Presentation on theme: "READING LIST THREE REVIEW IS8500, Fall 2014 Group 3 Eric Palmer Michael Sims Sorna Dhanabalan Irene Murigu-Hengerer."— Presentation transcript:
READING LIST THREE REVIEW IS8500, Fall 2014 Group 3 Eric Palmer Michael Sims Sorna Dhanabalan Irene Murigu-Hengerer
Puzzles in Organizational Learning: An Exercise in Disciplined Imagination By Karl E. Weick
OVERVIEW Theory construction is part of exercise in “disciplined imagination” Must pay attention to… Things we forget Values we push aside Goals we neglect Facts we avoid Questions we fear
Things we forget… Learning is as perceptual as it is computational We look for learning in wrong activities and overlook learning in obvious places. Learning and thinking Seeing through pattern matching Seeing situations as prototypes Observation of outcomes Developing and maintaining situation awareness Seeing what others miss Assessing situations for familiarity Sensing limitation of frameworks and redoing frameworks Knowledge is something that people share together.
Values we push aside… Values are important source of discipline Three Crucial Values Knowing, Respect, Happiness People value “knowing” and learn through “sense-making.” “Sense-making” is more dynamic and open for change then “decision making.” Animates People Provides Direction Encourages Updating Facilitates respectful interaction People value “respect” Is in short supply for novices due to harder time to develop skills Novices are more reluctant to speak up, answer questions, and have lack of confidence. People value “happiness” A depressed mood effects performance. Non-depressed moods tend to overestimate actions and outcomes.
Goals we neglect… Most goals focus on efficiency, cost containment, adaptation and speed. The problem is that these goals tend to ignore the complication that efficient performance is often low reliability performance. With better understanding of the goal of error-free performance, is a precondition for a better grasp of learning driver by more error laden goals. Jens Rasmussen on Errors (200): Errors reflect efforts to learn to interact effectively with a environment Rather then studying errors, we should focus on strategies to recover from unsuccessful explorations Improvisation In a standard organization routine, its easier to spot errors because it can be compared with a common goal. In improvised actions, you construct a routine and same moment you act out. An improvisation that doesn’t work only reveals itself in its aftermath.
Facts we avoid… Three facts that are not liked to be faced Learning involves disbelief All lessons learned are wrong Ambivalence is the optimal compromise Can uncertainty be reduced? When people experience uncertainty and gather information, it often backfires and increases uncertainty. The more information gathered, the more doubts. A simpler picture is needed to remain active. The saying “Keep it simple stupid” is a good strategy but a terrible practice. Questions we fear…
2013 Update: Connectivism - A learning theory for the digital age Connectivism is the integration of principles explored by chaos, network, complexity and self-organization theories. Principles of connectivism: Learning and knowledge rests in diversity of opinions. Learning is a process of connecting information sources. Learning may reside in non-human appliances. Capacity to know more is more critical than what is currently known. Nurturing and maintaining connections is needed to facilitate continual learning. Ability to see connections between fields, ideas, and concepts. Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities. Decision-making is itself a learning process.
FIRM STRATEGIES FOR RISK MANAGEMENT IN INNOVATION BY AUDLEY GENUS & ANNE-MARIE COLES
INTRODUCTION Main ideas: Study of tech change, risk management & strategy at micro level: Approach to Risk Analysis & Assessment Risk Management link to process of org culture & strategy Views on subjectivity of risk analysis Managerial identity, Knowledge and risk evaluation Success Factors How risk management has evolved in 2013-2014 Firms: Critical to generation & evaluation of new tech in developed economies Firm strategies: What matters to firms; influences performance/growth trajectory Risk: “effect of uncertainty on objectives” (ISO31000 Risk Management Standard) Risk Management: Iterative cycle to identify, assess, prioritize & mitigate risks Theoretical perspectives of risk assessment at micro-level How risks shape emergence of new technology Define & control risks because risks constrain innovations Innovation is risky business!
APPROACH TO RISK ANALYSIS & ASSESSMENT Economic/Accounting Approach (Neo-classic) - Capital budgeting Economic analysis of costs & future returns associated in investment in new tech Involves calculation of factors such as Net cash flow, present value, cost of capital etc. Shortcomings of approach “Management of Uncertainty”- foreseeable risks vs. “true” uncertainty (unpredictable) Statistical experiments-rely on few, limiting parameters (costly); mgmt. best guess Misses crucial issues e.g. firm size, Tech strategy, inter-firm trust etc. Reliance on measures-NPV, cost etc.-narrow criteria; misses indirect benefits ‘Paradox of time”-link between past successes & future failures; learning = success (think grand scale construction projects such Brooklyn Bridge, Sydney Opera House) Role of intangibles such as interpersonal trust
FACTORS IN RISK ANALYSIS & MANAGEMENT How do factors in Risk Management link to process of org culture & strategy? Crucial for firms to recognize risk management as a learning experience Certain org characteristics & routines influence conduct of risk analysis such as: Degree to which a firm is risk taking Nature of organizational culture Use of information systems Reason why managers avoid risk analysis? Can lead to non-funding of project Managers avoid quantification of risks by: Tactical ‘Padding’ of cost and time estimates Skimping on quality assurance or training activities Renegotiate scope of project
VIEWS ON SUBJECTIVITY OF RISK ANALYSIS 1.Strategic management of technology Strategic nature of innovation is planned with the customer (need) Firms have good understanding of what can be achieved & engineering limitations Nature of minimal risks involved with innovation : Mature technologies-innovation routine, incremental & under strict management control e.g. automotive engine production where few competitors exist Highly specialized firm’s routines, techniques & dedicated engineering personnel 2.Sharing of learning & experience across/between innovation projects (micro vs. macro) Consolidation of risk analysis past decisions/assumptions into org knowledge repository Strengthened knowledge base: Facilitates teaching of staff who are new to project/organization “Fountain model”-Parallel engineering=inter-project learning & coordination w/ firm ‘Coupling logic” due to deadlines, individuals work out solutions to meet specific goals Sharing knowledge from others or past projects
VIEWS ON SUBJECTIVITY OF RISK ANALYSIS (cont.) 3.Management of Innovation “Crises”: Explicit interpretive & political treatments of risks “Crises” impact innovating firms & result in poor risk management Crises allow areas of concern to become problems over time Crises conceived as threats to negotiated order Existing problem solving routines not satisfactory for managing risky/difficult projects Response to crises=reverse shift in power structure to those capable of resolving crises If crises repeat, shift can be irreversible & interpretive schemes altered permanently “Reframing of crises” may be product of manipulation of thinking by a group within org E.g. UK pharmaceutical facing failed innovations, undergoes internal reorg to reduce risk of product failure by improving internal practices 4.Management of R&D: Significant change in R&D culture & how orgs managed scientists & engineers Match ideas from R&D with potential market opportunity Competition between in-house R&D & external collaborations (C&D)
MANAGERIAL IDENTITY AND EVALUATION OF RISK Dev in social sciences; focuses on building identity & confidence of managers in their ability to act Evaluation of risk drawn from personal experiences, tech characteristics & org context Specifically around management of risky innovation projects: Participation in radical innovation can lead managers to upgrade reputation & career Leads to bias towards optimism & exaggerated perception of their control over risks Internal org can enhance or diminish perception of risk e.g. traditional matrix Passivity of decision makers content w/ established tech & protections (e.g. IPR) Concentration of decision making in select few; crucial “stop/do not stop” decision missing Undermining of managers optimism & self confidence in risk evaluation due to: Lack of technical literacy which results in feeling of little control over risk Issue of intangibility-“gut feel”/hunch vs. objective “hard” data required to persuade others Management fails to specify economic criteria to evaluate innovation until post launch Leaves subordinates with preordained feeling of “shooting at a moving target” Threshold tests: Need wider evaluation of risks to avoid under/over attention Lack of confidence & transparency in risk assessments
KNOWLEDGE & RISK EVALUATION Nature of risks pertaining to knowledge development process Uncertainty in firm-based innovation; Tech dev.=uncertainties in knowledge creation Need to create integrated socio-economic firm-level technological change Problem: fundamental contradictions between organization vs. knowledge Innovation & evolutionary theories minimize or externalize risk possibilities “Open the black box” analysis reveal uncertainties in knowledge dev. Critique of traditional innovative studies incl predictability & control of dev. process Org based innovation studies: Uncover uncertainties in firm learning & change, co-existence of tech & market, and intangibles such as trust, reputation etc. “Micro-political process” = shift alliances between interested groups/uncertain process SMEs most vulnerable to innovation risks-why? At leading edge of advanced tech; need to match entrepreneurial risk of tech to market High costs associated with innovation (e.g. R&D); Injection of external financial support Heavy reliance on collaborations (tech novel but dev. process uncertain) Informal feedback from engineers & customers
SUCCESS FACTORS Micro vs. Macro levels: Micro-level dynamics/Org: Encourage innovation & dev.; aligned w/ strategic goals Macro/Societal/Market- Monitor changes in market; Risk/Return tradeoff Organizational & Managerial identity: Corporate culture: Mgmt.'s attitude to risk (esp. in SMEs) & Employees attitudes to change Interpersonal interaction based on shared cultural norms Develop “warm relationship” based on openness/trust; individuals crucial in risk mgmt. Addition of decision makers; decoupling go/no go from stop/no stop decision making Expansion of threshold tests & more transparency from senior mgmt. Knowledge sharing & collaboration: Firm-specific literature for new knowledge production & socio-technical processes Reorg to encourage info sharing & breakdown boundaries between groups/sites/ roles Technology: Innovation at speed & aligned w/ core competencies (firm-specific) Training: Increase tech literacy in innovation/specialization
HOW RISK MANAGEMENT HAS EVOLVED View of Risk: Traditional: Bad & compliance issue; Focus on Avoidance, Acceptance, Minimize, or Transfer Now: Uncertainty & strategic; Focus: Optimization/value creation, response/recovery 3 Key trends in 2014 marked by new, pervasive, complex risks (Kapoor, 2014): Blurred lines of internal functions; global dispersion = ambiguous, expanding boundaries Leveraging risk info to drive business critical processes (innovation) & performance metrics Leveraging & harnessing big data to uncover risks & opportunities-elusive but promising Firms embarking on Governance, Risk and Compliance (GRC) journey Strengthen governance, ensure compliance, & build risk-aware corporate culture Risk-taking & Firm values (Hogan & Coote, 2013): Willingness to: Challenge status quo (how we have always done business) Experiment with new ideas & understand calculated risks
The New Economy, eValue and the Impact on User Acceptance of Pervasive ICT By Genus, A., Coles, A-M
User – System (ICT) Interaction : Value, PE, UA (ICT) System User User – System Interaction Impacts on User Acceptance (UA) when using ICT/Systems “Value” as a moderator of UA has been limited to PE (Performance Expectancy) Creating Value, “eValue” Instrumental Instrumental: where instrumentality means there is an objective external to user system interaction Hedonic Hedonic: to increase the acceptance Motivation What drives the motivation for the user Extrinsic: Extrinsic: rewards or benefits external to user system interaction Intrinsic: Intrinsic: derived based on user system interaction Salient Features
User – System (ICT) Interaction : Economy, eValue (ICT) System User User – System Interaction Attention Economy Attention being the scarcest resource in the value chain in information age Web 2.0 technologies triggered competition for attention through blogs, podcasts etc. The High-Tech Gift Economy Money commodity and Gift relationships co-exist with no conflicts Example: In Open Source software, where software is free, but it has business model for commercial support licenses eValue Use of a good (or ICT here) makes it a candidate for having “value” This should not be misunderstood with cost or property value of the good The Process of exchange that this value can translate in “economical value” Salient Features
Analytical Framework Analytical Framework Extends the Consumer Decision Process Model from Engel et al. 1995 Provides each stage of consumer action, and the impact on “value factor” in each context
Analytical Framework - Stages External Search Attention process starts with scanning Salient features increases the attention Prior Use Translation and Comprehensive mental mapping in episodic memory Constructive “meaning” based on use period or duration Use Exercised as normal activity or “routine” in semantic memory Multiple exposures to ICT Acceptance and Retention being the Sub-stages
Analytical Framework – Stages …(2) Habit Habituation of ICT Cognitive Processing with multiple exposures and repetitive behavior Internal Search Goal driven attention Subject’s need and desire determines the search Post Adaptive Behavior High or Low Elaboration Individual’s willingness and ability to see the position advanced due to the use of ICT ICT Example Google Drive, Cloud Storage
Surplus eValue: Effects On User Acceptance Surplus eBusiness Value = When a ICT business derives more value from a factor of innovation production (such as a new feature on a website that gets a user’s attention) than it takes to supply it. Also known as Economic Rent. Information Processing View (Torvalds 1998) Since attention itself is “property” located in a user’s mind, trusted networks of people are considered “property holders” of cyberspatial reputational capital. This capital remains “banked” with the networks of people and only a small amount needs to be monetized due to receiving greater economic rent over time.
Surplus eValue: Effects On User Acceptance Management View Strategic payoffs depend on how embedded an innovation is to the network. When an IS network becomes central to problem-solving and supporting higher-level aspects of work, Stages of IT Embeddedness (Fichman, 1999) 1. Routinization: The extent to which an innovation becomes a stable and regular part of organization procedures and behavior. 2. Infusion: The extent to which an innovation’s features are used in a complete and sophisticated way. 3. Assimilation 4. Institutionalization
Self-Fulfilling and Hedonic eValue – Effects on User Acceptance eConsumer Value = The value accrued to trusted networks of people (usually individual adopters) Attention structures should create process freedoms for users in order to facilitate adoption Value-Rational end consumer action is exhibited through pursuit of Hedonic and Self-fulfilling value. Examples: Freeware and Open Source developers (Linux, WordPress) who donate time and also receive benefits (enjoyment, satisfaction, use of product, etc.) IT Hedonism – Doing things on the Web “for the fun of it” is a stronger predictor of behavioral intention than mere instrumental value
Self-Fulfilling and Hedonic eValue – Effects on User Acceptance Conclusion Clarifying Surplus eValue (business) and Hedonic and Self-fulfilling eValue (consumers) helps to determine when eValue may play an important role in driving ICT usage behavior when it is less likely to do so.
References BERNACKI, E. (2013). Looking into the shadows. Charter, 84(3), 18-20. BROPHEY, G., BAREGHEH, A., & HEMSWORTH, D. (2013). INNOVATION PROCESS, DECISION-MAKING, PERCEIVED RISKS AND METRICS: A DYNAMICS TEST. International Journal Of Innovation Management, 17(3), 1-22. doi:10.1142/S1363919613400148 Genus, A., Coles, A-M. 06. Firm strategies for risk management in innovation. International Journal of Innovation Management, Jun2006, Vol. 10 Issue 2, p113-126, 14p. Hogan, S.J., & Coote, L.V. (2013). Organizational culture, innovation, and performance: A test of Schein's model. Journal of Business Research (2013). http://dx.doi.org/10.1016/j.jbusres.2013.09.007 McKINLEY, W., LATHAM, S., & BRAUN, M. (2014). ORGANIZATIONAL DECLINE AND INNOVATION: TURNAROUNDS AND DOWNWARD SPIRALS. Academy Of Management Review, 39(1), 88-110. doi:10.5465/amr.2011.0356 Merton, R. C. (2013). INNOVATION RISK. Harvard Business Review, 91(4), 48-56. Siemens, G. (2014). Connectivism: A learning theory for the digital age. Sgourev, S. V. (2013). The dynamics of risk in innovation: a premiere or an encore?. Industrial & Corporate Change, 22(2), 549-575. Weick, K. E. 2002. Puzzles in Organizational Learning: An Exercise in Disciplined Imagination.British Journal of Management, Sep2002 Supplement 2, Vol. 13, pS7
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