Web Technologies phần 8 pot

1818 Applying an Organizational Uncertainty Principle tation and rule processing (with XML formats, OWL ontologies and SWRL rule processors). A future option for the Semantic web, but one we pursue now in Case Study 1, may be electronic dashboards to link scientic publications and electronic medical records to associate disease, drug compounds, biology and pathway knowledge betweenR&Dgroups.AsanalconcernforHCL- SIG, there is today no widely recognized machine accessible semantic differentiation between a manuscript and publication; illustration and ex- perimental image data; or between an experiment, data, interpretations, and the hypothesis that an experiment was designed to validate. Initially, our rstweb-basedstudyinCaseStudy1addresses only parts of these problems with an adaptive electronic Institutional Review Board (eIRB) for research protocols rather than medical records; but associated with the eIRB, we are considering business intelligence for individual organization and system-wide performance metrics, and link- ingscienticpublicationsfrommultiplemilitary R&D groups to improve patient care. Brief Literature Review In addition to the literature reviewed in the back- ground, an additional but brief review is provided here to place our work in a historical context. On its face, Durkheim’s (1893) “social facts” stand against Weber’s (1958) methodological individu- alism, today ingrained in game theory, where the choices available to those playing games areinuencedbythesocialandreligiousnorms existing within a culture (Körding, 2007). As an example, the choice to cooperate with a partner in thePrisoner’sDilemmaGameisconguredwith a higher value than the choice to defect from a partner, even though from an information theory perspective,societyoftengainssignicantlymore socialbenetsfromcompetitionthancooperation (Lawless & Grayson, 2004). While social norms should not be disparaged but studied, neither should scientists favor the norm of cooperation by conguringitwithahighersocialwelfarevalue, similar to an industrial policy that chooses the winners for its society. But there are limitations to Durkehim’s view, too. If reality is bi-stable, social facts are open to multiple interpretations. Parsons and Luhman contributed to cybernet- ics and control theory as an information approach to controlling and modeling society. Parsons (1966) developed a systems approach as a grand theory of society. He used systems as a tool to analyze the function of society, concluding the sys- tems that adapt to their environment had evolved intomoreefcientsystems;however,inthatthe environment is ever changing, adaptation is not an optimal control strategy (Conant & Ashby, 1970). Parsons inuenced Luhmann’s (1984) theory of autopoietic, or self-organizing, systems. Luhmanbelievedthatautopoieticsystemsltered information from the environment, independently of other systems, making them autonomous, but also apart from society. Elias (1969) contributed to cybernetics with his ideas on gurational, networked or interconnected structures as the source of power over other systems. Crozier and Friedberg (1981) used game structures to explicitly analyze power and strategy between organizations and their members as interdependent actors. But the limitations remain for game theory from the inuenceofsocialnormsandthelackofatheory of uncertainty. Finally, and contrary to Weber’s view of dif- ferent beliefs producing structural differences in a society, Montesquieu (1949) suggested that checks and balances contribute to society by limit- ing power. Madison applied Montesquieu’s idea by building a constitutional government based on checks and balances (Hamilton, Madison, & Jay, 1787-1788), concluding that social structure controls and stabilizes government independently of the social norms in existence. Further, not only do checks and balances recognize the limits of situational awareness, motivated by the search for meaning at the individual level (Carley, 2002); but also, consensus rules and compromise dilute the 1819 Applying an Organizational Uncertainty Principle added information provided to society by checks and balances, their strength. However, compro- mise leads to an “action consensus” based on a concreteplanofaction,comparedtotheunied worldview of consensus seeking, which reduces the likelihood of action (Lawless et al., 2008b). This is not to conclude that Weber’s ideas missed the mark. Just the opposite. Weber understood that the tradeoffs between the incommensurable beliefs of Confucianism and Puritanism produced profound differences in the control of and social welfarebenetsfortwosocialsystems,which agrees with the uncertainty relations presented below. MAIN FOCUS OF THE CHAPTER In general, most of social science is predicated on the assumption that observations of behavior, especially the self-observations made in response to questionnaires, provide perfect or near perfect information about a target behavior, thereby leav- ing no room for an uncertainty principle. However, striking problems exist with asking agents about the causes of their behavior (self-reports, surveys, structured interviews, case studies). Baumeister et al. (2005) found that a 30-year meta-analysis of survey data on self-esteem correlated poorly with academic and work performance, casting doubt on one of the most studied phenomena in psychology and also on the ability of self-reports to capture targeted phenomena. Similarly, in an attempt to prove the value of air combat maneu- vering for Air Force educators, Lawless and his colleagues (2000) found no association between air combat outcomes (wins-losses) and examina- tion scores on air-combat knowledge. And at the end of his distinguished career in testing game matrices, Kelley (1992) found no association between the preferences as measured by surveys before games were played and the choices actually made during games. Along the same line, Axsom and Lawless (1992) found that scientists easily misinterpreted the causes of behavior measured in effort justication experiments designed to reduce public speaking anxiety even when the scientists observed the changes directly. Intheirreviewofdecisiontheory,Sharand LeBoef (2002) concluded that justications of actions already taken were not consistent with the actions taken, including for expert judges. In addition, they found that the widely held belief by theoreticians that expectations of well-being lead to well-being was systematically violated, even for experts. But even though the evidence in support of widespread claims based on self-reports does not exist, many social models continue to endorse the belief that cooperation enhances the value of social welfare more than competition. In agreement with Pfeffer and Fong, the lack of fundamentals has produced a subjective social science. In response, we take a more theoretical approach based on the impact that cooperation and dissonance have on the diminution or generation of information (Lawless & Grayson, 2004). To summarize, metrics must not interfere with the process of measurement; doing so collapses interdependence and invokes the organizational uncertainty principle (e.g, surveys of self-esteem at the individual level by Baumeister et al., 2004; decision-making at the organizational level; Law- less & Grayson, 2004). Perceptions are integral to behavior, as the Coca-Cola Company discovered when it decided to close out its traditional Coca- Cola brand due to its inability to best Pepsi-Cola in internal taste tests (en.wikipedia.org/wiki/ New_Coke). But following considerable public criticism,thermbroughtbackitstraditionalcola and re-branded it “Classic Coke”. As Baumeister has re-discovered, the measurement of percep- tions in interdependent states with behavior collapses the interdependence, producing static information. We plan to study organizations with compu- tational models. However, Bankes (2002) and Conzelmann and his colleagues (2004) have both concluded that current computational models 1820 Applying an Organizational Uncertainty Principle of organizations are not predictive, principally with Agent-Based Models (ABMs). We plan twocorrectives:rst,totestmodelsusingsocial congurationsaddressedbyourorganizational uncertainty model to reproduce the results of col- lapsed interdependent states that we have predicted andfoundintheeldandlaboratory;andsecond, to build bistable organizations constituted with bistablearticialagents. Organizational Theory and Uncertainty Principle In contrast to traditional social science, we have attempted to combine individuals with organiza- tions and systems, statics with dynamics, and empirical approaches with theory. We incorporate dynamics in our model with the effects of feed- back on oscillations within an organization, but as a metric for its performance. We incorporate organizations in our model by introducing control as organizations seek to improve in performing or revising their mission (Mattick & Gagen, 2005; also, May, 1973). Finally, in our approach, an empirical approach alone precludes formal ap- proaches and optimal solutions; our immediate goal, then, is to build and be guided by theory andempiricalndings. To implement control theory (Csete & Doyle, 2002), we need to quantify an organizational or system's level model. In line with earlier argu- ments, an organization controls at least four aspects of the decision-making process. First, by helping to set or choose its reference or threshold set-points (e.g., culture, decision processes, expec- tations, planning; and in Case Study 1, mission and vision). Second, by damping unexpected disturbances.Third,bylteringandtransform- ing incoming information about system internal states, inputs, and responses to form patterns and decisions. Finally, by taking actions then collecting feedback to revise decisions. However, Conant and Ashby (1970) concluded that feedback to minimize errors is not an optimal solution for control, that the optimum solution avoided errors (e.g,withaplanthatproducesthemostefcient operation possible). As metrics for our control theory, we have proposed inverting the organizational uncer- tainty principle in Figure 2 to link uncertainty between planning and execution as well as be- tween resource availability and the duration of plan execution. In Figure 2, uncertainty in the social interac- tion is represented by an interdependence between b u s ine ss m od e l s, s t ra t e g y, p l an s, o r k n o wl e dg e un- c er t ain t y ( ∆BM x , where the knowledge or business model is a function of the social location where it was learned; from Latané, 1981 and Sukthankar, 2008) and uncertainty in the rate of change in knowledge or its execution as ∆v = ∆ (∆BM/∆t). This relationship agrees with Levine and More- land (2004) that as consensus for a concrete plan increases (∆BM x reduces), the ability to execute the plan increases (∆v increases). By extension, Uncertainty in the execution of a plan Uncertainty in resources to execute a plan ΔvΔBM x ≈ c ≈ ΔRΔt Uncertainty in plans, worldview beliefs or Business Models Uncertainty in time to execute a plan Figure 2. Measurement problem The measurement problem occurs as the result of the orga- nizational uncertainty principle. The measurement problem arises from the interdependence between the two factors on each side of the equation. It states that both factors on either side of the equation cannot simultaneously be known exactly. For example, a decrease in the uncertainty in the strategy for an organization results in an increase in uncertainty for the execution of that strategy. In practice, decreasing strategic uncertainty increases action; increas- ing strategic uncertainty slows action (Busemeyer, 2008). At the same time, the uncertainty principle informs us that only one of the factors on either side of the equation can be known with certainty (Lawless et al., 2007). 1821 Applying an Organizational Uncertainty Principle interdependence also exists in the uncertainty in the resources expended to gain knowledge, ∆R, and by uncertainty in the time it takes to enact knowledge, ∆t. That these two sets of bistable fac- tors are interdependent means that a simultaneous exact knowledge of the two factors in either set is precluded, due to a collapse of interdependence. However, a partial or proportional collapse is not ruled out (i.e., tradeoffs). We have used the model in Figure 2 to study human organizations making decisions under uncertainty by addressing complex situations like the environmental cleanup of its nuclear facilities undertaken by the Department of Energy, or merg- ers and acquisitions. The primary characteristic ofthisinterdependenceisreectedintradeoffs between coordinating social objects communicat- ing to solve problems while in states of uncertainty (Lawless & Grayson, 2004). In Case Study 1, we apply Organizational Uncertainty theory to a system of seven MDRCs (Medical Department Research Training Center). Our goal is to help those MDRCs become more productive in meet- ing their assigned mission. This means that the MDRC system would shift from a fragmented to a more ordered group of organizations, thereby increasing productivity. In the future, to exploit the power of the semantic web, we propose to use a rate equation to measure in real-time with machines the system performance, thus offering management insight as to the factors to change in a tradeoff that enhances organizational per- formance. In addition, we have proposed that alignment of humans and thinking machines (agents) in an organization ranges from disordered in the lowest energy or individual state to one focused on the mission (Lawless et al., 2007). But, by focusing on the mission exclusively as in the latter case, organizations become vulnerable to change. Therefore, it is important to use feedback not only tonetuneanorganization's effectiveness over the short term, but to restructure by revising its mission over the long term (Smith & Tushman, 2005). We propose that the tension can be best constructed, maintained and controlled over time by using semantic web-based metrics. Evidence: Field Department of Energy Citizen Advisory Boards In our search for a classical organizational uncer- taintyprinciple,wehavefoundintheeldand conrmedinthelaboratoryaplanningcognitive- execution tradeoff between consensus-seeking and majority rule decision-making as citizen groups made decisions over complex issues like nuclear waste management (Lawless et al., 2005). Two forms of consensus were found to exist: Worldview consensus and action consensus. The former is more likely to be derived from coop- erative processes and the latter from competitive processes(Woodetal.,2008).Inthersteld study, we looked at the decisions of all nine of the Department of Energy’s Citizen Advisory Boards as they responded to DOE’s formal request to support DOE’s plans to speed the shipments of transuranic wastes to its repository in New Mexico (i.e., the WIPP facility; see www.wipp. energy.gov ) as part of its mission to accelerate the cleanup of DOE facilities across the U.S. These nine DOE Boards were geographically separated and located at the DOE sites where the transuranic wastes were being removed and shipped to WIPP. DOE’s plans were entailed in 13 concrete recom- mendations and explained to the various Boards by DOE engineers (e.g., recommendation #8: “ D O E i n c o ns ul t a ti o n wi t h s tak eh o l d e r s a n d re gu - lators initiate action to assure that WIPP has the capacity to accommodate all of the above listed TRUwaste”).Aspredicted,four-fthsofDOE’s m a j o r i ty - ru l e b o ar ds en d or sed th es e r eco mme nda - tions, while three-fourths of its consensus-ruled boards rejected them. In addition, the time spent in deciding for majority-ruled boards was about 1822 Applying an Organizational Uncertainty Principle one-fourth of the amount of time taken by the consensus-ruled boards. Inafollow-oneldstudyofconsensusdeci- sions by the Hanford Board in Washington State and majority rule decisions at the Savannah River Site Board in South Carolina, Boards located at the two DOE sites with the largest cleanup budgets, we found that consensus rule decisions produced a cognitive congestion that resulted in behavioral “gridlock” when the single worldview of the Board conictedwithDOE’svision,increasingsocial volatility (Lawless & Whitton, 2007). We have found that cognitive congestion is more likely under cooperative decision making because of the inability to accept challenges to illusions (Law- less et al., 2008b). In contrast, we have found that the cognitive disambiguation from competition improves social welfare with practical decisions thatfeedbackampliesoraccelerates. Relative to the SRS-CAB, Bradbury and her colleagues (2003) analyzed interviews and other self-reported measures to conclude that Hanford CAB members felt very positive about their consensus-seeking process, that they very much wanted a cleaned-up environment, and they felt that DOE at its Hanford site was very responsive totheirdemands.However,theresultsfromeld metrics at DOE Hanford and DOE Savannah River Site (SRS) across three measures of cleanup (high- level radioactive wastes, transuranic wastes, and the environmental remediation of contaminated sites) indicated the opposite (e.g., Lawless et al., 2005). Compared to the SRS CAB and the SRS site, this difference between perceptions at the Hanford CABandtheresultsintheeldrepresentedan increase in risk perceptions (i.e, an unchecked increase in the number of illusions) among the Hanford CAB members that had kept them from making concrete recommendations to accelerate the environmental cleanup at Hanford. Evidence: Laboratory Preliminary data from a laboratory experiment nearing completion with college students making recommendations to improve their college experi- ences appears to have fully replicated the DOE CAB study. In this study, we asked college students in 3-person groups (N = 53 groups) at a Historically Black College and a nearby University to proposed open-ended recommendations to improve op- erations affecting them at their schools (e.g., with cafeteria food, library, student government, etc.). Students were randomly assigned to three-person groups who made recommendations either under consensus (CR) or majority rules (MR). Time for both types of groups was held constant. Tentatively, we predicted and found that while CR produces signicantly more discussions (oscillations or jω), indicating less time available to craft recom- mendations,MRproducessignicantlymoretotal recommendations (our analyses are ongoing). Evidence: Case Study 1: Military Medical Department Research Training Centers (MDRCs) Guidedbyourtheoreticalandeldresultsinap- plying the organizational uncertainty principle, we have been assisting a system of seven military MDRCs (Wood et al., 2008) to become more pro- ductive; e.g., produce more research with greater scienticimpact;improvepatientcare;andreduce thecostsofcare.Specically,whenwebeganthis case study, we found little knowledge existed at the organizational level that directly linked each research product (publications, presentations, workshops) with MDRCs assigned mission. In- stead, MDRC collected basic citations for each publication; not all publications were captured in its data-base; nor were all conferences attended captured in their data base. 1823 Applying an Organizational Uncertainty Principle We began with a preliminary set of metrics that indicatedtheefciencyinmeetingMDRCsmission per research protocol across the factors of scholarly activity, personnel availability, space, and funding. But at the same time, these Centers wanted to be able to transform their mission as necessary. These two goals are contradictory. But Smith and Tush- man (2005) concluded that satisfying contradictory goals like these could make an organization more productive now, and more transformative in the future (see Figure 3). Based on feedback from metrics of organiza- tional performance linked to eIRB's, administrators have the ability to execute their mission effectively and efciently; e.g., with Lean Six Sigma pro- cesses.Butefciencyalonereducesadaptability to uncertain future missions (Smith & Tushman, 2005). Thus, concomitantly, a group internal to each MDRC and a national group of elite profes- sionals from all MDRC units could gather annually to transform its mission, goals, and rules guided by the same feedback and metrics. As these two systems compete in a bistable relationship to control the Mission, the two systems operate in tension (interdependence), producing a natural evolution of the system. Evidence: Case Study 1: Application of the Theoretical Model The military (Wood, 2007) has funded a secure web-based system for one MDRC for the submis- sion of IRB research protocols by its investigators (viz., human and animal research Institutional Review Boards). The other MDRCs are included in the product evaluation selection process in the hopethatthebenetsofthefundedeIRBwill Mission Tradeoffs: 1. Well-crafted mission supported by consensus-seeking versus consensus- action. Includes procedures, rules & metrics; e.g., fragmentation p innovation, impedes consensus. 2. Execution Vision Tradeoffs: 1. Vision transforms mission. New vision & mission are constructed by consensus-seeking versus consensus- action. 2. Top professionals at each MDRC propose vision and mission revisions based on mission demands & outcomes. 3. National meetings held to debate proposals. HQ adopts and publishes best, integrated proposal(s). 4. Revisions voted on by all MDRC, HQ & MRG professionals every 3 years versus Command Decision- Makin romotes of mission. The rate a pport a mission. tion or in plan is enacted. 3. Resources to su Effectiveness & efficiency versus resource requirements. 4. Timeliness with execu bringing assets to bear versus duration. g ( CDM ) . Negative Feedback Positive Feedback Metrics Feedback = = Planned Mission - Actual Mission = Actual Mission - Mission Vision Figure 3. Future proposal for a semantic-web based system of seven MDRCs The initial guidance based on theory were: Mission success makes a lean organization more efcient but also more vulner- able to change; change in a business model or its execution in reaction to environmental change was not optimum (Conant & Ashby, 1970); and a sweet spot exists where mission performance is optimum, errors are at a minimum, and at the same time the mission and the organizations it guides are modernized. 1824 Applying an Organizational Uncertainty Principle securefundingfortheothersites.ThersteIRB includes routing of submissions to IRB members; receipt of comments from IRB reviewers; trans- missionofmodicationrequeststoinvestigators; development of IRB meeting minutes; tracking of protocolstatus;automaticnoticationofinvestiga- tors of continuing review deadlines; and tracking metrics. The technology provides a platform for collaboration across the organization between Principal Investigators and team members when drafting protocol proposals. It provides feedback among IRB reviewers, the PI and study team, and Administrators. It tracks Adverse Events (medical and drugs); provides guided electronic input and assistance and error checking and reporting to PI’s and Administrators; but more importantly, it is a platform for integrated management and reporting. The vision for this eIRB project is to achieve an end state to: allow all research proposals, supporting docu- ments, and scholarly products to be submitted and managed by a secured web based electronic system that allows for the real time calculation of research metrics of workload, productivity and quality. Ad- ditionally, this kind of system will allow for better management of the necessary documentation for human research protection and ensure a better environment of operational security oversight for potentially actionable medical information. This will be developed with joint execution in mind and have input from our DoD counterparts. A system that effectively captures all aspects of the research process, from protocol submission and processing to publication of scholarly products or novel therapeutics will generate the highest quality data for productivity analysis and metric development. We believe this can best be achieved by development of an electronic protocol submis- sion and management system with the capacity to generate real time metrics of productivity and quality. (Wood, 2007, pp. 4-5) In installing the eIRB, MDRC will be better positioned to leverage business intelligence (BI) tools that automatically pull together data for metrics with machines from this new electronic system and from other disparate database systems already in place (e.g., electronic medical records). However, only until MDRC has database systems across all aspects of biomedical research and medical care delivery and the BI tools to link these often incongruent systems together will it be able to generate real time data for semantic-web machinestostudy,deneandimprovetheirpro- cesses. Once in place, MDRC can make decisions in real-time rather than with data many months old thereby closing the gap between the mission and the vision and pushing the organization faster towards innovation. The natural tension and gap between the mission and vision, as it closes, will decrease the cycle time between these two per- spectives propelling MDRC along the pathway of necessary transformation. We believe the ability to quickly and effectively manage knowledge is the key to organizational change. Knowledge management is one of the fast- est growing sectors in the business community. In parallel with the rapid growth of knowledge generated by automation systems, organizations having the capability and diversity of BI tools to analyze their performance against their own chosen metrics should help to accelerate system- wide transformation. These tools can afford a seamless reach across different platforms to easily allow for the automatic generation of dashboards that can visually depict metrics of organizational importance in a manner not previously available. As the present web evolves into the Semantic web, so will the capability of knowledge management with BI tools. Current Status. A case in point to demonstrate the power of web-based technology and knowl- edge management has been the virtual collabora- tion systems used by the MDRC working group planning for an eIRB. Leaders geographically separated were able to meet approximately thirty 1825 Applying an Organizational Uncertainty Principle times over almost two years and work together to solve common problems in a manner that would have been cost-prohibitive in the past. MDRC leaders from Hawaii, Washington State, Texas, Washington DC, and Georgia worked as a net- worked virtual organization for approximately 60 hours using web-based collaboration technology with visual and audio communication that lead ultimately to the successful funding of the eIRB system (for a review of Networked and Virtual Or- ganizations, see Lawless et al., 2008a). Members simply logged onto the web from the convenience oftheirownofcetoparticipateinproblemsolv- ing and closing the gap of tension between their mission and vision. Using this virtual collabora- tion in conjunction with a mind-mapping program (similar to a semantic network) for more effective brainstorming allowed the saving of thousands of dollars in travel and personnel time. Assessment of Case Study One. We began Case Study 1 by contrasting the organizational performanceofMDRCagainstthespecicslisted in its assigned mission: improving patient care in theeld;reducingthecostsofcare;andincreasing the impact of research products. We found no clear link between research products and the mission; no measure of publication impacts; and no direct way to measure organizational productivity against its peers (reduced or negligible states of interdepen- dence). In general, the organizations in the MDRC network appeared to be fragmented, with each p urs uin g i ts o wn p a th to mi ss i o n s u c c es s . N o o ve r - arching measure of system performance existed for the MDRCs that the separate organizations could follow to guide their collective behavior. As a consequence, long-term work practices and cultural differences predominated. Subsequently, the move to adopt a web-based eIRB has set the stage to turn around the lack of organizational and system-wide knowledge. MDRC is prepared for real-time organizational and system-wide based metrics, improvements and future transformations (based on maintaining interdependent states). We believe that the semantic web can enhance these metrics by operating in real-time with data col- lected by machines to distinguish between classes of data sources (using OWL’s vocabulary to label separately a site’s physician students, physician scientists and medical scientists across the dif- ferent sites, etc.). At the same time, we will be diligent in preventing web machines from either the inadvertent disclosure of patient records or the premature release, identity and location of researcher data. Evidence: Case Study 2: Application of Theoretical Model to a College After developing and applying metrics for a government organization whose primary mis- sion is training military physicians in medical research practices, it was helpful to apply similar web-based metrics to an organization with a very different purpose. The subject of Case Study 2 is an organization whose primary function is higher education. Although all institutions of higher education are tasked with the production of new knowledgewithineldswhereitoffersdegrees, this organization’s primary purpose is to train the next generation of citizens through the use of a liberal arts curriculum. In its Vision statement, technology is highlighted and indicates that the institution “provides information technologies that link its students’ total academic and social experi- ences to the global world.” (Bradley, 2008) Today’s institutions of higher education are faced with an interesting dilemma with faculty members who have come of age during a period of tremendous technological upheaval. During the last twenty years, institutions of higher educa- tionhavestartedmakingsignicantinvestments in administrative information systems. Higher education institutions are being asked by policy makers, accrediting bodies, the business com- munity and the public for evidence that college graduates have a demonstrated knowledge base predicated on their degree. With the mounting cost of higher education, consumers are asking 1826 Applying an Organizational Uncertainty Principle for accountability from colleges and universities (Bradley, 2008). Institutions of higher education as well as most organizations must focus on systems that must be in place to ensure that the decisions made in the future take advantage of the best data possible. Institutions are engaged in a delicate dance of remaining true to their purpose in society while responding to calls for accountability for their actions. Laws such as the Family Education Rights and Privacy Act (FERPA) caused some campusofcialstodevelopextremelystrictpoli- cies regarding information about student records. These policies were strictly enforced even when it was known that the aggregation and analysis of data from student records would provide the institution with invaluable information for mak- ing informed decisions about ways to improve academic programs, increase retention, and address issues being raised by outside entities. Institutional research projects were strangled by the fear of litigation regarding the privacy of student information (Green, 2007). According to Green (2007) “institutions of higher education have seen an emergence of a wide, rich, and mission-critical array of stu- dent and institutional services that are directly linked to core campus information services (or Enterprise Resource Planning (ERP) functions). Yet these new functions and servicesalumni services, course/learning management systems, digital content, e-portfolios, e-services (online registration, fee payment), and portalsare all rmlydependentnotonlyontheWebbutalso on real-time interaction with the core elements of the “old” management information system (MIS), particularly students records and institutional nances.”Manyofthesefunctionsatinstitutions, particularly small institutions are informal and units within the organization form their own efdomsmanytimesasawayofmanagingthe complexity of a system that is governed by external policies and procedures as well as the end users of the services. In an earlier age when students walked from one ofce to another to engage personnel in the business of enrolling in courses, acquiringnancialaid,payingtheirbills,andob- taining housing, these systems worked. However, in an age where information drives decisions for the organization as well as the consumer, the earlier model is no longer feasible. The organization employs approximately 200 individuals with the majority of individu- als serving as instructional personnel providing instruction for a student body of less than 1,000 individuals studying at the undergraduate level. Besides instructional staff (faculty), there are administrative staff members, staff who provide support services to students, a unit that manages thescalenterpriseoftheorganization, and a unit responsible for external partnerships and fund raising. All units of the institution rely on theefcientfunctionoftheotherareasbutare limited in the operational knowledge generated by these other units from the lack of technological (web-based) interconnectivity. Current Status Computing and technology support in an academic environment provide the technology infrastruc- ture for academic and administrative activities that have become essential for the operational effectiveness of institutions of higher education. There is a need to analyze the current technol- ogy infrastructure due to the present isolation between subsystems and organizational opera- tions. Multiple systems exist but each organiza- tional unit works with its own “preferred” one, producing fragmentation. The different systems are not integrated causing record sharing and management problems. Currently, information technology (IT) support is being done by two staff members, one deals with hardware issues and the other with support software plus the network as part of the college’s infrastructure. There is no system request form or work-list. Priority is given to network issues and calls from very important 1827 Applying an Organizational Uncertainty Principle persons (VIPs) within the organization, likely impeding performance. With a new administration, this organization has realized the need to evaluate the current IT infrastructure and the need for changes to fulll its vision and mission. After the pre- liminaryinvestigation,therstneedidentied was to overhaul and redesign the website. The previous version did not represent the academic organization due to its commercial feel. Then anITinventorysurveywasconductedtond out what systems are available, which system is being utilized by which unit (or not at all), the merit of these choices, and the costs associated witheachsystem.Tondanenterprise-wide solution, the institution is considering having an IT-consultant company to evaluate the current infrastructure (conceptual model), and suggest the best solution. The institution also needs a chiefinformationofcer(CIO)(orMISdirector) who is capable of implementing the plan. All institutional areas that are impacted by or use technology should be evaluated. Either after purchasing an enterprise information system (EIS) or after choosing from currently available systems for a single “main” system that supports most unit functions plus a Transaction Process System (TPS) forbusiness/nancialunitonlinetransactions,per- formance measurement should be enacted. Focus, however, would not be placed on the network p er s e , but on the organization’s performance as measured with its EIS. Critical Success Factors for an EIS in a higher education institute like this one which should be measured are: • Instructional support, as measured by the number of courses offered or supported via the Internet or other electronic methods, num- ber of instructional classrooms supported, number of student computer labs, student accounts, technology in residence halls and shared spaces (i.e. campus center) or other means • Researchsupport,asmeasuredbyaccessto research databases, high speed network con- nectivity, other data collection and analysis mechanisms, and specialized research func- tions • Costofservices,eithermeasuredintheaggre- gate, or on a per-student full-time equivalent (FTE) or per-faculty FTE basis, including comparisons with peer institutions • Numberofgraduatescomparedwithadmis- sion • Studentlearningoutcomes:assessmentssup- port Assessment of Case Study Two While it is too early in the process to assess this college, and while a measurable semantic-web based baseline is being built, certain areas to measure performance are already obvious. For example, after implementation of the EIS, do faculty publication numbers and the impact of research (quantity and quality) improve? Does the new IT web system improve or assist the College in its assessment processes? After the EIS system is operational, we plan to review its performance as well as the College’s. FUTURE TRENDS The most important future trend is the use of agent- based models (ABM’s) to model social and orga- nizational effects to measure their effectiveness with the semantic web. Agent based systems have been endowed with auction based mechanisms for distributing their resources (Gibson and Troxell, 2007). In this scenario, the various entities would “bid” for the information they require, ensuring that the entity that valued the information the most would receive it in the timeliest manner for their decision making. Double auctions have been used for similar analyses with genetic algorithms (Choi, Ahn and Han, 2008). [...]... previously published in Handbook of Research on Social Dimensions of Semantic Technologies and Web Services, edited by M M Cruz-Cunha; E F Oliveira; A J Tavares; L G Ferreira, pp 469- 488 , copyright 2009 by Information Science Reference (an imprint of IGI Global) 183 3 183 4 Chapter 7.2 Bridging the Gap between Mobile Application Contexts and Web Resources Stefan Dietze Open University, UK Alessio Gugliotta Open... Longitude Interval Float - 180 + 180 s1 History Ratio Float 0 100 s2 Culture Ratio Float 0 100 s3 Geography Ratio Float 0 100 s4 Language Ratio Float 0 100 Table 2 Prototypical members within L Prototype l1 (Latitude) l2 (Longitude) L1: Milton Keynes (UK) 52.044041 -0.699569 L2: London (UK) 51.500152 -0.126236 L3: Brighton (UK) 50 .82 0931 -0.13 984 6 L4: Paris (FR) 48. 85667 2.350 987 L5: Toulouse (FR) 43.604363... latitude-dimension) (has-value 50 .82 0931))) (def-instance brighton-valued-long-vector location-valued-dimension-vector ((values longitude-dimension) (has-value -0.13 984 6))) 184 6 Bridging the Gap between Mobile Application Table 4 Distances between E and targeted locations Prototype Euclidean Distance L1: Milton Keynes (UK 1.6125014961413195 L2: London 0 .84 063030296 081 79 L3: Brighton 0.4 280 775 986 5356176 Table 5 Distances... allgemeinen theorie, Frankfurt: Suhrkamp Hamilton, A., Madison, J., & Jay, J (1 787 -1 788 ) The federalist papers New York newspapers Mattick, J S., & Gagen, M J (2005) Accelerating networks Science, 307, 85 6 -8 May, R M (1973/2001) Stability and complexity in model ecosystems Princeton, NJ: Princeton University Press Metropolis, N (1 987 ) The beginning of the Monte Carlo method Los Alamos Science, Special Issue,... Distances between S1 and targeted subjects Subject Euclidean Distance S5 (50,0,50,0) 70.7106 781 186 5476 S6 (65,0,0,35) 49.497474 683 0 583 3 S7 (70, 30,0, 0) 35.355339059327 38 distance calculations According to the obtained situation parameters and the selected user goal, the SEE discovers and orchestrates annotated Web services, which show the capabilities to suit the given situation representation Whereas... Pervasive Computing Environments The Knowledge Engineering Review, 18, 197–207 doi:10.1017/S026 988 8904000025 Coalition, O W L.-S OWL-S 1.1 release (2004) http://www.daml.org/services/owl-s/1.1/ 184 9 Bridging the Gap between Mobile Application Cregan, A (2007) Symbol Grounding for the Semantic Web 4th European Semantic Web Conference 2007, Innsbruck, Austria Devore, J., & Peck, R (2001) Statistics—The Exploration... 55-90 DOI= http://dx.doi.org/10.1007/s10707-005- 488 6-9 World Wide Web Consortium W3C (2004a): Resource Description Framework, W3C Recommendation 10 February 2004, http://www w3.org/RDF/ World Wide Web Consortium W3C (2004b): Web Ontology Language Reference, W3C Recommendation 10 February 2004, http://www w3.org/TR/owl-ref/ WSMO Working Group (2004), D2v1.0: Web service Modeling Ontology (WSMO) WSMO Working... proceeds through the following steps utilizing the set of SWS capability descriptions: 1 2 3 4 Discovery of potentially relevant Web services Selection of set of Web services which best fit the incoming request Invocation of selected Web services whilst adhering to any data, control flow and Web service invocation constraints defined in the SWS capabilities Mediation of mismatches at the data or process... basic principles New York: Guilford Axelrod, R (1 984 ) The evolution of cooperation New York: Basic Axsom, D., & Lawless, W F (1992) Subsequent behavior can erase evidence of dissonanceinduced attitude change Journal of Experimental Social Psychology, 28, 387 -400 Bandura, A (1 989 ) Human agency in social cognitive theory American Psychologist, 44(9), 1175-1 184 Bandura, A (1977) Social learning theory New... describing latitude and longitude of Eastbourne: { E = (e1 = 50.76 686 8, e2 = 0. 284 804) ei Î L } To represent the current aim of the user, a user selects one of the subject prototypes (Section 0), in this case S1 (Table 3), which is added to the situation description Figure 5 depicts a screenshot of a mobile device showing the application web- interface while supporting a user to semi-automatically locate . Journal of Experimental Social Psychology, 28, 387 -400. Bandura, A. (1 989 ). Human agency in social cognitive theory. American Psychologist, 44(9), 1175-1 184 . Bandura, A. (1977). Social learning. 27). Video: Tim Berners-Lee on the Semantic Web. Retrieved February 20, 20 08, from http://www.technolo- gyreview.com/Infotech/ 184 51/ Bradley, G. (20 08) . Condential source: Presen- tation to the. McGraw-Hill. Luhmank, N. (1 984 ), Soziale systeme: Grundriß einer allge- meinen theorie, Frankfurt: Suhrkamp. Hamilton, A., Madison, J., & Jay, J. (1 787 -1 788 ). The federalist papers. New