as adaptations of legislative entities to smooth the way for thedevelopments within a new industrial revolution.

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9 Future researchThis thesis has created insights in the characteristics of the adaptation phase of theinnovation implementation process by utilising a multitude of source. For future research,it will be important to build a similar study with an in-depth case study approach toconfirm and expand the developed research model and framework. Focus should belaid on gathering observations along the whole innovation implementation process andto observe the changes of user’s perception of usefulness and ease of use over time.To create more vivid data and understanding, the primary data should be collectedfrom both managers and users, but also other organisational groups involved in theprocess. Another interesting topic that was crystallised from the findings of this thesisis the relationship of financial resource availability, training, and knowledge and skills.And finally, it is suggested to conduct a study that concentrates on the scalability of atechnology in relation to its perceived ease of use and usefulness.

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10 References

Ajzen, I. and Fishbein, M. (1980). Understanding attitudes and predicting social behavior.Englewood Cliffs, N.J. : Prentice-Hall, cop. 1980.

Azuma, R. (1997). A survey of augmented reality. PRESENCE-TELEOPERATORSAND VIRTUAL ENVIRONMENTS, 6(4):355 – 385.

Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., and MacIntyre, B. (2001).Recent advances in augmented reality. IEEE COMPUTER GRAPHICS AND AP-PLICATIONS, 21(6):34 – 47.

Bauer, K., Diegner, D. B., Diemer, J., Dorst, W., Ferber, D. S., Glatz, R., Hellinger, A.,Herfs, D. W., Horstmann, M., Kaufmann, D. T., Kurz, D. C., LÃűwen, D. U., andStumpf, V. (2013). Umsetzungsempfehlungen fuer das zukunftsprojekt industrie 4.0.Plattform INDUSTRIE 4.0.

Baumol, W. (2014). The free-market innovation machine: Analyzing the growth miracleof capitalism. Princeton University Press.

bbc Research (2016). Wearable computing: Technologies, applications and global mar-kets.

Billinghurst, M., Clark, A., and Gun, L. (2015a). A survey of augmented reality. Foun-dations and Trends in Human-Computer Interaction, 8(2):73 – 272.

Billinghurst, M., Clark, A., and Lee, G. (2015b). A survey of augmented reality. Foun-dations and trends in human-computer interaction: 8:2-3. Hanover, Massachusetts :Now Publishers, [2015].

Billinghurst, M., Weghorst, S., and Furness III, T. (1998). Shared space: An augmentedreality approach for computer supported collaborative work. Virtual Reality, 3(1):25–36.

Burmeister, C., Luettgens, D., and Piller, F. T. (2015). Business model innovation forindustrie 4.0: Why the "industrial internet" mandates a new perspective on innovation.RWTH-TIM Working Paper Revised Version 2.0.

Choi, J. N. and Moon, W. (2013). Multiple forms of innovation implementation: The roleof innovation, individuals, and the implementation context. Organizational Dynamics,42(4):290 – 297.

Compeau, D. R. and Higgins, C. A. (1995). Computer self-efficacy: Development of ameasure and initial test. MIS Quarterly, 19(2):189 – 211.

Cooper, R. B. and Zmud, R. W. (1990). Information technology implementation re-search: A technological diffusion approach. Management Science, 36(2):123 – 139.

75

Daft, R. L. (1978). A dual-core model of organizational innovation. The Academy ofManagement Journal, (2):193.

Damanpour, F. (1991). Organizational innovation: A meta-analysis of effects of deter-minants and moderators. The Academy of Management Journal, (3):555.

Damanpour, F. and Evan, W. M. (1984). Organizational innovation and performance:The problem of ’organizational lag’. Administrative Science Quarterly, (3):392.

Damanpour, F. and Schneider, M. (2006). Phases of the adoption of innovation inorganizations: Effects of environment, organization and top managers. British Journalof Management, 17(3):215–236.

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-userinformation systems : theory and results.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance ofinformation technology. MIS Quarterly.

Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computertechnology: A comparison of two theoretical models. Management Science, 35(8):982–1003.

Dean, D., DiGrande, S., Field, D., Lundmark, A., O’Day, J., Pineda, J., and Zwillenberg,P. (2012). The internet economy in the g-20. bcg perspectives - The Connected World.

Denzin, N. K. (2001). Interpretive interactionism. Applied social research methodsseries: 16. Thousand Oaks, Calif. : Sage Publications, 2001.

Denzin, N. K. and Lincoln, Y. S. (2000). Handbook of qualitative research. ThousandOaks, Calif. : Sage, 2000.

Dong, L., Neufeld, D. J., and Higgins, C. (2008). Testing klein and sorras innovationimplementation model: An empirical examination. Journal of Engineering and Tech-nology Management, 25:237 – 255.

Elsbach, K. D. and Kramer, R. M. (2016). Handbook of qualitative organizational re-search: Innovative pathways and methods. Routledge/Taylor & Francis Group.

Emerson, R. W. (2015). Convenience sampling, random sampling, and snowball sam-pling: How does sampling affect the validity of research?. Journal of Visual Impair-ment & Blindness, 109(2):164 – 168 5p.

Ensminger, D. and Surry, D. (2008). Relative ranking of conditions that facilitate in-novation implementation in the usa. Australasian Journal of Educational Technology,24(5):611–626.

76

Etikan, I., Musa, S. A., and Alkassim, R. S. (2016). Comparison of convenience sam-pling and purposive sampling. American Journal of Theoretical and Applied Statistics,5(1):1–4.

Evan, W. M. (1966). Organizational lag. Human Organization, 25:51–53.

Fussell, S. R., Setlock, L. D., Jie, Y., Jiazhl, O., Mauer, E., and Kramer, A. D. I.(2004). Gestures over video streams to support remote collaboration on physicaltasks. Human-Computer Interaction, 19(3):273 – 309.

Gopalakrishnan, S. and Damanpour, F. (1997). A review of innovation research ineconomics, sociology and technology management. Omega, (1):15.

Greenhalgh, C. and Rogers, M. (2010). Innovation, intellectual property, and economicgrowth: Christine Greenhalgh & Mark Rogers. Princeton University Press.

Grudin, J. (1994). Computer-supported cooperative work: History and focus. Computer,27(5):19–26.

Gurevich, P., Cohen, B., and Lanir, J. (2015). Design and implementation of teleadvi-sor: a projection-based augmented reality system for remote collaboration. ComputerSupported Cooperative Work: CSCW: An International Journal, page 36p.

Henderson, R. M. and Clark, K. B. (1990). Architectural innovation: The reconfigurationof existing product technologies and the failure of established firms. AdministrativeScience Quarterly, 35:9–30.

Holahan, P. J., Aronson, Z. H., Jurkat, M. P., and Schoorman, F. D. (2004). Implement-ing computer technology: a multiorganizational test of klein and sorraâĂŹs model.Journal of Engineering and Technology Management, 21(Research on the Human Con-nection in Technological Innovation):31 – 50.

Johansen, R. (1988). Groupware: Computer support for business teams. Free Press.

Johnson, J. D. (2001). Success in innovation implementation. Journal of CommunicationManagement, 5(4):341 – 359.

Jonathan, G. and Steven, P. (2014). Taxonomy and theory in computer supportedcooperative work. the oxford handbook of industrial and organizational psychology.

Jørnø, R., Gynther, K., and Christensen, O. (2013). Challenging the cscw matrix: Arough draft of a new conceptualisation of collaborative practices in learning environ-ments. Open Learning, 28(3):239–254.

Kagermann, P. H., Wahlster, P. W., and Helbig, D. J. (2012). Im fokus: Das zukunft-sprojekt industrie 4.0 - handlungsempfehlungen zur umsetzung. Bericht der Promo-torengruppe Kommunikation.

77

Khan, S. and VanWynsberghe, R. (2008). Cultivating the under-mined: Cross-caseanalysis as knowledge mobilization. Qualitative Research, 9(1).

King, N. (1990). Innovation at work: The research literature., pages 15 – 59. John Wiley& Sons.

Klein, K. J., Conn, A. B., and Sorra, J. S. (2001). Implementing computerized technol-ogy: An organizational analysis. Journal of Applied Psychology, 86(5):811 – 824.

Klein, K. J. and Knight, A. P. (2005). Innovation implementation: Overcoming thechallenge. Current Directions in Psychological Science, (5):243.

Klein, K. J. and Sorra, J. S. (1996). The challenge of innovation implementation. TheAcademy of Management Review, (4):1055.

Knight, K. E. (1967). A descriptive model of the intra-firm innovation process. TheJournal of Business, (4):478.

Kwon, T. H. and Zmud, R. W. (1987). Critical issues in information systems research.chapter Unifying the Fragmented Models of Information Systems Implementation,pages 227–251. John Wiley & Sons, Inc., New York, NY, USA.

Li, D., Mattsson, S., Fast-Berglund, A., and Akerman, M. (2016). Testing operatorsupport tools for a global production strategy. ScienceDirect, 6th CIRP Conferenceon Assembly Technologies and Systems (CATS).

Mann, S. (1994). Mediated reality. MIT-ML Percom, TR-260.

Mann, S. (2002). Mediated reality with implementations for everyday life. PresenceConnect.

Meredith, J. (1998). Building operations management theory through case and fieldresearch. Journal of Operations Management, 16(4):441–454.

Milgram, P. and Kishino, F. (1994). A taxonomy of mixed reality visual-displays. IEICETransactions on information and system, E77D(12):1321 – 1329.

Perey, C. (2016). Augmented reality in the aerospace industry. AREA Webi-nar. http://thearea.org/area-webinar-augmented-reality-in-the-aerospace-industry-february18-2016/.

Pichlak, M. (2015). The innovation adoption process: A multidimensional approach.Journal of Management and Organization, pages 1–19.

Pierce, J. L. and Delbecq, A. L. (1977). Organization structure, individual attitudes andinnovation. The Academy of Management Review, (1):27.

PR Newswire (2016). Augmented reality (ar) market forecast to 2024 - hmd segment todominate market - research and markets. PR Newswire US.

78

Rekimoto, J. and Nagao, K. (1995). The world through the computer: Computer aug-mented interaction with real world environments. In Proceedings of the 8th annualACM symposium on user interface and software technology, pages 29–36. ACM.

Research and Markets (2015). Mobile augmented reality market by component, appli-cation, vertical, and geography - global forecast to 2022. Business Wire (English).

Research and Markets (2016). Global mobile augmented reality market 2016-2020 -market to grow at a cagr of 89% - research and markets. Business Wire (English).

Rogers, E. M. (1983). Diffusion of Innovation. The Free Press, 3 edition.

Scavo, B. and Perey, C. (2016). Ar-enhanced remote assistence: An area technical report.Technical report, Augmented Reality for Enterprise Alliance (AREA).

Schneider, B. (1975). Organizational climates: An essay. Personnel Psychology, 28:447–479.

Slappendel, C. (1996). Perspectives on innovation in organizations. Organization Studies,17(1):107–129.

Sung, S. and Choi, J. (2014). The roles of individual differences and innovation propertiesin multiple forms of innovation implementation. Social Behavior and Personality,42(7):1201–1220.

Sussmann, M. and Vecchio, R. P. (1982). A social influence interpretation of workermotivation. The Academy of Management Review, (2):177.

Sutherland, I. E. (1968). A head-mounted three dimensional display. Proceedings of theDecember 9-11, 1968, Fall Joint Computer Conference, Part I, page 757.

Svensson, J. (2016). Study of whether remote guidance, with augmented reality, canenhance organizational efficiency. Master’s thesis, Linkøping University.

Syberfeldt, Anna, A., Danielsson, Oscar, A., Holm, Magnus, A., Wang, Lihui, A.,Høgskolan i Skøvde, Institutionen før ingenjørsvetenskap, O., and Høgskolan i Skøvde,Forskningscentrum før Virtuella system, O. (2015). Visual assembling guidance usingaugmented reality. Procedia Manufacturing, page 98.

Tait, M., Tsai, T., Sakata, N., Billinghurst, M., and Vartiainen, E. (2013). A pro-jected augmented reality system for remote collaboration. Extended Abstracts of theIEEE International Symposium on Mixed and Augmented Reality 2013 and Scienceand Technology Proceedings.

Thompson, V. A. (1965). Buraucracy and innovation. Administrative Science Quarterly,10:1–20.

79

Tiefenbacher, P., Gehrlich, T., and Rigoll, G. (2015). Impact of annotation dimension-ality under variable task complexity in remote guidance. In 2015 IEEE Symposiumon 3D User Interfaces, 3DUI 2015 - Proceedings, number 2015 IEEE Symposium on3D User Interfaces, 3DUI 2015 - Proceedings, pages 189–190, Institute for Human-Machine Communication, Technische Universitaet Muenchen.

Tiefenbacher, P., Lehment, N., and Rigoll, G. (2014). Virtual, Augmented and MixedReality - DesDesign and Developing virtual and Augmented Environments. 6th Inter-national Conference, VAMR 2014 Held as Part of HCI International 2014 Heraklion,Crete, Greece, June 22 to 27, 2014, Proceedings, Part I, volume 8525 LNCS of LectureNotes in Computer Science, chapter Augmented reality evaluation: A concept utiliz-ing virtual reality. Springer Verlag, Institute for Human-Machine Communication,Technische Universitaet Muenchen.

Tushman, M. L. and Anderson, P. (1986). Technological discontinuities and organiza-tional environments. Administrative Science Quarterly, 31:439–465.

Venkatesh, V. and Brown, S. A. (2001). A longitudinal investigation of personal com-puters in homes: Adoption determinants and emerging challenges. MIS Quarterly,25(1):71 – 102.

Voss, C. A., Tsikriktsis, N., Frohlich, M., and Place, S. (2011). Case research in opera-tions management.

Wilson, J. (1966). Approach to Organization design, chapter Innovation in Organisation:Notes toward a theory. University of Pittsburg Press.

Yin, R. K. (2014). Case study research : design and methods. London : SAGE, cop.2014.

Zmud, R. W. and Apple, L. E. (1989). Measuring information technology infusion.unpublished manuscript.

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