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1. List the three (3) requirements defining Liabilities: 2. On July 1, 20X4, Working Dog, Inc., borrowed $20,000 on a four-year, 6% note payable. At December 31, 20X4, a journal entry should be made to record: 3. The employee is responsible for which payroll taxes? 4. Alpine Corporation sells $50,000 of goods, and you collect sales tax of 7%. What is the journal entry to record the transaction? 5. Technology Corporation has a lawsuit pending from a customer claiming damages of $128,000. Technology Corporation’s attorney advises that the likelihood the customer will win is reasonably possible. How is this contingent liability reported? 6. Cooper Company owed Estimated Warranty Payable of $2,200 at the end of 20X4. During 20X5, Cooper Company made sales of $280,000 and expects product warranties to cost the company 3% of the sales. During 20X5, Cooper Company paid $5,000 for warranties. What is Cooper Company’s estimated warranty payable at the end of 20X5? 7. At December 31, Haute Hardware owes employees for four days of the five-day workweek. The total payroll for the week is $51,000. What journal entry should you make at December 31? 8. Yolanda’s Yoga Studio has Unearned Revenue of $15,000, Salaries Payable of $28,000, and Allowance for Uncollectible Accounts of $4,200. What amount would Yolanda’s Yoga Studio report as total current liabilities? 9. A five-year, $100,000, 4% note payable was issued on December 31, 20X4. The note requires principal payments of $20,000 plus interest due each year beginning December 31, 20X5. The entry to record the annual payment at the end of year two on December 31, 20X6,: 10. Bon Voyage Company’s trial balance shows $800,000 face value of bonds with a discount balance of $12,000. The bonds mature in 20 years. How will the bonds be presented on the balance sheet? 11. Rivers and Streams Corporation issued $400,000 of 8% serial bonds at face value on December 31, 2018. Half of the bonds mature January 1, 2021, while the other half of the bonds mature January 1, 2029. On December 31, 2020, the balance sheet will show which of Bonds Payable? 12. Nolan Corporation has total assets of $500,000. Current liabilities are $10,000, and long-term liabilities are $90,000. What is the debt to equity ratio? 13. A $400,000 bond priced at 102 can be bought or issued for: 14. Cooper Corporation issued its 4%, 20-year bonds payable at a price of $288,500 (face value is $300,000). The company uses the straight-line amortization method for the bonds. Interest expense for each year is: 15. Skinny Smoothies has $850,000 of 20-year bonds payable outstanding. These bonds had a discount of $42,000 at issuance, which was 8 years ago. The company uses the straight-line amortization method. The carrying amount of these bonds payable today is: 16. What is the correct journal entry to record the issuance of a $250,000 face value bond at 95? 17. Hughes Industries signed a 20-year note payable on January 1, 2018. The note requires annual pr ...

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1. List the three (3) requirements defining Liabilities: 2. On July 1, 20X4, Working Dog, Inc., borrowed $20,000 on a four-year, 6% note payable. At December 31, 20X4, a journal entry should be made to record: 3. The employee is responsible for which payroll taxes? 4. Alpine Corporation sells $50,000 of goods, and you collect sales tax of 7%. What is the journal entry to record the transaction? 5. Technology Corporation has a lawsuit pending from a customer claiming damages of $128,000. Technology Corporation’s attorney advises that the likelihood the customer will win is reasonably possible. How is this contingent liability reported? 6. Cooper Company owed Estimated Warranty Payable of $2,200 at the end of 20X4. During 20X5, Cooper Company made sales of $280,000 and expects product warranties to cost the company 3% of the sales. During 20X5, Cooper Company paid $5,000 for warranties. What is Cooper Company’s estimated warranty payable at the end of 20X5? 7. At December 31, Haute Hardware owes employees for four days of the five-day workweek. The total payroll for the week is $51,000. What journal entry should you make at December 31? 8. Yolanda’s Yoga Studio has Unearned Revenue of $15,000, Salaries Payable of $28,000, and Allowance for Uncollectible Accounts of $4,200. What amount would Yolanda’s Yoga Studio report as total current liabilities? 9. A five-year, $100,000, 4% note payable was issued on December 31, 20X4. The note requires principal payments of $20,000 plus interest due each year beginning December 31, 20X5. The entry to record the annual payment at the end of year two on December 31, 20X6,: 10. Bon Voyage Company’s trial balance shows $800,000 face value of bonds with a discount balance of $12,000. The bonds mature in 20 years. How will the bonds be presented on the
balance sheet? 11. Rivers and Streams Corporation issued $400,000 of 8% serial bonds at face value on December 31, 2018. Half of the bonds mature January 1, 2021, while the other half of the bonds mature January 1, 2029. On December 31, 2020, the balance sheet will show which of Bonds Payable? 12. Nolan Corporation has total assets of $500,000. Current liabilities are $10,000, and long-term liabilities are $90,000. What is the debt to equity ratio? 13. A $400,000 bond priced at 102 can be bought or issued for: 14. Cooper Corporation issued its 4%, 20-year bonds payable at a price of $288,500 (face value is $300,000). The company uses the straight-line amortization method for the bonds. Interest expense for each year is: 15. Skinny Smoothies has $850,000 of 20-year bonds payable outstanding. These bonds had a discount of $42,000 at issuance, which was 8 years ago. The company uses the straight-line amortization method. The carrying amount of these bonds payable today is: 16. What is the correct journal entry to record the issuance of a $250,000 face value bond at 95? 17. Hughes Industries signed a 20-year note payable on January 1, 2018. The note requires annual principal payments plus interest. The entry to record the annual payment on December 31, 2018 includes a _______ to Interest Expense 18. Yes or No, Is Mutual Agency a characteristic of Corporations? 19. What is the effect of the purchase of treasury stock on the number of shares issued? 20. Backyard Emporium issued 500,000 shares of $1 par common stock at $5 per share. What journal entry correctly records the issuance of this stock? 21. Two years ago, Tonya Williams purchased a building for $210,000. This year, Williams gave the building, which now has a current market value of $240,000, to Beyond Corporation in exchange for 5,000 shares of $10-par common stock. What
journal entry by Beyond Corporation correctly records the issuance of this stock? 22. Good For You Foods has outstanding 6,000 shares of $3 par common stock, which was issued at $15 per share, and 2,000 shares of $10 par cumulative preferred stock, which was issued at par. Good For You Foods also has a deficit balance in Retained Earnings of $26,000. How much is Good For You Foods’ total stockholders’ equity? 23. Happy Pets Corporation has 10,000 shares of 5%, $20 par noncumulative preferred stock, and 37,000 shares of common stock outstanding. Ink declared no dividends in 20X4. In 20X5, Ink declares a total dividend of $54,000. How much of the dividends go to the common stockholders? 24. True or False, Stock Splits increase the number of shares of stock issued while decreasing par value per share. 25. Retained Earnings can be subject to appropriation by whom? Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy
Lecture 4 Week 4 Structure of the session: Ground Rules What did we cover last two weeks? Aim and intended learning outcomes of this week A bit of lecture Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey Ground Rules for today: There is no wrong and right answer to my questions. Any answer is much appreciated. Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What is the aim of the lecture and workshop in Week 4?
To develop a multi-aspectual understanding of types of human- machine collaboration for the enterprise. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for week 4? Why do we need multi-aspectual understanding? What do we mean by multi-aspectual understanding? How can we develop such an understanding of human-machine collaboration? Human Use of Digital Technologies EIT : Engaging with Interface & Technology or HCI: Human- Computer Interaction. EMC: Engaging with Meaningful Content or ERC: Engaging with Represented Content. ELI : Engaging in Life with ICT (Information & Communication Technology) or HLC: Human Living with Computers.
ELI HLC ERC EMC HCI EIT HCI EIT
Can Human-Machine Collaboration and Human Use of Digital Technologies be explained through Socio-Technical Approach? 12 13
Yes, we can take a Socio-Technical Approach and explain for Human-Machine collaboration and Human-Use of Emergent Technologies. But is that sufficient? Can we explain everything through this approach? 14 Critical Research in Information Systems tell us that the social world is unjust and prevents individuals from living up to their potential. Current social structures are problematic. Walsham (2005), Stahl (2007) 15 Lee (2004) In Socio-Technical Approach , Information has not been seen as important as social and technological factor!
16 We need to know a way to explain for the reality of Human Use of Emergent Technologies and Human-Machine Collaboration 17 18
Dooyeweerd Aspects Aspects Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical Pistic Kernel Meaning Discrete amount Continuous space Movement Energy + Mass, Forces Life functions + Organisms Sense, Feeling, Emotion Distinction, Conceptualization Achievement, History, Technology Meaning carried by symbols “We”: Relationships, Roles
Frugal management of resources Harmony, play, enjoyment Due: Responsibilities + Right Self-giving love, generosity Vision, Motivation, Belief, aspiration 20 Use Aspects to Analyse some of Digital Divide Issues A simple example of Dooyeweerd’s philosophy application 21 Aspects Down-to-earth Issues? Quantitative Frequent of usage? For how long? Spatial At trusted public places or/and at home? Kinematic What is the internet speed like? PhysicalFor elderly people, can they hold tablets, mobile, laptops in their hand? What happens if they drop it? Biotic Are we providing with having different age range in mind?Psychic/SensitiveAre we providing with having visually and hearing impaired people in mind? Is flashing red, blue, green light on Wi-Fi hub helping? Analytic Are they really clear about what to expect? Clear about internet package they receive? FormativeDo they understand what they can achieve with internet?
LingualCan we translate a technical term for a lay person or we just say “don’t worry about it just press this button every time”? SocialDo we have the right social skills on the front line (our digital champions) to connect with them and build the essential rapport? EconomicNot paying for mobile phone plan means no hotspot to connect other devices? How much time is enough for assisting them? AestheticHow the internet access is harmonised with the rest of their life? How is it made enjoyable for them? JuridicalDo they know with a simple search they are exposed to number of opaque AI algorithm? How about Data Protection? Data Privacy? EthicalDo we know when and how we can donate our unused mobile data? Pistic Is giving internet access really for helping them? what is our real motivation? What other issues can you think of? Benefits! – If you have 100 of these , how would you sort them out? Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical
Pistic Think better Gain confidence Communicate with loved ones Save time Enjoy sharing photos Achieve more Aspects can help us with managing the diversity of things, a heterogeneous list. 23 Core Message: The aspects help to separate thing out without reducing one to another. In a real-life complex situation aspects help with bringing up things that are often taken for granted. References: Stahl, B.C., 2007. ETHICS, Morality and Critique: An Essay on Enid Mumford¡¯ s Socio-Technical Approach. Journal of the Association for Information Systems, 8(9), p.28. Walsham, G., 2005. Learning about being critical. Information Systems Journal, 15(2), pp.111-117.
Lee, A.S., 2004. Thinking about social theory and philosophy for information systems. Social theory and philosophy for information systems, 1, p.26. https://dooy.info/aspects.html Your feedback on the Lecture 4 is much appreciated. https://forms.office.com/r/9BqP4FPdQK It takes less than 5 minutes. Thank you 27
Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 2 Week 2 Structure of the session: Ground Rules What did we cover last week? Aim and indented learning outcomes A bit of lecture Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey Ground Rules for today: There is no wrong and right answer to my questions. Any answer is much appreciated. Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What was the aim of the lecture and workshop in Week 1?
To recognise the importance of the concept of “emerging technology”. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What were the intended learning outcomes in week 1? To define an “emergent technology”. To discuss what do we mean by “emergent”? To identify and argue what qualify a technology to be “emergent”? What is the aim of the lecture and workshop in Week 2? To discuss the nature of digital technologies from two perspectives of being-in-itself and being-in-the-world. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for this week? What do we mean by being-in-itself?
What do we mean by being-in-the-world? What are the 5 exponential digital technologies and their use cases? Being-in-itself Being-in-itself answer tries to find the essence of X, which can fully explain X, or from which all about it can be derived. Example: To say “a calculator is a machine that process numbers” Being-in-the-world Being-in-the-world answers the question by looking at the context that forms the thing. Example: To say “this iPhone is my communication lifeline” 5 Exponential Digital Technologies Artificial Intelligence Smart Processing Automation Blockchain Robots Special-Function Technologies Saldanha (2019)
10 What is the “use case”? An application of specific tool to a given problem. Saldanha (2019) 11 What is the “use case”? Now you give an example 12 Artificial Intelligence What is Artificial Intelligence?
14 15 Being-in-itself Artificial intelligence is defined as a “computing systems that
are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks” (Popenici & Kerr, 2017 cited in Crompton 2021). Being-in-the-world Example: Predictive Maintenance in Manufacturing. To say “just what we need to avoid another plant shutdown” Smart Process Automation What is Smart Process Automation?
19 What is RPA? Can you find a definition and cite it for me? Can you find me an Use Case of RPA? Blockchain What is Blockchain?
24 Being-in-itself The blockchain is defined as ”a distributed database of records, or public ledger of all transactions or digital events that have been executed and shared among participating parties” Atlam et al (2018) Being-in-the-world
Example: Emerging market Solar Energy Sharing. In Estonia: WePower has been testing just how well a choice-driven energy market could work by teaming up with an independent energy provider who shares their energy data in real time. It means “being independent” Robotics What is robotics? Being-in-itself International Federation of Robotics (IFR) define a robot as being “a machine which can be programmed to perform tasks
which involve manipulative and, in some cases, locomotive actions under automatic control.” Yahya et al (2019) Being-in-itself Robotics is defined as “the engineering science and technology of robots, and their design manufacture, application, and structural disposition and related to electronics, mechanical, and software.” Yahya et al (2019) Being-in-the-world Example: Any task that needs physical seeing, sensing, assisting, moving, measuring or delivering is fair game for robotics. To say “it is so convenient”. 33
Special-Function Technologies What is 3D Printing, Internet of Things,Nanotech, Energy Storage, Biotechnology and advanced materials? 35 36 Being-in-itself
A special-purpose sensor that measures and transmits necessary information and can be made smart enough to make some decisions. Saldanha (2019) Being-in-the-world Example: A Nest thermostat does measure and transmit necessary information to do with temperature, carbon monoxide and video image information. “It reduces cost” What is the aim of the lecture and workshop in Week 2? To discuss the nature of digital technologies from two perspectives of being-in-itself and being-in-the-world. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for this week?
What do we mean by being-in-itself? What do we mean by being-in-the-world? What are the 5 exponential digital technologies and their use cases? Your feedback on the Lecture 2 is much appreciated. Link to the feedback form. https://forms.office.com/r/Tzi3EQyTVq It takes less than 5 minutes. References: Yahya, M.Y.B., Hui, Y.L., Yassin, A.B.M., Omar, R., anak Robin, R.O. and Kasim, N., 2019. The Challenges of the Implementation of Construction Robotics Technologies in the Construction. In MATEC Web of Conferences (Vol. 266, p. 05012). EDP Sciences. Saldanha, T., 2019. Why digital transformations fail: The surprising disciplines of how to take off and stay ahead.
Berrett-Koehler Publishers. https://www.forbes.com/sites/jamesellsmoor/2019/04/27/blockc hain-is-the-next-big-thing-for-renewable- energy/?sh=e3470c948c1b Atlam, H.F., Alenezi, A., Alassafi, M.O. and Wills, G., 2018. Blockchain with internet of things: Benefits, challenges, and future directions. International Journal of Intelligent Systems and Applications, 10(6), pp.40-48. Keys, B. and Zhang, Y.J., 2020. Introducing RPA in an Undergraduate AIS Course: Three RPA Exercises on Process Automations in Accounting. Journal of Emerging Technologies in Accounting, 17(2), pp.25-30. Crompton, H., 2021. The Potential of Artificial Intelligence in Higher Education. Revista Virtual Universidad Católica del Norte, 62. Thank you 43
Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 3 Week 3 Dr Sina Joneidy 11/10/2021 is happy I wish everyone Structure of the session: Ground Rules What did we cover last two weeks? Aim and indented learning outcomes of this week A bit of lecture Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey
Ground Rules for today: There is no wrong and right answer to my questions. Any answer is much appreciated. Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What was the aim of the lecture and workshop in Week 1? To recognise the importance of the concept of “emerging technology”. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What were the intended learning outcomes in week 1? To define an “emergent technology”. To discuss what do we mean by “emergent”? To identify and argue what qualify a technology to be “emergent”?
What was the aim of the lecture and workshop in Week 2? To discuss the nature of digital technologies from two perspectives of being-in-itself and being-in-the-world. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What were the intended learning outcomes for week 2? What do we mean by being-in-itself? What do we mean by being-in-the-world? What are the 5 exponential digital technologies and their use cases? What is the aim of the lecture and workshop in Week 3? To identify types of human-machine collaboration for the enterprise and discuss the implications for the managers and workers. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for this week?
What do we mean by instrumenting the human? What do we mean by socialising the machine? What are the types of human-machine collaborations in the enterprise? What are the implications of instrumenting the human and socialising machine for the managers and workers? Disruptive Technologies Transformation the future of workplace Can AI Replace Human? 12
Can AI Replace Human? 13 We want to re-imagine the work processes in the context of mutual human-machine collaboration. 14 to optimise the blend of human–machine participation and interaction within the digital workplace.
Augmentation! 15 Work in greater harmony together. 16
Mapping the division of labour: human–machine collaboration 17 Human–machine work scenarios where machines augment humans and vice versa 18 ‘Physical–physical’ – meaning both humans and machines play a physical role such as caregivers working with smart mobile robots to deliver medicines and supplies in hospitals. 19
Physical – Physical Health Care Tug Autonomous Mobile Robot An example of Socialising Machin ‘Physical–virtual’ – meaning humans play a physical role and machines play a virtual role at the point where work is performed, such as warehouse employees using smart glasses for navigation and picking instructions to boost productivity. 22 Physical – Virtual DHL Smart Glasses An example of Instrumenting Human
‘Virtual–physical’ – meaning humans play a virtual role at the point work is performed and machines play a physical role such as doctors performing telepresence surgery. 25 Virtual – Physical Health Care Da Vinci Surgical System An example of Instrumenting Human ‘Virtual–virtual’ – meaning both humans and machines play a virtual role such as in call centres, with human agents working in tandem with virtual cognitive
agents. 28 Virtual – Virtual Virtual Service Desk Amelia Cognitive Agent An example of Socialising Machin Implications for managers
31 Use the 2X2 Matrix Be flexible Find the sweet spot for human–machine collaboration based on the nature of the work. 32 Implications for workers
33 Be flexible Be adaptable 34 What is the aim of the lecture and workshop in Week 3? To identify types of human-machine collaboration for the enterprise and discuss the implications for the managers and workers. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1.
What are the intended learning outcomes for this week? What do we mean by instrumenting the human? What do we mean by socialising the machine? What are the types of human-machine collaborations in the enterprise? What are the implications of instrumenting the human and socialising machine for the managers and workers? Your feedback on the Lecture 3 is much appreciated. It takes less than 5 minutes.
References: Evans, N.D., 2017. Mastering digital business. BCS Learning&D Thank you 39 Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 7
Week 7 Structure of the session: Aim and intended learning outcomes of this week. Linking your learning to the assessment. A bit of lecture and watching videos. Individual interactive activity. A bit of hint for your assessment. What is the aim of the lecture and workshop in Week 7? To develop a multi-modal understanding of types of human- machine collaboration for the enterprise. Linked to ECA Part 1 & ECA Part 2 – where you need to demonstrate multi-modal analysis What are the intended learning outcomes for week 7? To run a multi-modal analysis of your everyday life experience. To run a multi-modal analysis on issues and benefits of human- machine collaboration.
Link to the ECA Part 2 – 80% 6
Dooyeweerd Aspects Aspects Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical Pistic Kernel Meaning Discrete amount Continuous space Movement Energy + Mass, Forces Life functions + Organisms Sense, Feeling, Emotion Distinction, Conceptualization Achievement, History, Technology Meaning carried by symbols “We”: Relationships, Roles Frugal management of resources Harmony, play, enjoyment Due: Responsibilities + Right Self-giving love, generosity Vision, Motivation, Belief, aspiration
8 An extensive introduction of aspect 9 10 Use Dooyeweerd’s Aspects for analysing your Halloween
experience 11 How your experience of Halloween is meaningful from Dooyeweerd’s perspective? Human-Machine Collaborations Issues Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical Pistic Lack of Trust Not enough Visibility
Computer Anxiety Argument for change Gender inequality Aspects can help us with managing the diversity of things, a heterogeneous list. Low Self-efficacy Lack of transitional support Lack of compatibility Lack of facilitating condition Access cost Job Insecurity Negative social influence Lack of training 13 Human-Machine Collaborations Issues 14 References: Stahl, B.C., 2007. ETHICS, Morality and Critique: An Essay on Enid Mumford¡¯ s Socio-Technical Approach. Journal of the Association for Information Systems, 8(9), p.28. Walsham, G., 2005. Learning about being critical. Information Systems Journal, 15(2), pp.111-117.
Lee, A.S., 2004. Thinking about social theory and philosophy for information systems. Social theory and philosophy for information systems, 1, p.26. Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐ analysis of the TAM: Part 1. Journal of Modelling in Management. https://dooy.info/aspects.html Thank you 16 Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 5 Week 5
Your Voice Matters Your feedback is vital and we are listening. We want to know what is working well, and also any concerns you may have. Final year undergraduate students will receive an online survey about your experiences so far. Please take the time to complete this survey and help us further improve your student experience. #YourVoiceMatters tees.ac.uk/studentvoice #YourVoiceMatters This video provides a summary of the Your Voice Matters campaign. tees.ac.uk/studentvoice #YourVoiceMatters Structure of the session: Ground Rules Aim and outcomes of this week A bit of lecture
Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey Ground Rules for today: Running question: How am I going to use the learning from today’s lecture in my assessment? Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What is the aim of the lecture and workshop in Week 5? Introduce you the role and impotence of Digital Business Models at Enterprise. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 2. What are the intended learning outcomes for week 5? To define the concept of digital business model
To describe the importance of key dimensions in shaping digital business models. To distinguish between different digital business models using the four key dimensions we are introducing in the session. Digital Business Models 10 Joneidy, Sina (JS) - What is your Digital Business Model? 12 Joneidy, Sina (JS) -
What is your digital business model? A 2X2 Matrix What is your digital business model? What is your digital business model? References P. Weill and S.L. Woerner, "What's Your Digital Business
Model?: Six Questions to Help You Build the Next- Generation Enterprise", Harvard Business School Press Books, 2018, p. 1. Your feedback on the Lecture 4 is much appreciated. https://forms.office.com/r/SwZare95L9 It takes less than 5 minutes. Thank you 20 W
D a b c U a A R R A A K E C D A O D F S I S 1 i i p i m
g t c e b i c 2 S d h 0 Research Policy 44 (2015) 1827–1843 Contents lists available at ScienceDirect Research Policy j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / r e s p o l hat is an emerging technology? aniele Rotolo a,b,∗ , Diana Hicks b, Ben R. Martin a,c SPRU – Science Policy Research Unit, University of Sussex, Brighton BN1 9SL, United Kingdom School of Public Policy, Georgia Institute of Technology, Atlanta 30332-0345, United States Centre for Science and Policy (CSAP) and Centre for Business Research, Judge Business School, University of Cambridge, Cambridge CB2 1QA, nited Kingdom
r t i c l e i n f o rticle history: eceived 11 December 2014 eceived in revised form 15 June 2015 ccepted 16 June 2015 vailable online 9 August 2015 eywords: merging technologies onceptualisation efinition ttributes of emergence a b s t r a c t There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet, as an area of study, emerging technologies lack key foundational ele- ments, namely a consensus on what classifies a technology as ‘emergent’ and strong research designs that operationalise central theoretical concepts. The present paper aims to fill this gap by developing a definition of ‘emerging technologies’ and linking this conceptual effort with the development of a frame- work for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of ‘emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent
impact, and (v) uncertainty and ambiguity. The perationalisation etection and analysis ramework cientometrics ndicators cience and Technology Studies (STS) framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indica- tors and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence. © 2015 Elsevier B.V. All rights reserved. . Introduction Emerging technologies have been the subject of much debate n academic research and a central topic in policy discussions and nitiatives. Evidence of the increasing attention being paid to the henomenon of emerging technologies can be found in the grow - ng number of publications dealing with the topic and news articles entioning emerging technologies (in their headlines or lead para- raphs), as depicted in Fig. 1. Increasing policy interest in emerging echnologies, however, must be set against a literature where no
onsensus has emerged as to what qualifies a technology to be mergent. Definitions proposed by a number of studies overlap, ut also point to different characteristics. For example, certain def- nitions emphasise the potential impact emerging technologies are apable of exerting on the economy and society (e.g. Porter et al., 002), especially when they are of a more ‘generic’ nature (Martin, ∗ Corresponding author at: SPRU – Science Policy Research Unit, University of ussex, Brighton BN1 9SL, United Kingdom. Tel.: +44 1273 872980. E-mail addresses: [email protected] (D. Rotolo), [email protected] (D. Hicks), [email protected] (B.R. Martin). ttp://dx.doi.org/10.1016/j.respol.2015.06.006 048-7333/© 2015 Elsevier B.V. All rights reserved. 1995), while others give great importance to the uncertainty asso- ciated with the emergence process (e.g. Boon and Moors, 2008) or to the characteristics of novelty and growth (e.g. Small et al., 2014). The understanding of emerging technologies also depends on the analyst’s perspective. An analyst may consider a technology emer- gent because of its novelty and expected socio-economic impact, while others may see the same technology as a natural extension of an existing technology. Also, emerging technologies are
often grouped together under ‘general labels’ (e.g. nanotechnology, syn- thetic biology), when they might be better treated separately given their different socio-technical features (e.g. technical difficulties, involved actors, applications, uncertainties). The lack of consensus over definitions is matched by an ‘eclec- tic’ and ad hoc approach to measurement. A wide variety of methodological approaches have been developed, especially by the scientometric community, for the detection and analysis of emer- gence in science and technology domains (e.g. Porter and Detampel, 1995; Boyack et al., 2014; Glänzel and Thijs, 2012). These methods, favoured, because they take advantage of growing computational power and large new datasets and allow one to work with more sophisticated indicators and models, lack strong connections to well thought out concepts that one is attempting to measure, a basic dx.doi.org/10.1016/j.respol.2015.06.006 http://www.sciencedirect.com/science/journal/00487333 http://www.elsevier.com/locate/respol http://crossmark.crossref.org/dialog/?doi=10.1016/j.respol.2015 .06.006&domain=pdf mailto:[email protected] mailto:[email protected] mailto:[email protected]
dx.doi.org/10.1016/j.respol.2015.06.006 1828 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 Year N u m b e r o f p u b lic a tio n s 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 0 5
0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 Year 0 5 0 0 1 0
0 0 1 5 0 0 2 0 0 0 2 5 0 0 3 0 0 0 3 5 0 0 N u
m b e r o f n e w s a rt ic le s News articles Publications in all disciplines Publications in social sciences Fig. 1. Publications (left axis) and news articles (right axis) including the variations of the term “emerging technologies”. Publications were retrieved by querying SCOPUS: “TITLE(“emerg* technol*”) OR TITLE(“emergence of* technolog*”) OR TITLE(“techn* emergence”) OR TITLE(“emerg* scien* technol*”)”. Publications in social sciences were defined as those assigned to the SCOPUS categories “Business, Management and Accounting”, “Decision Sciences”, “Economics, Econometrics and Finance”, “Multidisciplinary”, “Psychology”, and “Social Sciences”. News articles were
identified by searching for “emerg* near2 technolog*” in article headlines and lead paragraphs as reported in FACTIVA. F conc 1 ly gro S t c f t o I a d n g a a n o c t n c u o t o g o c s i
a a i r rom 1980 to 2013, the average yearly growth rates of the number of publications 2.5% and 23.8%, respectively. The total number of publications in SCOPUS has year ource: search performed by authors on SCOPUS and FACTIVA. enet of good research design. Often no definition of the central con- ept of an emerging technology is provided. It is no surprise there- ore that approaches to the detection and analysis of emergence end to differ greatly even with the use of the same or similar meth- ds. The operationalisation of emergence is also in a state of flux. t changes as new categorisations (e.g. new terms in institution- lised vocabularies, new technological classes) are created within atabases. This, in turn, makes less clear the exact nature of the phe- omena that these scientometric methods enable us to examine. These problems in the effort to understand emerging technolo- ies limit the utility of the research and so may hamper resource llocation and the development of regulations, which, in turn, have major role in supporting and shaping the directionality of tech- ological emergence.
The present paper addresses both the conceptual and method- logical gaps. We aim to elaborate a framework that links what is onceptualised as ‘emerging technologies’ with its measurement, hus providing guidance to future research (e.g. development of ovel methods for the detection of emergence and analysis of its haracteristics) and to policy-making (e.g. resource allocation, reg- lation). To do so, we first attempt to clarify the conceptualisation f emerging technologies by integrating different conceptual con- ributions on the topic into a more precise and coherent definition f ‘emerging technology’. We begin with the definition of ‘emer- ence’ or ‘emergent’, which is the process of coming into being, or f becoming important and prominent. This is then enriched and ontextualised with a review of major contributions to innovation tudies that have focused on technological emergence, highlight- ng both their common and contradictory features. Conceptual ttempts to grapple with emergence in complex systems theory are lso discussed where relevant to the idea of emergent technology. The result is the delineation of five key attributes that qual - fy a technology as emerging. These are: (i) radical novelty, (ii) elatively fast growth, (iii) coherence, (iv) prominent impact, and erning emerging technologies in all disciplines and in social sciences have been of wn on average by 4.9%.
(v) uncertainty and ambiguity. Specifically, we conceive of an emerging technology as a radically novel and relatively fast growing technology characterised by a certain degree of coherence persisting over time and with the potential to exert a considerable impact on the socio-economic domain(s) which is observed in terms of the compo- sition of actors, institutions and patterns of interactions among those, along with the associated knowledge production processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous. Second, the framework for operationalising emerging technolo- gies is developed on the basis of the attributes we identified. The scientometric literature forms the core of the methods dis - cussed because, as mentioned, this field has been remarkably active in developing methodologies for the detection and analysis of emergence in science and technology. The reviewed methods are grouped into five main categories: (i) indicators and trend analysis, (ii) citation analysis (including direct citation and co-citation anal- ysis, and bibliographic coupling), (iii) co-word analysis, (iv) overlay mapping, and (v) hybrid approaches that combine two or more of the above. Because scientometric techniques cannot address all the attributes comprehensively, we also discuss approaches developed
in other fields. The paper is organised as follows. The next section introduces the concept of emergence and its various components. In Sec- tion 3, these elements are integrated with key innovation studies proposing definitions of technological emergence, and a definition of emerging technologies is then elaborated. Section 4 reviews methods to both detect and analyse emergence, and then examines the use of those approaches to operationalise the proposed defini- tion and the various attributes of emerging technologies. Section 5 discusses the limits of current methodologies for the detection and analysis of emerging technologies and identifies directions for future research. Section 6 summarises the main conclusions of the study. U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight
D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 1829 Table 1 Dictionary definitions of the concept of emergence. Dictionary definition of ‘emerge’/‘emergent’ Attributes “the process of coming into being, or of becoming important and prominent” (New Oxford American Dictionary) come into being; important; prominent “to become manifest: become known [. . .]” (Merriam- Webster’s Collegiate Dictionary) become manifest; become known “to rise up or come forth [. . .] to become evident [. . .] to come into existence” (The American Heritage Desk Dictionary and Thesaurus) evident; come into existence “move out of something and become visible [. . .] come into existence or greater prominence [. . .] become known [. . .] in the process of coming into being or prominence” (Concise Oxford English Dictionary) visible; prominent; become known; come into being “starting to exist or to become known [. . .] to appear by coming out of something or out from behind become known; to appear
S 2 i A e D o t i d t e l t O c e p d i t L a s s s i a n f g
s e G n p o d p t ( i o ( ( s c to identify those that develop or provide definitions of emerg- ing technologies — we searched for ‘defining’ sentences within the publication full-text by using the keywords listed above. This 1 The terminology of ‘emerging technologies’ has become central to a number of research traditions and especially to the scientometric, bibliometric and tech- mining domains (cf. Avila-Robinson and Miyazaki, 2011), which, as discussed, have something” (Cambridge Dictionaries Online) ource: search performed by authors on major English dictionaries. . The concept of emergence
The word ‘emerge’ or ‘emergent’ means “the process of coming nto being, or of becoming important and prominent” (New Oxford merican Dictionary) or “to rise up or come forth [. . .] to become vident [. . .] to come into existence” (The American Heritage Desk ictionary and Thesaurus). Table 1 presents dictionary definitions f emergent. The primary attribute of emergence is ‘becoming’ — hat is, coming into existence. Emergent is not a static property; t is a label for a process. The endpoint of the process is variously escribed as visible, evident, important or prominent. Thus, among he dictionaries there is some disagreement as to whether acknowl- dged existence is enough for emergence, or beyond that, a certain evel of prominence is needed in order to merit application of the erm emergence. There is a second definition of emergent given the by the New xford American Dictionary as: a property arising as an effect of omplex causes and not analysable simply as the sum of their ffects. An additional definition is: arising and existing only as a henomenon of independent parts working together, and not pre- ictable on the basis of their properties. This concept of emergence s used in the study of complex systems. It can be traced back o the 19th Century in the proto-emergentism movement when ewes (1875) referred to ‘emergent effects’ in chemical reactions s those effects that cannot be reduced to the components of the ystem, i.e. the effects for which it is not possible to trace all the
teps of the processes that produced them. Its application in the tudy of the dynamics of complex systems in physics, mathemat- cs, and computer science gave rise to other fundamental theories nd schools of thought such as complex adaptive system theory, on-linear dynamical system theory, the synergetics school, and ar-from-equilibrium thermodynamics (see Goldstein, 1999). A number of studies focusing on the definitional issue of emer - ence were produced by scholars in complex system theory — ee Table A1 in Appendix for an overview of the definitions of mergence proposed by major studies in complex system theory. oldstein (1999), for example, defined emergence as “the arising of ovel and coherent structures, patterns, and properties during the rocess of self-organization in complex systems” (1999, p. 49). An ntological and epistemological definition of emergence is instead eveloped by de Haan (2006). Ontological emergence is “about the roperties of wholes compared to those of their parts, about sys - ems having properties that their objects in isolation do not have” 2006, p. 294), while epistemological emergence it is about “the nteractions between the objects that cause the coming into being f those properties, in short the mechanisms producing novelty” 2006, p. 294). Though research on complex systems may have a certain cachet and perhaps for this reason scholars of emerging technologies ometimes attempt to work with the meaning of emergent as onceived by the complex system approach), we maintain that questions about emerging technologies are not fundamentally about understanding the origins and the causal nature of full system
interaction; rather they are about uncertainty, novelty, identifica- tion at an early stage, and visibility and prominence. It is true that some technologies in themselves may be complex systems in the sense of exhibiting adaptation, self-organisation, and emergence, an example being parts of materials science (Ivanova et al., 1998). However, other technologies exhibit ‘complicatedness’ rather than ‘complexity’ as defined in complex system theory — for example, engineering systems. These systems are designed for specific pur- poses, but they do not adapt and self-organise to changes in the environment (Ottino, 2004). It is also true that emerging tech- nologies may arise from complex innovation systems (Katz, 2006), but we would contend that in the phrase ‘emerging technology’, ‘emerging’ is generally understood in the standard sense, not the complex system usage. 3. Defining emerging technologies To further clarify what is meant by emerging technology, we reviewed literature in innovation studies dealing with definitional issues of emerging technologies. To identify relevant studies, we searched for “emerg* technolog*”, “tech* emergence”, “emergence of* technolog*”, or “emerg* scien* technol*” in publication titles
by querying SCOPUS (see the left-hand column of Table 2).1 We restricted the search to the title field to limit results to publications primarily focused on emerging technologies. The search identified a total of 2201 publications from 1971 to mid 2014.2 Within this sample we selected those publications in social science domains, thus reducing the sample to 501 records (see Fig. 1). We then read the abstracts and accessed the full-text of these studies where necessary both to identify additional documents from the list of cited references and to exclude studies that are not relevant to the scope of this paper. We found that about 50% of the studies in the sample refer to a specific industrial context (e.g. listing and discussing emerging technologies in a given industry) or to the educational sector (e.g. emergence of novel technologies to improve education and learning). These were deemed not rele- vant to our study. The remaining studies were further examined been remarkably active in developing methods for the operationalisation of emer- gence. In other words, ‘emerging technologies’ have become a category of its own. For this reason, we do not include epistemologically related terms, such as ‘radical’, ‘disruptive’, ‘discontinuous’, ‘nascent’ and ‘breakthrough’. 2 The search was performed on 13th May 2014.
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U0033385 Highlight 1830 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 Table 2 Search strategies used to identify the set of relevant publications for the conceptualisation and operationalisation of emerging technologies. Conceptualisation Operationalisation Search terms “emerg* technolog*” “emerg* technolog*” “tech* emergence” “tech* emergence” “emergence of* technolog*” “emergence of* technolog*” “emerg* scien* technol*” “emerg* scien* technol*” “emerg* topic*” “emergence of* topic*” Field(s) of search Title Title, abstract, keywords Focus Social sciences Scientometric journals: Journal of the Association for Information Science &Technology (formerly the Journal of the American Society for Information Science &Technology), Journal of Informetrics, Research Evaluation, Research Policy, Scientometrics, Technological Forecasting &Social Change, Technology Analysis &Strategic Management S l
p m e t i T T D S Number of studies 501 155 ource: authors’ elaboration as based on SCOPUS data. ed to a core set of 12 studies from science and technology (S&T) olicy studies, evolutionary economics, management, and sciento- etrics that contributed to the conceptualisation of technological mergence. These are listed with their definitions of emerging echnologies in Table 3. We analysed the textual content of the def- nitions reported in Table 3 to extract all the component concepts. hese were grouped into the attributes discussed below and used to able 3 efinitions of emerging technologies (studies are chronologically ordered). Study Domain Definition (elaborated
Martin (1995) S&T policy “A ‘generic emerging benefits for a wide ran Day and Schoemaker (2000) Management “[. . .] emerging techn industry or transform innovations [. . .] as w separate research stre Porter et al. (2002) S&T policy “Emerging technologi in the coming (roughl Corrocher et al. (2003) Evolutionary economics “The emergence of a n institutional and socia firms or research labo and evolution of know Hung and Chu (2006) S&T policy “Emerging technologi changing the basis of Boon and Moors (2008) S&T policy “Emerging technologi aspects, such as the ch actor network and the Srinivasan (2008) Management “I conceptualize emer characteristics [. . .] an emerging technologie application’ — four ch technologies, converg technologies — shiftin within the firm to out Cozzens et al. (2010) S&T policy “Emerging technology settled down into any emerging technologie
in the process of trans yet; (4) increasingly s Stahl (2011) S&T policy “[. . .] emerging techn relevance within the n development process [. . .] Despite this, thes capabilities, constrain Alexander et al. (2012) S&T policy “Technical emergence members of an expert extension of) human u Halaweh (2013) Management Characteristics of (IT) ethical concerns, cost 108) Small et al. (2014) Scientometrics “[. . .] there is nearly u newness) and growth ource: search performed by authors on SCOPUS and extended to cited references. construct our definition of emerging technologies. Extracted con- cepts excluded from our list of attributes will also be discussed. The first defining attribute of emerging technolo gy, explicitly included in two of the 12 core articles, is radicalnovelty: “novelty (or newness)” (Small et al., 2014) may take the form of “dis - continuous innovations derived from radical innovations” (Day and Schoemaker, 2000) and may appear either in the method or or adopted)
technology’ is defined [. . .] as a technology the exploitation of which will yield ge of sectors of the economy and/or society” (p. 165) ologies as science-based innovation that have the potential to create a new an existing ones. They include discontinuous innovations derived from radical ell as more evolutionary technologies formed by the convergence of previously ams” (p. 30) es are defined [. . .] as those that could exert much enhanced economic influence y) 15-year horizon.” (p. 189) ew technology is conceptualised [. . .] as an evolutionary process of technical, l change, which occurs simultaneously at three levels: the level of individual ratories, the level of social and institutional context, and the level of the nature ledge and the related technological regime.” (p. 4) es are the core technologies, which have not yet demonstrated potential for competition” (p. 104) es are technologies in an early phase of development. This implies that several aracteristics of the technology and its context of use or the configuration of the ir related roles are still uncertain and non-specific” (p. 1915) ging technologies in terms of three broad subheads: their sources [. . .], their d their effects [. . .] Specifically, I consider two aspects of the sources of s — the ‘relay race evolution’ of emerging technologies, and
‘revolution by aracteristics of emerging technologies — the clockspeed nature of emerging ence, dominant designs, and network effects — and three effects of emerging g value chains, digitization of goods, and the shifting locus of innovation (from side the firm).” (pp. 633–634) — a technology that shows high potential but hasn’t demonstrated its value or kind of consensus.” (p. 364). “The concepts reflected in the definitions of s, however, can be summarised four-fold as follows: (1) fast recent growth; (2) ition and/or change; (3) market or economic potential that is not exploited fully cience-based.” (pp. 365–366) ologies are defined as those technologies that have the potential to gain social ext 10 to 15 years. This means that they are currently at an early stage of their . At the same time, they have already moved beyond the purely conceptual stage. e emerging technologies are not yet clearly defined. Their exact forms, ts, and uses are still in flux” (pp. 3–4) is the phase during which a concept or construct is adopted and iterated by [. . .] community of practice, resulting in a fundamental change in (or significant nderstanding or capability.” (p. 1289) emerging technologies “are uncertainty, network effect, unseen
social and , limitation to particular countries, and a lack of investigation and research.” (p. niversal agreement on two properties associated with emergence — novelty (or .” (p. 2) U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight
h Polic t p p e u t w t b e c e ( t w a t a ( T b n t p t t t s e n t a w
i e e n a H t t a t t ( “ n s k t a a t m o a o t T e t D. Rotolo et al. / Researc he function of the technology. To achieve a new or a changed urpose/function, emerging technologies build on different basic rinciples (Arthur, 2007) (e.g. cars with an internal combustion
ngine vs. an electric engine, cytology-based techniques vs. molec- lar biology technologies). Novelty is not only a characteristic of echnologies deriving from technical revolutions, i.e. technologies ith relatively limited prior developments (e.g. DNA sequencing echnologies, molecular biology, nano-materials), but it may also e generated by putting an existing technology to a new use. The volutionary theory of technological change views this as the spe- iation process of technology, that is the process of applying an xisting technology from one domain to another domain or ‘niche’ Adner and Levinthal, 2002). The niche is characterised by a selec- ion process that is different from the one where the technology as initially applied. The niche specifically may differ in terms of daptation (the needs of the niche) and abundance of resources. The echnology applied in the niche may adapt and then emerge as well s potentially invading other domains including the initial domain giving rise to a ‘revolution’ or a process of ‘creative destruction’). his implies that ‘evolutionary’ technology (those not characterised y revolutionary technical developments) can also be radically ovel in domains of application different from those where the echnology was initially developed. Adner and Levinthal (2002) rovided a compelling example of the speciation process by repor-
ing on the evolution of wireless communication technology. This echnology was created for laboratory purposes, and specifically for he measurement of electromagnetic waves. Yet, it found numerous ubsequent applications. Wireless communication technology first nabled communication with locations (e.g. lighthouses) otherwise ot reachable with wired telegraphy. Then, applications expanded o the transmission of voice (radiotelephony and broadcasting), nd, more recently, to data transmission (Wi-Fi). With each shift, ireless communication technology appeared radically novel in ts new domain of application, although the technology itself had xisted since the early laboratory and telegraphy applications. The volutionary theory of technological change teaches us that radical ovelty may characterise innovations based on both revolutionary nd evolutionary inventions resulting from the speciation process. owever, the term ‘evolutionary’ is also used to refer to incremen- al technological advances. To avoid ambiguity, we opted to use he term ‘radical novelty’ rather than ‘revolutionary/evolutionary’ nd to contextualise it in relation to the domain(s) in which the echnology is arising.3 The second defining attribute of emerging technologies, iden- ified by three of the 12 core articles is “clockspeed nature” Srinivasan, 2008) or “fast growth” (Cozzens et al., 2010), or at
least growth” (Small et al., 2014). Growth may be observed across a umber of dimensions such as the number of actors involved (e.g. cientists, universities, firms, users), public and private funding, nowledge outputs produced (e.g. publications, patents), proto- ypes, products and services, etc. As with the radical novelty ttribute, the fast growth of a technology needs to be contextu- lised. A technology may grow rapidly in comparison with other echnologies in the same domain(s), therefore relativelyfastgrowth ay be a better term. The third attribute of emerging technologies, identified by four f the 12 core articles is coherence that persists over time. The core rticles variously describe this attribute as “convergence of previ- usly separated research streams” (Day and Schoemaker, 2000), 3 The word ‘novelty’ alone may also create ambiguity with regard to the types of echnologies we aim to include in our conceptualisation of emerging technologies. echnologies of a more incremental nature, as derived from the improvement of xisting technologies, are somewhat novel. For the sake of conceptual clarity, we herefore prefer to add the attribute ‘radical’ to the word ‘novelty’. y 44 (2015) 1827–1843 1831 “convergence in technologies” (Srinivasan, 2008), and technolo- gies that “have already moved beyond the purely conceptual stage” (Stahl, 2011). Alexander et al. (2012) point instead to the role of
“an expert community of practice”, which adopts and iterates the concepts or constructs underlying the particular emerging tech- nology. The concept of a community of practice suggests that both a number of people and a professional connection between those people are necessary. Coming together, intertwining and staying together are all entailed in coherence. Coherence refers to internal characteristics of a group such as ‘sticking together’, ‘being united’, ‘logical interconnection’ and ‘congruity’. The status of external rela- tions is also important. The emerging technology must detach itself from its technological ‘parents’ to some degree to merit a separate identity. Furthermore, it must stay detached for some period of time to be seen as self-sustaining (Glänzel and Thijs, 2012). As we stated above, emergence is a process and coherence, detachment and identity do not characterise a final state, but are always in the process of realisation, presenting challenging issues of boundary delineation and classification. Perspective matters since an analyst may see an exciting emerging technology about to make a major economic impact in something a scientist sees as long past the exciting emerging phase. The fourth defining attribute of emerging technologies, identi - fied by nine of the 12 core articles is to yield “benefits for a wide range of sectors” (Martin, 1995), “create new industry or trans -
form existing ones” (Day and Schoemaker, 2000), “exert much enhanced economic influence” (Porter et al., 2002), or change “the basis of competition” (Hung and Chu, 2006). Corrocher et al. (2003) also point to the pervasiveness of the impact that the emerg- ing technology may exert by crosscutting multiple levels of the socio-economic system, i.e. organisations and institutions, as well as knowledge production processes and technological regimes. Accordingly, we identify prominentimpact as another key attribute of emerging technologies. Most of the core articles conceived the prominent impact of emerging technologies as exerted on the entire socio-economic system. In this usage the concept of emerg- ing technologies becomes very close to that of ‘general purpose technologies’ and so excludes technologies prominent within a specific domain. We wish to include relatively smaller scale promi- nence in our definition. For example, a diagnostic technology may emerge and significantly reshape the clinical practices associated with a given disease, profoundly affecting one disease domain but not others. In other words, our definition allows for prominent impact with narrow scope (emergence in one or a few domains), as well as wide-ranging impact across domains and potentially the entire socio-economic system (e.g. ICT and molecular biology). Such a perspective suggests, as with the attributes of radical novelty and relatively fast growth, the importance of contextualising the
promi- nent impact of the observed technolo gy within the domain(s) from which the technology emerges. The final defining attribute of emerging technologies, identified in seven of the 12 core articles is that the prominent impact of emerging technologies lies somewhere in the future — the tech- nology is not finished. Thus, uncertainty features in the emergence process. The non-linear and multi-factor nature of emergence pro- vides emergence with a certain degree of autonomy, which in turn makes predicting a difficult task (de Haan, 2006; Mitchel, 2007). As a consequence, knowledge of the probabilities associated with each possible outcome (e.g. potential applications of the technol- ogy, financial support for its development, standards, production costs) may be particularly problematic (Stirling, 2007). Core articles expressed this attribute in terms of the ‘potential’ that emerging technologies have for changing the existing ‘ways of doing things’ (e.g. Boon and Moors, 2008; Hung and Chu, 2006; Stahl, 2011). However, these definitions seem not to disentangle explic- itly another important aspect of emergence from the concept of U0033385
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Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight 1 h Policy 44 (2015) 1827–1843 u a t i s u 2 e c k i p s o f t o e 1
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x x x x x x x x x x x 832 D. Rotolo et al. / Researc ncertainty. This is ambiguity. Ambiguity arises because proposed pplications are still malleable, fluid and in some cases contradic- ory, i.e. even the knowledge of possible outcomes of emergence
s incomplete. A variety of possible outcomes may occur because ocial groups encountered during emergence hold diverging val - es and ascribe different meanings to the technology (Mitchel, 007). It is worth noting that uncertainty and ambiguity are, how- ver, not mutually exclusive (Stirling, 2007). These are not discrete onditions. A continuum exists as defined by the extent to which nowledge of possible outcomes and likelihood for each outcome s incomplete. For example, it may be problematic evaluating the robabilities associated with known possible outcomes, but at the ame time there may also be a lack of knowledge of other possible utcomes such as unintended/undesirable consequences deriving rom the (potentially uncontrolled) use of the technology. Uncer - ainty and ambiguity are key starting concepts for a wide variety f science and technology studies (STS) focusing on the role of the xpectations in technological emergence (e.g. van Lente and Rip, 998). The studies reviewed here introduced various additional oncepts such as the science-based-ness, network effects, and arly-stage development of emerging technologies. While the last f these seems to be implicit in the definition of emergence and the ey role of networks (of users adopting the technology) is certainly ot a unique feature of emerging technologies, the association ith science-based-ness is less clear. The importance of science especially public science) for the development of industrial tech- ologies is widely accepted on the basis of substantial evidence
e.g. Narin et al., 1997). However, even today not all technological evolutions may depend on breakthrough advances in science. In ertain domains, a technology can be developed without the need or deep scientific understanding of how the phenomenon under - ying it works — “it is possible to know how to produce an effect ithout knowing how an effect is produced” (Nightingale, 2014, p. ). For example, Vincenti (1984) provided evidence of this in the ase of the construction of airplanes in the 1930s. The different arts of an airplane were initially joined using rivets with dome- haped heads. These types of rivets, however, caused resistance o the air, thus reducing the aerodynamic efficiency of the plane. s other dimensions of airplane performance were improving (e.g. peed), the aerodynamic efficiency became increasingly relevant. he dome-shaped rivets were therefore replaced with rivets flush ith the surface of the airplane. This was a major improvement for he aerodynamics of airplanes in 1930s, but it required no major cientific breakthrough.4 A more recent example is the develop- ent of smartphones which did not require major advancements n science since most of the technologies used already existed — he integration of these technologies, and advances in design for he creation of novel user interfaces instead provided the founda - ion of the innovation.5 For these reasons, ‘science-based-ness’ does ot feature in our definition of emerging technologies. In summary, as reported in Table 4, our review of innovation tudies identified five main defining characteristics or attributes
f emerging technologies: (i) radical novelty, (ii) relatively fast rowth, (iii) coherence, (iv) prominent impact, and (v) uncertainty nd ambiguity. Combining these attributes, we define an emerging 4 Other classical examples include prehistoric cave dwellers using fire for cooking ithout any scientific understanding of it, the development of steam engines that redated the development of thermodynamics, or the Wright brothers testing flying evices before the field of aerodynamics was established. 5 The innovation was architectural rather than modular according to the distinc- ion proposed by Henderson and Clark (1990). Also, smartphone technology can e considered as an example of emerging technology of an evolutionary nature. s discussed above, the radical novelty of this technology is the result of existing echnologies converging in new domains of applications. T a b le 4 A tt ri
b u te s o f em er g en ce an d re v ie w ed k ey
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au th o rs ’ e la b o ra ti o n . U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight
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e o w n i f t a f c a t e e t n i d b i t e b w m t s i t t t t t n w
i o g i t a w t S s l c a c I c e t W a 4 g Time Attribute Pre-emergence Emergence Post-emergence Relatively fast growth Coherence Prominent impact
Radical novelty Uncertainty and ambiguity Fig. 2. Pre-emergence, emergence, and post-emergence: attributes and ‘stylised’ trends. gence. This process led to a final set of 55 publications,6 which were then classified in terms of the methodological approach adopted to detect or analyse emergence (e.g. indicators, citation 6 We excluded 76 studies that did not operationalise emergence (e.g. use of emerging technologies as empirical context for various analyses, examination of ethical issues associated with emerging technologies), three studies focused on the review of scientometric methods for the analysis of emerging technologies, and two studies elaborating document search strategies based on a modular lexical approach. 33 studies that were concerned with Future-oriented Technology Analysis (FTA) techniques (e.g. foresight, forecasting, roadmapping, Constructive Technol- ogy Assessment (CTA)) were also not included in the review. While about 67% of these do not rely on scientometrics, the remaining FTA studies in the sample pro- pose frameworks for selecting, rather than identifying, emerging technologies, or adopt conventional scientometric/bibliometric approaches, which will be instead discussed with the review of the selected scientometric studies. FTA methods, how- ever, remain crucial for more prospective analyses of emerging
technologies and decision-making on possible future scenarios (e.g. Porter et al., 2004; Irvine and Martin, 1984; Ciarli et al., 2013). 14 STS studies included in the sample will be instead referenced in our review and discussion when the operationalisation of the attributes of emergence with the use of scientometric approaches is limited by a lack of data or by the nature of the considered attribute. It is worth noting that our search did not capture ‘technometric’ studies (e.g. Grupp, 1994; Saviotti and D. Rotolo et al. / Researc echnology as a radically novel and relatively fast growing technol- gy characterised by a certain degree of coherence persisting over time nd with the potential to exert a considerable impact on the socio- conomic domain(s) which is observed in terms of the composition f actors, institutions and patterns of interactions among those, along ith the associated knowledge production processes. Its most promi- ent impact, however, lies in the future and so in the emergence phase s still somewhat uncertain and ambiguous. It is reasonable to assume that the attributes of emergence range rom ‘low’ to ‘high’ levels. Nonetheless, to try and pin them down o some absolute level is rather meaningless. As discussed, the
ttributes of emergence (especially radical novelty and relatively ast growth) provide an indication of emergence when they are onsidered in the domain in which the given technology is arising nd therefore in relation to other technologies that may exist in hat domain. Most importantly, these attributes are likely to co- volve and assume very different levels over different periods of mergence. In the early stage of emergence (‘pre-emergence’), a echnology is likely to be characterised by high levels of radical ovelty as compared to other technologies in the domain in which t is arising. However, the impact the technology can exert on that omain is still relatively low. The technology has not yet gone eyond the purely conceptual stage, multiple communities are nvolved in its development, and the delineation of the boundary of he technology is particularly problematic (i.e. low levels of coher- nce). As a consequence, its growth is relatively slow or not yet egun, and high levels of uncertainty and ambiguity are associated ith the future developments of the technology — the technology ay not even emerge. The technology may then acquire a cer- ain momentum. Some trajectories of development may have been elected out and certain dimensions of performance prioritised and mproved. A community of practice may have also emerged. The echnology thus becomes more coherent. Its impact is also rela- ively less uncertain and ambiguous, and the technology starts to ake off in terms of publications, patents, researchers, firms, pro- otypes/products, etc. However, at the same time, it is likely that
he radical novelty of the technology will diminish — other tech- ologies that exploit different basic principles may be emerging as ell in the domain in which the considered technology is emerg- ng. We conceived ‘emergence’ as this phase where the attributes f emergence are subject to dramatic change. Finally, impact and rowth may enter a stable or declining phase, the technology loses ts radical novelty, knowledge of the possible outcomes of the echnology becomes more complete (probabilities can be perhaps ssigned to outcomes), and the community of practice may become ell-established (e.g. regular conferences, dedicated journals). The echnology enters in a ‘post-emergence’ period. In line with the -shaped patterns highlighted in early studies on the growth of cience (e.g. De Solla Price, 1963) and in technological adoption iterature (e.g. Mansfield, 1961; Rogers, 1962), we ‘stylised’ the hange in the levels of the attributes of emergence as following n S curve (or more strictly, a reversed S curve in two of the five ases). This is qualitatively depicted in Fig. 2. Defining ‘emerging technology’ is, however, only half the battle. f the definition is to be useful, we must show how the attributes an be measured and thus how technologies can be classified as merging or not. In the next section, we link our definition to he operationalisation of our definition of emerging technologies. e rely mainly on scientometric techniques, bringing in other pproaches to fill certain gaps.
. A framework for the operationalisation of emergence Scientometric research has developed methods to detect emer - ence in science and technology and is therefore central to Source: authors’ elaboration. operationalising our definition. From the vast literature that touches on emerging technologies, we drew upon studies that offer ideas on operationalising our five attributes. We identified relevant scientometric studies by including the term ‘topic’ in the search string we used to select research works dealing with definitional issues of emerging technologies — ‘topic’ is often used in sciento- metrics to refer to the emergence of a new set of research activities in science and technology (e.g. Small et al., 2014; Glänzel and Thijs, 2012). The search was also extended to publication titles, abstracts and keywords, but narrowed to journals mainly or to a significant extent oriented toward the publication of novel scientometric tech- niques (see the right-hand column of Table 2). The search in SCOPUS returned 155 publications. The examination of cited references of these publications enabled us to retrieve additional studies that were not cap- tured with the search string, but are potentially relevant to for our analysis. This increased the initial sample to 183 studies. We then analysed these publications to identify studies that were relevant to the operationalisation of the attributes of emer-
Metcalfe, 1984; Sahal, 1985). This research stream has been particularly important for the measurement of technology and technological change. Nonetheless, techno- metric models tend to rely on a variety of assumptions and often require data, the collection of which can be particularly labour-intensive (e.g. extraction and coding of data on the features of the considered technologies) (e.g. Coccia, 2005). U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight 1834 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843
Table 5 Methods for the detection and analysis of emergence in science and technology (studies are ordered by technique and publication year). Method/study Data Operationalisation of emergence Indicators and trends Porter and Detampel (1995) Publications/patents Count of keywords in publication abstracts and trend analysis based on Fisher–Pry curves Kleinberg (2002) Publications/e-mails ‘Burst of activity’ detected as state transitions of an infinite-state automaton Bengisu (2003) Publications Positive slope of the line derived by regressing the number of publications on time and no decrease of more than 10% or stability (no increase) in the last period or continuous decline in the last three periods of observation Watts and Porter (2003) Publications Indicators of emergence: cohesion (based on cosine similarity between documents), entropy, and F-measure Bettencourt et al. (2008) Publications Epidemic model to describe the increasing number of authors involved in an emerging field Bettencourt et al. (2009) Publications Increasing densification (average number of edges per node), stable/decreasing diameter (average path length between nodes), and increasing fractional count of edges in the largest component of the co-authorship network Moed (2010) Publications Journals characterised by high values
of Source Normalised Impact per Paper (SNIP) indicator Schiebel et al. (2010) and Roche et al. (2010) Publications Publication keywords initially labelled as “unusual terms”, by using tf-idf and Gini coefficient, that subsequently become “cross section terms”, i.e. they diffuse in several research domains Guo et al. (2011) Publications Indicators of emergence: frequency of keywords (ISI WoS keywords, authors’ keywords, and MeSH terms), growing number of authors, and interdisciplinarity (based year-average Rao-Stirling diversity index) of cited references Järvenpää et al. (2011) Mixed Absolute and cumulative count of the number of basic and applied research publications, patents, and news Abercrombie et al. (2012) Mixed Normalised number of publications and citations, patents, and web news fitted to a polynomial function Jun (2012) and Jun et al. (2014) News Normalised searching traffic (Google trends) Avila-Robinson and Miyazaki (2013b,a) Publications/patents Overview of indicators to analyse emergence de Rassenfosse et al. (2013) Patents Count of the priority patent applications filed by a country’s inventor, regardless of the patent
office in which the application is filed Ho et al. (2014) Publications Cumulative number of publications fitted to a logistic curve Citations analysis Direct citation Seminal paper: Garfield et al. (1964) Publications – Kajikawa and Takeda (2008), Kajikawa et al. (2008) and Takeda and Kajikawa (2008) Publications Clusters of publications with the highest average publication year Scharnhorst and Garfield (2010) Publications Historiographic approach combined with ‘field mobility’ of publications Shibata et al. (2011) Publications Clusters of publications with the highest values of betweenness centrality Iwami et al. (2014) Publications Publications (‘leading papers’) with high values of in-degree (‘height’), large variation of in-degree between one year and the next year (‘slope’), or large cumulative in-degree (‘area’) as defined on the basis of the yearly direct citation network Co-citation Seminal paper: Small (1973) Publications –
Small (2006) Publications Clusters with no continuing publications from the prior period Cho and Shih (2011) Patents Technological patent classes (IPC) that span structural holes in the co-citation network Érdi et al. (2012) Patents Clusters of patents present in a given time period and not in the previous period Boyack et al. (2014) Publications Yearly clustered publications of which references overlap less than 30% with references cited by previous clusters Bibliographic coupling Seminal paper: Kessler (1963) Publications – Morris et al. (2003) Publications Clusters of publications that cite more recent clusters of publications, namely emerging research fronts Kuusi and Meyer (2007) Patents Clusters of patents as source to identify guiding images (‘leitbild’) of technological development Co-word analysis Seminal paper: Callon et al. (1983) Publications – Lee (2008) Publications Clusters in the co-word network that show low values of degree, high betweenness, and low closeness, i.e. those clusters that are more likely to turn into hub in the future. Ohniwa et al. (2010) Publications MeSH terms (clustered with co-word analysis) that are included in the top-5% by incremental rate in a given year — the increment rate for a MeSH term is
defined as the number of time the terms occurred at the time t, t + 1, and t + 2 out the number of times the term occurred at t − 1, t, t + 1, and t + 2 Yoon et al. (2011) Patents Small and dense sub-networks in the ‘invention property-function’ network Furukawa et al. (2015) Publications Sessions of conferences in which previous sessions converge according to the average cosine similarity (based on tf-idf-identified keywords) between the papers included in the sessions Zhang et al. (2014) Publications Combination of cluster analysis with term clumping and principal component analysis Overlay mapping Rafols et al. (2010) Publications Overlays of publications projected on a basemap of ISI WoS subject categories linked by cosine similarity of co-citations patterns between journals Bornmann and Leydesdorff (2011) Publications Overlays of publications on Google maps to identify cities publishing more than expected D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 1835 Table 5 (Continued) Method/study Data Operationalisation of emergence
Leydesdorff and Rafols (2011) Publications Overlays of publications and co-authorship networks on Google maps to trace collaboration activity Leydesdorff et al. (2012) Publications Overlays of publications projected on a basemap of MeSH terms linked by cosine similarity (based on the co-occurrence of MeSH terms at the publication level) Leydesdorff and Bornmann (2012) Patents Overlays of patents on Google maps to identify cities patenting more than expected Leydesdorff et al. (2013) Publications Overlays of publications projected on the basemap of journals linked by cosine similarity of co-citations patterns between journals Kay et al. (2014) Patents Overlays of patents projected on the basemap of 466 IPC classes linked by cosine similarity of citing-to-cited relationships between classes — the basemap is built by using patents included in 2011 PATSTAT Leydesdorff et al. (2014) Patents Overlays of patents projected on the basemap of 124 3-digit or 630 4-digit IPC classes linked by cosine similarity based on co-citations between classes — the basemap is built by using patents granted at the United States Patent and Trademark Office (USPTO) from 1976 to 2011 Hybrid
Chen (2006): co-citation analysis and burst detection Publications Trends in the bipartite network of research-front terms (burst detection) and intellectual base articles — the network includes three types of links: co- occurring research front terms, co-cited intellectual base articles, and a research-front term citing an intellectual base article Leydesdorff et al. (1994): co-citation analysis and bibliographic coupling Publications New journals that build on multiple existing areas, i.e. they load on multiple factors obtained by the factor analysis of the matrix of the cited references, and have unique ‘being cited’ patterns, i.e. they are ‘central tendency journals’ reporting highest load on a given factor as obtained by the factor-analysis of the matrix of received citations Glänzel and Thijs (2012): co-word, direct citation analyses and bibliographic coupling Publications Existing clusters with exceptional growth, completely new clusters with roots in other clusters, and existing clusters with a topic shift Gustafsson et al. (2015): co-occurrence of IPC classes
Patents Technological co-classification to identify clusters of patents and detect guiding images or ‘leitbild’ from patent full-text Small et al. (2014): direct and co-citation analyses Publications Clusters of publications that show high growth and are new both to the direct citation and co-citation models Yan (2014): co-word analysis and topic modelling Publications Topics that are not a close variation of other topics, i.e. a topic i in the year t is emerging if no predecessors are found and no other topics are transformed into topic i at t + 1 Chang and Breitzman (2009), Breitzman and Thomas (2015): direct citation and co-citation analyses Patents Clusters of patents (co-citation clustering) that form around ‘hot’ patents — defined as those patents that are highly cited (top 5–10%) by patents issued in the last two years and the citations of which mostly come from patents issued in the last two years S refere p d p
s g b f w a w w a a e e g u W t e h r o b T r e e ource: search performed by authors on SCOPUS and extended to publication cited atterns between documents, co-occurrence of words in text), ata sources used (e.g. publications, patents, news articles), and roposed operationalisation of emergence. This information is ummarised in Table 5 where studies are grouped into five roups: (i) indicators and trend analysis studies that are mainly ased on document counts; (ii) citation analysis studies which ocus on examining citation patterns between documents; (iii)
co- ord analysis studies that build on the co-occurrence of words cross document text; (iv) overlay mapping technique studies, hich use projections to position a given set of documents ithin a wider or more global structure (e.g. a map of science); nd (v) hybrid studies that combine two or more of the above pproaches. Table 5 shows how definitions of emergence varied, ven within the same group of techniques, thus providing further vidence of the low level of consensus on what constitutes emer - ence. Given the definitional weaknesses in the original studies, our se of a particular study often varies from that of its authors. e will briefly introduce the major techniques and our interpre- ation of the contribution they make to measuring attributes of merging technologies. For each attribute, we will first describe ow it can be operationalised for contemporary and then for ret- ospective cases of emerging technologies. When data scarcity r the nature of the attribute of emergence limit the applica- ility of scientometrics, we will discuss qualitative approaches. he role of experts remains crucial for the validation of the esults obtained with the use of the techniques discussed below, specially for qualitative approaches to the operationalisation of mergence. nces. 4.1. Radical novelty Emerging technologies are radically novel, i.e. they fulfill a given function by using a different basic principle as compared to
what was used before to achieve a similar purpose. Publications and patents are of limited use in assessing radical novelty in contem- porary technology. In contrast, news articles, editorials, review and perspective articles in professional as well as academic journals represent valuable sources, providing participant perspectives on if and why a technology is viewed as radically novel. These docu- ments may also provide an understanding of the basic principles underpinning the examined technology. In contrast, in retrospective analyses citation and co-word anal- yses can be particularly effective for identifying radical novelty. Relatively large amounts of data can be exploited to map the cog- nitive networks of a knowledge domain over time. Citation analysis builds on citation patterns among documents to generate a network in which nodes are documents and links between nodes repre- sent (i) a direct citation between two documents (direct citation analysis) (Garfield et al., 1964), (ii) the extent to which two doc- uments are cited by the same documents (co-citation analysis) (Small, 1973), or (iii) to what extent two documents cite the same set of documents (bibliographic coupling) (Kessler, 1963). Co- word analysis instead exploits the text of documents to create a network of keywords (or key phrases) that are linked according to the
text to which they co-occur across the set of selected documents (Callon et al., 1983). On the premise that clusters of documents or words in these networks represent different knowledge areas of a domain or U0033385 Highlight U0033385 Highlight U0033385 Highlight 1 h Polic d h i a c t c t s o ( d t
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e s s g d c e R S l c S 836 D. Rotolo et al. / Researc ifferent literatures on which the domain builds, several studies ave considered the appearance of clusters not previously pres ent n the network as a signal of novelty (e.g. Érdi et al., 2012; Kajikawa nd Takeda, 2008). Others dispute this interpretation. Given the ontinuous evolution of science and technology, one is unlikely o find a cluster again in subsequent annual networks so the per- entage of clusters that would qualify as newly appearing tends o be relatively high. For this reason, additional criteria have been uggested such as the appearance of new clusters that also link therwise weakly connected (e.g. betweenness centrality) clusters e.g. Shibata et al., 2011; Furukawa et al., 2015), that form around ocuments that are highly cited by recent documents and the cita- ions of which also are mostly from recent documents (Breitzman nd Thomas, 2015), or that cite more recent clusters as identi- ed by the (Salton) similarity of their references (Morris et al.,
003). Small et al. (2014) have recently proposed a hybrid approach ased on a combination of direct citation and co-citation models as pplied to publication data. This approach is particularly focused on he detection of novelty, which is defined in terms of clusters that re new to the co-citation model — that is, clusters with limited verlap with the cited documents included in clusters in previ - us years (Boyack et al., 2014) — as well as to a parallel direct itation model. By combining bibliographic coupling, co-word anal- sis, and direct citation analysis, Glänzel and Thijs (2012) instead efined novelty (namely emerging topics) as three cases of clus - ers: those that show exceptional growth, those that are completely ew but with their roots in other clusters, or already existing ones hat exhibit a topic shift. Yan (2014) combined co-word analysis ith Natural Language Process (NLP) approaches (topic modelling). mergence, as reflected in novelty, is then associated with the ppearance of topics that are not a close variation of other topics alculated on the basis of the Jenson–Shannon Divergence.7 Specif- cally, a topic i appearing at time t is considered to be emerging f it has no predecessors and none of the identified topics trans - orms into topic i at t + 1. A different perspective is provided by charnhorst and Garfield (2010) who extended the analysis of his- oriographs (based on direct citations) to trace the extent to which
ublications move across fields as they receive citations from new elds (namely ‘field mobility’). Assuming that these publications re associated with a basic principle used for technological appli - ations, this approach enables one to identify which fields may be sing a different knowledge base and thus in which fields radi - ally novel technologies are potentially emerging. However, this equires a priori knowledge of the basic principle and the set of ocuments associated with it. Research in scientometrics has also focused on the develop- ent of techniques to expand the ‘local’ (domain) perspective that itation or text-based approaches may provide. This effort has gen- rated a number of overlay mapping techniques (for an overview ee Rotolo et al., 2014), which in turn may be particularly well uited to detecting radical novelty. The basic idea is to project a iven set of documents (e.g. publications associated with a research omain) on a basemap through the use of an overlay. The basemap an represent the ‘global’ science structure at the level of the sci- ntific discipline (ISI Web of Science (WoS) subject categories) (e.g. afols et al., 2010), journal (e.g. Leydesdorff et al., 2013), Medical ubject Headings (MeSH) (Leydesdorff et al., 2012), or the techno- ogical structure at the level of patent classes (e.g. Kay et al., 2014;
7 The Jenson–Shannon Divergence is a measure of similarity between empiri- ally determined distributions (e.g. co-occurrence of words in documents) based on hannon entropy measures (for more details see Lin, 1991). y 44 (2015) 1827–1843 Leydesdorff et al., 2014).8 Once the set of documents (publications or patents) associated with a given domain has been identified, the projection of these documents over different time slices on the global map of science or technology may reveal the increas- ing involvement of new scientific or technological areas. This may suggest that new knowledge areas are being accessed to conduct research, and thus that potentially different basic pri nciples are drawn upon to achieve a given purpose. Among the studies within the ‘indicators and trends’ group of techniques, Moed (2010) proposed the source normalised impact per paper (SNIP) indicator for the evaluation of journals’ impact and claims it is relevant for identifying emerging technologies. This indicator is defined as the ratio between the journal’s raw impact per paper (number of citations in the year of analysis to the jour- nal’s papers published in the three previous years, divided by the number of the journal’s papers in these three years) and the rela- tive database citation potential in the subject field covered by the
journal (mean number of 1–3-year-old references per paper citing the journal and published in journals included in the considered database divided by that for the median journal in the database). Moed (2010) argued that the SNIP indicator, and specifically high values of this indicator, also provides information on the extent to which a considered journal covers emerging topics. Given the focus on recent citations and database coverage, the SNIP indicator is clearly associated with the radical novelty attribute of emergence. This indicator is, however, evaluated at the aggregate level of the journal and journal-by-journal. It is therefore less clear whether signals of radical novelty (i.e. relatively high values of SNIP) are associated with one or multiple emerging topics the considered journal may cover. In addition, the SNIP may not capture signals of radical novelty in those instances of journals that cover few emerging topics and therefore characterised by low values of SNIP. All these techniques have various advantages and limitations. The qualitative analysis of news articles, editorials, review and per- spective articles, for example, may be effective for contemporary analyses. However, the technical language used in these documents may be an important barrier to a non-expert’s efforts to inde- pendently assess radical novelty. The application of citation and co-word analyses is strongly dependent on time. Data need to be longitudinal in order to permit the tracing of cognitive dynamics
and associated changes in the knowledge structure. Co-word anal- ysis and bibliographic coupling are, however, less sensitive to time than direct citation and co-citation analyses and can be applied as documents become available (e.g. Breitzman and Thomas, 2015). Finally, overlay mapping provides a global perspective on emer - gence for the assessment of radical novelty, but interpretation of the resulting maps is mainly based on visual inspection. 4.2. Relatively fast growth Emerging technologies show relatively fast growth rates com- pared to non-emerging technologies. The assessment of this attribute is particularly problematic for contemporary analyses. Growth is not yet observed in terms of publications and patents, for example, so scientometric indicators cannot be used. Early indica- tions of growth may be revealed from the analysis of funding data, big data, and altmetrics. This is an important research direction for future studies on the operationalisation of the relatively fast growth attribute, as we will discuss later in the paper. 8 The elements of the basemap are linked according to similarity based on the co-occurrence of citations or, in the case of MeSH, the co- occurrence of terms. The
same approach can be used to project a sample of publications and patents onto geo- graphical maps (e.g. Google maps) to reveal the most active cities and collaborative activities (see Table 5). U0033385 Highlight U0033385 Highlight h Polic p s n t b c e o t p o M i d a o f e p t
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1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J
1. List the three (3) requirements defining Liabilities 2. On J

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1. List the three (3) requirements defining Liabilities 2. On J

  • 1. 1. List the three (3) requirements defining Liabilities: 2. On July 1, 20X4, Working Dog, Inc., borrowed $20,000 on a four-year, 6% note payable. At December 31, 20X4, a journal entry should be made to record: 3. The employee is responsible for which payroll taxes? 4. Alpine Corporation sells $50,000 of goods, and you collect sales tax of 7%. What is the journal entry to record the transaction? 5. Technology Corporation has a lawsuit pending from a customer claiming damages of $128,000. Technology Corporation’s attorney advises that the likelihood the customer will win is reasonably possible. How is this contingent liability reported? 6. Cooper Company owed Estimated Warranty Payable of $2,200 at the end of 20X4. During 20X5, Cooper Company made sales of $280,000 and expects product warranties to cost the company 3% of the sales. During 20X5, Cooper Company paid $5,000 for warranties. What is Cooper Company’s estimated warranty payable at the end of 20X5? 7. At December 31, Haute Hardware owes employees for four days of the five-day workweek. The total payroll for the week is $51,000. What journal entry should you make at December 31? 8. Yolanda’s Yoga Studio has Unearned Revenue of $15,000, Salaries Payable of $28,000, and Allowance for Uncollectible Accounts of $4,200. What amount would Yolanda’s Yoga Studio report as total current liabilities? 9. A five-year, $100,000, 4% note payable was issued on December 31, 20X4. The note requires principal payments of $20,000 plus interest due each year beginning December 31, 20X5. The entry to record the annual payment at the end of year two on December 31, 20X6,: 10. Bon Voyage Company’s trial balance shows $800,000 face value of bonds with a discount balance of $12,000. The bonds mature in 20 years. How will the bonds be presented on the
  • 2. balance sheet? 11. Rivers and Streams Corporation issued $400,000 of 8% serial bonds at face value on December 31, 2018. Half of the bonds mature January 1, 2021, while the other half of the bonds mature January 1, 2029. On December 31, 2020, the balance sheet will show which of Bonds Payable? 12. Nolan Corporation has total assets of $500,000. Current liabilities are $10,000, and long-term liabilities are $90,000. What is the debt to equity ratio? 13. A $400,000 bond priced at 102 can be bought or issued for: 14. Cooper Corporation issued its 4%, 20-year bonds payable at a price of $288,500 (face value is $300,000). The company uses the straight-line amortization method for the bonds. Interest expense for each year is: 15. Skinny Smoothies has $850,000 of 20-year bonds payable outstanding. These bonds had a discount of $42,000 at issuance, which was 8 years ago. The company uses the straight-line amortization method. The carrying amount of these bonds payable today is: 16. What is the correct journal entry to record the issuance of a $250,000 face value bond at 95? 17. Hughes Industries signed a 20-year note payable on January 1, 2018. The note requires annual principal payments plus interest. The entry to record the annual payment on December 31, 2018 includes a _______ to Interest Expense 18. Yes or No, Is Mutual Agency a characteristic of Corporations? 19. What is the effect of the purchase of treasury stock on the number of shares issued? 20. Backyard Emporium issued 500,000 shares of $1 par common stock at $5 per share. What journal entry correctly records the issuance of this stock? 21. Two years ago, Tonya Williams purchased a building for $210,000. This year, Williams gave the building, which now has a current market value of $240,000, to Beyond Corporation in exchange for 5,000 shares of $10-par common stock. What
  • 3. journal entry by Beyond Corporation correctly records the issuance of this stock? 22. Good For You Foods has outstanding 6,000 shares of $3 par common stock, which was issued at $15 per share, and 2,000 shares of $10 par cumulative preferred stock, which was issued at par. Good For You Foods also has a deficit balance in Retained Earnings of $26,000. How much is Good For You Foods’ total stockholders’ equity? 23. Happy Pets Corporation has 10,000 shares of 5%, $20 par noncumulative preferred stock, and 37,000 shares of common stock outstanding. Ink declared no dividends in 20X4. In 20X5, Ink declares a total dividend of $54,000. How much of the dividends go to the common stockholders? 24. True or False, Stock Splits increase the number of shares of stock issued while decreasing par value per share. 25. Retained Earnings can be subject to appropriation by whom? Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy
  • 4. Lecture 4 Week 4 Structure of the session: Ground Rules What did we cover last two weeks? Aim and intended learning outcomes of this week A bit of lecture Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey Ground Rules for today: There is no wrong and right answer to my questions. Any answer is much appreciated. Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What is the aim of the lecture and workshop in Week 4?
  • 5. To develop a multi-aspectual understanding of types of human- machine collaboration for the enterprise. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for week 4? Why do we need multi-aspectual understanding? What do we mean by multi-aspectual understanding? How can we develop such an understanding of human-machine collaboration? Human Use of Digital Technologies EIT : Engaging with Interface & Technology or HCI: Human- Computer Interaction. EMC: Engaging with Meaningful Content or ERC: Engaging with Represented Content. ELI : Engaging in Life with ICT (Information & Communication Technology) or HLC: Human Living with Computers.
  • 7. Can Human-Machine Collaboration and Human Use of Digital Technologies be explained through Socio-Technical Approach? 12 13
  • 8. Yes, we can take a Socio-Technical Approach and explain for Human-Machine collaboration and Human-Use of Emergent Technologies. But is that sufficient? Can we explain everything through this approach? 14 Critical Research in Information Systems tell us that the social world is unjust and prevents individuals from living up to their potential. Current social structures are problematic. Walsham (2005), Stahl (2007) 15 Lee (2004) In Socio-Technical Approach , Information has not been seen as important as social and technological factor!
  • 9. 16 We need to know a way to explain for the reality of Human Use of Emergent Technologies and Human-Machine Collaboration 17 18
  • 10. Dooyeweerd Aspects Aspects Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical Pistic Kernel Meaning Discrete amount Continuous space Movement Energy + Mass, Forces Life functions + Organisms Sense, Feeling, Emotion Distinction, Conceptualization Achievement, History, Technology Meaning carried by symbols “We”: Relationships, Roles
  • 11. Frugal management of resources Harmony, play, enjoyment Due: Responsibilities + Right Self-giving love, generosity Vision, Motivation, Belief, aspiration 20 Use Aspects to Analyse some of Digital Divide Issues A simple example of Dooyeweerd’s philosophy application 21 Aspects Down-to-earth Issues? Quantitative Frequent of usage? For how long? Spatial At trusted public places or/and at home? Kinematic What is the internet speed like? PhysicalFor elderly people, can they hold tablets, mobile, laptops in their hand? What happens if they drop it? Biotic Are we providing with having different age range in mind?Psychic/SensitiveAre we providing with having visually and hearing impaired people in mind? Is flashing red, blue, green light on Wi-Fi hub helping? Analytic Are they really clear about what to expect? Clear about internet package they receive? FormativeDo they understand what they can achieve with internet?
  • 12. LingualCan we translate a technical term for a lay person or we just say “don’t worry about it just press this button every time”? SocialDo we have the right social skills on the front line (our digital champions) to connect with them and build the essential rapport? EconomicNot paying for mobile phone plan means no hotspot to connect other devices? How much time is enough for assisting them? AestheticHow the internet access is harmonised with the rest of their life? How is it made enjoyable for them? JuridicalDo they know with a simple search they are exposed to number of opaque AI algorithm? How about Data Protection? Data Privacy? EthicalDo we know when and how we can donate our unused mobile data? Pistic Is giving internet access really for helping them? what is our real motivation? What other issues can you think of? Benefits! – If you have 100 of these , how would you sort them out? Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical
  • 13. Pistic Think better Gain confidence Communicate with loved ones Save time Enjoy sharing photos Achieve more Aspects can help us with managing the diversity of things, a heterogeneous list. 23 Core Message: The aspects help to separate thing out without reducing one to another. In a real-life complex situation aspects help with bringing up things that are often taken for granted. References: Stahl, B.C., 2007. ETHICS, Morality and Critique: An Essay on Enid Mumford¡¯ s Socio-Technical Approach. Journal of the Association for Information Systems, 8(9), p.28. Walsham, G., 2005. Learning about being critical. Information Systems Journal, 15(2), pp.111-117.
  • 14. Lee, A.S., 2004. Thinking about social theory and philosophy for information systems. Social theory and philosophy for information systems, 1, p.26. https://dooy.info/aspects.html Your feedback on the Lecture 4 is much appreciated. https://forms.office.com/r/9BqP4FPdQK It takes less than 5 minutes. Thank you 27
  • 15. Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 2 Week 2 Structure of the session: Ground Rules What did we cover last week? Aim and indented learning outcomes A bit of lecture Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey Ground Rules for today: There is no wrong and right answer to my questions. Any answer is much appreciated. Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What was the aim of the lecture and workshop in Week 1?
  • 16. To recognise the importance of the concept of “emerging technology”. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What were the intended learning outcomes in week 1? To define an “emergent technology”. To discuss what do we mean by “emergent”? To identify and argue what qualify a technology to be “emergent”? What is the aim of the lecture and workshop in Week 2? To discuss the nature of digital technologies from two perspectives of being-in-itself and being-in-the-world. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for this week? What do we mean by being-in-itself?
  • 17. What do we mean by being-in-the-world? What are the 5 exponential digital technologies and their use cases? Being-in-itself Being-in-itself answer tries to find the essence of X, which can fully explain X, or from which all about it can be derived. Example: To say “a calculator is a machine that process numbers” Being-in-the-world Being-in-the-world answers the question by looking at the context that forms the thing. Example: To say “this iPhone is my communication lifeline” 5 Exponential Digital Technologies Artificial Intelligence Smart Processing Automation Blockchain Robots Special-Function Technologies Saldanha (2019)
  • 18. 10 What is the “use case”? An application of specific tool to a given problem. Saldanha (2019) 11 What is the “use case”? Now you give an example 12 Artificial Intelligence What is Artificial Intelligence?
  • 19. 14 15 Being-in-itself Artificial intelligence is defined as a “computing systems that
  • 20. are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks” (Popenici & Kerr, 2017 cited in Crompton 2021). Being-in-the-world Example: Predictive Maintenance in Manufacturing. To say “just what we need to avoid another plant shutdown” Smart Process Automation What is Smart Process Automation?
  • 21. 19 What is RPA? Can you find a definition and cite it for me? Can you find me an Use Case of RPA? Blockchain What is Blockchain?
  • 22. 24 Being-in-itself The blockchain is defined as ”a distributed database of records, or public ledger of all transactions or digital events that have been executed and shared among participating parties” Atlam et al (2018) Being-in-the-world
  • 23. Example: Emerging market Solar Energy Sharing. In Estonia: WePower has been testing just how well a choice-driven energy market could work by teaming up with an independent energy provider who shares their energy data in real time. It means “being independent” Robotics What is robotics? Being-in-itself International Federation of Robotics (IFR) define a robot as being “a machine which can be programmed to perform tasks
  • 24. which involve manipulative and, in some cases, locomotive actions under automatic control.” Yahya et al (2019) Being-in-itself Robotics is defined as “the engineering science and technology of robots, and their design manufacture, application, and structural disposition and related to electronics, mechanical, and software.” Yahya et al (2019) Being-in-the-world Example: Any task that needs physical seeing, sensing, assisting, moving, measuring or delivering is fair game for robotics. To say “it is so convenient”. 33
  • 25. Special-Function Technologies What is 3D Printing, Internet of Things,Nanotech, Energy Storage, Biotechnology and advanced materials? 35 36 Being-in-itself
  • 26. A special-purpose sensor that measures and transmits necessary information and can be made smart enough to make some decisions. Saldanha (2019) Being-in-the-world Example: A Nest thermostat does measure and transmit necessary information to do with temperature, carbon monoxide and video image information. “It reduces cost” What is the aim of the lecture and workshop in Week 2? To discuss the nature of digital technologies from two perspectives of being-in-itself and being-in-the-world. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for this week?
  • 27. What do we mean by being-in-itself? What do we mean by being-in-the-world? What are the 5 exponential digital technologies and their use cases? Your feedback on the Lecture 2 is much appreciated. Link to the feedback form. https://forms.office.com/r/Tzi3EQyTVq It takes less than 5 minutes. References: Yahya, M.Y.B., Hui, Y.L., Yassin, A.B.M., Omar, R., anak Robin, R.O. and Kasim, N., 2019. The Challenges of the Implementation of Construction Robotics Technologies in the Construction. In MATEC Web of Conferences (Vol. 266, p. 05012). EDP Sciences. Saldanha, T., 2019. Why digital transformations fail: The surprising disciplines of how to take off and stay ahead.
  • 28. Berrett-Koehler Publishers. https://www.forbes.com/sites/jamesellsmoor/2019/04/27/blockc hain-is-the-next-big-thing-for-renewable- energy/?sh=e3470c948c1b Atlam, H.F., Alenezi, A., Alassafi, M.O. and Wills, G., 2018. Blockchain with internet of things: Benefits, challenges, and future directions. International Journal of Intelligent Systems and Applications, 10(6), pp.40-48. Keys, B. and Zhang, Y.J., 2020. Introducing RPA in an Undergraduate AIS Course: Three RPA Exercises on Process Automations in Accounting. Journal of Emerging Technologies in Accounting, 17(2), pp.25-30. Crompton, H., 2021. The Potential of Artificial Intelligence in Higher Education. Revista Virtual Universidad Católica del Norte, 62. Thank you 43
  • 29. Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 3 Week 3 Dr Sina Joneidy 11/10/2021 is happy I wish everyone Structure of the session: Ground Rules What did we cover last two weeks? Aim and indented learning outcomes of this week A bit of lecture Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey
  • 30. Ground Rules for today: There is no wrong and right answer to my questions. Any answer is much appreciated. Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What was the aim of the lecture and workshop in Week 1? To recognise the importance of the concept of “emerging technology”. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What were the intended learning outcomes in week 1? To define an “emergent technology”. To discuss what do we mean by “emergent”? To identify and argue what qualify a technology to be “emergent”?
  • 31. What was the aim of the lecture and workshop in Week 2? To discuss the nature of digital technologies from two perspectives of being-in-itself and being-in-the-world. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What were the intended learning outcomes for week 2? What do we mean by being-in-itself? What do we mean by being-in-the-world? What are the 5 exponential digital technologies and their use cases? What is the aim of the lecture and workshop in Week 3? To identify types of human-machine collaboration for the enterprise and discuss the implications for the managers and workers. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1. What are the intended learning outcomes for this week?
  • 32. What do we mean by instrumenting the human? What do we mean by socialising the machine? What are the types of human-machine collaborations in the enterprise? What are the implications of instrumenting the human and socialising machine for the managers and workers? Disruptive Technologies Transformation the future of workplace Can AI Replace Human? 12
  • 33. Can AI Replace Human? 13 We want to re-imagine the work processes in the context of mutual human-machine collaboration. 14 to optimise the blend of human–machine participation and interaction within the digital workplace.
  • 34. Augmentation! 15 Work in greater harmony together. 16
  • 35. Mapping the division of labour: human–machine collaboration 17 Human–machine work scenarios where machines augment humans and vice versa 18 ‘Physical–physical’ – meaning both humans and machines play a physical role such as caregivers working with smart mobile robots to deliver medicines and supplies in hospitals. 19
  • 36. Physical – Physical Health Care Tug Autonomous Mobile Robot An example of Socialising Machin ‘Physical–virtual’ – meaning humans play a physical role and machines play a virtual role at the point where work is performed, such as warehouse employees using smart glasses for navigation and picking instructions to boost productivity. 22 Physical – Virtual DHL Smart Glasses An example of Instrumenting Human
  • 37. ‘Virtual–physical’ – meaning humans play a virtual role at the point work is performed and machines play a physical role such as doctors performing telepresence surgery. 25 Virtual – Physical Health Care Da Vinci Surgical System An example of Instrumenting Human ‘Virtual–virtual’ – meaning both humans and machines play a virtual role such as in call centres, with human agents working in tandem with virtual cognitive
  • 38. agents. 28 Virtual – Virtual Virtual Service Desk Amelia Cognitive Agent An example of Socialising Machin Implications for managers
  • 39. 31 Use the 2X2 Matrix Be flexible Find the sweet spot for human–machine collaboration based on the nature of the work. 32 Implications for workers
  • 40. 33 Be flexible Be adaptable 34 What is the aim of the lecture and workshop in Week 3? To identify types of human-machine collaboration for the enterprise and discuss the implications for the managers and workers. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 1.
  • 41. What are the intended learning outcomes for this week? What do we mean by instrumenting the human? What do we mean by socialising the machine? What are the types of human-machine collaborations in the enterprise? What are the implications of instrumenting the human and socialising machine for the managers and workers? Your feedback on the Lecture 3 is much appreciated. It takes less than 5 minutes.
  • 42. References: Evans, N.D., 2017. Mastering digital business. BCS Learning&D Thank you 39 Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 7
  • 43. Week 7 Structure of the session: Aim and intended learning outcomes of this week. Linking your learning to the assessment. A bit of lecture and watching videos. Individual interactive activity. A bit of hint for your assessment. What is the aim of the lecture and workshop in Week 7? To develop a multi-modal understanding of types of human- machine collaboration for the enterprise. Linked to ECA Part 1 & ECA Part 2 – where you need to demonstrate multi-modal analysis What are the intended learning outcomes for week 7? To run a multi-modal analysis of your everyday life experience. To run a multi-modal analysis on issues and benefits of human- machine collaboration.
  • 44. Link to the ECA Part 2 – 80% 6
  • 45. Dooyeweerd Aspects Aspects Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical Pistic Kernel Meaning Discrete amount Continuous space Movement Energy + Mass, Forces Life functions + Organisms Sense, Feeling, Emotion Distinction, Conceptualization Achievement, History, Technology Meaning carried by symbols “We”: Relationships, Roles Frugal management of resources Harmony, play, enjoyment Due: Responsibilities + Right Self-giving love, generosity Vision, Motivation, Belief, aspiration
  • 46. 8 An extensive introduction of aspect 9 10 Use Dooyeweerd’s Aspects for analysing your Halloween
  • 47. experience 11 How your experience of Halloween is meaningful from Dooyeweerd’s perspective? Human-Machine Collaborations Issues Quantitative Spatial Kinematic Physical Biotic Sensitive Analytic Formative Lingual Social Economic Aesthetic Juridical Ethical Pistic Lack of Trust Not enough Visibility
  • 48. Computer Anxiety Argument for change Gender inequality Aspects can help us with managing the diversity of things, a heterogeneous list. Low Self-efficacy Lack of transitional support Lack of compatibility Lack of facilitating condition Access cost Job Insecurity Negative social influence Lack of training 13 Human-Machine Collaborations Issues 14 References: Stahl, B.C., 2007. ETHICS, Morality and Critique: An Essay on Enid Mumford¡¯ s Socio-Technical Approach. Journal of the Association for Information Systems, 8(9), p.28. Walsham, G., 2005. Learning about being critical. Information Systems Journal, 15(2), pp.111-117.
  • 49. Lee, A.S., 2004. Thinking about social theory and philosophy for information systems. Social theory and philosophy for information systems, 1, p.26. Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐ analysis of the TAM: Part 1. Journal of Modelling in Management. https://dooy.info/aspects.html Thank you 16 Emerging Technologies for the Enterprise BIN3025 Module Leader & Tutor Dr Sina Joneidy Lecture 5 Week 5
  • 50. Your Voice Matters Your feedback is vital and we are listening. We want to know what is working well, and also any concerns you may have. Final year undergraduate students will receive an online survey about your experiences so far. Please take the time to complete this survey and help us further improve your student experience. #YourVoiceMatters tees.ac.uk/studentvoice #YourVoiceMatters This video provides a summary of the Your Voice Matters campaign. tees.ac.uk/studentvoice #YourVoiceMatters Structure of the session: Ground Rules Aim and outcomes of this week A bit of lecture
  • 51. Questions for you A bit of lecture and watching videos Review of the aim and intended learning outcomes Preparation for the workshop Feedback Survey Ground Rules for today: Running question: How am I going to use the learning from today’s lecture in my assessment? Respect yourself, your peers and your instructor by being present in the session. Your engagement influence your success in the assessment! Believe me ;) Keep the noise level down. When you write a feedback for me at the end, please make it constructive. What is the aim of the lecture and workshop in Week 5? Introduce you the role and impotence of Digital Business Models at Enterprise. Linked to ECA Part 1 & ECA Part 2 Assessment Criteria 2. What are the intended learning outcomes for week 5? To define the concept of digital business model
  • 52. To describe the importance of key dimensions in shaping digital business models. To distinguish between different digital business models using the four key dimensions we are introducing in the session. Digital Business Models 10 Joneidy, Sina (JS) - What is your Digital Business Model? 12 Joneidy, Sina (JS) -
  • 53. What is your digital business model? A 2X2 Matrix What is your digital business model? What is your digital business model? References P. Weill and S.L. Woerner, "What's Your Digital Business
  • 54. Model?: Six Questions to Help You Build the Next- Generation Enterprise", Harvard Business School Press Books, 2018, p. 1. Your feedback on the Lecture 4 is much appreciated. https://forms.office.com/r/SwZare95L9 It takes less than 5 minutes. Thank you 20 W
  • 56. g t c e b i c 2 S d h 0 Research Policy 44 (2015) 1827–1843 Contents lists available at ScienceDirect Research Policy j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / r e s p o l hat is an emerging technology? aniele Rotolo a,b,∗ , Diana Hicks b, Ben R. Martin a,c SPRU – Science Policy Research Unit, University of Sussex, Brighton BN1 9SL, United Kingdom School of Public Policy, Georgia Institute of Technology, Atlanta 30332-0345, United States Centre for Science and Policy (CSAP) and Centre for Business Research, Judge Business School, University of Cambridge, Cambridge CB2 1QA, nited Kingdom
  • 57. r t i c l e i n f o rticle history: eceived 11 December 2014 eceived in revised form 15 June 2015 ccepted 16 June 2015 vailable online 9 August 2015 eywords: merging technologies onceptualisation efinition ttributes of emergence a b s t r a c t There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet, as an area of study, emerging technologies lack key foundational ele- ments, namely a consensus on what classifies a technology as ‘emergent’ and strong research designs that operationalise central theoretical concepts. The present paper aims to fill this gap by developing a definition of ‘emerging technologies’ and linking this conceptual effort with the development of a frame- work for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of ‘emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent
  • 58. impact, and (v) uncertainty and ambiguity. The perationalisation etection and analysis ramework cientometrics ndicators cience and Technology Studies (STS) framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indica- tors and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence. © 2015 Elsevier B.V. All rights reserved. . Introduction Emerging technologies have been the subject of much debate n academic research and a central topic in policy discussions and nitiatives. Evidence of the increasing attention being paid to the henomenon of emerging technologies can be found in the grow - ng number of publications dealing with the topic and news articles entioning emerging technologies (in their headlines or lead para- raphs), as depicted in Fig. 1. Increasing policy interest in emerging echnologies, however, must be set against a literature where no
  • 59. onsensus has emerged as to what qualifies a technology to be mergent. Definitions proposed by a number of studies overlap, ut also point to different characteristics. For example, certain def- nitions emphasise the potential impact emerging technologies are apable of exerting on the economy and society (e.g. Porter et al., 002), especially when they are of a more ‘generic’ nature (Martin, ∗ Corresponding author at: SPRU – Science Policy Research Unit, University of ussex, Brighton BN1 9SL, United Kingdom. Tel.: +44 1273 872980. E-mail addresses: [email protected] (D. Rotolo), [email protected] (D. Hicks), [email protected] (B.R. Martin). ttp://dx.doi.org/10.1016/j.respol.2015.06.006 048-7333/© 2015 Elsevier B.V. All rights reserved. 1995), while others give great importance to the uncertainty asso- ciated with the emergence process (e.g. Boon and Moors, 2008) or to the characteristics of novelty and growth (e.g. Small et al., 2014). The understanding of emerging technologies also depends on the analyst’s perspective. An analyst may consider a technology emer- gent because of its novelty and expected socio-economic impact, while others may see the same technology as a natural extension of an existing technology. Also, emerging technologies are
  • 60. often grouped together under ‘general labels’ (e.g. nanotechnology, syn- thetic biology), when they might be better treated separately given their different socio-technical features (e.g. technical difficulties, involved actors, applications, uncertainties). The lack of consensus over definitions is matched by an ‘eclec- tic’ and ad hoc approach to measurement. A wide variety of methodological approaches have been developed, especially by the scientometric community, for the detection and analysis of emer- gence in science and technology domains (e.g. Porter and Detampel, 1995; Boyack et al., 2014; Glänzel and Thijs, 2012). These methods, favoured, because they take advantage of growing computational power and large new datasets and allow one to work with more sophisticated indicators and models, lack strong connections to well thought out concepts that one is attempting to measure, a basic dx.doi.org/10.1016/j.respol.2015.06.006 http://www.sciencedirect.com/science/journal/00487333 http://www.elsevier.com/locate/respol http://crossmark.crossref.org/dialog/?doi=10.1016/j.respol.2015 .06.006&domain=pdf mailto:[email protected] mailto:[email protected] mailto:[email protected]
  • 61. dx.doi.org/10.1016/j.respol.2015.06.006 1828 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 Year N u m b e r o f p u b lic a tio n s 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 0 5
  • 64. m b e r o f n e w s a rt ic le s News articles Publications in all disciplines Publications in social sciences Fig. 1. Publications (left axis) and news articles (right axis) including the variations of the term “emerging technologies”. Publications were retrieved by querying SCOPUS: “TITLE(“emerg* technol*”) OR TITLE(“emergence of* technolog*”) OR TITLE(“techn* emergence”) OR TITLE(“emerg* scien* technol*”)”. Publications in social sciences were defined as those assigned to the SCOPUS categories “Business, Management and Accounting”, “Decision Sciences”, “Economics, Econometrics and Finance”, “Multidisciplinary”, “Psychology”, and “Social Sciences”. News articles were
  • 65. identified by searching for “emerg* near2 technolog*” in article headlines and lead paragraphs as reported in FACTIVA. F conc 1 ly gro S t c f t o I a d n g a a n o c t n c u o t o g o c s i
  • 66. a a i r rom 1980 to 2013, the average yearly growth rates of the number of publications 2.5% and 23.8%, respectively. The total number of publications in SCOPUS has year ource: search performed by authors on SCOPUS and FACTIVA. enet of good research design. Often no definition of the central con- ept of an emerging technology is provided. It is no surprise there- ore that approaches to the detection and analysis of emergence end to differ greatly even with the use of the same or similar meth- ds. The operationalisation of emergence is also in a state of flux. t changes as new categorisations (e.g. new terms in institution- lised vocabularies, new technological classes) are created within atabases. This, in turn, makes less clear the exact nature of the phe- omena that these scientometric methods enable us to examine. These problems in the effort to understand emerging technolo- ies limit the utility of the research and so may hamper resource llocation and the development of regulations, which, in turn, have major role in supporting and shaping the directionality of tech- ological emergence.
  • 67. The present paper addresses both the conceptual and method- logical gaps. We aim to elaborate a framework that links what is onceptualised as ‘emerging technologies’ with its measurement, hus providing guidance to future research (e.g. development of ovel methods for the detection of emergence and analysis of its haracteristics) and to policy-making (e.g. resource allocation, reg- lation). To do so, we first attempt to clarify the conceptualisation f emerging technologies by integrating different conceptual con- ributions on the topic into a more precise and coherent definition f ‘emerging technology’. We begin with the definition of ‘emer- ence’ or ‘emergent’, which is the process of coming into being, or f becoming important and prominent. This is then enriched and ontextualised with a review of major contributions to innovation tudies that have focused on technological emergence, highlight- ng both their common and contradictory features. Conceptual ttempts to grapple with emergence in complex systems theory are lso discussed where relevant to the idea of emergent technology. The result is the delineation of five key attributes that qual - fy a technology as emerging. These are: (i) radical novelty, (ii) elatively fast growth, (iii) coherence, (iv) prominent impact, and erning emerging technologies in all disciplines and in social sciences have been of wn on average by 4.9%.
  • 68. (v) uncertainty and ambiguity. Specifically, we conceive of an emerging technology as a radically novel and relatively fast growing technology characterised by a certain degree of coherence persisting over time and with the potential to exert a considerable impact on the socio-economic domain(s) which is observed in terms of the compo- sition of actors, institutions and patterns of interactions among those, along with the associated knowledge production processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous. Second, the framework for operationalising emerging technolo- gies is developed on the basis of the attributes we identified. The scientometric literature forms the core of the methods dis - cussed because, as mentioned, this field has been remarkably active in developing methodologies for the detection and analysis of emergence in science and technology. The reviewed methods are grouped into five main categories: (i) indicators and trend analysis, (ii) citation analysis (including direct citation and co-citation anal- ysis, and bibliographic coupling), (iii) co-word analysis, (iv) overlay mapping, and (v) hybrid approaches that combine two or more of the above. Because scientometric techniques cannot address all the attributes comprehensively, we also discuss approaches developed
  • 69. in other fields. The paper is organised as follows. The next section introduces the concept of emergence and its various components. In Sec- tion 3, these elements are integrated with key innovation studies proposing definitions of technological emergence, and a definition of emerging technologies is then elaborated. Section 4 reviews methods to both detect and analyse emergence, and then examines the use of those approaches to operationalise the proposed defini- tion and the various attributes of emerging technologies. Section 5 discusses the limits of current methodologies for the detection and analysis of emerging technologies and identifies directions for future research. Section 6 summarises the main conclusions of the study. U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight
  • 70. D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 1829 Table 1 Dictionary definitions of the concept of emergence. Dictionary definition of ‘emerge’/‘emergent’ Attributes “the process of coming into being, or of becoming important and prominent” (New Oxford American Dictionary) come into being; important; prominent “to become manifest: become known [. . .]” (Merriam- Webster’s Collegiate Dictionary) become manifest; become known “to rise up or come forth [. . .] to become evident [. . .] to come into existence” (The American Heritage Desk Dictionary and Thesaurus) evident; come into existence “move out of something and become visible [. . .] come into existence or greater prominence [. . .] become known [. . .] in the process of coming into being or prominence” (Concise Oxford English Dictionary) visible; prominent; become known; come into being “starting to exist or to become known [. . .] to appear by coming out of something or out from behind become known; to appear
  • 72. s e G n p o d p t ( i o ( ( s c to identify those that develop or provide definitions of emerg- ing technologies — we searched for ‘defining’ sentences within the publication full-text by using the keywords listed above. This 1 The terminology of ‘emerging technologies’ has become central to a number of research traditions and especially to the scientometric, bibliometric and tech- mining domains (cf. Avila-Robinson and Miyazaki, 2011), which, as discussed, have something” (Cambridge Dictionaries Online) ource: search performed by authors on major English dictionaries. . The concept of emergence
  • 73. The word ‘emerge’ or ‘emergent’ means “the process of coming nto being, or of becoming important and prominent” (New Oxford merican Dictionary) or “to rise up or come forth [. . .] to become vident [. . .] to come into existence” (The American Heritage Desk ictionary and Thesaurus). Table 1 presents dictionary definitions f emergent. The primary attribute of emergence is ‘becoming’ — hat is, coming into existence. Emergent is not a static property; t is a label for a process. The endpoint of the process is variously escribed as visible, evident, important or prominent. Thus, among he dictionaries there is some disagreement as to whether acknowl- dged existence is enough for emergence, or beyond that, a certain evel of prominence is needed in order to merit application of the erm emergence. There is a second definition of emergent given the by the New xford American Dictionary as: a property arising as an effect of omplex causes and not analysable simply as the sum of their ffects. An additional definition is: arising and existing only as a henomenon of independent parts working together, and not pre- ictable on the basis of their properties. This concept of emergence s used in the study of complex systems. It can be traced back o the 19th Century in the proto-emergentism movement when ewes (1875) referred to ‘emergent effects’ in chemical reactions s those effects that cannot be reduced to the components of the ystem, i.e. the effects for which it is not possible to trace all the
  • 74. teps of the processes that produced them. Its application in the tudy of the dynamics of complex systems in physics, mathemat- cs, and computer science gave rise to other fundamental theories nd schools of thought such as complex adaptive system theory, on-linear dynamical system theory, the synergetics school, and ar-from-equilibrium thermodynamics (see Goldstein, 1999). A number of studies focusing on the definitional issue of emer - ence were produced by scholars in complex system theory — ee Table A1 in Appendix for an overview of the definitions of mergence proposed by major studies in complex system theory. oldstein (1999), for example, defined emergence as “the arising of ovel and coherent structures, patterns, and properties during the rocess of self-organization in complex systems” (1999, p. 49). An ntological and epistemological definition of emergence is instead eveloped by de Haan (2006). Ontological emergence is “about the roperties of wholes compared to those of their parts, about sys - ems having properties that their objects in isolation do not have” 2006, p. 294), while epistemological emergence it is about “the nteractions between the objects that cause the coming into being f those properties, in short the mechanisms producing novelty” 2006, p. 294). Though research on complex systems may have a certain cachet and perhaps for this reason scholars of emerging technologies ometimes attempt to work with the meaning of emergent as onceived by the complex system approach), we maintain that questions about emerging technologies are not fundamentally about understanding the origins and the causal nature of full system
  • 75. interaction; rather they are about uncertainty, novelty, identifica- tion at an early stage, and visibility and prominence. It is true that some technologies in themselves may be complex systems in the sense of exhibiting adaptation, self-organisation, and emergence, an example being parts of materials science (Ivanova et al., 1998). However, other technologies exhibit ‘complicatedness’ rather than ‘complexity’ as defined in complex system theory — for example, engineering systems. These systems are designed for specific pur- poses, but they do not adapt and self-organise to changes in the environment (Ottino, 2004). It is also true that emerging tech- nologies may arise from complex innovation systems (Katz, 2006), but we would contend that in the phrase ‘emerging technology’, ‘emerging’ is generally understood in the standard sense, not the complex system usage. 3. Defining emerging technologies To further clarify what is meant by emerging technology, we reviewed literature in innovation studies dealing with definitional issues of emerging technologies. To identify relevant studies, we searched for “emerg* technolog*”, “tech* emergence”, “emergence of* technolog*”, or “emerg* scien* technol*” in publication titles
  • 76. by querying SCOPUS (see the left-hand column of Table 2).1 We restricted the search to the title field to limit results to publications primarily focused on emerging technologies. The search identified a total of 2201 publications from 1971 to mid 2014.2 Within this sample we selected those publications in social science domains, thus reducing the sample to 501 records (see Fig. 1). We then read the abstracts and accessed the full-text of these studies where necessary both to identify additional documents from the list of cited references and to exclude studies that are not relevant to the scope of this paper. We found that about 50% of the studies in the sample refer to a specific industrial context (e.g. listing and discussing emerging technologies in a given industry) or to the educational sector (e.g. emergence of novel technologies to improve education and learning). These were deemed not rele- vant to our study. The remaining studies were further examined been remarkably active in developing methods for the operationalisation of emer- gence. In other words, ‘emerging technologies’ have become a category of its own. For this reason, we do not include epistemologically related terms, such as ‘radical’, ‘disruptive’, ‘discontinuous’, ‘nascent’ and ‘breakthrough’. 2 The search was performed on 13th May 2014.
  • 78. U0033385 Highlight 1830 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 Table 2 Search strategies used to identify the set of relevant publications for the conceptualisation and operationalisation of emerging technologies. Conceptualisation Operationalisation Search terms “emerg* technolog*” “emerg* technolog*” “tech* emergence” “tech* emergence” “emergence of* technolog*” “emergence of* technolog*” “emerg* scien* technol*” “emerg* scien* technol*” “emerg* topic*” “emergence of* topic*” Field(s) of search Title Title, abstract, keywords Focus Social sciences Scientometric journals: Journal of the Association for Information Science &Technology (formerly the Journal of the American Society for Information Science &Technology), Journal of Informetrics, Research Evaluation, Research Policy, Scientometrics, Technological Forecasting &Social Change, Technology Analysis &Strategic Management S l
  • 79. p m e t i T T D S Number of studies 501 155 ource: authors’ elaboration as based on SCOPUS data. ed to a core set of 12 studies from science and technology (S&T) olicy studies, evolutionary economics, management, and sciento- etrics that contributed to the conceptualisation of technological mergence. These are listed with their definitions of emerging echnologies in Table 3. We analysed the textual content of the def- nitions reported in Table 3 to extract all the component concepts. hese were grouped into the attributes discussed below and used to able 3 efinitions of emerging technologies (studies are chronologically ordered). Study Domain Definition (elaborated
  • 80. Martin (1995) S&T policy “A ‘generic emerging benefits for a wide ran Day and Schoemaker (2000) Management “[. . .] emerging techn industry or transform innovations [. . .] as w separate research stre Porter et al. (2002) S&T policy “Emerging technologi in the coming (roughl Corrocher et al. (2003) Evolutionary economics “The emergence of a n institutional and socia firms or research labo and evolution of know Hung and Chu (2006) S&T policy “Emerging technologi changing the basis of Boon and Moors (2008) S&T policy “Emerging technologi aspects, such as the ch actor network and the Srinivasan (2008) Management “I conceptualize emer characteristics [. . .] an emerging technologie application’ — four ch technologies, converg technologies — shiftin within the firm to out Cozzens et al. (2010) S&T policy “Emerging technology settled down into any emerging technologie
  • 81. in the process of trans yet; (4) increasingly s Stahl (2011) S&T policy “[. . .] emerging techn relevance within the n development process [. . .] Despite this, thes capabilities, constrain Alexander et al. (2012) S&T policy “Technical emergence members of an expert extension of) human u Halaweh (2013) Management Characteristics of (IT) ethical concerns, cost 108) Small et al. (2014) Scientometrics “[. . .] there is nearly u newness) and growth ource: search performed by authors on SCOPUS and extended to cited references. construct our definition of emerging technologies. Extracted con- cepts excluded from our list of attributes will also be discussed. The first defining attribute of emerging technolo gy, explicitly included in two of the 12 core articles, is radicalnovelty: “novelty (or newness)” (Small et al., 2014) may take the form of “dis - continuous innovations derived from radical innovations” (Day and Schoemaker, 2000) and may appear either in the method or or adopted)
  • 82. technology’ is defined [. . .] as a technology the exploitation of which will yield ge of sectors of the economy and/or society” (p. 165) ologies as science-based innovation that have the potential to create a new an existing ones. They include discontinuous innovations derived from radical ell as more evolutionary technologies formed by the convergence of previously ams” (p. 30) es are defined [. . .] as those that could exert much enhanced economic influence y) 15-year horizon.” (p. 189) ew technology is conceptualised [. . .] as an evolutionary process of technical, l change, which occurs simultaneously at three levels: the level of individual ratories, the level of social and institutional context, and the level of the nature ledge and the related technological regime.” (p. 4) es are the core technologies, which have not yet demonstrated potential for competition” (p. 104) es are technologies in an early phase of development. This implies that several aracteristics of the technology and its context of use or the configuration of the ir related roles are still uncertain and non-specific” (p. 1915) ging technologies in terms of three broad subheads: their sources [. . .], their d their effects [. . .] Specifically, I consider two aspects of the sources of s — the ‘relay race evolution’ of emerging technologies, and
  • 83. ‘revolution by aracteristics of emerging technologies — the clockspeed nature of emerging ence, dominant designs, and network effects — and three effects of emerging g value chains, digitization of goods, and the shifting locus of innovation (from side the firm).” (pp. 633–634) — a technology that shows high potential but hasn’t demonstrated its value or kind of consensus.” (p. 364). “The concepts reflected in the definitions of s, however, can be summarised four-fold as follows: (1) fast recent growth; (2) ition and/or change; (3) market or economic potential that is not exploited fully cience-based.” (pp. 365–366) ologies are defined as those technologies that have the potential to gain social ext 10 to 15 years. This means that they are currently at an early stage of their . At the same time, they have already moved beyond the purely conceptual stage. e emerging technologies are not yet clearly defined. Their exact forms, ts, and uses are still in flux” (pp. 3–4) is the phase during which a concept or construct is adopted and iterated by [. . .] community of practice, resulting in a fundamental change in (or significant nderstanding or capability.” (p. 1289) emerging technologies “are uncertainty, network effect, unseen
  • 84. social and , limitation to particular countries, and a lack of investigation and research.” (p. niversal agreement on two properties associated with emergence — novelty (or .” (p. 2) U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight
  • 86. i e e n a H t t a t t ( “ n s k t a a t m o a o t T e t D. Rotolo et al. / Researc he function of the technology. To achieve a new or a changed urpose/function, emerging technologies build on different basic rinciples (Arthur, 2007) (e.g. cars with an internal combustion
  • 87. ngine vs. an electric engine, cytology-based techniques vs. molec- lar biology technologies). Novelty is not only a characteristic of echnologies deriving from technical revolutions, i.e. technologies ith relatively limited prior developments (e.g. DNA sequencing echnologies, molecular biology, nano-materials), but it may also e generated by putting an existing technology to a new use. The volutionary theory of technological change views this as the spe- iation process of technology, that is the process of applying an xisting technology from one domain to another domain or ‘niche’ Adner and Levinthal, 2002). The niche is characterised by a selec- ion process that is different from the one where the technology as initially applied. The niche specifically may differ in terms of daptation (the needs of the niche) and abundance of resources. The echnology applied in the niche may adapt and then emerge as well s potentially invading other domains including the initial domain giving rise to a ‘revolution’ or a process of ‘creative destruction’). his implies that ‘evolutionary’ technology (those not characterised y revolutionary technical developments) can also be radically ovel in domains of application different from those where the echnology was initially developed. Adner and Levinthal (2002) rovided a compelling example of the speciation process by repor-
  • 88. ing on the evolution of wireless communication technology. This echnology was created for laboratory purposes, and specifically for he measurement of electromagnetic waves. Yet, it found numerous ubsequent applications. Wireless communication technology first nabled communication with locations (e.g. lighthouses) otherwise ot reachable with wired telegraphy. Then, applications expanded o the transmission of voice (radiotelephony and broadcasting), nd, more recently, to data transmission (Wi-Fi). With each shift, ireless communication technology appeared radically novel in ts new domain of application, although the technology itself had xisted since the early laboratory and telegraphy applications. The volutionary theory of technological change teaches us that radical ovelty may characterise innovations based on both revolutionary nd evolutionary inventions resulting from the speciation process. owever, the term ‘evolutionary’ is also used to refer to incremen- al technological advances. To avoid ambiguity, we opted to use he term ‘radical novelty’ rather than ‘revolutionary/evolutionary’ nd to contextualise it in relation to the domain(s) in which the echnology is arising.3 The second defining attribute of emerging technologies, iden- ified by three of the 12 core articles is “clockspeed nature” Srinivasan, 2008) or “fast growth” (Cozzens et al., 2010), or at
  • 89. least growth” (Small et al., 2014). Growth may be observed across a umber of dimensions such as the number of actors involved (e.g. cientists, universities, firms, users), public and private funding, nowledge outputs produced (e.g. publications, patents), proto- ypes, products and services, etc. As with the radical novelty ttribute, the fast growth of a technology needs to be contextu- lised. A technology may grow rapidly in comparison with other echnologies in the same domain(s), therefore relativelyfastgrowth ay be a better term. The third attribute of emerging technologies, identified by four f the 12 core articles is coherence that persists over time. The core rticles variously describe this attribute as “convergence of previ- usly separated research streams” (Day and Schoemaker, 2000), 3 The word ‘novelty’ alone may also create ambiguity with regard to the types of echnologies we aim to include in our conceptualisation of emerging technologies. echnologies of a more incremental nature, as derived from the improvement of xisting technologies, are somewhat novel. For the sake of conceptual clarity, we herefore prefer to add the attribute ‘radical’ to the word ‘novelty’. y 44 (2015) 1827–1843 1831 “convergence in technologies” (Srinivasan, 2008), and technolo- gies that “have already moved beyond the purely conceptual stage” (Stahl, 2011). Alexander et al. (2012) point instead to the role of
  • 90. “an expert community of practice”, which adopts and iterates the concepts or constructs underlying the particular emerging tech- nology. The concept of a community of practice suggests that both a number of people and a professional connection between those people are necessary. Coming together, intertwining and staying together are all entailed in coherence. Coherence refers to internal characteristics of a group such as ‘sticking together’, ‘being united’, ‘logical interconnection’ and ‘congruity’. The status of external rela- tions is also important. The emerging technology must detach itself from its technological ‘parents’ to some degree to merit a separate identity. Furthermore, it must stay detached for some period of time to be seen as self-sustaining (Glänzel and Thijs, 2012). As we stated above, emergence is a process and coherence, detachment and identity do not characterise a final state, but are always in the process of realisation, presenting challenging issues of boundary delineation and classification. Perspective matters since an analyst may see an exciting emerging technology about to make a major economic impact in something a scientist sees as long past the exciting emerging phase. The fourth defining attribute of emerging technologies, identi - fied by nine of the 12 core articles is to yield “benefits for a wide range of sectors” (Martin, 1995), “create new industry or trans -
  • 91. form existing ones” (Day and Schoemaker, 2000), “exert much enhanced economic influence” (Porter et al., 2002), or change “the basis of competition” (Hung and Chu, 2006). Corrocher et al. (2003) also point to the pervasiveness of the impact that the emerg- ing technology may exert by crosscutting multiple levels of the socio-economic system, i.e. organisations and institutions, as well as knowledge production processes and technological regimes. Accordingly, we identify prominentimpact as another key attribute of emerging technologies. Most of the core articles conceived the prominent impact of emerging technologies as exerted on the entire socio-economic system. In this usage the concept of emerg- ing technologies becomes very close to that of ‘general purpose technologies’ and so excludes technologies prominent within a specific domain. We wish to include relatively smaller scale promi- nence in our definition. For example, a diagnostic technology may emerge and significantly reshape the clinical practices associated with a given disease, profoundly affecting one disease domain but not others. In other words, our definition allows for prominent impact with narrow scope (emergence in one or a few domains), as well as wide-ranging impact across domains and potentially the entire socio-economic system (e.g. ICT and molecular biology). Such a perspective suggests, as with the attributes of radical novelty and relatively fast growth, the importance of contextualising the
  • 92. promi- nent impact of the observed technolo gy within the domain(s) from which the technology emerges. The final defining attribute of emerging technologies, identified in seven of the 12 core articles is that the prominent impact of emerging technologies lies somewhere in the future — the tech- nology is not finished. Thus, uncertainty features in the emergence process. The non-linear and multi-factor nature of emergence pro- vides emergence with a certain degree of autonomy, which in turn makes predicting a difficult task (de Haan, 2006; Mitchel, 2007). As a consequence, knowledge of the probabilities associated with each possible outcome (e.g. potential applications of the technol- ogy, financial support for its development, standards, production costs) may be particularly problematic (Stirling, 2007). Core articles expressed this attribute in terms of the ‘potential’ that emerging technologies have for changing the existing ‘ways of doing things’ (e.g. Boon and Moors, 2008; Hung and Chu, 2006; Stahl, 2011). However, these definitions seem not to disentangle explic- itly another important aspect of emergence from the concept of U0033385
  • 107. x x x x x x x x x x x 832 D. Rotolo et al. / Researc ncertainty. This is ambiguity. Ambiguity arises because proposed pplications are still malleable, fluid and in some cases contradic- ory, i.e. even the knowledge of possible outcomes of emergence
  • 108. s incomplete. A variety of possible outcomes may occur because ocial groups encountered during emergence hold diverging val - es and ascribe different meanings to the technology (Mitchel, 007). It is worth noting that uncertainty and ambiguity are, how- ver, not mutually exclusive (Stirling, 2007). These are not discrete onditions. A continuum exists as defined by the extent to which nowledge of possible outcomes and likelihood for each outcome s incomplete. For example, it may be problematic evaluating the robabilities associated with known possible outcomes, but at the ame time there may also be a lack of knowledge of other possible utcomes such as unintended/undesirable consequences deriving rom the (potentially uncontrolled) use of the technology. Uncer - ainty and ambiguity are key starting concepts for a wide variety f science and technology studies (STS) focusing on the role of the xpectations in technological emergence (e.g. van Lente and Rip, 998). The studies reviewed here introduced various additional oncepts such as the science-based-ness, network effects, and arly-stage development of emerging technologies. While the last f these seems to be implicit in the definition of emergence and the ey role of networks (of users adopting the technology) is certainly ot a unique feature of emerging technologies, the association ith science-based-ness is less clear. The importance of science especially public science) for the development of industrial tech- ologies is widely accepted on the basis of substantial evidence
  • 109. e.g. Narin et al., 1997). However, even today not all technological evolutions may depend on breakthrough advances in science. In ertain domains, a technology can be developed without the need or deep scientific understanding of how the phenomenon under - ying it works — “it is possible to know how to produce an effect ithout knowing how an effect is produced” (Nightingale, 2014, p. ). For example, Vincenti (1984) provided evidence of this in the ase of the construction of airplanes in the 1930s. The different arts of an airplane were initially joined using rivets with dome- haped heads. These types of rivets, however, caused resistance o the air, thus reducing the aerodynamic efficiency of the plane. s other dimensions of airplane performance were improving (e.g. peed), the aerodynamic efficiency became increasingly relevant. he dome-shaped rivets were therefore replaced with rivets flush ith the surface of the airplane. This was a major improvement for he aerodynamics of airplanes in 1930s, but it required no major cientific breakthrough.4 A more recent example is the develop- ent of smartphones which did not require major advancements n science since most of the technologies used already existed — he integration of these technologies, and advances in design for he creation of novel user interfaces instead provided the founda - ion of the innovation.5 For these reasons, ‘science-based-ness’ does ot feature in our definition of emerging technologies. In summary, as reported in Table 4, our review of innovation tudies identified five main defining characteristics or attributes
  • 110. f emerging technologies: (i) radical novelty, (ii) relatively fast rowth, (iii) coherence, (iv) prominent impact, and (v) uncertainty nd ambiguity. Combining these attributes, we define an emerging 4 Other classical examples include prehistoric cave dwellers using fire for cooking ithout any scientific understanding of it, the development of steam engines that redated the development of thermodynamics, or the Wright brothers testing flying evices before the field of aerodynamics was established. 5 The innovation was architectural rather than modular according to the distinc- ion proposed by Henderson and Clark (1990). Also, smartphone technology can e considered as an example of emerging technology of an evolutionary nature. s discussed above, the radical novelty of this technology is the result of existing echnologies converging in new domains of applications. T a b le 4 A tt ri
  • 120. Radical novelty Uncertainty and ambiguity Fig. 2. Pre-emergence, emergence, and post-emergence: attributes and ‘stylised’ trends. gence. This process led to a final set of 55 publications,6 which were then classified in terms of the methodological approach adopted to detect or analyse emergence (e.g. indicators, citation 6 We excluded 76 studies that did not operationalise emergence (e.g. use of emerging technologies as empirical context for various analyses, examination of ethical issues associated with emerging technologies), three studies focused on the review of scientometric methods for the analysis of emerging technologies, and two studies elaborating document search strategies based on a modular lexical approach. 33 studies that were concerned with Future-oriented Technology Analysis (FTA) techniques (e.g. foresight, forecasting, roadmapping, Constructive Technol- ogy Assessment (CTA)) were also not included in the review. While about 67% of these do not rely on scientometrics, the remaining FTA studies in the sample pro- pose frameworks for selecting, rather than identifying, emerging technologies, or adopt conventional scientometric/bibliometric approaches, which will be instead discussed with the review of the selected scientometric studies. FTA methods, how- ever, remain crucial for more prospective analyses of emerging
  • 121. technologies and decision-making on possible future scenarios (e.g. Porter et al., 2004; Irvine and Martin, 1984; Ciarli et al., 2013). 14 STS studies included in the sample will be instead referenced in our review and discussion when the operationalisation of the attributes of emergence with the use of scientometric approaches is limited by a lack of data or by the nature of the considered attribute. It is worth noting that our search did not capture ‘technometric’ studies (e.g. Grupp, 1994; Saviotti and D. Rotolo et al. / Researc echnology as a radically novel and relatively fast growing technol- gy characterised by a certain degree of coherence persisting over time nd with the potential to exert a considerable impact on the socio- conomic domain(s) which is observed in terms of the composition f actors, institutions and patterns of interactions among those, along ith the associated knowledge production processes. Its most promi- ent impact, however, lies in the future and so in the emergence phase s still somewhat uncertain and ambiguous. It is reasonable to assume that the attributes of emergence range rom ‘low’ to ‘high’ levels. Nonetheless, to try and pin them down o some absolute level is rather meaningless. As discussed, the
  • 122. ttributes of emergence (especially radical novelty and relatively ast growth) provide an indication of emergence when they are onsidered in the domain in which the given technology is arising nd therefore in relation to other technologies that may exist in hat domain. Most importantly, these attributes are likely to co- volve and assume very different levels over different periods of mergence. In the early stage of emergence (‘pre-emergence’), a echnology is likely to be characterised by high levels of radical ovelty as compared to other technologies in the domain in which t is arising. However, the impact the technology can exert on that omain is still relatively low. The technology has not yet gone eyond the purely conceptual stage, multiple communities are nvolved in its development, and the delineation of the boundary of he technology is particularly problematic (i.e. low levels of coher- nce). As a consequence, its growth is relatively slow or not yet egun, and high levels of uncertainty and ambiguity are associated ith the future developments of the technology — the technology ay not even emerge. The technology may then acquire a cer- ain momentum. Some trajectories of development may have been elected out and certain dimensions of performance prioritised and mproved. A community of practice may have also emerged. The echnology thus becomes more coherent. Its impact is also rela- ively less uncertain and ambiguous, and the technology starts to ake off in terms of publications, patents, researchers, firms, pro- otypes/products, etc. However, at the same time, it is likely that
  • 123. he radical novelty of the technology will diminish — other tech- ologies that exploit different basic principles may be emerging as ell in the domain in which the considered technology is emerg- ng. We conceived ‘emergence’ as this phase where the attributes f emergence are subject to dramatic change. Finally, impact and rowth may enter a stable or declining phase, the technology loses ts radical novelty, knowledge of the possible outcomes of the echnology becomes more complete (probabilities can be perhaps ssigned to outcomes), and the community of practice may become ell-established (e.g. regular conferences, dedicated journals). The echnology enters in a ‘post-emergence’ period. In line with the -shaped patterns highlighted in early studies on the growth of cience (e.g. De Solla Price, 1963) and in technological adoption iterature (e.g. Mansfield, 1961; Rogers, 1962), we ‘stylised’ the hange in the levels of the attributes of emergence as following n S curve (or more strictly, a reversed S curve in two of the five ases). This is qualitatively depicted in Fig. 2. Defining ‘emerging technology’ is, however, only half the battle. f the definition is to be useful, we must show how the attributes an be measured and thus how technologies can be classified as merging or not. In the next section, we link our definition to he operationalisation of our definition of emerging technologies. e rely mainly on scientometric techniques, bringing in other pproaches to fill certain gaps.
  • 124. . A framework for the operationalisation of emergence Scientometric research has developed methods to detect emer - ence in science and technology and is therefore central to Source: authors’ elaboration. operationalising our definition. From the vast literature that touches on emerging technologies, we drew upon studies that offer ideas on operationalising our five attributes. We identified relevant scientometric studies by including the term ‘topic’ in the search string we used to select research works dealing with definitional issues of emerging technologies — ‘topic’ is often used in sciento- metrics to refer to the emergence of a new set of research activities in science and technology (e.g. Small et al., 2014; Glänzel and Thijs, 2012). The search was also extended to publication titles, abstracts and keywords, but narrowed to journals mainly or to a significant extent oriented toward the publication of novel scientometric tech- niques (see the right-hand column of Table 2). The search in SCOPUS returned 155 publications. The examination of cited references of these publications enabled us to retrieve additional studies that were not cap- tured with the search string, but are potentially relevant to for our analysis. This increased the initial sample to 183 studies. We then analysed these publications to identify studies that were relevant to the operationalisation of the attributes of emer-
  • 125. Metcalfe, 1984; Sahal, 1985). This research stream has been particularly important for the measurement of technology and technological change. Nonetheless, techno- metric models tend to rely on a variety of assumptions and often require data, the collection of which can be particularly labour-intensive (e.g. extraction and coding of data on the features of the considered technologies) (e.g. Coccia, 2005). U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight U0033385 Highlight 1834 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843
  • 126. Table 5 Methods for the detection and analysis of emergence in science and technology (studies are ordered by technique and publication year). Method/study Data Operationalisation of emergence Indicators and trends Porter and Detampel (1995) Publications/patents Count of keywords in publication abstracts and trend analysis based on Fisher–Pry curves Kleinberg (2002) Publications/e-mails ‘Burst of activity’ detected as state transitions of an infinite-state automaton Bengisu (2003) Publications Positive slope of the line derived by regressing the number of publications on time and no decrease of more than 10% or stability (no increase) in the last period or continuous decline in the last three periods of observation Watts and Porter (2003) Publications Indicators of emergence: cohesion (based on cosine similarity between documents), entropy, and F-measure Bettencourt et al. (2008) Publications Epidemic model to describe the increasing number of authors involved in an emerging field Bettencourt et al. (2009) Publications Increasing densification (average number of edges per node), stable/decreasing diameter (average path length between nodes), and increasing fractional count of edges in the largest component of the co-authorship network Moed (2010) Publications Journals characterised by high values
  • 127. of Source Normalised Impact per Paper (SNIP) indicator Schiebel et al. (2010) and Roche et al. (2010) Publications Publication keywords initially labelled as “unusual terms”, by using tf-idf and Gini coefficient, that subsequently become “cross section terms”, i.e. they diffuse in several research domains Guo et al. (2011) Publications Indicators of emergence: frequency of keywords (ISI WoS keywords, authors’ keywords, and MeSH terms), growing number of authors, and interdisciplinarity (based year-average Rao-Stirling diversity index) of cited references Järvenpää et al. (2011) Mixed Absolute and cumulative count of the number of basic and applied research publications, patents, and news Abercrombie et al. (2012) Mixed Normalised number of publications and citations, patents, and web news fitted to a polynomial function Jun (2012) and Jun et al. (2014) News Normalised searching traffic (Google trends) Avila-Robinson and Miyazaki (2013b,a) Publications/patents Overview of indicators to analyse emergence de Rassenfosse et al. (2013) Patents Count of the priority patent applications filed by a country’s inventor, regardless of the patent
  • 128. office in which the application is filed Ho et al. (2014) Publications Cumulative number of publications fitted to a logistic curve Citations analysis Direct citation Seminal paper: Garfield et al. (1964) Publications – Kajikawa and Takeda (2008), Kajikawa et al. (2008) and Takeda and Kajikawa (2008) Publications Clusters of publications with the highest average publication year Scharnhorst and Garfield (2010) Publications Historiographic approach combined with ‘field mobility’ of publications Shibata et al. (2011) Publications Clusters of publications with the highest values of betweenness centrality Iwami et al. (2014) Publications Publications (‘leading papers’) with high values of in-degree (‘height’), large variation of in-degree between one year and the next year (‘slope’), or large cumulative in-degree (‘area’) as defined on the basis of the yearly direct citation network Co-citation Seminal paper: Small (1973) Publications –
  • 129. Small (2006) Publications Clusters with no continuing publications from the prior period Cho and Shih (2011) Patents Technological patent classes (IPC) that span structural holes in the co-citation network Érdi et al. (2012) Patents Clusters of patents present in a given time period and not in the previous period Boyack et al. (2014) Publications Yearly clustered publications of which references overlap less than 30% with references cited by previous clusters Bibliographic coupling Seminal paper: Kessler (1963) Publications – Morris et al. (2003) Publications Clusters of publications that cite more recent clusters of publications, namely emerging research fronts Kuusi and Meyer (2007) Patents Clusters of patents as source to identify guiding images (‘leitbild’) of technological development Co-word analysis Seminal paper: Callon et al. (1983) Publications – Lee (2008) Publications Clusters in the co-word network that show low values of degree, high betweenness, and low closeness, i.e. those clusters that are more likely to turn into hub in the future. Ohniwa et al. (2010) Publications MeSH terms (clustered with co-word analysis) that are included in the top-5% by incremental rate in a given year — the increment rate for a MeSH term is
  • 130. defined as the number of time the terms occurred at the time t, t + 1, and t + 2 out the number of times the term occurred at t − 1, t, t + 1, and t + 2 Yoon et al. (2011) Patents Small and dense sub-networks in the ‘invention property-function’ network Furukawa et al. (2015) Publications Sessions of conferences in which previous sessions converge according to the average cosine similarity (based on tf-idf-identified keywords) between the papers included in the sessions Zhang et al. (2014) Publications Combination of cluster analysis with term clumping and principal component analysis Overlay mapping Rafols et al. (2010) Publications Overlays of publications projected on a basemap of ISI WoS subject categories linked by cosine similarity of co-citations patterns between journals Bornmann and Leydesdorff (2011) Publications Overlays of publications on Google maps to identify cities publishing more than expected D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 1835 Table 5 (Continued) Method/study Data Operationalisation of emergence
  • 131. Leydesdorff and Rafols (2011) Publications Overlays of publications and co-authorship networks on Google maps to trace collaboration activity Leydesdorff et al. (2012) Publications Overlays of publications projected on a basemap of MeSH terms linked by cosine similarity (based on the co-occurrence of MeSH terms at the publication level) Leydesdorff and Bornmann (2012) Patents Overlays of patents on Google maps to identify cities patenting more than expected Leydesdorff et al. (2013) Publications Overlays of publications projected on the basemap of journals linked by cosine similarity of co-citations patterns between journals Kay et al. (2014) Patents Overlays of patents projected on the basemap of 466 IPC classes linked by cosine similarity of citing-to-cited relationships between classes — the basemap is built by using patents included in 2011 PATSTAT Leydesdorff et al. (2014) Patents Overlays of patents projected on the basemap of 124 3-digit or 630 4-digit IPC classes linked by cosine similarity based on co-citations between classes — the basemap is built by using patents granted at the United States Patent and Trademark Office (USPTO) from 1976 to 2011 Hybrid
  • 132. Chen (2006): co-citation analysis and burst detection Publications Trends in the bipartite network of research-front terms (burst detection) and intellectual base articles — the network includes three types of links: co- occurring research front terms, co-cited intellectual base articles, and a research-front term citing an intellectual base article Leydesdorff et al. (1994): co-citation analysis and bibliographic coupling Publications New journals that build on multiple existing areas, i.e. they load on multiple factors obtained by the factor analysis of the matrix of the cited references, and have unique ‘being cited’ patterns, i.e. they are ‘central tendency journals’ reporting highest load on a given factor as obtained by the factor-analysis of the matrix of received citations Glänzel and Thijs (2012): co-word, direct citation analyses and bibliographic coupling Publications Existing clusters with exceptional growth, completely new clusters with roots in other clusters, and existing clusters with a topic shift Gustafsson et al. (2015): co-occurrence of IPC classes
  • 133. Patents Technological co-classification to identify clusters of patents and detect guiding images or ‘leitbild’ from patent full-text Small et al. (2014): direct and co-citation analyses Publications Clusters of publications that show high growth and are new both to the direct citation and co-citation models Yan (2014): co-word analysis and topic modelling Publications Topics that are not a close variation of other topics, i.e. a topic i in the year t is emerging if no predecessors are found and no other topics are transformed into topic i at t + 1 Chang and Breitzman (2009), Breitzman and Thomas (2015): direct citation and co-citation analyses Patents Clusters of patents (co-citation clustering) that form around ‘hot’ patents — defined as those patents that are highly cited (top 5–10%) by patents issued in the last two years and the citations of which mostly come from patents issued in the last two years S refere p d p
  • 134. s g b f w a w w a a e e g u W t e h r o b T r e e ource: search performed by authors on SCOPUS and extended to publication cited atterns between documents, co-occurrence of words in text), ata sources used (e.g. publications, patents, news articles), and roposed operationalisation of emergence. This information is ummarised in Table 5 where studies are grouped into five roups: (i) indicators and trend analysis studies that are mainly ased on document counts; (ii) citation analysis studies which ocus on examining citation patterns between documents; (iii)
  • 135. co- ord analysis studies that build on the co-occurrence of words cross document text; (iv) overlay mapping technique studies, hich use projections to position a given set of documents ithin a wider or more global structure (e.g. a map of science); nd (v) hybrid studies that combine two or more of the above pproaches. Table 5 shows how definitions of emergence varied, ven within the same group of techniques, thus providing further vidence of the low level of consensus on what constitutes emer - ence. Given the definitional weaknesses in the original studies, our se of a particular study often varies from that of its authors. e will briefly introduce the major techniques and our interpre- ation of the contribution they make to measuring attributes of merging technologies. For each attribute, we will first describe ow it can be operationalised for contemporary and then for ret- ospective cases of emerging technologies. When data scarcity r the nature of the attribute of emergence limit the applica- ility of scientometrics, we will discuss qualitative approaches. he role of experts remains crucial for the validation of the esults obtained with the use of the techniques discussed below, specially for qualitative approaches to the operationalisation of mergence. nces. 4.1. Radical novelty Emerging technologies are radically novel, i.e. they fulfill a given function by using a different basic principle as compared to
  • 136. what was used before to achieve a similar purpose. Publications and patents are of limited use in assessing radical novelty in contem- porary technology. In contrast, news articles, editorials, review and perspective articles in professional as well as academic journals represent valuable sources, providing participant perspectives on if and why a technology is viewed as radically novel. These docu- ments may also provide an understanding of the basic principles underpinning the examined technology. In contrast, in retrospective analyses citation and co-word anal- yses can be particularly effective for identifying radical novelty. Relatively large amounts of data can be exploited to map the cog- nitive networks of a knowledge domain over time. Citation analysis builds on citation patterns among documents to generate a network in which nodes are documents and links between nodes repre- sent (i) a direct citation between two documents (direct citation analysis) (Garfield et al., 1964), (ii) the extent to which two doc- uments are cited by the same documents (co-citation analysis) (Small, 1973), or (iii) to what extent two documents cite the same set of documents (bibliographic coupling) (Kessler, 1963). Co- word analysis instead exploits the text of documents to create a network of keywords (or key phrases) that are linked according to the
  • 137. text to which they co-occur across the set of selected documents (Callon et al., 1983). On the premise that clusters of documents or words in these networks represent different knowledge areas of a domain or U0033385 Highlight U0033385 Highlight U0033385 Highlight 1 h Polic d h i a c t c t s o ( d t
  • 139. e s s g d c e R S l c S 836 D. Rotolo et al. / Researc ifferent literatures on which the domain builds, several studies ave considered the appearance of clusters not previously pres ent n the network as a signal of novelty (e.g. Érdi et al., 2012; Kajikawa nd Takeda, 2008). Others dispute this interpretation. Given the ontinuous evolution of science and technology, one is unlikely o find a cluster again in subsequent annual networks so the per- entage of clusters that would qualify as newly appearing tends o be relatively high. For this reason, additional criteria have been uggested such as the appearance of new clusters that also link therwise weakly connected (e.g. betweenness centrality) clusters e.g. Shibata et al., 2011; Furukawa et al., 2015), that form around ocuments that are highly cited by recent documents and the cita- ions of which also are mostly from recent documents (Breitzman nd Thomas, 2015), or that cite more recent clusters as identi- ed by the (Salton) similarity of their references (Morris et al.,
  • 140. 003). Small et al. (2014) have recently proposed a hybrid approach ased on a combination of direct citation and co-citation models as pplied to publication data. This approach is particularly focused on he detection of novelty, which is defined in terms of clusters that re new to the co-citation model — that is, clusters with limited verlap with the cited documents included in clusters in previ - us years (Boyack et al., 2014) — as well as to a parallel direct itation model. By combining bibliographic coupling, co-word anal- sis, and direct citation analysis, Glänzel and Thijs (2012) instead efined novelty (namely emerging topics) as three cases of clus - ers: those that show exceptional growth, those that are completely ew but with their roots in other clusters, or already existing ones hat exhibit a topic shift. Yan (2014) combined co-word analysis ith Natural Language Process (NLP) approaches (topic modelling). mergence, as reflected in novelty, is then associated with the ppearance of topics that are not a close variation of other topics alculated on the basis of the Jenson–Shannon Divergence.7 Specif- cally, a topic i appearing at time t is considered to be emerging f it has no predecessors and none of the identified topics trans - orms into topic i at t + 1. A different perspective is provided by charnhorst and Garfield (2010) who extended the analysis of his- oriographs (based on direct citations) to trace the extent to which
  • 141. ublications move across fields as they receive citations from new elds (namely ‘field mobility’). Assuming that these publications re associated with a basic principle used for technological appli - ations, this approach enables one to identify which fields may be sing a different knowledge base and thus in which fields radi - ally novel technologies are potentially emerging. However, this equires a priori knowledge of the basic principle and the set of ocuments associated with it. Research in scientometrics has also focused on the develop- ent of techniques to expand the ‘local’ (domain) perspective that itation or text-based approaches may provide. This effort has gen- rated a number of overlay mapping techniques (for an overview ee Rotolo et al., 2014), which in turn may be particularly well uited to detecting radical novelty. The basic idea is to project a iven set of documents (e.g. publications associated with a research omain) on a basemap through the use of an overlay. The basemap an represent the ‘global’ science structure at the level of the sci- ntific discipline (ISI Web of Science (WoS) subject categories) (e.g. afols et al., 2010), journal (e.g. Leydesdorff et al., 2013), Medical ubject Headings (MeSH) (Leydesdorff et al., 2012), or the techno- ogical structure at the level of patent classes (e.g. Kay et al., 2014;
  • 142. 7 The Jenson–Shannon Divergence is a measure of similarity between empiri- ally determined distributions (e.g. co-occurrence of words in documents) based on hannon entropy measures (for more details see Lin, 1991). y 44 (2015) 1827–1843 Leydesdorff et al., 2014).8 Once the set of documents (publications or patents) associated with a given domain has been identified, the projection of these documents over different time slices on the global map of science or technology may reveal the increas- ing involvement of new scientific or technological areas. This may suggest that new knowledge areas are being accessed to conduct research, and thus that potentially different basic pri nciples are drawn upon to achieve a given purpose. Among the studies within the ‘indicators and trends’ group of techniques, Moed (2010) proposed the source normalised impact per paper (SNIP) indicator for the evaluation of journals’ impact and claims it is relevant for identifying emerging technologies. This indicator is defined as the ratio between the journal’s raw impact per paper (number of citations in the year of analysis to the jour- nal’s papers published in the three previous years, divided by the number of the journal’s papers in these three years) and the rela- tive database citation potential in the subject field covered by the
  • 143. journal (mean number of 1–3-year-old references per paper citing the journal and published in journals included in the considered database divided by that for the median journal in the database). Moed (2010) argued that the SNIP indicator, and specifically high values of this indicator, also provides information on the extent to which a considered journal covers emerging topics. Given the focus on recent citations and database coverage, the SNIP indicator is clearly associated with the radical novelty attribute of emergence. This indicator is, however, evaluated at the aggregate level of the journal and journal-by-journal. It is therefore less clear whether signals of radical novelty (i.e. relatively high values of SNIP) are associated with one or multiple emerging topics the considered journal may cover. In addition, the SNIP may not capture signals of radical novelty in those instances of journals that cover few emerging topics and therefore characterised by low values of SNIP. All these techniques have various advantages and limitations. The qualitative analysis of news articles, editorials, review and per- spective articles, for example, may be effective for contemporary analyses. However, the technical language used in these documents may be an important barrier to a non-expert’s efforts to inde- pendently assess radical novelty. The application of citation and co-word analyses is strongly dependent on time. Data need to be longitudinal in order to permit the tracing of cognitive dynamics
  • 144. and associated changes in the knowledge structure. Co-word anal- ysis and bibliographic coupling are, however, less sensitive to time than direct citation and co-citation analyses and can be applied as documents become available (e.g. Breitzman and Thomas, 2015). Finally, overlay mapping provides a global perspective on emer - gence for the assessment of radical novelty, but interpretation of the resulting maps is mainly based on visual inspection. 4.2. Relatively fast growth Emerging technologies show relatively fast growth rates com- pared to non-emerging technologies. The assessment of this attribute is particularly problematic for contemporary analyses. Growth is not yet observed in terms of publications and patents, for example, so scientometric indicators cannot be used. Early indica- tions of growth may be revealed from the analysis of funding data, big data, and altmetrics. This is an important research direction for future studies on the operationalisation of the relatively fast growth attribute, as we will discuss later in the paper. 8 The elements of the basemap are linked according to similarity based on the co-occurrence of citations or, in the case of MeSH, the co- occurrence of terms. The
  • 145. same approach can be used to project a sample of publications and patents onto geo- graphical maps (e.g. Google maps) to reveal the most active cities and collaborative activities (see Table 5). U0033385 Highlight U0033385 Highlight h Polic p s n t b c e o t p o M i d a o f e p t