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Productivity dimensions in the management of information
TELEMATICS and
IN FORMATICS
Vol. 1, No. 3, pp. 309-319, 1984 Copyright © 1984 Pergamon Press Ltd. Printed in the USA 0736-5853/84 S3.00+ .00
P R O D U C T I V I T Y D I M E N S I O N S IN THE M A N A G E M E N T OF I N F O R M A T I O N H. Charles Chase Abstract--The impact of new development in information technologies and the effective management and implementation of the systems are discussed with reference to productivity enhancement in market-driven human systems. Basic productivity measures for the emerging information economy are developed as are the specific contributions of decision support systems, emergent technologies, and the new managerial roles for the measurement of productivity among managers and information support staff. The geometric growth rate in scientific information portends a spike in the ranks of future information specialists :just as 90~ of all scientists who ever lived now live, that number will more than double during the next 30 years. 1 The resulting knowledge explosion, synergizing with tomorrow's artificial intelligence systems, portends one of history's most interesting periods. Strassman 2 stresses the transition from yesterday's industrial economy to a new services and information economy. Historically, process-linked workers achieved diminishing marginal costs and economies of manufacturing scale; in the information economy, productivity is leveraged by the distribution of information to knowledge workers, thereby orienting the firm to its key environmental features, interlinking internal resources, and synergizing the outcomes of organizational members' work. The unequivocal result will be improved productivity ratios, as measured by the ratio of clientvalued outcomes to allocated inputs.
THE I N F O R M A T I O N E C O N O M Y : A P A R A D I G M SHIFT Barter economies, still common in information-poor LDC's, could only approximate human value-added in primitive service or craft processes. The leap to calibrated valueindexing by currency units created accurate value measures for pre-industrial production. The emergence of credit, however, was a quantum leap forward, based on expected future value of present transactions. In turn, this encouraged the accumulation of intangible future wealth and so portended today's information economy, in which technology and information become a transferable form of potential, future wealth creation: a true paradigm shift in the history of economic exchange. Already, information exchange impacts on organization productivity in such areas as : • •
technology transfers human resource management
Dr. Chase is Director, Institute for Productivity Development at Towson State University, Towson, Maryland.
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decision support systems (DSS) for both large and small enterprises control of accelerating technological change and of turbulence in the firm's competitive environment.
International, intra-firm information exchanges are already highly visible in the information economy : in Europe, 50~ of all organizational data exchanges are within the originating firm, but across national boundaries 3 such international, integrative information transfers will further multiply with the continued emergence of a truly global information economy. Indeed, transborder information flows already percuss on national policy, as nations--in particular, the information-poor--increasingly tax such flows on an information value-added basis. A further concern in the emerging global information economy is the widening gap between the information-rich and the information-poor: while significant, newsworthy events reach the homes of information-rich consumers on a nearly real-time basis, information consumers in LDC's are conditioned to consumerism from endless reruns of "I Love Lucy." According to McPhail,4 LDCs attempt to balance their information transfer deficit more equitably, and to rectify "electronic colonialism" by leveraging the international politics of information at such global agencies as UNESCO.
PRODUCTIVITY-RELEVANT INFORMATION PARAMETERS Processed or interpreted data yields information, which--when systematized for use in accomplishing specified goals--yields technology. Technologies include, but are by no means limited to, applied sciences, engineering, and managerial psychology and organizational behavior. Specifically, information is derived from data which has been codified, sorted, verified, or measured. Once information is so treated, or managed, it acquires value and may be exchanged, bought, or sold. In the information economy, productivity-relevant information, in particular, circulates among such knowledge specialists as managers and their information support staffs (ISS), much as did the agricultural or manufactured goods of the pre-information economy. Indeed, presently produced goods are increasingly "information intense," both in their embodied technology, and quite literally as integrated microprocessors are increasingly incorporated in product design. Productivity value-added (enhancement of the ratio of process or product outcomes over such inputs as cost, energy, or human labor) is the goal of such information intensity. At a macro-industrial level, the information economy has already impacted on the restructuring of key industries. Just as railroads redefined their business as "transportation," and the steel industry is redefining its domain as the materials industry, so too the following information-intense businesses are rapidly redefining their roles in terms of information brokerage : • • •
• • • •
banking accounting law risk management (insurance) stock brokerage/foreign exchange news/entertainment market research.
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While intensity of information exchange is a dominant common characteristic of the new information brokers, information also serves the strategic planning, innovation, production, and marketing functions of these same information brokers. Consequently, demand for relevant information is doubly acute among the managers or knowledge leaders in information brokerage businesses for whom timely information is a doubly indispensible productivity prerequisite.
BASIC P R O D U C T I V I T Y M E A S U R E S FOR THE I N F O R M A T I O N ECONOMY The productivity concept need not remain ambiguous ; Ranftl 5 states that : "Productivity can be defined as the ratio of valuable output to input ; i.e., the efficiency and effectiveness with which available resources--personnel, machines, facilities, capital, time--are utilized to produce a valuable output." Previously, apppropriate measures for managerial productivity were considered virtually intractable, but the concept of value-added intensity offers an applicable solution. Valueadded stresses the additional utility of goods produced or of information after its collection, codification, verification and application. Added values in production and information management may include the following: • • • • • •
access value time value form transformation value package or format value language or encoded value applications value.
The value-added to by managers' information output may be measured either by profit contributions, or by the sale value to clients of the output or information produced. In either case, cost of production or information must be subtracted from its sale value to compute value-added. The formula becomes: VA = S V I - C P , where VA = Value-added, SVI = Sale value of production or processed information, CP = Cost of production or processing. The value-added concept is converted to a true productivity measure by adherence to the formula of outcomes (useful outputs) over inputs. Since direct (wages) and indirect (recruitment, training, etc.) costs of personnel are exceptionally salient, the input "number of personnel" results in a measure of value-added intensity : VAI =
SVI-CP nP '
where VAI = Value-added intensity, n P = number of personnel (including management) who input to information or processed information value-added. SVI, of course, prices the firm's information or production output at the equilibrium point of the supply and demand curves for that good or service. The firm's market share of total information (e.g., news, publications, software, research) or production (microprocessors) may be increased by such non-price elements as market promotion (itself the management of information for productivity), quality of product of information, and
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positioning (tailoring of the product or information to a specific market niche or segment). Segments are distinct submarkets nonetheless extensive enough to support necessary economies of scale in production or information processing; niches by definition are not, so that a niche strategy must target the needs of distinct consumers in multiple niches through precise calibration of information or production to the specific demands of such consumers. VAI registers productivity of information processing or manufacturing on a net valueadded, per-person basis: improvements or decrements in VAI ratios for departments within the organization may be tracked monthly or quarterly, yielding a trend analysis of productivity specific areas. Management may enhance the productivity of information workers through the following practices: • • •
upgrade skill levels and interest through job rotation, enrichment (re-designing a job to include decision-making with a corresponding accountability for results); design incentives for output such as gainsharing; 6 use staff feedback to managers (e.g., quality circles).
While short-term ROI (e.g., quarterly return on investment) indicates the firm's current success in productivity, longer-term corporate performance is measured by discounted or de-inflated equity growth and ROE, or return on equity. Profit maximization--the simplistic shibboleth of an earlier economic era--has been supplemented by equity growth, profit optimization (long-range harvesting of returns), consumer primacy, and technology leadership in today's complex, competitive information economy.
THE DECISION S U P P O R T S Y S T E M C O N T R I B U T I O N TO INFORMATION PRODUCTIVITY
Value-added intensity is relevant to smaller as well as multi-operation or multi-sited firms. Both require a close fit of Decision Support Systems (DSS) with core manufacturing or information processing functions. And, since cost-efficient and results-effective information management is key to productivity, a critical information link between the firm and its clients insures that product performance, design, and cost are compatible with perceived client needs. DSS provides critical support for the market research, bench-marking, and product prototype development required for client-centeredness in operations; in addition, DSS raises effectiveness and efficiency (i.e., productivity) in operations by function-based support (see Table 1), rather than by database category. Specific DSS capabilities including those cited by Jaffe 7 are shown in Table 1. Such DSS applications should be horizontally and vertically integrated. Vertical integration means the DSS accesses one database management system for efficiency and effectiveness of set-up, system maintenance, and executive use. Horizontal DSS integration means all DSS applications should communicate through a common language. Data entered at one DSS module should be available at all others as required. DSS differs from data processing (DP) and management information systems (MIS) by formatting information for productivity in such decision areas as production and inventory cost control, value-engineering support, product performance, service and delivery, and even benchmarking. Since in current parlance "benchmarking" is development of information regarding parameters of product quality, design/performance characteristics, or even analysis of competitor performance to improve the firm's performance standards, it is a form of information management for productivity.
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Table 1. Typical support modules in DSS
Inventory Stocking policy Cost/investment Stocking levels Service levels Differential analysis by category (usage, value, criticality, lead time)
Procurement~Purchasing Price change/discount/time analysis Inventory consolidation analysis Material bid-evaluation Quantity discount analysis Vendor analysis
Warehouse Layout/design Storage allocation Space utilization
Transportation Freight consolidation Routing and scheduling Transportation budgeting Shipment planning Lane analysis/balancing Carrier analysis
Production Planning and Scheduling Master production schedule "What if" production/inventory tradeoffs Capacity planning "What if" for machine failures Production materials flow control Process scheduling
Reflecting organizational priorities, the volatility of product technology and market demand, and competitive pressure, good DSS should encourage maximum "hands-on" use by managers. Unfortunately, while information support staff(ISS) usually have time to get familiar with a new DSS, the system's intended users--executives--seldom have either the time or inclination to learn complex new systems. Consequently, DSS must be designed for non-technically-oriented and time-pressured users. In short, consumerprimacy information (characteristics of managers) is a productivity key in DSS design and development. Besides solving time-consuming problems, the DSS information system expands managers' analytic grasp of multi-factor impact on organization productivity. In earlier management information systems, centralized rather than distributed information management, was emphasized and productivity-relevant information was further removed from decision-maker involvement. Future productivity gains will result from interactive DSS/AI (Artificial Intelligence, or fifth-generation systems), leading to greater hands-on involvement of managers with productivity-convergent information technologies. INFORMATION
BASED PRODUCTIVITY HORIZON
ON THE T E C H N O L O G Y
Such convergence of information technologies means that dedicated expert systems will also come onstream to enahance manufacturing productivity. McKibbon 8 states : "Expert systems can help plan, schedule and control the production process, monitor and replenish inventories, design original, customized products, diagnose malfunctions and alert proper parties about the problem. When expert systems combine with natural language processing like document understanding, the factory system might even be able to read market reports and make adjustments in the production planning process."
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The combination of expert systems with the heuristics capability of artificial intelligence means that older-style MIS (management information systems) will continue to serve in repetitive control functions and in low change administrative areas such as personnel records-keeping, payroll, etc., but that in more complex productivity problems, expert systems will imitate the problem-solution performance of a highly competent specialist in a given expertise such as product design, or design of manufacturing flows. Obermeier 9 states : "In their performance they go beyond mere data retrieval of knowledgebased systems by exhibiting reasoning capacity derived from an individual knowledge base and a set of heuristics that were previously gathered from a human expert." Presently, AI research and development incorporates the now-classic McCarthy-Hayes ~° distinction between epistemology (the representation and use of knowledge), and heuristics (helping to discover, goal-seeking, further information). Obermeier 9 continues : "Under the assumption that inference mechanisms can be filtered/abstracted out of the thinking process, one of the leading current paradigms in expert systems makes use of an external 'inference' engine (e.g., MYCIN) that can be attached to any data base without further modification." Near-future information management trends will also be registered in the following productivity areas : •
•
•
managerial domains: international business and international marketing will be new emphases in the globalization of productivity competition through process and information management. integrated decision-making: catalytic systems for teleconferencing, office automation, and LAN's (local area networks) will synergize inputs from decentralized decision-makers. vendor integration : vendors will become team players as backward integration for quality control and delivery coordination becomes a fact of life in the pursuit of lower defect rates, slashed inventory, and innovative vendor-designed components.
Balanced against these gains will be, however, an emerging politics of information, in which divergent information technologies will generate partisan loyalties to methods rather than goals. Yet another result will be accelerated managerial obsolescence, or its substitution by permanent on-the-job training and learning. Increasingly, managers--knowledge leaders--are expected to unravel such issues as personnel differences in information assimilation, and to show flexibility and adaptation to change, particularly the change from directive, charismatic leadership to the new information leadership paradigm which harnesses information flows within complex organizations in pursuit of productivity: the ratio enhancement of market-valuable outcomes per unit of allocated inputs. Also, information leadership will increasingly substitute for charismatic, personal leadership styles in the trend toward process control of complex and continuous materials, energy, and information flows. Already, mere handfuls of supervisory personnel monitor massive flows in their role as attentive observers of signals synthesized by complex information array panels. Even mid-level managerial roles are impacted by the shift from leadership of persons to leadership of process in the context of information management
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systems and hardware which Jaffe ~ describes as dynamic, time-limited and highly complex. CALCULATING
INFORMATION
SUPPORT
STAFF PRODUCTIVITY
The calculation of information support staff (ISS) productivity is an increasingly important managerial responsibility which can and must be realized. Because of the intangible output of these "knowledge workers", managers have been tardy in monitoring their significant "allocated inputs" role in the productivity equation. Hammond ~1 reports an ISS productivity measurement method developed by IBM. This method calculates productivity gains on the key variable "time saved per information worker". This "time saved" over previous information work cycles is expressed in units per knowledge worker. Next, "time saved" is stated as a percentage of working time, then translated into a dollar rate according to rates of pay. Figures are aggregated for user departments. ~2 This productivity measure may also be time-series analyzed for improvement/decrement. Measured improvements are generally results of this method, meaning that "present information systems staff can be maintained although the number of applications is increased,,..12 This trend analysis method of monitoring information productivity also conforms to Ranftl, 5 who notes the utility of ratcheting up managerial expectations for future ISS performance as a reasonable function of past performance and elapsed time. A compatible manager-usable format for weighted time-series trend analysis of productivity has been described by Chase.~3"~4 The approaches cited above conform to the cardinal principle that ISS productivity should be measured at the point where work is performed, to insure both pertinence and validity.
MANAGERIAL
ROLES IN I N F O R M A T I O N
PRODUCTIVITY
As noted, successful "knowledge leadership" by managers in the information economy requires the integration of information flows within the firm's productivity-driven functions. Strategic planning must therefore be integrated at the following levels : • • •
strategic goals: defined priorities and goals with two to five year time horizons permit investment decisions in specific technologies and systems. strategic support : specific need-to-know parameters defined for pertinent areas such as market or consumer research, research, competitor analysis, etc. strategic implementation: specific DSS go on-line as ISS translate, format, and transmit priority information to managers (knowledge leaders).
Increasingly, strategy focuses on consumer need-satisfaction to energize the firm's productivity in an increasingly competitive world, populated by competent foreign performers enjoying significant labor-cost leadership. Only information leadership can countervail this tremendous productivity advantage, providing the knowledge derived from market and in-depth consumer research which permits the firm to meet the needs of its targeted niche and segment markets in an increasingly "splintered" mass market.15 Accelerated global competition means the productivity of managers must be assessed and rewarded, in spite of difficulties presented by the extraordinary variability in their tasks. According to Grove, 16 a manager's productivity "depends on eliciting more output from this team". Grove ~6 adds that "a manager's own output is the output of his
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organization--no more, no less--". Elsewhere, Grove 17 notes that managerial or knowledge leader productivity begins with the concept of "leverage": Managerial Output = Output of the organization = L I xAI +L2xA2
+''"
This means that managerial activities A t, A2, etc., exhibit an effectiveness or efficiency gain resulting from the multiplicative, not merely additive, effects of such leverage factors as L 1, L 2, etc. These leverage factors may include : •
coordination and focusing of ISS activities in pertinent areas of organization functioning, up to but not beyond equilibrium of marginal cost/internal marginal demand for ISS services. • brief, highly focused managerial interventions such as training, work re-design for job enrichment, gainsharing, and quality circles. • investment decisions in knowledge/information systems to raise informationintensity of both processes and products. • focusing on the outcomes side of the productivity equation when possible, making information technology investments based on enhancement of value added rather than over-emphasis on input-side cost-cutting. Managerial productivity is also leveraged by making job-pertinent information available to operating personnel. The resulting information exchange and processing among staff creates an information economy in microcosm. Along with this downward displacement of the micro-information economy within the firm to the frontier of each specific job, management also achieves job enrichment by de-centralizing decision-making to the job experts themselves. According to the NYSE Office of Economic Research, 6 this delegating of information and decisional discretion to the job frontier typically leverages personal and work-group productivity. The cost of information inputs is a productivity determinant ; managerial "willingness to pay" for information is a practical, surrogate measure of the perceived productivity value of information. Hammond tt and Phillips t2 both discuss such perceived value in terms of management's willingness to re-budget existing resources for information acquisition, thus creating a "bottom-line" information value criterion. Such resource allocation decisions, however, must link management's felt information needs to proposed system benefits, an awareness which Drucker ts says is unfortunately unrealistic: "At a meeting of upper-level managers all of them basically said, 'I expect the information specialist to tell me what information I need'. But that's like going to the doctor and saying, 'What medication do I need?' without giving him a chance to do the diagnosis." The opinions of information staff are not a guide to the real information needs of management. Mortensen 19 writing on the expertise areas of information specialists, states : "'these technologists often have little, or only theoretical understanding of the organizational settings in which new technology will be introduced." Ultimately, information must be managed by management itself, since only those actually responsible for productivity can determine the information and information resources necessary to accomplish their productivity responsibilities.
Productivity dimensions in the management of information
INFORMATION
MANAGEMENT
IN M A R K E T - D R I V E N
317
PRODUCTIVITY
To recoup massive share-of-market losses to sophisticated foreign producers such as Japan, an orientation toward high product quality and meeting of specific consumer needs must be adopted. In short, US management must mainstream information from consumer behavior studies and market research into each transfer phase in the product development cycle. The product development cycle extends from product planning to product development and prototype testing, to manufacturing, quality control, and marketing. While market research (i.e., data on aggregate purchase behavior) is common in the U.S., the analysis of potential consumer preferences for undeveloped or prototype products is of very recent vintage. Foreign performers have scored information coups with in-depth consumer studies, as in the now-classic study commissioned by Toyota to ascertain VW "beetle" owners' "most-desired changes" and to re-design a new Toyota line to incorporate this information. The outcome is now information management history: the once-ubiquitous VW beetle is virtually a collector's item ; 1984 Toyota selling prices were 20Vo above sticker. Consumer "focus groups" emerged from group dynamics research in the 60's and 70's. Skilled group facilitators create a climate of synergistic information exchange in which group members--consumers--explore their reactions to and preferences for potential products. These fast-paced product-concept explorations have produced numerous market winners, including the microcomputer "touch screen" method of icon manipulation. Focus groups embody the Peters and Waterman 2° dictum of"staying close to the consumer", a paradigm for information management in the pursuit of corporate excellence.
INFORMATION
MANAGEMENT
IN T H E X E R O X CASE
This prototypic technology pioneer fell from a 96~ share-of-market in plain-paper copiers in 1970 to less than 45~ in 1982, as reported in B u s i n e s s W e e k . 2~ Seen from an information management perspective, specific factors in the precipitous Xerox-productivity decline were : • • •
failure to evaluate information on market rival performance. cumbersome layers of information-filtering bureaucracy. failure to act on critical information (the initial penetration by Japanese firms of the low-cost end of the U.S. copier market).
In contrast, the recent Xerox turnaround resulted from implementing effective new strategies in information management: • • • •
•
increased information inputs from market and consumer research to new-product design and development. multiplied information exchanges on SBU (strategic business unit) priorities among staff at each SBU for strategy and product development improvements. immediate feedback on new-product idea-feasibility, using information-based "product synthesis" teams to weed out losers. shortened product development cycles through direct, frequent information exchanges between development engineering, product engineering, and SBU vicepresidents. integrated information sharing with key vendors/supplier for more vendor-initiated component design and quality improvements.
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i m p l e m e n t e d " ' k a n b a n " o r "just in time" i n v e n t o r y i n f o r m a t i o n c o n t r o l to cut d r a c o n i a n i n v e n t o r y c a r r y i n g costs.
SUMMARY
AND CONCLUSION
The c h a s m between the i n f o r m a t i o n rich a n d the i n f o r m a t i o n p o o r will widen in n e a r - t e r m d e v e l o p m e n t of the i n f o r m a t i o n e c o n o m y . W i t h i n the family, the rift will d e e p e n b e t w e e n c o m p u t e r illiterate p a r e n t s a n d their h y p e r - l i t e r a t e progeny. H e u r i s t i c " e x p e r t s y s t e m s " will signal a q u a n t u m a d v a n c e in b o t h " m a c h i n e p s y c h o l o g y " a n d in the i n f o r m a t i o n industry. T h e m e r g e r of r o b o t i c s a n d AI is further a l t e r i n g the l a n d s c a p e o f h u m a n experience a n d o r g a n i z a t i o n a l p r o d u c t i v i t y . A l r e a d y we perceive the d a y of true cortical prostheses, when surgically i m p l a n t e d t e r m i n a l s will p e r m i t direct n e t w o r k i n g of the h u m a n cortex with host c o m p u t e r s a n d o t h e r brains, not unlike t o d a y ' s "local a r e a n e t w o r k s " ( L A N ' s ) which a l r e a d y link i n d i v i d u a l w o r k s t a t i o n c o m p u t e r s with central m a i n f r a m e s to create the electronic office. As F. W. H o r t o n 22 c o m m e n t s : "'Few enterprises yet understand how the very nature oftheir business is being fundamentally transformed by the impact of modern information and communication technologies. Most only dimly discern how the value of online databases, carefully synchronized with internal data, in the context of decision support systems, can immeasurably improve the timeliness of decision-making and problem-solving." In this real-time scenario, successful m a n a g e m e n t techniques will i n c r e a s i n g l y focus on m u l t i - v a r i a t e p r o d u c t i v i t y t h r o u g h the m a n a g e m e n t of i n f o r m a t i o n in a g l o b a l information economy.
REFERENCES I. Merrifield, D. Bruce. Forces of Change Affecting High Technology Industries. Nat. J. 253-256 (29 January 1983J. 2. Strassman, P. Oral presentation made at the U.S. Department of Commerce Product!vity Steering Group. U.S. Department of Commerce, Washington, D.C., 28 April 1983. 3. Bushkin, Arthur Emerging Barriers to International Information Exchange. Conference address at How to Fund. Manage, and Market High-Tech Research, Reston, VA., 7-8 March 1984. 4. McPhail, Thomas L. Electronic Colonialism: The Future of International Broadcasting and Communication. Beverly Hills: Sage Publications, 1983. 5. Ranftl, Robert M. Personal, Managerial, and Organizational Productivity Seminar. Los Angeles: privately published, 1983. 6. New York Stock Exchange, Office of Economic Research. People and Productivity. New York : April, 1983. 7. Jaffe, M. Decision Support Systems for Manufacturing. It~systems 112-114 (July 1983). 8. McKibbin, W. L. Will AI Clash with MIS in the Factory? hgbsystems 99 (August 1983). 9. Obermeier, Klaus K. Expert Systems Enhancement of Productivity. In : Producticity in the Information Age (Edited by Vondran et al.). White Plains: Knowledge Industry Publications, 1983. 10. McCarthy, J. and Hayes, P. J. Some Philosophical Problems From the Standpoint of Artificial Intelligence. Edinburgh:Edinburgh University Press, 1969. I I. Hammond, L. W. Management Considerations for an Information Center. IBM Systems J. 21, 131-161 (1982). 12. Phillips, Delores. The Information Centre For End-User Programming: Relationship to Productivity. in: Productirity in the Information Age (Edited by Vondran et al.). White Plains: Knowledge Industry Publications, 1983. 13. Chase, H. Charles. Innovation : The Overlooked Productivity Dimension, Telephone Eng. Manag. ( 15 April 1984). 14. Chase H. Charles. Export Expansion and Small Business Productivity. Am. J. Small Business. {June, 1984). 15. Steinburg, Bruce. The mass market is splitting apart. Fortune 76-82 (28 November 1983). 16. Steinburg, Bruce. Why training is the boss's job, Fortune 109, 94 (23 January 1984).
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17. Grove, Andrew S. Hiyh Output Management, pp. 62-66. New York : Random'House, 1983. 18. Drucker, Peter. Automating the executive suite, Supplement to the New York Times, 11 (3 October 19821. 19. Mortensen, Erik. Office Automation : Agenda for Organizational Charge. In : Productivity in the Information Aye (Edited by Vondran et al.). White Plains: Knowledge Industry Publications, 1983. 20. Peters, Thomas J. and Waterman, Robert H. Jr. In Search t~'Excellence. New York : Harper and Row, 1982. 21. Business Week. How Xerox speeds up the birth of new products, Business Week, 58-59 (19 March 1984). 22. Horton, Forest Woody. "'Harnessing Information Assets and Resources to Increase Productivity." A paper prepared for the National Commission on Libraries and Information Science, Washington, D.C., May, 1983.
IN FORMATICS
Vol. 1, No. 3, pp. 309-319, 1984 Copyright © 1984 Pergamon Press Ltd. Printed in the USA 0736-5853/84 S3.00+ .00
P R O D U C T I V I T Y D I M E N S I O N S IN THE M A N A G E M E N T OF I N F O R M A T I O N H. Charles Chase Abstract--The impact of new development in information technologies and the effective management and implementation of the systems are discussed with reference to productivity enhancement in market-driven human systems. Basic productivity measures for the emerging information economy are developed as are the specific contributions of decision support systems, emergent technologies, and the new managerial roles for the measurement of productivity among managers and information support staff. The geometric growth rate in scientific information portends a spike in the ranks of future information specialists :just as 90~ of all scientists who ever lived now live, that number will more than double during the next 30 years. 1 The resulting knowledge explosion, synergizing with tomorrow's artificial intelligence systems, portends one of history's most interesting periods. Strassman 2 stresses the transition from yesterday's industrial economy to a new services and information economy. Historically, process-linked workers achieved diminishing marginal costs and economies of manufacturing scale; in the information economy, productivity is leveraged by the distribution of information to knowledge workers, thereby orienting the firm to its key environmental features, interlinking internal resources, and synergizing the outcomes of organizational members' work. The unequivocal result will be improved productivity ratios, as measured by the ratio of clientvalued outcomes to allocated inputs.
THE I N F O R M A T I O N E C O N O M Y : A P A R A D I G M SHIFT Barter economies, still common in information-poor LDC's, could only approximate human value-added in primitive service or craft processes. The leap to calibrated valueindexing by currency units created accurate value measures for pre-industrial production. The emergence of credit, however, was a quantum leap forward, based on expected future value of present transactions. In turn, this encouraged the accumulation of intangible future wealth and so portended today's information economy, in which technology and information become a transferable form of potential, future wealth creation: a true paradigm shift in the history of economic exchange. Already, information exchange impacts on organization productivity in such areas as : • •
technology transfers human resource management
Dr. Chase is Director, Institute for Productivity Development at Towson State University, Towson, Maryland.
309
310
• •
H. Charles Chase
decision support systems (DSS) for both large and small enterprises control of accelerating technological change and of turbulence in the firm's competitive environment.
International, intra-firm information exchanges are already highly visible in the information economy : in Europe, 50~ of all organizational data exchanges are within the originating firm, but across national boundaries 3 such international, integrative information transfers will further multiply with the continued emergence of a truly global information economy. Indeed, transborder information flows already percuss on national policy, as nations--in particular, the information-poor--increasingly tax such flows on an information value-added basis. A further concern in the emerging global information economy is the widening gap between the information-rich and the information-poor: while significant, newsworthy events reach the homes of information-rich consumers on a nearly real-time basis, information consumers in LDC's are conditioned to consumerism from endless reruns of "I Love Lucy." According to McPhail,4 LDCs attempt to balance their information transfer deficit more equitably, and to rectify "electronic colonialism" by leveraging the international politics of information at such global agencies as UNESCO.
PRODUCTIVITY-RELEVANT INFORMATION PARAMETERS Processed or interpreted data yields information, which--when systematized for use in accomplishing specified goals--yields technology. Technologies include, but are by no means limited to, applied sciences, engineering, and managerial psychology and organizational behavior. Specifically, information is derived from data which has been codified, sorted, verified, or measured. Once information is so treated, or managed, it acquires value and may be exchanged, bought, or sold. In the information economy, productivity-relevant information, in particular, circulates among such knowledge specialists as managers and their information support staffs (ISS), much as did the agricultural or manufactured goods of the pre-information economy. Indeed, presently produced goods are increasingly "information intense," both in their embodied technology, and quite literally as integrated microprocessors are increasingly incorporated in product design. Productivity value-added (enhancement of the ratio of process or product outcomes over such inputs as cost, energy, or human labor) is the goal of such information intensity. At a macro-industrial level, the information economy has already impacted on the restructuring of key industries. Just as railroads redefined their business as "transportation," and the steel industry is redefining its domain as the materials industry, so too the following information-intense businesses are rapidly redefining their roles in terms of information brokerage : • • •
• • • •
banking accounting law risk management (insurance) stock brokerage/foreign exchange news/entertainment market research.
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While intensity of information exchange is a dominant common characteristic of the new information brokers, information also serves the strategic planning, innovation, production, and marketing functions of these same information brokers. Consequently, demand for relevant information is doubly acute among the managers or knowledge leaders in information brokerage businesses for whom timely information is a doubly indispensible productivity prerequisite.
BASIC P R O D U C T I V I T Y M E A S U R E S FOR THE I N F O R M A T I O N ECONOMY The productivity concept need not remain ambiguous ; Ranftl 5 states that : "Productivity can be defined as the ratio of valuable output to input ; i.e., the efficiency and effectiveness with which available resources--personnel, machines, facilities, capital, time--are utilized to produce a valuable output." Previously, apppropriate measures for managerial productivity were considered virtually intractable, but the concept of value-added intensity offers an applicable solution. Valueadded stresses the additional utility of goods produced or of information after its collection, codification, verification and application. Added values in production and information management may include the following: • • • • • •
access value time value form transformation value package or format value language or encoded value applications value.
The value-added to by managers' information output may be measured either by profit contributions, or by the sale value to clients of the output or information produced. In either case, cost of production or information must be subtracted from its sale value to compute value-added. The formula becomes: VA = S V I - C P , where VA = Value-added, SVI = Sale value of production or processed information, CP = Cost of production or processing. The value-added concept is converted to a true productivity measure by adherence to the formula of outcomes (useful outputs) over inputs. Since direct (wages) and indirect (recruitment, training, etc.) costs of personnel are exceptionally salient, the input "number of personnel" results in a measure of value-added intensity : VAI =
SVI-CP nP '
where VAI = Value-added intensity, n P = number of personnel (including management) who input to information or processed information value-added. SVI, of course, prices the firm's information or production output at the equilibrium point of the supply and demand curves for that good or service. The firm's market share of total information (e.g., news, publications, software, research) or production (microprocessors) may be increased by such non-price elements as market promotion (itself the management of information for productivity), quality of product of information, and
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positioning (tailoring of the product or information to a specific market niche or segment). Segments are distinct submarkets nonetheless extensive enough to support necessary economies of scale in production or information processing; niches by definition are not, so that a niche strategy must target the needs of distinct consumers in multiple niches through precise calibration of information or production to the specific demands of such consumers. VAI registers productivity of information processing or manufacturing on a net valueadded, per-person basis: improvements or decrements in VAI ratios for departments within the organization may be tracked monthly or quarterly, yielding a trend analysis of productivity specific areas. Management may enhance the productivity of information workers through the following practices: • • •
upgrade skill levels and interest through job rotation, enrichment (re-designing a job to include decision-making with a corresponding accountability for results); design incentives for output such as gainsharing; 6 use staff feedback to managers (e.g., quality circles).
While short-term ROI (e.g., quarterly return on investment) indicates the firm's current success in productivity, longer-term corporate performance is measured by discounted or de-inflated equity growth and ROE, or return on equity. Profit maximization--the simplistic shibboleth of an earlier economic era--has been supplemented by equity growth, profit optimization (long-range harvesting of returns), consumer primacy, and technology leadership in today's complex, competitive information economy.
THE DECISION S U P P O R T S Y S T E M C O N T R I B U T I O N TO INFORMATION PRODUCTIVITY
Value-added intensity is relevant to smaller as well as multi-operation or multi-sited firms. Both require a close fit of Decision Support Systems (DSS) with core manufacturing or information processing functions. And, since cost-efficient and results-effective information management is key to productivity, a critical information link between the firm and its clients insures that product performance, design, and cost are compatible with perceived client needs. DSS provides critical support for the market research, bench-marking, and product prototype development required for client-centeredness in operations; in addition, DSS raises effectiveness and efficiency (i.e., productivity) in operations by function-based support (see Table 1), rather than by database category. Specific DSS capabilities including those cited by Jaffe 7 are shown in Table 1. Such DSS applications should be horizontally and vertically integrated. Vertical integration means the DSS accesses one database management system for efficiency and effectiveness of set-up, system maintenance, and executive use. Horizontal DSS integration means all DSS applications should communicate through a common language. Data entered at one DSS module should be available at all others as required. DSS differs from data processing (DP) and management information systems (MIS) by formatting information for productivity in such decision areas as production and inventory cost control, value-engineering support, product performance, service and delivery, and even benchmarking. Since in current parlance "benchmarking" is development of information regarding parameters of product quality, design/performance characteristics, or even analysis of competitor performance to improve the firm's performance standards, it is a form of information management for productivity.
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Table 1. Typical support modules in DSS
Inventory Stocking policy Cost/investment Stocking levels Service levels Differential analysis by category (usage, value, criticality, lead time)
Procurement~Purchasing Price change/discount/time analysis Inventory consolidation analysis Material bid-evaluation Quantity discount analysis Vendor analysis
Warehouse Layout/design Storage allocation Space utilization
Transportation Freight consolidation Routing and scheduling Transportation budgeting Shipment planning Lane analysis/balancing Carrier analysis
Production Planning and Scheduling Master production schedule "What if" production/inventory tradeoffs Capacity planning "What if" for machine failures Production materials flow control Process scheduling
Reflecting organizational priorities, the volatility of product technology and market demand, and competitive pressure, good DSS should encourage maximum "hands-on" use by managers. Unfortunately, while information support staff(ISS) usually have time to get familiar with a new DSS, the system's intended users--executives--seldom have either the time or inclination to learn complex new systems. Consequently, DSS must be designed for non-technically-oriented and time-pressured users. In short, consumerprimacy information (characteristics of managers) is a productivity key in DSS design and development. Besides solving time-consuming problems, the DSS information system expands managers' analytic grasp of multi-factor impact on organization productivity. In earlier management information systems, centralized rather than distributed information management, was emphasized and productivity-relevant information was further removed from decision-maker involvement. Future productivity gains will result from interactive DSS/AI (Artificial Intelligence, or fifth-generation systems), leading to greater hands-on involvement of managers with productivity-convergent information technologies. INFORMATION
BASED PRODUCTIVITY HORIZON
ON THE T E C H N O L O G Y
Such convergence of information technologies means that dedicated expert systems will also come onstream to enahance manufacturing productivity. McKibbon 8 states : "Expert systems can help plan, schedule and control the production process, monitor and replenish inventories, design original, customized products, diagnose malfunctions and alert proper parties about the problem. When expert systems combine with natural language processing like document understanding, the factory system might even be able to read market reports and make adjustments in the production planning process."
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The combination of expert systems with the heuristics capability of artificial intelligence means that older-style MIS (management information systems) will continue to serve in repetitive control functions and in low change administrative areas such as personnel records-keeping, payroll, etc., but that in more complex productivity problems, expert systems will imitate the problem-solution performance of a highly competent specialist in a given expertise such as product design, or design of manufacturing flows. Obermeier 9 states : "In their performance they go beyond mere data retrieval of knowledgebased systems by exhibiting reasoning capacity derived from an individual knowledge base and a set of heuristics that were previously gathered from a human expert." Presently, AI research and development incorporates the now-classic McCarthy-Hayes ~° distinction between epistemology (the representation and use of knowledge), and heuristics (helping to discover, goal-seeking, further information). Obermeier 9 continues : "Under the assumption that inference mechanisms can be filtered/abstracted out of the thinking process, one of the leading current paradigms in expert systems makes use of an external 'inference' engine (e.g., MYCIN) that can be attached to any data base without further modification." Near-future information management trends will also be registered in the following productivity areas : •
•
•
managerial domains: international business and international marketing will be new emphases in the globalization of productivity competition through process and information management. integrated decision-making: catalytic systems for teleconferencing, office automation, and LAN's (local area networks) will synergize inputs from decentralized decision-makers. vendor integration : vendors will become team players as backward integration for quality control and delivery coordination becomes a fact of life in the pursuit of lower defect rates, slashed inventory, and innovative vendor-designed components.
Balanced against these gains will be, however, an emerging politics of information, in which divergent information technologies will generate partisan loyalties to methods rather than goals. Yet another result will be accelerated managerial obsolescence, or its substitution by permanent on-the-job training and learning. Increasingly, managers--knowledge leaders--are expected to unravel such issues as personnel differences in information assimilation, and to show flexibility and adaptation to change, particularly the change from directive, charismatic leadership to the new information leadership paradigm which harnesses information flows within complex organizations in pursuit of productivity: the ratio enhancement of market-valuable outcomes per unit of allocated inputs. Also, information leadership will increasingly substitute for charismatic, personal leadership styles in the trend toward process control of complex and continuous materials, energy, and information flows. Already, mere handfuls of supervisory personnel monitor massive flows in their role as attentive observers of signals synthesized by complex information array panels. Even mid-level managerial roles are impacted by the shift from leadership of persons to leadership of process in the context of information management
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systems and hardware which Jaffe ~ describes as dynamic, time-limited and highly complex. CALCULATING
INFORMATION
SUPPORT
STAFF PRODUCTIVITY
The calculation of information support staff (ISS) productivity is an increasingly important managerial responsibility which can and must be realized. Because of the intangible output of these "knowledge workers", managers have been tardy in monitoring their significant "allocated inputs" role in the productivity equation. Hammond ~1 reports an ISS productivity measurement method developed by IBM. This method calculates productivity gains on the key variable "time saved per information worker". This "time saved" over previous information work cycles is expressed in units per knowledge worker. Next, "time saved" is stated as a percentage of working time, then translated into a dollar rate according to rates of pay. Figures are aggregated for user departments. ~2 This productivity measure may also be time-series analyzed for improvement/decrement. Measured improvements are generally results of this method, meaning that "present information systems staff can be maintained although the number of applications is increased,,..12 This trend analysis method of monitoring information productivity also conforms to Ranftl, 5 who notes the utility of ratcheting up managerial expectations for future ISS performance as a reasonable function of past performance and elapsed time. A compatible manager-usable format for weighted time-series trend analysis of productivity has been described by Chase.~3"~4 The approaches cited above conform to the cardinal principle that ISS productivity should be measured at the point where work is performed, to insure both pertinence and validity.
MANAGERIAL
ROLES IN I N F O R M A T I O N
PRODUCTIVITY
As noted, successful "knowledge leadership" by managers in the information economy requires the integration of information flows within the firm's productivity-driven functions. Strategic planning must therefore be integrated at the following levels : • • •
strategic goals: defined priorities and goals with two to five year time horizons permit investment decisions in specific technologies and systems. strategic support : specific need-to-know parameters defined for pertinent areas such as market or consumer research, research, competitor analysis, etc. strategic implementation: specific DSS go on-line as ISS translate, format, and transmit priority information to managers (knowledge leaders).
Increasingly, strategy focuses on consumer need-satisfaction to energize the firm's productivity in an increasingly competitive world, populated by competent foreign performers enjoying significant labor-cost leadership. Only information leadership can countervail this tremendous productivity advantage, providing the knowledge derived from market and in-depth consumer research which permits the firm to meet the needs of its targeted niche and segment markets in an increasingly "splintered" mass market.15 Accelerated global competition means the productivity of managers must be assessed and rewarded, in spite of difficulties presented by the extraordinary variability in their tasks. According to Grove, 16 a manager's productivity "depends on eliciting more output from this team". Grove ~6 adds that "a manager's own output is the output of his
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organization--no more, no less--". Elsewhere, Grove 17 notes that managerial or knowledge leader productivity begins with the concept of "leverage": Managerial Output = Output of the organization = L I xAI +L2xA2
+''"
This means that managerial activities A t, A2, etc., exhibit an effectiveness or efficiency gain resulting from the multiplicative, not merely additive, effects of such leverage factors as L 1, L 2, etc. These leverage factors may include : •
coordination and focusing of ISS activities in pertinent areas of organization functioning, up to but not beyond equilibrium of marginal cost/internal marginal demand for ISS services. • brief, highly focused managerial interventions such as training, work re-design for job enrichment, gainsharing, and quality circles. • investment decisions in knowledge/information systems to raise informationintensity of both processes and products. • focusing on the outcomes side of the productivity equation when possible, making information technology investments based on enhancement of value added rather than over-emphasis on input-side cost-cutting. Managerial productivity is also leveraged by making job-pertinent information available to operating personnel. The resulting information exchange and processing among staff creates an information economy in microcosm. Along with this downward displacement of the micro-information economy within the firm to the frontier of each specific job, management also achieves job enrichment by de-centralizing decision-making to the job experts themselves. According to the NYSE Office of Economic Research, 6 this delegating of information and decisional discretion to the job frontier typically leverages personal and work-group productivity. The cost of information inputs is a productivity determinant ; managerial "willingness to pay" for information is a practical, surrogate measure of the perceived productivity value of information. Hammond tt and Phillips t2 both discuss such perceived value in terms of management's willingness to re-budget existing resources for information acquisition, thus creating a "bottom-line" information value criterion. Such resource allocation decisions, however, must link management's felt information needs to proposed system benefits, an awareness which Drucker ts says is unfortunately unrealistic: "At a meeting of upper-level managers all of them basically said, 'I expect the information specialist to tell me what information I need'. But that's like going to the doctor and saying, 'What medication do I need?' without giving him a chance to do the diagnosis." The opinions of information staff are not a guide to the real information needs of management. Mortensen 19 writing on the expertise areas of information specialists, states : "'these technologists often have little, or only theoretical understanding of the organizational settings in which new technology will be introduced." Ultimately, information must be managed by management itself, since only those actually responsible for productivity can determine the information and information resources necessary to accomplish their productivity responsibilities.
Productivity dimensions in the management of information
INFORMATION
MANAGEMENT
IN M A R K E T - D R I V E N
317
PRODUCTIVITY
To recoup massive share-of-market losses to sophisticated foreign producers such as Japan, an orientation toward high product quality and meeting of specific consumer needs must be adopted. In short, US management must mainstream information from consumer behavior studies and market research into each transfer phase in the product development cycle. The product development cycle extends from product planning to product development and prototype testing, to manufacturing, quality control, and marketing. While market research (i.e., data on aggregate purchase behavior) is common in the U.S., the analysis of potential consumer preferences for undeveloped or prototype products is of very recent vintage. Foreign performers have scored information coups with in-depth consumer studies, as in the now-classic study commissioned by Toyota to ascertain VW "beetle" owners' "most-desired changes" and to re-design a new Toyota line to incorporate this information. The outcome is now information management history: the once-ubiquitous VW beetle is virtually a collector's item ; 1984 Toyota selling prices were 20Vo above sticker. Consumer "focus groups" emerged from group dynamics research in the 60's and 70's. Skilled group facilitators create a climate of synergistic information exchange in which group members--consumers--explore their reactions to and preferences for potential products. These fast-paced product-concept explorations have produced numerous market winners, including the microcomputer "touch screen" method of icon manipulation. Focus groups embody the Peters and Waterman 2° dictum of"staying close to the consumer", a paradigm for information management in the pursuit of corporate excellence.
INFORMATION
MANAGEMENT
IN T H E X E R O X CASE
This prototypic technology pioneer fell from a 96~ share-of-market in plain-paper copiers in 1970 to less than 45~ in 1982, as reported in B u s i n e s s W e e k . 2~ Seen from an information management perspective, specific factors in the precipitous Xerox-productivity decline were : • • •
failure to evaluate information on market rival performance. cumbersome layers of information-filtering bureaucracy. failure to act on critical information (the initial penetration by Japanese firms of the low-cost end of the U.S. copier market).
In contrast, the recent Xerox turnaround resulted from implementing effective new strategies in information management: • • • •
•
increased information inputs from market and consumer research to new-product design and development. multiplied information exchanges on SBU (strategic business unit) priorities among staff at each SBU for strategy and product development improvements. immediate feedback on new-product idea-feasibility, using information-based "product synthesis" teams to weed out losers. shortened product development cycles through direct, frequent information exchanges between development engineering, product engineering, and SBU vicepresidents. integrated information sharing with key vendors/supplier for more vendor-initiated component design and quality improvements.
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i m p l e m e n t e d " ' k a n b a n " o r "just in time" i n v e n t o r y i n f o r m a t i o n c o n t r o l to cut d r a c o n i a n i n v e n t o r y c a r r y i n g costs.
SUMMARY
AND CONCLUSION
The c h a s m between the i n f o r m a t i o n rich a n d the i n f o r m a t i o n p o o r will widen in n e a r - t e r m d e v e l o p m e n t of the i n f o r m a t i o n e c o n o m y . W i t h i n the family, the rift will d e e p e n b e t w e e n c o m p u t e r illiterate p a r e n t s a n d their h y p e r - l i t e r a t e progeny. H e u r i s t i c " e x p e r t s y s t e m s " will signal a q u a n t u m a d v a n c e in b o t h " m a c h i n e p s y c h o l o g y " a n d in the i n f o r m a t i o n industry. T h e m e r g e r of r o b o t i c s a n d AI is further a l t e r i n g the l a n d s c a p e o f h u m a n experience a n d o r g a n i z a t i o n a l p r o d u c t i v i t y . A l r e a d y we perceive the d a y of true cortical prostheses, when surgically i m p l a n t e d t e r m i n a l s will p e r m i t direct n e t w o r k i n g of the h u m a n cortex with host c o m p u t e r s a n d o t h e r brains, not unlike t o d a y ' s "local a r e a n e t w o r k s " ( L A N ' s ) which a l r e a d y link i n d i v i d u a l w o r k s t a t i o n c o m p u t e r s with central m a i n f r a m e s to create the electronic office. As F. W. H o r t o n 22 c o m m e n t s : "'Few enterprises yet understand how the very nature oftheir business is being fundamentally transformed by the impact of modern information and communication technologies. Most only dimly discern how the value of online databases, carefully synchronized with internal data, in the context of decision support systems, can immeasurably improve the timeliness of decision-making and problem-solving." In this real-time scenario, successful m a n a g e m e n t techniques will i n c r e a s i n g l y focus on m u l t i - v a r i a t e p r o d u c t i v i t y t h r o u g h the m a n a g e m e n t of i n f o r m a t i o n in a g l o b a l information economy.
REFERENCES I. Merrifield, D. Bruce. Forces of Change Affecting High Technology Industries. Nat. J. 253-256 (29 January 1983J. 2. Strassman, P. Oral presentation made at the U.S. Department of Commerce Product!vity Steering Group. U.S. Department of Commerce, Washington, D.C., 28 April 1983. 3. Bushkin, Arthur Emerging Barriers to International Information Exchange. Conference address at How to Fund. Manage, and Market High-Tech Research, Reston, VA., 7-8 March 1984. 4. McPhail, Thomas L. Electronic Colonialism: The Future of International Broadcasting and Communication. Beverly Hills: Sage Publications, 1983. 5. Ranftl, Robert M. Personal, Managerial, and Organizational Productivity Seminar. Los Angeles: privately published, 1983. 6. New York Stock Exchange, Office of Economic Research. People and Productivity. New York : April, 1983. 7. Jaffe, M. Decision Support Systems for Manufacturing. It~systems 112-114 (July 1983). 8. McKibbin, W. L. Will AI Clash with MIS in the Factory? hgbsystems 99 (August 1983). 9. Obermeier, Klaus K. Expert Systems Enhancement of Productivity. In : Producticity in the Information Age (Edited by Vondran et al.). White Plains: Knowledge Industry Publications, 1983. 10. McCarthy, J. and Hayes, P. J. Some Philosophical Problems From the Standpoint of Artificial Intelligence. Edinburgh:Edinburgh University Press, 1969. I I. Hammond, L. W. Management Considerations for an Information Center. IBM Systems J. 21, 131-161 (1982). 12. Phillips, Delores. The Information Centre For End-User Programming: Relationship to Productivity. in: Productirity in the Information Age (Edited by Vondran et al.). White Plains: Knowledge Industry Publications, 1983. 13. Chase, H. Charles. Innovation : The Overlooked Productivity Dimension, Telephone Eng. Manag. ( 15 April 1984). 14. Chase H. Charles. Export Expansion and Small Business Productivity. Am. J. Small Business. {June, 1984). 15. Steinburg, Bruce. The mass market is splitting apart. Fortune 76-82 (28 November 1983). 16. Steinburg, Bruce. Why training is the boss's job, Fortune 109, 94 (23 January 1984).
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17. Grove, Andrew S. Hiyh Output Management, pp. 62-66. New York : Random'House, 1983. 18. Drucker, Peter. Automating the executive suite, Supplement to the New York Times, 11 (3 October 19821. 19. Mortensen, Erik. Office Automation : Agenda for Organizational Charge. In : Productivity in the Information Aye (Edited by Vondran et al.). White Plains: Knowledge Industry Publications, 1983. 20. Peters, Thomas J. and Waterman, Robert H. Jr. In Search t~'Excellence. New York : Harper and Row, 1982. 21. Business Week. How Xerox speeds up the birth of new products, Business Week, 58-59 (19 March 1984). 22. Horton, Forest Woody. "'Harnessing Information Assets and Resources to Increase Productivity." A paper prepared for the National Commission on Libraries and Information Science, Washington, D.C., May, 1983.