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The Information and Services Economy a.k.a. Business Architecture and Services Science. IS210, Week 6 Profs Bob Glushko & Anno Saxenian UC Berkeley School of Information Fall 2006. A new dominant logic for marketing. Marketing in the goods economy: financial optimization and the 4 P’s
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The Information and Services Economya.k.a.Business Architecture and Services Science IS210, Week 6 Profs Bob Glushko & Anno Saxenian UC Berkeley School of Information Fall 2006
A new dominant logic for marketing • Marketing in the goods economy: financial optimization and the 4 P’s • Product • Price • Placement • Promotion • Marketing in the services economy: communication across organizational boundaries • An ongoing social and economic process • Knowledge is fundamental source of competitive advantage • Inherently customer-oriented and relational • Goods as distribution/delivery mechanisms for services
Emerging services-centered logic Intangible resources are key • Service provision, not goods, is fundamental to economic exchange • Specialized competences (skills and knowledge) or services are primary goal of exchange; • Goods are intermediate, not end, products that transmit knowledge and are used by consumers in value-creation process; Customers co-create value • Customers always co-producers of services via relational exchange; • Value is perceived & determined by consumer; Role of the enterprise • The enterprise can only make value propositions; • Wealth is obtained through application and exchange of specialized knowledge and skill.
A services economy curriculum • Marketing strategy: competences and capabilities in creation of value, resource advantage theory • Management of cross-functional business processes to support development of capabilities & competences for market-driven organization • Integrated marketing communication • Consumer behavior: relational • Pricing: building and maintaining value propositions, management of long-term customer equity • Marketing channels: coordinating marketing networks and systems • Supply chain mgmt: management of value constellations and service flows
The core competence of the corporation • “Competitiveness in long run derives from ability to build, at lower cost and faster than competitors, the core competencies that spawn unanticipated products.” • Core competences are collective learning in the organization—particularly how to coordinate diverse production skills & integrate multiple technology streams Sony’s miniaturization capabilities Citicorp’s operating system for 24/7 operation • Core competence is communication, involvement, and deep commitment to working across organizational boundaries: need to blend deeply specialized and different types of expertise
Core competence of the corporation II • Core competence does not diminish with use, but needs to be nurtured & protected, serve as engines for new business development 3M sticky tape competence => “post-it” notes, coated abrasives, magnetic tape, photo film, pressure-sensitive tape… (substrates, adhesives, coatings.. .) • Tests for identifying core competencies • Provides access to variety of markets • Makes significant contribution to perceived customer benefit of end product • Difficult for competitors to imitate
From core competences to core products • Core products are the tangible link between core competences and end products – the components or subassemblies that contribute value to end products (e.g. Canon’s desktop laser printer “engines.”) • To sustain core competence companies seek to maximize world manufacturing share in core products: producing for both external and internal customers provides market feedback as well as revenue that insures maintenance of core competencies. • A dominant share in core products allows company to shape the evolution of applications and end markets.
From SBU model to core competencies • SBU model of the corporation sees company as portfolio of autonomous businesses. • Unit of analysis is discrete businesses with related products • Resources get trapped (imprisoned) in business units • Innovation is bounded by immediate opportunities; hinders hybrid opportunities for innovation • View company as portfolio of corecompetencies, core products, and market-focusedcore businesses. • Unit of analysis is businesses and core competencies • Top management enunciates strategic architecture, builds competencies for long term • Strategic architecture makes resource allocation priorities transparent; provides template for allocation decisions, forces organization to identify and commit to technical and business linkages across businesses that will provide competitive advantage
The challenge: learning v. monitoring What do modern economic organizations need to do? • Motivate talent, encourage initiative, innovation, development of core competencies • Coordinate/monitor activities of/between internal and external units Organizational options: Devolve decision-making authority and access to relevant information within corporation, focus on core competences/products Governance options: Maintain ownership of assets but decentralize internally; Spin-off units to market, focus assets on core competences; Result is less a hierarchy than a federation or network
Learning by monitoring Governance options in a network Arms-length, market relationships between units– discrete transactions, maximizes autonomy, no trust Hybrid relationships of co-production, co-design—”learning by monitoring” with “studied trust” Institutionalization of continuous, joint conversation about common goals as well as apportionment of gains and losses => mutual experimentation and definition of roles Ownership relationships between units— blind trust, maximizes control
Business processes, collaborations, and transactions “The model of business organization shapes need to exchange information across organizational boundaries.” Bob Glushko, Document Engineering • Business processes are synchronized by loosely coupled information exchanges using documents Business process Business process transaction transaction transaction transaction COLLABORATIONS transaction transaction transaction transaction Enterprise Boundary
New directions in the social sciences • W. Brian Arthur “Complexity and the Economy” Science, 1999 • Duncan J. Watts Six Degrees: The Science of a Connected Age, 2002 “The story of the sciences in the twentieth Century in one of a steady loss of certainty. Much of what was real and machine-like and objective and determinate at the start of the century, by mid-century was a phantom, unpredictable, subjective, and indeterminate.” W. Brian Arthur “The End of Certainty in Economics” Einstein Meets Magritte Conference, 1994
The end of certainty in economics • What defined science at start of century? • The power to predict • The clear distinction between subject & object • What does the loss of predictive power in sciences mean for economics? Other social sciences? • Economics claims to be a science: a body of well-reasoned knowledge; has maintained “certainty” • But is the economy like a gigantic machine?
Origins of modern economics English and Scottish enlightenment, 18th c. All nature is but Art* unknown to Thee All Chance, Direction, which thou canst not see All Discord, Harmony, not understood All partial Evil, universal Good: And, spite of Pride, in erring Reason’s spite One truth is clear, “Whatever IS, is RIGHT” Alexander Pope An Essay on Man, 1733 *Art: artifice, technique, or mechanism
The search for a grand theory • Hidden simplicity behind traffickings of traders and manufactories and butchers and bakers…the “invisible hand” • Economy as a gigantic machine; if we understood the working of its parts we could predict the whole. • Goal: Grand Unified Theory of economics • Theory of the consumer, rational human behavior + theory of the firm = microeconomics • Aggregate theory of the economy = macroeconomics • Economics as predictive science becomes mathematics (e.g. models of rational expectations)
But there were problems . . . • Human beings: not orderly machine components—they have foibles, caprices, emotions. • Finessed with “economic man”: perfectly rational being who reasons perfectly deductively on well defined problems • Technology: destroys the orderly machine by changing the entire economy. • Couldn’t be finessed so technology either ignored or treated as exogenous by economics. Economic man (subject) needs to operate on well-defined Problems (object) to make orderly, predictive theory possible. And well defined Problems should have well-defined Solutions. The solutions are building blocks for next aggregated level of theory. • Works for simple problems with one decision maker….
Economics and indeterminacy • Once you acknowledge that people don’t have complete information and well defined, predetermined preferences you encounter problems of logical indeterminacy. • People create world that forms their expectations, but don’t (can’t) do so in a perfectly logical deductive way; • Our ideas and preferences co-create the world that our forecasts attempt to predict, and problems are indeterminate. • Impossibility of separating subjects of the economy (the people that form it) from the object (the economy itself) creates large areas of indeterminism. • Examples: Crashes and bubbles
Economy constructed by its agents • Economy emerges from our subjective beliefs, which in aggregate structure the micro economy, shape financial markets, direct flows of capital and govern strategic behavior and negotiations. • These subjective beliefs are not determinate in advance: they co-evolve, arise, decay, change, mutually reinforce and mutually negate. • Subject and object cannot be neatly separated. • Creates possibility of “studied trust” rather than either opportunism or “blind trust”
Complexity and the economy Complexity economics as non-equilibrium theory (vs. standard economics seeks static patterns in behavioral equilibrium) with nonlinearities and positive feedbacks: multiple equilibria, increasing returns, importance of small events. • Complex systems with multiple elements adapting or reacting to patterns created by the elements; • In natural sciences: elements (cells in immune system, ions in a spin glass) co-create; systems evolve • Application to economics: human agents become the elements in the systems (bankers, consumers, firms, investors) but they do have strategic intent, behavior
The economy as a complex system The El Farol Bar Problem • Agents cannot assume or rationally deduce expectations; must discover them over time • Failure of beliefs, expectations to converge over time as predicted by standard economic models; rather divergent beliefs that exhibit mutually reinforcing expectations among sub-populations • Alternating periods of high and low volatility (comparable to the bubbles and crashes in financial markets) Out-of-equilibrium theory of the economy: economy as process-dependent, organic, always evolving
Six degrees: science of a connected age • Why does a large complex connected systems behave differently than a dissociated collection of components? • Small disease outbreak => epidemic • Crickets chirping => synchronization • Single genes => genetic traits • How does individual behavior aggregate to collective behavior? Parts don’t sum up in a simple fashion, but interact to generate “bewildering” emergent behavior • The “science of networks” recognizes that “what happens and how it happens depends upon the network” which itself has evolved historically.
Emergent effects • Complex systems are self-constituting and coherent systems driven by interaction of equals, without any central authority or control. • Need to understand dynamics of the network and dynamics on the network. • Importance of phase transitions in different spheres, from social to chemical to physical.