Service Science: Research and Innovationsin the Service Economy

Series EditorsBill HefleyWendy Murphy

More information about this series at http://www.springer.com/series/8080

Paul P. Maglio • Cheryl A. KieliszewskiJames C. Spohrer • Kelly LyonsLia Patrício • Yuriko SawataniEditors

Handbook of Service Science,Volume II

EditorsPaul P. MaglioUniversity of California, MercedMerced, CA, USA

Cheryl A. KieliszewskiIBM Research—AlmadenSan Jose, CA, USA

James C. SpohrerIBM Cognitive Opentech, AlmadenSan Jose, CA, USA

Kelly LyonsUniversity of TorontoToronto, ON, Canada

Lia PatrícioINESCTEC and Faculty of EngineeringUniversity of PortoPorto, Portugal

Yuriko SawataniGraduate School of ManagementNagoya University of Commerce and BusinessBusiness SchoolNagoya, Aichi, Japan

ISSN 1865-4924 ISSN 1865-4932 (electronic)Service Science: Research and Innovations in the Service EconomyISBN 978-3-319-98511-4 ISBN 978-3-319-98512-1 (eBook)https://doi.org/10.1007/978-3-319-98512-1

Library of Congress Control Number: 2010924288

© Springer Nature Switzerland AG 2019This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of thematerial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,broadcasting, reproduction on microfilms or in any other physical way, and transmission or informationstorage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodologynow known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.The publisher, the authors, and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor the authors orthe editors give a warranty, express or implied, with respect to the material contained herein or for anyerrors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

Foreword by Martin Fleming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Foreword by Irene Ng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiii

1 Introduction: Why Another Handbook? . . . . . . . . . . . . . . . . . . . . 1Paul P. Maglio, Cheryl A. Kieliszewski, James C. Spohrer,Kelly Lyons, Lia Patrício, and Yuriko Sawatani

Part I Service Experience – On the Human-Centered Nature of Service

2 Service Timing: Designing and Executing Servicein a Dynamic Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Ruth N. Bolton

3 Designing Service Systems to Enhance PerceivedDecision Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Sriram Dasu and Alexandra Brunner-Sperdin

4 The Sequence of Service: An Affect Perspectiveto Service Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Michael J. Dixon and Liana Victorino

5 Customer Acceptance of AI in Service Encounters:Understanding Antecedents and Consequences . . . . . . . . . . . . . . . 77Amy L. Ostrom, Darima Fotheringham, and Mary Jo Bitner

6 Optimal Structure of Experiential Services:Review and Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Guillaume Roels

v

7 A Human-Centred, Multidisciplinary, and TransformativeApproach to Service Science: A Service Design Perspective . . . . . . 147Daniela Sangiorgi, Filipe Lima, Lia Patrício, Maíra Prestes Joly,and Cristina Favini

8 Emerging Design Research Themes: A Research Reviewfrom Design, Service, and Management Studies . . . . . . . . . . . . . . . 183Yuriko Sawatani

9 High-Tech vs. High Touch Service Design in Healthcare:A Case for Considering the Emotional Biorhythm of thePatient in Technology Interventions . . . . . . . . . . . . . . . . . . . . . . . . 193Alexis Strong and Rohit Verma

10 Customer Experience Analytics: DynamicCustomer-Centric Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207Mohamed Zaki and Andy Neely

Part II Service Systems – On the Nature of Service Interactions

11 The Future of Service Systems: From Synergeticsto Multi-Sided Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237Jennifer D. Chandler

12 Using Employees’ Collective Intelligence for Service Innovation:Theory and Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249Niels Feldmann, Hansjörg Fromm, Gerhard Satzger,and Ronny Schüritz

13 A Multilayer Framework for Service System Analysis . . . . . . . . . . 285Robert Blair Frost, Michael Cheng, and Kelly Lyons

14 People and Social Interaction: Drivers of Service Innovation . . . . . 307Cheryl A. Kieliszewski and Laura Challman Anderson

15 Queues in Service Systems: Some Unusual Applications . . . . . . . . 327Luna An, Mallika Machra, Abigail M. Moser, Sanja Simonovikj,and Richard C. Larson

16 Clarifying the Concept of Smart Service System . . . . . . . . . . . . . . 349Chiehyeon Lim and Paul P. Maglio

17 Exploring the Journey to Services . . . . . . . . . . . . . . . . . . . . . . . . . 377Veronica Martinez, Andy Neely, Chander Velu,Stewart Leinster-Evans, and Dav Bisessar

18 Digital Workers in Service Systems: Challengesand Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409Paolo Piciocchi, Clara Bassano, Maria Cristina Pietronudo,and James C. Spohrer

vi Contents

19 Visualizing and Improving Service Processeswith PCN Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433Scott E. Sampson

Part III Service Ecosystems – On the Broad Context of Service

20 Value-in-Context: An Exploration of the Contextof Value and the Value of Context . . . . . . . . . . . . . . . . . . . . . . . . . 457Melissa Archpru Akaka and Glenn Parry

21 On the Evolution of Service Ecosystems: A Studyof the Emerging API Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . 479Rahul C. Basole

22 Institutionalization Process of Service Innovation: OvercomingCompeting Institutional Logics in Service Ecosystems . . . . . . . . . . 497Elina Jaakkola, Leena Aarikka-Stenroos, and Paavo Ritala

23 Innovation in Sociomaterial Practices: The Caseof IoE in The Healthcare Ecosystem . . . . . . . . . . . . . . . . . . . . . . . 517Cristina Mele and Tiziana Russo-Spena

24 Toward the Service Science of Education . . . . . . . . . . . . . . . . . . . . 545Oleg V. Pavlov and Frank Hoy

25 Leveraging Big Data Platform Technologies and Analyticsto Enhance Smart City Mobility Services . . . . . . . . . . . . . . . . . . . . 567Robin G. Qiu, Tianhai Zu, Ying Qian, Lawrence Qiu,and Youakim Badr

26 Population Health as a Network of Services: Integrationof Health, Education, and Social Services . . . . . . . . . . . . . . . . . . . 589William B. Rouse, Kara M. Pepe, and Michael M. E. Johns

27 Incremental and Radical Service Innovation in Healthcare . . . . . . 619Peter Samuelsson, Lars Witell, Patrik Gottfridsson, and Mattias Elg

Part IV Challenges – On Rethinking the Theory and Foundationsof Service Science

28 Further Advancing Service Science with Service-Dominant Logic:Service Ecosystems, Institutions, and Their Implications forInnovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641Melissa Archpru Akaka, Kaisa Koskela-Huotari,and Stephen L. Vargo

29 On the Ethical Implications of Big Data in Service Systems . . . . . . 661Christoph F. Breidbach, Michael Davern, Graeme Shanks,and Ida Asadi-Someh

Contents vii

30 Service-Dominant Logic: Inward and Outward Views . . . . . . . . . . 675Luigi Cantone, Pierpaolo Testa, and Teresa Marrone

31 Service Economies and Complexity . . . . . . . . . . . . . . . . . . . . . . . . 711Benoît Desmarchelier

32 From Whence to Where in Service Science: A Perspectiveon the Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729Janet R. McColl-Kennedy

33 Embedding Humans into Service Systems Analysis:The Evolution of Mathematical Thinking About Services . . . . . . . 743Alexandra Medina-Borja

34 The Demolition of Service Scientists’ Cultural-Boundaries . . . . . . 773Francesco Polese, Sergio Barile, Vincenzo Loia, and Luca Carrubbo

35 Asset-Based Strategies for Capturing Valuein the Service Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785Jochen Wirtz and Michael Ehret

36 Service as Intersubjective Struggle . . . . . . . . . . . . . . . . . . . . . . . . . 811Yutaka Yamauchi

viii Contents

Foreword by Martin Fleming

As hotel guests, we very rarely have any idea what room rate our fellow guests arepaying. We quietly check in and out at the registration desk with other guests ideallyknowing nothing about our transaction. Unless we are repeat visitors, we also do notactually know the quality of service to be delivered. Will the room be comfortable,have enough space, be clean? Will the hotel be quiet? Will the staff be responsive?Will the food be to our liking?

As economist Arthur Okun wrote nearly 40 years ago, this is the hotel problem(Okun 1981). Price and service quality information can only be gained throughexperience, though shopping can help.

The advent of hotel shopping sites, such as Orbitz, Trivago, Hotwire, andTripAdvisor, can help as travelers have the opportunity to share experiences.Similarly, online intermediate sites, such as Airbnb, can also help with room ratingsavailable. However, across all sites, information is often incomplete. In addition,artificial intelligence capabilities allow for transaction-by-transaction price determi-nation. There is no substitute for personal experience. There is no auctioneer callingout prices for standardized offerings.

Hoteliers, of course, understand the quandary guests face and have built success-ful business models to satisfy their guests. Hotels have learned to pledge certainty ofoffer in which service quality is consistent from visit to visit and site to site. Servicequality is the focus, with client relationship management dominating businessprocesses and employee behavior.

The expense of enticing a guest for an initial stay in turn creates high value fromrepeat guests and the lifetime value of the client relationship. It is ultimately theproductivity, skill, engagement, and satisfaction of employees that provide the highlevel of customer satisfaction motivating repeat guests and delivering high profit-

ix

ability (Heskett et al. 2008).1 Value-added per worker, even adjusting for inflation, ishigh and, typically, rising at above average rates.

In contrast to standardized offerings in commodity markets, in the hotel problem,room rates are not much used to match demand and supply. Okun was amacroeconomist and was eager to show that shifts in demand are met by slowlychanging prices with unit sales more rapidly absorbing adjustments. When businessconditions are favorable, hoteliers are likely to forgo rate increases, for a time, as thevalue of obtaining new guests exceeds, over the long run, the value of an immediaterate increase. Conversely, when demand weakens, stable room rates rule out theattraction of bargain hunters in favor of building future profitable guests.

The Ritz-Carlton Hotel Company is well known for achieving high levels ofcustomer satisfaction and building repeat business by providing employees withwide latitude and productivity-enhancing tools to meet their guests’ every need. Thecompany has for years given staff broad discretion to solve any customer complaintin the manner the employee feels is appropriate. With definitions of standards foremployees to follow combined with the autonomy necessary to address clientconcerns quickly, the Ritz-Carlton has built consistent, sustainable business success(Inghilleri and Solomon 2010).

The hotel problem is a rich problem to have. As wealth, incomes, and standards ofliving have risen around the globe, services have grown to make up an increasingportion of economic activity. A wide range of service providers—travel, financial,healthcare, education, legal, technology, consulting—all face a challenge similar tothe hotel problem. In the absence of known prices and quality, repeat business andcustomer satisfaction delivered by productive, skilled, engaged, and satisfiedemployees are necessary for revenue growth and profitability.

Of course, services have not always dominated economic activity. Over250 years, the industrial era has produced 5 monumental epochs. Each epoch hasbeen characterized by massive physical capital investment, both public and private;substantial human capital accumulation; innovative new technology, including newenergy technology; shifting flows of financial capital, including a deep financialmarket crash; and ultimately a fundamental reconfiguration of social, political,cultural, and economic institutions and arrangements (Perez 2002).

In the early 1880s, the power loom was at the leading edge of technology. Theability to more productively manufacture textiles in volume at ever lower costs madeclothing and apparel significantly less expensive. An expanded personal wardrobewas more affordable and much less of a luxury. Substantial water-powered textilefactories were built in both England and New England, employing skilled andtalented workers who migrated from the agriculture sector.

By the late nineteenth century, productivity in the textile industry had risen by98% with prices falling similarly. With the completion of rail networks across NorthAmerica and Western Europe, demand grew to such an extent, accompanied by

1Despite the current day challenges faced by service providers, service profit chain scholarship andresearch was largely settled 20 years ago. The classic reference is Heskett et al. (1997).

x Foreword by Martin Fleming

wage and employment growth, that workers learned for the first time they couldleave one job and employers would competitively bid for their services in another.The emergence of the competitive labor market was a racial, social, and culturaltransformation (Bessen 2015).

The global economy is now in the fifth industrial epoch. Electronics and infor-mation technology is in full deployment providing inexpensive and abundant com-puting and storage, allowing for radical new innovations in digital commerce,analytics, artificial intelligence, and deep learning. After the 2008–2009 financialmarkets crash, balance sheets have been cleansed, and renewable energy sources aretaking hold; the Internet and the global telecommunications network are fundamen-tally altering the nature of commerce, increasing human capital requirements andredefining economic activity.

Despite the historic dominance of global manufacturing activity, the sector hasfaded as a source of employment in recent decades. The share of jobs in the servicesector has risen in advanced economies, replacing manufacturing jobs, and indeveloping economies, replacing agricultural jobs. While aggregate productivityand income growth has appeared to slow as a result of the shift of capital andlabor into services, the slowdown has not been uniform. The services sector is vastand consists of many subsectors with varying productivity levels and growth rates.Recent advances in technology and the global tradability of services have acceler-ated gains (International Monetary Fund 2018).

In the services sector, information technology has a long track record of valua-tion. Examples are abundant: ticketing systems for travel, supply chain managementapplications in retail trade, transaction processing in financial services, and patientclinical records in healthcare. The hotel industry has also extensively deployed self-service, digital reservation capabilities.2

In enhancing the effectiveness of services sector employees, there are three routesto improving productivity trends. First, efforts focusing on skill development andeducation can ameliorate sizable cross-organizational and transnational gaps. Sec-ond, trade policies that remove barriers to entry increase globally tradable servicesand create competitive pressures that drive enterprise transformation. Third, recentinformation technology developments are having meaningful impacts on servicessector productivity growth and value-added creation.

Machine learning, artificial intelligence, neural networks, deep learning, andother innovations have created fundamentally new capabilities that have improvedworkers’ ability to diagnose, decide, and act more effectively and productively.

2In the US hotel industry, both productivity and employment are growing. The Bureau of LaborStatistics reports productivity grew at an above average annual rate of 2.4% over the 2007–2016period, twice the 1.2% productivity growth in the much broader nonfarm business sector. Mean-while in 2016 at 1.882 million, industry employment was 47,700 workers above its 2007 peak and171,600 above its 2010 low point after the 2008–2009 Great Recession.

Foreword by Martin Fleming xi

Building on vast bodies of structured and unstructured data, such tools provide anassessment of probabilistic outcomes that allow workers to be more effective.3

Growth and value creation in the services sector has transformed the sector into acritical element of the global economy. Applying science to what in an earlier eramight have been person-to-person courtesy is now important for economic growth.Service science as a discipline has proven to be a necessary component of thesector’s maturation.

In the second volume of the Handbook of Services Science, Paul Maglio, CherylKieliszewski, and Jim Spohrer build on the seminal work of volume one which tookthe first major steps in clarifying the definition, role, and future of the, then, nascentfield. Now, more than 7 years later, to assess the progress toward a new interdisci-plinary services science, the handbook has been expanded by collecting new chap-ters written by researchers and scholars who have grown up with service science,mobile phones, cloud computing, big data, and artificial intelligence.

By looking through the eyes of today’s new services scientists, volume twoclarifies the value and grand challenges emerging from the integration of theories,methods, and techniques outlined in the original volume. I applaud the continuingeffort to build our understanding of this scientifically fascinating and economicallysignificant area of study—the journey continues.

Armonk, NY Martin FlemingVice President, Chief Analytics Officer

and Chief Economist, IBM

References

Bessen, J. (2015). Learning by Doing: The Real Connection between Innovation, Wages, andWealth. Yale University Press.

Heskett, J.L., Jones, T.O., Loveman, G.W., Sasser, W.E. and Schlesinger, L.A. (2008). “Putting theService-Profit Chain to Work”. Harvard Business Review. July-August.

Heskett, J.L., Sasser, W.E., and Schlesinger, L.A. (1997). The Service Profit Chain: How LeadingCompanies Link Profit and Growth to Loyalty, Satisfaction and Value. The Free Press.

Inghilleri, L. and Solomon, M. (2010). Exceptional Service, Exceptional Profit: The Secrets ofBuilding a Five-Star Customer Service Organization. AMACOM.

International Monetary Fund (2018). Cyclical Upswing, Structural Change. Chapter 3.“Manufacturing Jobs: Implications for Productivity and Inequality”. Washington, DC, April.

Okun, A. (1981). Prices and Quantities: A Macroeconomic Analysis. The Brookings Institution.Perez, C. (2002). Technological Revolutions and Financial Capital: The Dynamics of Bubbles and

Golden Ages. Edward Elgar.

3In addition to employee productivity tools, online shopping sites and online intermediated sitesusing vast stores of structured and unstructured data allow offers based on the demographics,interests, and experiences of individual consumers, creating unique room rates and moving close tothe perfect price discrimination hypothesized by Okun.

xii Foreword by Martin Fleming

Martin Fleming is IBM’s VP, Chief Analytics Officer, and Chief Economist leading IBM’scognitive enterprise transformation. He co-leads the Shared Prosperity initiative of IBM-MITWatson AI Lab. Previously, within IBM Corporate Strategy, he led IBM’s Smarter Planet strategyand execution, including supporting the formation of SSME (Service Science, Management, andEngineering). His career started at MIT’s System Dynamics Group. He served on NABE (NationalAssociation of Business Economists) Board as Conference Business Economists Chair. His workhas been published in the Journal of Economic and Social Measurement, Business Economics andAmerican Demographics, The New York Times, and The Wall Street Journal. He is a member of theAmerican Economic Association.

Foreword by Martin Fleming xiii

Foreword by Irene Ng

What is simplicity? I ask this question to be provocative of course, but I do mean itseriously. It seems like the world is becoming ever more “complex”—and wheneversomeone says that, everyone seems to nod in violent agreement. Some great thinkershave given us much wisdom in understanding complexity, such as Senge’s distinc-tion between detail complexity, arising because of the number of variables, anddynamic complexity, arising from the way interactions create subtle causes andeffects (Senge 1990). But is the world really more complex than it used to be? Oris it that our increasing understanding of the world makes it seem more complex?

The reason I start from simplicity is because I don’t think you can call somethingcomplex if you can’t define its opposite. You can’t say “it’s dark” until you canexplain that “dark” is the absence of light (apologies to physicists that study darkmatter). Thus, nothing is just “complex.” There is an absence of simplicity. Soasking “What is simplicity?” may just help us understand complexity. In a nutshell,simplicity evokes notions of atomic, unadorned, straightforward, or obvious,whereas complexity is considered to be compound, elaborate, difficult, or opaque.

My own quest for a much deeper understanding of service systems and ways tomore purposefully intervene started about 10 years ago when I first moved toCambridge—with 3 years buyout of my time to do any research I wanted to do—amentor of mine, Professor Chris Todd from UCL (who has sadly passed away since)visited me. We had tea in a lovely cafe overlooking a great view of King’s College.When he asked me what I would do in those 3 years, I presented my plans for A(4*) publications. He looked disappointed and said “Irene, you love research, andyou now have time to do it. Why don’t you do something hard.” His comment hasstayed with me since, and it has driven the research I choose to do still today.

Hard problems are inconvenient. Yet they are incredibly rewarding, and they cantruly make a difference. Working on something that is complex and making it simpleis a hard problem. In our quest to understand complexity, we forget that we shouldreally be trying to understand why there may be a lack of simplicity. The larger

xv

question of course is whether we will take the power to identify the simplicitieswithin a complexity to enable change of a system through redesign andreengineering or whether we only wish to observe and manage complexity. Toooften, I find researchers choose to be passive, as if their place in the world is merelyto describe and to create understanding and insights. I am not diminishing thatcontribution, but I lament that we do not feel empowered to do more.

Service scientists have a lot on their plate in understanding service in a hyper-connected and complex world of science, technology, humans, cognition, andbehavioral and social lives. We live in a noun-based world—engineers and scientistsfocusing on the artifacts, the things, the objects, the structures, and even the systems.Most people, however, create meaning from verbs—eating, seeing, reading, travel-ing, posting, tweeting, sleeping, and running. The combination of the two createsinstitutionalized rules and heuristics from a social angle; tools and data flow from thetechnological angle. Combining them means the combining of different approaches,methods, cultures, mindsets, skills, and training. A look across the room, a meetingof eyes, and an instant connection between two people seem too remote in conceptcompared to the connection between two API end points. Our natural instincts asservice researchers investigating cyber-social-physical service systems are to scopeit down, ignore one type of phenomenon, or change the question so that the harderquestions can be put aside. Yet, there are researchers who take the opportunities totry and decipher the simple from the complex.

In thinking about systems, particularly human-centered service systems, there arenatural drivers of simplicity that help us navigate the complexity to elicit simplicity.These drivers are conventions that demonstrate themselves as rules of thumb(heuristics), repetitive action (procedural memory), norms and rules (institutions),representation (models), limits (boundaries), results or conclusions (outcomes),instruments (tools and technologies), explicit expression (languages), and organizedfacts (information). In the world of cyber-social-physical service systems, each ofthese conventions are used to different degrees and in different combinations to gaineither an understanding or create an improvement in how services are enabled,measured, delivered, and established within society to improve livelihoods andquality of the planet.

Heuristics. These are “practical methods not guaranteed to be optimal or perfect,but sufficient for the immediate goals” (Simon 1996). Heuristics are mental short-cuts. They are used when we don’t wish to expend too much cognitive power; whenwe don’t really want to think too hard. Marketing uses heuristics a lot, so thatfamiliarity with a brand will help you make decisions to buy quickly, withoutsearching for too much information. Common sense and rules of thumb—these areall heuristics. With heuristics, something that may be complex is perceived to besimple.

Muscle memory. As I type these words, procedural memory goes some waytoward making us believe in the simplicity of repeated action (Gray and Lindstedt2017; Shapiro 2010). Showing how to brush your teeth ascribes muscle-memorizingaction without cognitive effort of description. Sometimes, it is simpler to show

xvi Foreword by Irene Ng

someone how to do something, rather than explaining how to do it because whencreating a pattern of action, one creates repetition, stability, and, yes, heuristics.

Institutions. Institutions are social norms and rules (Ostrom 2005). They gener-ate recurring behaviors that also reinforce the norm. Eating with chopsticks anddriving on one side of the road—these are rules that have been institutionalized.Driving can be complex, but if you understand and believe that the car on the otherside of the road will not come over to your side of the road, you won’t panic whenyou see a car coming toward you. Instead, you are relaxed because your actions areembedded in muscle memory and everyone generally follows the rules, makingdriving reasonably simple (most of the time).

Models. As Box’s aphorism (1976) goes, “All models are wrong but some areuseful.” The map to guide you around the city is probably wrong too, but it isincredibly useful. Often in understanding the world, we try to be as close to reality aspossible. If we do that with maps, we will never have useful maps. The simplicity ofmaps comes from having just enough information to guide and no more. Modelmaking is simplifying to be useful.

Boundaries. The easiest way to force simplicity is to set limits (constraints). Put aman in a cage and his actions become incredibly simple. Widen his freedom to a cityand you get more complexity. The point here is not that we should cage people butthat we should understand why and how boundaries matter in complexity. And Idon’t mean merely physical boundaries, but also sociological ones like in-groupsand out-groups and economic ones like transaction and payment boundaries. Bound-aries drive human behavior, and putting them at the right places will change theincentives and the behavior of people within. When data was more expensive,people texted or called. When it became cheaper, they emailed and used WhatsApp.When it became cheaper still, they watched movies. Boundaries can create bothsimplicity and complexity. Most of all, boundaries define what is possible within asystem. How high can you throw a ball? The answer is not derived from how highyou have thrown it before or how good is your throwing skill. The answer is howhigh is the ceiling. And your behavior? If you know there is a ceiling, you won’tthrow as hard. Boundaries can align behaviors or destroy the workings of a system.

Outcomes. To create simplicity, we can define just one outcome. If the outcomeof going to London is just to get to London, it’s relatively simple. If it’s to get toLondon cheaper than £30, it gets a little more complicated. If it’s to get to Londonwith a group of friends from different parts of the UK at 4 pm, it gets complex. Andwhen one of them can’t afford to go but others would like him there, it gets political.Complexity often arises when there are multiple stakeholders that want differentoutcomes.

Tools. The technological answer to human heuristics is physical tooling. Thesmartphone has created what economists call externalities, side effects, which maybe positive or negative. Positive externalities come from better coordination betweenfriends, better tools for productivity and efficiency. Negative externalities come fromprivacy loss and addiction. Human lives are made simpler with tools. Schedulingmeetings is simpler with doodle; coordination is simpler with WhatsApp.

Foreword by Irene Ng xvii

Language. Simplicity is created often with an explicit language, like mathematicsor music. Such “languages” have very little ambiguity, which is why mathematics isused for models and music is a representation of the emotions of the creator (Cooke1959/1989). Other human languages have modalities, that is, they are like signs andthey reflect a status of reality that requires interpretation by others. The more wordswe have for sad or happy, the richer our descriptions become—but also the morecomplex. Words are performative in that they can be self-fulfilling, and using wordschanges us and changes others. Using words, whether to describe complexity or tocreate simplicity, immediately creates a description that is value laden.

Information. Information can create simplicity. When I go to London, I knowwhere I am going. A third party observing my movements and predicting where I gonext will find my decisions and movements hard to understand and may deem them“complex.” For me, even if I deviate along the route, I know why I deviated and whatI will do next. My actions are not complex to me at all. Simplicity is therefore amatter of information and perspective. Information and heuristics combine to createtemplates of behaviors around the goods and services we buy and use. They makethe world simple to us, though it may be complex to observers. The question to askas researchers is whether a system is truly complex or whether we simply do notunderstand it or know it, making a judgment that it is complex. Together withinformation come the four types of information drivers of simplicity and complexity:asymmetry (something I know but you don’t), incomplete (something that is notknown now), uncertain (we’re not sure if the information is true or false), andambiguous (there are two meanings, but we don’t know which one is the rightone). Together with assumptions of human rationality, we take a stand on how weview a system. Most economists like to use perfect rationality with symmetric,complete, certain, and unambiguous information in their models. The reason forthis is to create models that establish the “height of the ceiling.” It is the boundarythat helps us understand all human behavior that would lie within and below it. Itisn’t important that such a model may not exist. They can serve as a boundary guide.Like stars in our solar system, we may not be able to travel to them, but they areincredibly useful for navigation.

Service science is a discipline of service to humanity. The work captured in thissecond volume of the Handbook of Service Science embraces the challenge of doingsomething hard. My hope is that researchers and practitioners in this field continue totake up the baton and meet that challenge through understanding complexity fromsimplicity and remain empowered to change systems through redesign andreengineering. You should do something hard too.

Coventry, UK Irene NgProfessor of Marketing and Service Systems

University of Warwick

xviii Foreword by Irene Ng

References

Box, G. E.P. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799.

Cooke, D. (1959/1989). The language of music. Oxford University Press.

Gray, W. D., & Lindstedt, J. K. (2017). Plateaus, dips, and leaps: Where to look for inventions anddiscoveries during skilled performance. Cognitive science, 41(7), 1838–1870.

Ostrom, E. (2005) Understanding institutional diversity. Princeton University Press.

Senge, P. (1990). The fifth discipline: The art and science of the learning organization. New York:Currency Doubleday.

Shapiro, L. (2010). Embodied cognition. Routledge.Simon, H. A. (1996). The sciences of the artificial. MIT press.

Irene Ng is Professor of Marketing and Service Systems and Director of the International Institutefor Product and Service Innovation at WMG, University of Warwick. She was CEO of SA Toursand Travel group and founded Empress Cruise Lines. An industrial economist by training, herresearch includes transdisciplinary study of value, markets, and economic/business model design.Her awards include ESRC, NIHR, InnovateUK Innovation Caucus Thought Leader, and UKAdvanced Institute of Management (AIM) Research Services Fellowships. As entrepreneur andacademic, Irene is passionate about the link between practice and research. She advises startups onnew financial models in digital businesses.

Foreword by Irene Ng xix

Preface

We didn’t plan to compile a second volume of the Handbook of Service Science10 years ago when we started to compile the first one. We didn’t know where thefield of service science would be or, frankly, whether there would be a field at all.But a few years ago, we were deeply gratified to learn that theHandbookwas in wideuse and considered an important reference. Yet, as we all know, things change, andwe started to ask colleagues to think about the progress that had been made in thestudy of service. We asked them—and we asked ourselves—where is service sciencetoday?

After all, the world has changed significantly in 10 years. Advancing technolo-gies—smartphones, cloud computing, social platforms, big data analytics, artificialintelligence—have transformed business models and are reshaping public policy,from retail and hospitality to transportation and communications. The first volume ofthe Handbook marked the emergence of the field of service science: integratingestablished disciplinary studies of business-to-customer service systems with theneeds of a new era of business-to-business and societal scale service ecosystems (theso-called Smarter Planet era). Nevertheless, most chapters in that volume focused onone aspect of service from a single disciplinary perspective. We decided to expandthe Handbook by collecting a new set of chapters for Volume II, written mainly byresearchers and academics who have grown up with service science. We thought thatby looking through the eyes of today’s new service scientists, we might begin to seevalue and grand challenges emerging from the integration of theories, methods, andtechniques brought together in the original volume. Now, in this volume, we seesome familiar themes but rooted more deeply in service-dominant logic and systemsthinking. We see needed clarity in how to identify, enable, and measure service, andwe see new ideas and connections made to physics, design, computer science, anddata science and analytics for advancing service innovation and the welfare ofsociety.

We thank Martin Fleming and Irene Ng, who each generously provided fore-words to the volume, providing considerable insight into some of the challenges andopportunities facing service research. Martin tells us “the global economy is in the

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fifth industrial epoch,” in which current technologies are enabling radical newinnovation that will have a direct impact on the growth and importance of service.Irene tells us that “hard problems are terribly inconvenient,” yet they are everywherein service. Luckily, many smart and talented people are ready to suffer this incon-venience to shed light on otherwise often complex and opaque problems andsituations. Chapters in the first volume represent seminal thinking about the scien-tific study of service, service systems, and service innovation. They helped spurconversations across fields and establish a common language for talking aboutservice. Chapters in Volume II represent a snapshot of where we are today: to alarge extent mixing methods and disciplines, working from a common vocabulary,and going beyond simple characterizations of service to understand, design, andinfluence aspects of the global service system in all of its complexity.

We also thank everyone else who helped bring together this collection of work:our associate editors, Kelly Lyons, Lia Patrício, and Yuriko Sawatani, who solicitedchapters and shepherded the review process, working with authors and reviewers todevelop a set of truly exciting papers; Melissa Fearon and Carolyn Flanagan atSpringer who encouraged us and established a smooth production process; and ofcourse, our contributors, whose extraordinary work we are lucky to showcase here.

Where will service science be 10 years from now? Let’s find out!

Merced, CA, USA Paul P. MaglioSan Jose, CA, USA Cheryl A. KieliszewskiSan Jose, CA, USA James C. SpohrerToronto, ON, Canada Kelly LyonsPorto, Portugal Lia PatrícioNagoya, Aichi, Japan Yuriko SawataniJune 1, 2018

xxii Preface

Contributors

Leena Aarikka-Stenroos Tampere University of Technology, Tampere, Finland

Melissa Archpru Akaka Daniels College of Business, University of Denver,Denver, CO, USA

Luna An Wellesley College, Wellesley, MA, USA

Laura Challman Anderson IBM Research—Almaden, San Jose, CA, USA

Ida Asadi-Someh The University of Melbourne, Parkville, VIC, Australia

Youakim Badr Engineering Division, Big Data Lab, Penn State University,Malvern, PA, USA

University of Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, Lyon, France

Sergio Barile Department of Management, Sapienza University, Rome, Italy

Rahul C. Basole College of Computing, Georgia Institute of Technology, Atlanta,GA, USA

Clara Bassano Department of Management Studies and Quantitative Methods,Parthenope University, Naples, Italy

Dav Bisessar International Business Machines Corporation, Armonk, NY, USA

Mary Jo Bitner W. P. Carey School of Business, Arizona State University, Tempe,AZ, USA

Ruth N. Bolton Center for Service Leadership, Arizona State University, Tempe,AZ, USA

Christoph F. Breidbach The University of Melbourne, Parkville, VIC, Australia

Alexandra Brunner-Sperdin University of Applied Sciences, Kufstein, Austria

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Luigi Cantone Department of Economics, Management, Institutions, Federico IIUniversity of Naples, Naples, Italy

Luca Carrubbo Department of Medicine, Surgery and Dentistry, University ofSalerno, Baronissi, SA, Italy

Jennifer D. Chandler California State University, Fullerton, Fullerton, CA, USA

Michael Cheng University of Toronto, Toronto, ON, Canada

Sriram Dasu Marshall School of Business, University of Southern California, LosAngeles, CA, USA

Michael Davern The University of Melbourne, Parkville, VIC, Australia

Benoît Desmarchelier The University of Lille, Lille, France

Michael J. Dixon Jon M Huntsman School of Business, Utah State University,Logan, UT, USA

Michael Ehret Nottingham Business School, Nottingham Trent University,Nottingham, UK

Mattias Elg Industrial Engineering and Management, Linköping University,Linköping, Sweden

Cristina Favini Logotel, Milan, Italy

Niels Feldmann Karlsruhe Institute of Technology, Karlsruhe, Germany

Darima Fotheringham W. P. Carey School of Business, Arizona State University,Tempe, AZ, USA

Hansjörg Fromm Karlsruhe Institute of Technology, Karlsruhe, Germany

Robert Blair Frost University of Toronto, Toronto, ON, Canada

Patrik Gottfridsson CTF Service Research Center, Karlstad University, Karlstad,Sweden

Frank Hoy Worcester Polytechnic Institute, Worcester, MA, USA

Elina Jaakkola Turku School of Economics, University of Turku, Turku, Finland

Michael M. E. Johns Schools of Medicine and Public Health, Emory University,Atlanta, GA, USA

Maíra Prestes Joly Design Department, Politecnico di Milano, Milan, Italy

INESCTEC, Faculty of Engineering, University of Porto, Porto, Portugal

Cheryl A. Kieliszewski IBM Research—Almaden, San Jose, CA, USA

Kaisa Koskela-Huotari Karlstad University, Karlstad, Sweden

Richard C. Larson Massachusetts Institute of Technology, Cambridge, MA, USA

xxiv Contributors

Stewart Leinster-Evans BAE Systems plc, London, UK

Chiehyeon Lim Ulsan National Institute of Science and Technology, Ulsan,Republic of Korea

Filipe Lima Design Department, Politecnico di Milano, Milan, Italy

Vincenzo Loia Department of Business Sciences – Management & InnovationSystems, University of Salerno, Fisciano, SA, Italy

Kelly Lyons University of Toronto, Toronto, ON, Canada

Mallika Machra Wellesley College, Wellesley, MA, USA

Paul P. Maglio University of California, Merced, Merced, CA, USA

Teresa Marrone Department of Economics, Management, Institutions, Federico IIUniversity of Naples, Naples, Italy

Veronica Martinez Cambridge Service Alliance, University of Cambridge,Cambridge, UK

Janet R. McColl-Kennedy UQ Business School, University of Queensland,Brisbane, QLD, Australia

Alexandra Medina-Borja Department of Industrial Engineering, University ofPuerto Rico-Mayaguez, Mayaguez, PR, USA

Cristina Mele Department of Economics, Management, Institutions, University ofNaples Federico II, Naples, Italy

Abigail M. Moser Massachusetts Institute of Technology, Cambridge, MA, USA

Andy Neely Cambridge Service Alliance, University of Cambridge, Cambridge,UK

Amy L. Ostrom W. P. Carey School of Business, Arizona State University,Tempe, AZ, USA

Glenn Parry Bristol Business School, University of the West of England, Bristol,UK

Lia Patrício INESCTEC and Faculty of Engineering, University of Porto, Porto,Portugal

Oleg V. Pavlov Worcester Polytechnic Institute, Worcester, MA, USA

Kara M. Pepe Schools of Medicine and Public Health, Emory University, Atlanta,GA, USA

Paolo Piciocchi Department of Political, Social and Communication Studies,University of Salerno, Salerno, Italy

Contributors xxv

Maria Cristina Pietronudo Department of Management Studies and QuantitativeMethods, Parthenope University, Naples, Italy

Francesco Polese Department of Business Sciences – Management & InnovationSystems, University of Salerno, Fisciano, SA, Italy

Ying Qian Engineering Division, Big Data Lab, Penn State University, Malvern,PA, USA

Robin G. Qiu Engineering Division, Big Data Lab, Penn State University,Malvern, PA, USA

Lawrence Qiu School of EE & CS, Penn State University, University Park, PA,USA

Paavo Ritala Lappeenranta University of Technology, Lappeenranta, Finland

Guillaume Roels INSEAD, Fontainebleau, France

William B. Rouse Center for Complex Systems and Enterprises, Stevens Instituteof Technology, Hoboken, NJ, USA

Tiziana Russo-Spena Department of Economics, Management, Institutions, Uni-versity of Naples Federico II, Naples, Italy

Scott E. Sampson Marriott School of Business, Brigham Young University, Provo,UT, USA

Peter Samuelsson CTF Service Research Center, Karlstad University, Karlstad,Sweden

Daniela Sangiorgi Design Department, Politecnico di Milano, Milan, Italy

Gerhard Satzger Karlsruhe Institute of Technology, Karlsruhe, Germany

Yuriko Sawatani Graduate School of Management, Nagoya University of Com-merce and Business, Business School, Nagoya, Aichi, Japan

Ronny Schüritz Karlsruhe Institute of Technology, Karlsruhe, Germany

Graeme Shanks The University of Melbourne, Parkville, VIC, Australia

Sanja Simonovikj Massachusetts Institute of Technology, Cambridge, MA, USA

James C. Spohrer IBM Cognitive Opentech, Almaden, San Jose, CA, USA

Alexis Strong Cornell University, Ithaca, NY, USA

Pierpaolo Testa Department of Economics, Management, Institutions, Federico IIUniversity of Naples, Naples, Italy

Stephen L. Vargo Shidler College of Business, University of Hawai’i at Mānoa,Honolulu, HI, USA

xxvi Contributors

Chander Velu Cambridge Service Alliance, University of Cambridge, Cambridge,UK

Rohit Verma Cornell University, Ithaca, NY, USA

Liana Victorino Gustavson School of Business, University of Victoria, Victoria,BC, Canada

Jochen Wirtz National University of Singapore, Singapore, Singapore

Lars Witell CTF Service Research Center, Karlstad University, Karlstad, Sweden

Industrial Engineering and Management, Linköping University, Linköping, Sweden

Yutaka Yamauchi Graduate School of Management, Kyoto University, Kyoto,Japan

Mohamed Zaki Department of Engineering, Institute for Manufacturing, Univer-sity of Cambridge, Cambridge, UK

Tianhai Zu Engineering Division, Big Data Lab, Penn State University, Malvern,PA, USA

Contributors xxvii