T Summit: The logic of future talent, work, and higher education.

In the age of cognitive systems and social networks, a type of “Moore’s Law for Education” may arise.  To understand this core “smart service system” it is necessary to first think about human talent more precisely…

1. Talent: What is the difference between I’s and T’s?

Today’s institutes of higher education are well designed to produce I’s (I-shaped professionals).  I’s have depth in one area of study, practice, and culture.  More and more employers are looking for T’s (T-shaped professionals).  T’s have depth and breadth.  So T’s have something extra that makes them highly sought after in the search for talent.  For example, Professional Science Masters (business on top of science degrees) can help with boundary spanning abilities; general education (liberal arts) can help provide some of the T’s communication skills; Service Science Management and Engineering courses can also help with breadth; all of these are especially effective when projects (challenge-based, multidisciplinary team projects with industry mentors) are well-integrated into courses across the curriculum spectrum from science and engineering, to management and public policy, to social sciences and economics, to humanities and arts, adopting an essential element of professional schools, like medicine, law, education, and architecture, that build the professional social networks of students before graduation.

Talent is a blend of basic competences, professional skills, and attitudes.  Basic competences (by definition) can be assessed with multiple-choice questions, and certificates of competence are sought after by students and practitioners in search of new employment opportunities.  On the other hand, professional skills cannot be fully assessed except by other professionals, usually during a performance or challenge, such as playing piano, operating machinery, or working well on a team project with others.  Attitudes shape our responses to situations we cannot control, and are fundamental to human performance in challenging situations, but are quite difficult to assess without participating in diverse projects and activities together.  Interaction with others, mentors and role models, is essential for the development of skills and attitudes that go beyond more-easily-tested basic competences.

2. Work: What is the T advantage (especially in the age of cognitive computing, and smarter machines)?

Breadth provides T’s more boundary spanning skills and attitudes,  which sets the stage for improved teamwork, adaptive capacity, and innovation capacity.   Smarter machines are on the way, such as IBM’s Watson system which won against the world’s best players of the TV game show” Jeopardy!,”  and in the age of cognitive computing these smarter machines will easily pass multiple choice tests of competence in more and more areas where human experts have “competence depth.”  Eventually, these systems competence depth (as assessed by multiple-choice competence tests) will be greater than the depth of the best I-shaped human experts, in part because these systems can rapidly “learn” by absorbing millions of pages of text in seconds, and in fact these systems will actually support the generation of new multiple-choice basic competence tests.  In the era of cognitive computing, which has already begun, the T advantage includes boundary spanning skills and attitudes that improve teamwork (empathy and communications), adaptive capacity (co-learning rates), and innovation capacity (creativity and non-routine problem solving).

3. Higher Education: How to foster and inspire more T’s?

Faculty and their courses are at the heart of higher education today, and we expect the same will be true in the future.  Top faculty are among the best I-shaped human experts, the 1% club of intellectual abilities in their nations for their chosen fields of specialization.  When students take courses in a specialized area their “depth competence”increases in a way that can be objectively measured by multiple choice testing.  What is different in the future is that faculty will play an increasingly active role in changing their courses year over year to benefit from new knowledge (discovery, research) and new applications (engagement, entrepreneurship).  MOOCs (Massively Open Online Courses) and other technologies will be used by faculty to shift standardized, routine lecture materials into student pre-requisites (self-service learning of depth competences); thus freeing up faculty time to inspire students on the frontiers of knowledge and applications, which is inherently social (current world issues, by people alive and contributing today).  The skills and attitudes needed to help students develop social networks to others, including researchers and industry practitioners, will foster greater breadth, and provide more access to role models to learn skills and attitudes.  Both faculty and students are likely to become more T-shaped, as well as the practitioners will become more T-shaped lifelong learners through these challenge-based project interactions – beyond basic competences, skills and attitudes will need to be brought to bear.   A type of “Moore’s Law for Education” may also arise if 10% of most course’s lectures are newly added every year to reflect the fore-front of research and practice, while the pre-requisite MOOC lectures become better integrated and optimized to prepare students to take the more advanced version of the course that emerges each year.   Industry and higher education will both be transformed, co-creating more T’s and fewer I’s – where T’s are better adapted than I’s for a rapidly changing world of opportunities.

This so-called  “law” we envision is quite simple to explain, and requires faculty to shift 10% of their older course lectures to MOOCs (Massively Open Online Courses) each year, and add 10% new lectures based on topical themes in research (top journals) and business/societal applications (top new business and social innovations related to course topics). Each year the collective of course-prerequisite MOOC lectures gets integrated and compressed by 10%.  No decrease in faculty.  No increase in number of lectures (except as new courses are added).  Increasing student access to course-prerequisite MOOCs.  Success depends on several factors, including re-integration of knowledge, and more-student self-service via MOOCs. Each year the students entering the courses are better prepared, students graduating know more, faculty stay more engaged as stewards of their course areas, and faculty and students expand their social networks connecting to top researchers and top entrepreneurs. The “law” is based on principles of service science, especially the principle of run-transform-innovate (“improve the engine while the car is running,” or systematic exploitation-exploration learning model (Prof. James March, Stanford) – applied to service system entities).

Like the original Moore’s Law this is a “self-fulfilling human law” of investment and change, not a law of nature.

The Shape of Analytics Certification explains T-shaped data scientists for big data analytics
https://www.informs.org/ORMS-Today/Public-Articles/February-Volume-39-Number-1/The-shape-of-analytics-certification

Nick Donofrio (IBM Fellow Emeritus and retired SVP Innovation) explains T-shaped innovators
http://www.kauffman.org/advancing-innovation/innovation-that-matters.aspx

IDEO CEO Tim Brown explains T-shaped design thinkers
http://chiefexecutive.net/ideo-ceo-tim-brown-t-shaped-stars-the-backbone-of-ideoae%E2%84%A2s-collaborative-culture

Prof. Henry Chesbrough (Berkeley, Open Innovation) explains T-shaped managers
http://www.forbes.com/sites/henrychesbrough/2011/04/28/the-war-for-talent-and-open-innovation/

Gary Beach blogs about Critical Thinking an Quantitative Skills
http://citizenibm.com/2013/08/gary-beach.html

Business Higher Education Forum describes T-shapes and deeper learning
http://www.bhef.com/publications/promoting-effective-dialogue-between-business-and-education-around-need-deeper-learning

Dr. Phil Gardner (MSU, Collegiate Employment Reearch Institute) provides references to T-shaped professionals
http://www.ceri.msu.edu/t-shaped-professionals/

If you would like to find out more:

The T Summit is coming…

In preparation for this event – NSF Virtual Forum…

 Platform Technologies and Smart Service Systems

Here is my presentation:
http://www.slideshare.net/spohrer/nsf-virtual-forum-20130923-v2

Here are some short answers to questions:

The role of technology in service systems

1. What are the essential research elements to make possible the integration of platform technologies into service systems?

Platform technologies are owned by providers, who have to negotiate rights and responsibilities with customers, as value propositions.

Service systems are dynamic configurations of resources (people, technology, organizations, information) connected by value propositions.

Customers will not adopt platforms where the  value propositions are not viewed as win-win.

So an essential research element is around understanding the relationships between, platform technologies, smart service systems, and value propositions.

Some call the value propositions ‘business model design.”

What is different about the design of service systems and service design?

Service design has a heavy emphasis on customer experience, and is often done by the service provider.

Design of service systems is a bigger design space, and includes modeling competitors, governing authorities (regulations), etc.

How can the deployment of smart systems be accelerated?

Perhaps the wrong question.  Think of service systems as evolving over time – getting smarter (e.g., better information for all stakeholders to make better win-win decisions, co-elevate capabilities, trust parties more, improve faster, be sustainable, be resilient, etc.).   So the real question is service system innovation mechanisms, both to make existing service systems smarter and create new types of service systems as well.   One way to accelerate would be to have a good historical analysis done – for this we need a Linneaus and Darwin of service system taxonomy and evolution, respectively.   Modularity of service systems is one item that will jump out as a way to accelerate innovation of technology platforms – think of a car as a technology platform and all the innovation of each module – engine, seats, windows, doors, seat belts, driver interfaces to information systems, etc.

2. Can you share your experience regarding the use of smart technology in service system settings?

Yes, all IBM’s Smarter Planet examples.  My favorite is the Smarter Cities Intelligent Operations Center (SC IOC) in Rio.  My second favorite is Watson in Healthcare.

3. What qualifies as a “smart”service system?  Can you provide examples?  Good starter question for Jim.

Yes, as above SC IOC is a platform technology and each city that has it can be an example of a Smart Service System (over 2500 worldwide inlcluding Rio)

How many hospitals have a Watson advisor (technology platform), only a small number.  Each hospital is an example of a Smart Service System, if they use Watson or other technology platforms to improve measures.

The measures of smartness include productivity, quality, compliance, innovativeness, sustainability, resilience, and much more.

In general, smart service systems are instrumented, interconnected, and intelligent.  Instrumented means sensors, sensors everywhere – more of the information (real-time and historical, as well as monte carlo predictive runs) that stakeholders, providers, customers, governing authorities, etc. – need to make better win-win (value co-creation, capability co-elevating) decisions is available.  Interconnected means people have easy access to information about a particular service system, as well as others that interact with it via value propositions, perhaps displayed on their smartphones.   Intelligent means recommendations systems that work to provide stakeholders useful choices – for example, Watson-style recommendation systems, or Amazon-style recommendation systems.

The technology platform is a shared part of many smart service systems – service systems made smarter by access to the technology platform of some provider.

Service systems are dynamic configurations of resources (technology, people, organizations, information) connected internally and externally by value propositions.

Because of the diversity of resources that make up service system, service science, which is the study of service systems and value co-creation processes, requires multiple disciplines working together (engineering, social sciences, management, economics and law, etc.).

About academe-industrial partnerships:

4. What are the ingredients, in your experience,that are essential for a successful partnership with industry and academia?

Industry-academia partnerships in my experience, work best when there is a clear focus on real-world challenges, real-world tools, real-world data, and real-world mentorships – where students work in teams (ideally multidisciplinary) to apply what they have learned in their courses to real-world challenges.

These experiences can help the faculty and students who have entrepreneurial inclinations do university-based startups.

These experiences can help the industry mentors identify top students for internship and employment opportunities.

Do you think that these would be any different in partnerships for service system innovation?

The focus on real-world challenges, tools, data, mentorships should work great for service system innovations.

The main thing is to make sure the team of students is multi-disciplinary, and the real-world challenge can be managed in a meaningful way with the time and course content constraints of faculty.

5. How do you go about finding the right academic and industrial partners?  How could NSF facilitate this?

IBM has relationships with 5000 universities around the world, 1/3 in the US.  We have many programs to engage on multiple topics.

I think NSF does a great job with the award programs, and experimenting with LinkedIn Groups, Virtual Summits, etc.   Challenges like mini-X-prizes are also an important area to explore, especially if done with Reginal Economic Development groups around the US.

About human innovation capacity:

6. What are your insights regarding human innovation capacity in these type of partnerships?

Human innovation capacity at universities can be estimated by looking at the startups generated by the university (one method of estimation).   Technology platforms for smarter service systems are aligned quite well with the startups coming out of universities these days.  To grow rapidly startups often create or align with existing technology platforms to accelerate scaling up globally, and maximize customers, etc.

The NAE list of engineering grand challenges are also well aligned with real-world challenges.  http://www.engineeringchallenges.org

How can the experience of students be enhanced?

The student experience should include: growing their personal brand, on-line professional persona, and membership in professional associations (they may change universities, change jobs, but advancing in professional associations last a life-time).  The professional association also builds their global network of professional contacts.

The industry mentor also becomes part of the students professional network.

The outcome of mentorships should also be posted publically to social media platforms, and build the students online portfolio.

About Service Science:

7.  What is the potential role of service science researchers in service system  innovation?  In this solicitation?

At SJSU (Prof. Lou Freund, service system engineering) and VaTech (Prof. Ralph Badinelli, business and IT service system operations) already have industry projects for their students to analyze service systems.  Other service science researchers in academic are doing the same.  With a focus on industry mentorships related to technology platforms and smart service systems, service science researchers and faculty teaching about service systems should be able to work closely together.   Service science researchers in industry can help guide as well, especially with real-world challenges about the win-win-win benefits of university-startup, large-industry-integrators, and customers for platform technologies for smart service systems.

Previously, I posted these answers to these other questions:
https://service-science.info/archives/3209

I have also made these posts about the NSF efforts:
https://service-science.info/archives/3189

Today I was at the Global 1000 Startup Showcase in Menlo Park, CA.

There was a good discussion about industry models of working with universities and startup companies from Huawei, Tyco, Dell, Microsoft, Intel, and IBM.

The win-win-win – where big company, startup company, and customers all benefit – was highlighted.

I mentioned the following IBM programs and learned a lot about the programs from other large industry players represented on the panel.

IBM Smart Camps – global events and IBM mentors

IBM Global Entrepreneur Program – access to platform and tools

IBM Partnerworld – programs for partners

IBM Supplier Connect – small and medium companies selling to global 1000

IBM Acquisitions – about one a month for the last ten years

IBM University Programs – 6 R’s (research, readiness, recruiting, revenue, responsibilities, and regions)

Two key challenges: (1) working with universities to create more T-shaped graduates with depth and breadth across disciplines, sectors, cultures, and (2) simplifying complex integration of multiple companies’ offerings.

ISSIP (International Society of Service Innovation Professionals) exists to promote T-shaped innovators professional development, across disciplines, sectors, and cultures.  ISSIP is also promoting mentorships between industry and academics – perhaps a 10x increase in mentorships could lead to a 2x increase in internships.  If you have an interest in T-shapes, mentorships, and the the transformation of universities to align with regional economic development groups, please feel free to contact me at spohrer@us.ibm.com.

In preparation for the NSF Smart Service Systems Virtual Forum, here are some questions and responses:

1.       What do we mean by smart service systems?

Smart service systems benefit customers and providers.

Service providers try to compete for customers by (1) improving existing offerings to customers, (2) innovating new types of offerings, (3) evolving their portfolio of offerings, (4) changing their relationships to suppliers and others in the ecosystem.

“Smart” refers to improvement in key performance indicators over time (e.g., productivity indicators, quality indicators, compliance indicators, sustainability indicators, resilience indicators, etc.).  “Smart” often implies increasing system capabilities over time, that overcome system constraints.  An important constraint in many service systems is the trade-off between productivity and quality…. often the way to break out of that constraint requires new technology, information systems, and customer skills for self-service… for example, a bank’s ATM (automatic teller machines) systems introduced in the 1970’s improved both productivity and quality indicators for customer to withdrawn their money day or night. However, it required customers learning new skills.  Retail store self-service check-out systems require even more skills for self-service.

“Service” refers to the application of knowledge (e.g., technology, organizational forms, business models, etc.) for mutual benefits, between a provider entity and customer entity (and often including other stakeholder entities, such as government agencies concerned with regulator compliance, investors concerned with return on investment, etc.).  Within the service science community “service” is synonymous with “value co-creation between entities” and includes many forms of cooperation, coordination, and even mutually beneficial forms of competition.

Most service phenomena derives from division of labor between entities (service for service exchange), creating mutual dependencies between entities in vast networks of interaction and exchange.   Entities apply knowledge, including technological tools, forms of organizations, to create mutual benefits that improve productivity (provider concern) and quality (customer concern) and other measures.

“Service systems” (in general) refer to dynamic configurations of resources (people, technology, information, organizations) interconnected by value propositions internally and externally to other service systems. A bus stop may have structure for passengers to wait for the bus and stay dry, often they are in disrepair – however, many cities lease access to that resource to advertising agencies – and get a new revenue stream to support public transportation, as well maintenance service.   Adding display screens for ads, makes them “smart” in some ways, and may require cameras for extra security.

“Service systems” (specifically) have been evolved and designed for all aspects of business and society – transportation, water & waste, agriculture and manufacturing (servitization, such as Rolls-Royce “Power-by-the-hour”), energy and electricity (“smart grids), ICT, building, retail & hospitality, finance & banking, healthcare, education, government.   Types of service system entities – entities with rights and responsibility to offer service to others include – people, businesses, universities, hospitals, cities, states, nations, etc.  A better taxonomy of service system entities and understanding of the distribution of types of service systems in different societies is much needed.

“Service innovations” scale the benefits of new knowledge, globally and rapidly.

2.       What are the major hurdles to introduce new knowledge and innovations (e.g., technologies, organizational forms, methods of interaction, etc.)  developed in academic institutions into commercial smart service systems?

Apple’s innovations have often been based on customer experience and ease-of-use.  Interaction design and new business model design are aspect of smart service system design.

The lack of ability for academics to interact with real customers and evolve new knowledge and innovations limits their commercialization efforts (e.g., lean startups are often better than licensing efforts for bringing difficult-to-scale new knowledge to market).  Facebook interacted with students as customers.  Google interacted with faculty and students as customers.   SPSS interacted with faculty and students as customers.  Tools that are accessible on-line provide a platform for academics to reach more customers.  Amazon, Google, Apple, IBM/Softlayer, HP, Cisco, and many others are providing academics access to Cloud based capabilities that allow new modes of customer-provider and customer-customer, etc. interactions.  The internet, the internet-of-things, and other platforms can improve academics ability to interact with customers.

a.       Resources – Kickstarter and other crowd-funding sites offer ways for customers to invest in the innovations they want to see become real
b.       Technology – Real tools and real data often limit academics abilities to take on real challenges
c.       Policy – Privacy and protection of data is a very real concern these days, especially as cyber-security is increasingly necessary to stop criminals
d.       Business Models – lean startups pivot their business models and business concepts quickly based on experiments with real customers
e.       Industry Mentorships for students in course – learning to apply knowledge to take on real-world challenges is key

University four missions:
Learning – teaching: knowledge transfer
Discovery – research: knowledge creation
Engagement – entrepreneurship: knowledge application
Wholeness – citizenship: knowledge integration

3.       Why are behavioral and cognitive considerations essential to further adapt technologies to be used in service systems?

Behavioral and cognitive scientists can help help understand both customers and other people inside service systems, how they interact, what incentives are required to change behaviors, etc.

a.       What is the role of service science in this process?

Service science is sometimes described in the literature as the short version of service science, management, engineering, design, arts, and pubic policy (SSME+DAPP) – with the clear indication that it is an emerging transdiscipline, that borrows from many existing disciplines, but does not replace any of them.

The emerging service science community links academics, industry, and government to build the body of knowledge about the nested, networked service systems in which we all live and work.  The service science community has many members who aspire to be better boundary-spanners, better T-shapes to work collaboratively on innovation teams.  ISSIP.org is an umbrella professional association working on that goal.

4.       What are the characteristics of successful industry-academe partnerships in service systems innovation?

One measure of success: how many mentorships are created to allow students to engage with industry mentors working on real-world challenges, where the application of knowledge that faculty have inspired the students to learn about is encouraged.  Because real-world challenges do not respect discipline boundaries, and require engineers, managers, social scientists, and arts and humanities working together to engage a customer with a prototype solution – these teams are more likely to create viable service system innovations.

Industry and government provides platforms, tools, data, challenges and the academy provides multidisciplinary student teams and faculty guides to create viable solutions to challenges that can be tried out in the real-world with real-customers either as startups (some based on industry provided platforms) or as compelling proof-of-concepts that build the personal brands of students and prepare them to be T-shaped adaptive innovators, ready to hit-the-ground running in industry jobs.

5.       What is your take on the new BIC solicitation?  What do you expect will come up from this?

Fabulous solicitation.  Will impact both human capital and close knowledge gaps.

Human capital:  I expect students working on new startup ideas will come out of this, leveraging some existing industry platforms and creating some new platforms.   Better prepared students and graduates – more T-shaped adaptive innovators who can be better prepared to be in both start-ups or other industry positions on leading edge service offerings.

Knowledge gaps:  Many knowledge gaps exist related to understanding service systems, and how to scale the benefits of new knowledge, globally and rapidly – new technologies, new organizational forms, new business models, new skills in people, etc.

Just learned of this interesting global student challenge lead by UC San Diego Rady School of Management:

“The project is made possible by a $1.7 million Ideas to Innovation Challenge grant from the Graduate Management Admission Council (GMAC).

“This visionary project could drastically, and positively, alter the MBA experience in terms of how students gain information, how they interact with each other and how they develop skills,” said Rady School Dean Robert Sullivan.

Alex Howland, Project Manager of VirBELA, explained that the project’s inaugural program will be a cross-university MBA business simulation competition. This will be the first business competition to take place in the virtual world and unlike many traditional competitions, the teams will be comprised of MBA students from multiple universities around the globe.

“The goal of this competition will be to develop global competencies by giving students experience working across time zones and with different cultures,” Howland said.

The business simulation is “green” focused and teams will work together to design their product, figure out where to invest and how to incorporate green technology. Because the competition is taking place in the virtual world, researchers will have the opportunity to observe how the teams interact. “We will research how people collaborate in the business competition and facilitate discussions with participants about those collaborations providing direct feedback to students about their teamwork skills,” Howland said.”

More information at:

http://virbela.com/launch.html 

http://rady.ucsd.edu/news/newsletter/2012/fall/virbela/

Please feel free to distribute this to anyone you think may be interested.

The Business School at the University of Hull (UK) is making a major investment in systems thinking and logistics, two of the research areas where we already have a strong international reputation and see scope for continued growth and development. There are two professorships, one senior lectureship and one lectureship available.

The Professor of Systems Thinking will join both the Management Systems Subject Group and the Centre for Systems Studies. The latter has 22 staff members and 38 PhD students, making it one of the largest systems thinking research groups in the Western world. We have a strong and well established international reputation in the systems community, and a growing national and international reputation in operational research and other management research communities. The new Professor will take forward and enhance this reputation.

We are also advertising Senior Lecturer and Lecturer positions. Ideally, one of these will be filled by a systems thinker and the other by a logistics specialist.

In addition, we are recruiting a Professor of Logistics & Supply Chain Management to join our internationally renowned Logistics Institute and play a leading role in taking forward our supply chain research.

I would like to encourage applications for these positions, which have a deadline of 20 September 2013. Links to them can be found below.

Professor of Systems Thinking (BS0009)
https://jobs.hull.ac.uk/Vacancy.aspx?ref=BS0009

Professor of Logistics & Supply Chain Management (BS0010)
https://jobs.hull.ac.uk/Vacancy.aspx?ref=BS0010

Senior Lecturer in Management Systems (BS0011)
https://jobs.hull.ac.uk/Vacancy.aspx?ref=BS0011

Lecturer in Management Systems (BS0012)
https://jobs.hull.ac.uk/Vacancy.aspx?ref=BS0012

Best wishes,
Gerald Midgley

………………………………………………………………………………………………..
Professor Gerald Midgley
Director, Centre for Systems Studies
Associate Dean for Research and Enterprise

g.r.midgley@hull.ac.uk
T +44 (0)1482 463316

Hull University Business School
University of Hull
Hull, HU6 7RX, UK
www.hull.ac.uk/hubs
………………………………………………………………………………………………..

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Smart service systems are proliferating in business and society.

Recently, I invited about 70 researchers with publications (see https://service-science.info/archives/3166) related to service science, management, engineering, design, arts, public policy,  etc. to share their perspectives and examples of smart service systems on the new LinkedIn Group (see https://service-science.info/archives/3121) started by NSF (National Science Foundations of the US).

I asked each of them to consider contributing an example of a smart service system of their choice, ideally one that exists in the literature or press publications.

So here is my version of the above request – with general framing, examples of smart service systems, and references to the literature:

First, what types of entities organize service systems?

Service systems are organized by entities that can be small or large, local or global, but all service systems begin with an entity capable of provisioning service offerings to others.

Global business social media platforms, like Facebook, Twitter, and LinkedIn, are exemplars of service systems organized by business entities.  These business entities offer customers a service system platform that allows customers or members of the communities to post content for sharing with others.

Other business entities, including banks, insurance companies, IT and business process outsourcing companies, hotels, restaurants, provide service offerings to customers.   Even manufacturers and agricultural conglomerates offer upstream and downstream service offerings to their customers (e.g., from financing to maintenance, from provenancing to recycling).

Besides businesses, government and societal entities provision service offerings for their citizens and beneficiaries.

For example, Rio De Janeiro’s Smarter Cities Intelligent Operations Center is an example of a candidate smart service system for smarter urban service and operations (Naphade et al, 2011).

Even buildings as service systems are getting smarter with better air quality for inhabitants, better energy efficiency, better earthquake safety, and better faster construction mechanisms that are continuously improved  (Kibert 2012).

A taxonomy of smart service systems, like a taxonomy of service systems, must begin with an organizing framework for business and societal entities that provision service to customers and citizens, as well as other beneficiaries and stakeholders.

And remember,  wherever there is an instance of “service failure,” there is also the possibility of smarter service systems that avoid or mitigate service failures.

For citations see:

Service Science Researchers and Smart Service Systems: https://service-science.info/archives/3166

For additional references see:

The Well-Read Service Scientist: https://service-science.info/archives/2708

Service-related HBR Articles:  https://service-science.info/archives/2210

Early SSMED Reading List:  http://www.cob.sjsu.edu/ssme/refmenu.asp

Insights into smart service systems can be found in the following references, many by service science researchers. (Service science is short for service science, management, engineering, design, arts, public policy, etc.):

Akella, R., Xu, Z., Barajas, J., & Caballero, K. (2009, August). Knowledge sciences in services automation: integration models and perspectives for service centers. In Automation Science and Engineering, 2009. CASE 2009. IEEE International Conference on (pp. 71-78). IEEE.

Alter, S. (2008). Service system fundamentals: Work system, value chain, and life cycle. IBM Systems Journal, 47(1), 71-85.

Apte, U. M., Cavaliere, R. A., & Kulkarni, S. S. (2010). Analysis and Improvement of Information‐Intensive Services: Evidence from Insurance Claims Handling Operations. Production and Operations Management, 19(6), 665-678.

Baba, M. L. (1999). Dangerous liaisons: Trust, distrust, and information technology in American work organizations. Human Organization, 58(3), 331-346.

Basole, R. C., & Rouse, W. B. (2008). Complexity of service value networks: conceptualization and empirical investigation. IBM systems journal, 47(1), 53-70.

Berg, D and N G Einspruch, “Some Characteristics of Human Resources in the Service Sector””, in , Service Science, 1, pp. 107-114, 2009

Bitner, M. J., Brown, S. W., & Meuter, M. L. (2000). Technology infusion in service encounters. Journal of the Academy of marketing Science, 28(1), 138-149.

Bohmann, T., Junginger, M., & Krcmar, H. (2003, January). Modular service architectures: a concept and method for engineering it services. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on (pp. 10-pp). IEEE.

Bolton, R. N., Kannan, P. K., & Bramlett, M. D. (2000). Implications of loyalty program membership and service experiences for customer retention and value. Journal of the academy of marketing science, 28(1), 95-108.

Bowen, D. E., & Lawler, E. E. (1994). The empowerment of service workers. Training and Development Sourcebook, 413.

Cardoso, J., Sheth, A., Miller, J., Arnold, J., & Kochut, K. (2004). Quality of service for workflows and web service processes. Web Semantics: Science, Services and Agents on the World Wide Web, 1(3), 281-308.

Chang, C. M. (2010). Service Systems Management and Engineering: Creating Strategic Differentiation and Operational Excellence. Wiley.

Chase, R. B., & Apte, U. M. (2007). A history of research in service operations: What’s the< i> big idea</i>?. Journal of Operations Management, 25(2), 375-386.

Chesbrough, H., & Spohrer, J. (2006). A research manifesto for services science. Communications of the ACM, 49(7), 35-40.

Cohen, M., Kamesam, P. V., Kleindorfer, P., Lee, H., & Tekerian, A. (1990). Optimizer: IBM’s multi-echelon inventory system for managing service logistics. Interfaces, 20(1), 65-82.

Daim, T. U., & Oliver, T. (2008). Implementing technology roadmap process in the energy services sector: A case study of a government agency. Technological Forecasting and Social Change, 75(5), 687-720.

Davis, M. M., Spohrer, J. C., & Maglio, P. P. (2011). Guest editorial: how technology is changing the design and delivery of services. Operations Management Research, 4(1), 1-5.

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