8,985 research outputs found

    Do management accounting systems influence organizational change or vice-versa? Evidence from a case of constructive research in the Healthcare Sector

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    The paper aims to analyze the process of change of management accounting system (MAS) as a consequence of changes in the complexity of organizational structure in healthcare. It analyzes the process of change of MAS according with the theoretical frameworks of Habermas (1987) and Laughlin (1991).In this organizational changes are seen as the consequence of the interaction between tangible and intangible elements of the organization and between the organization and the external environment. The process of change was not studied from an external standpoint, but through an active participation and contribution of the researchers in the process of change itself. Using a constructive approach, the researchers were actively involved with the actors of the change in developing the process of change, and in facilitating the overcoming of some cultural gaps and resistance which could arise in professional organization. The paper provides empirical insights of the characteristics of the process of change of MAS in a Heath Care setting with a particular focus on aspects characterizing the process of change itself. Finding suggests the importance of putting high attention in the development of the process of change and underlines how the attention to peculiarities of the organization, in to this phase, could make the MAS able to impact on the behaviours and culture of professionals.Management Accounting Change, Healthcare Accounting, Habermas

    Formal verification of automotive embedded UML designs

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    Software applications are increasingly dominating safety critical domains. Safety critical domains are domains where the failure of any application could impact human lives. Software application safety has been overlooked for quite some time but more focus and attention is currently directed to this area due to the exponential growth of software embedded applications. Software systems have continuously faced challenges in managing complexity associated with functional growth, flexibility of systems so that they can be easily modified, scalability of solutions across several product lines, quality and reliability of systems, and finally the ability to detect defects early in design phases. AUTOSAR was established to develop open standards to address these challenges. ISO-26262, automotive functional safety standard, aims to ensure functional safety of automotive systems by providing requirements and processes to govern software lifecycle to ensure safety. Each functional system needs to be classified in terms of safety goals, risks and Automotive Safety Integrity Level (ASIL: A, B, C and D) with ASIL D denoting the most stringent safety level. As risk of the system increases, ASIL level increases and the standard mandates more stringent methods to ensure safety. ISO-26262 mandates that ASILs C and D classified systems utilize walkthrough, semi-formal verification, inspection, control flow analysis, data flow analysis, static code analysis and semantic code analysis techniques to verify software unit design and implementation. Ensuring software specification compliance via formal methods has remained an academic endeavor for quite some time. Several factors discourage formal methods adoption in the industry. One major factor is the complexity of using formal methods. Software specification compliance in automotive remains in the bulk heavily dependent on traceability matrix, human based reviews, and testing activities conducted on either actual production software level or simulation level. ISO26262 automotive safety standard recommends, although not strongly, using formal notations in automotive systems that exhibit high risk in case of failure yet the industry still heavily relies on semi-formal notations such as UML. The use of semi-formal notations makes specification compliance still heavily dependent on manual processes and testing efforts. In this research, we propose a framework where UML finite state machines are compiled into formal notations, specification requirements are mapped into formal model theorems and SAT/SMT solvers are utilized to validate implementation compliance to specification. The framework will allow semi-formal verification of AUTOSAR UML designs via an automated formal framework backbone. This semi-formal verification framework will allow automotive software to comply with ISO-26262 ASIL C and D unit design and implementation formal verification guideline. Semi-formal UML finite state machines are automatically compiled into formal notations based on Symbolic Analysis Laboratory formal notation. Requirements are captured in the UML design and compiled automatically into theorems. Model Checkers are run against the compiled formal model and theorems to detect counterexamples that violate the requirements in the UML model. Semi-formal verification of the design allows us to uncover issues that were previously detected in testing and production stages. The methodology is applied on several automotive systems to show how the framework automates the verification of UML based designs, the de-facto standard for automotive systems design, based on an implicit formal methodology while hiding the cons that discouraged the industry from using it. Additionally, the framework automates ISO-26262 system design verification guideline which would otherwise be verified via human error prone approaches

    Graduate School: Course Decriptions, 1972-73

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    Official publication of Cornell University V.64 1972/7

    Theoretical analysis of the philosophy and practice of disciplined inquiry

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    2015 Spring.Includes bibliographical references.This dissertation theoretically examined the process of disciplined inquiry in the social sciences from its philosophical foundations to its extensions into practice. Key to conceptualization of disciplined inquiry were two regulative ideals: the commitment to the concepts that define the possibility of experience and the commitment to processes for combining the concepts of experience. The paradigm theory of Lincoln, Lynham, and Guba (e.g., Lincoln & Lynham, 2011; Lincoln, Lynham, & Guba, 2011) provided a sophisticated explanation of the possibility of experience that inquirers can commit to when engaging in disciplined inquires. Review of literature revealed an inadequacy in the state of theoretical understanding of processes for combining the concepts of experience. To develop a theoretical agenda of research for disciplined inquiry, the literature on paradigm theory and theory building was analyzed. A historical analysis of paradigm theory revealed milestones in more than 40 years of inquiry focused on conceptualization of the theory. A reverse engineering analysis theoretically examined paradigm theory and its milestones identified from the historical analysis for key features of the theoretical process. A revised conceptualization of disciplined inquiry was presented and a theoretical agenda for developing the underlying theoretical framework for the processes of combining the concepts of experience was outlined

    Research and Regions. a KWIC Indexed Bibliography

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    Computerized techniques applied to economics to produce bibliography of related materia

    VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

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    The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

    The ecology of technology : the co-evolution of technology and organization

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    In this day and age, arguing that technology is a powerful force that drives many economic processes is like preaching to the choir. Nevertheless, despite the widespread realization of the important role of technology in our modern day society, an intimate understanding of the process of technological change is still lacking. This study seeks to provide more insight into the concept of technological change by characterizing it as a socio-cultural evolutionary process of variation, selection and retention. According to this logic, variety (or novelty) is created by (random or non-random) mutations (i.e., organizations and individuals that (re-) combine existing components in novel ways). This variety is subsequently selected out by the stakeholders in the environment, such as individuals, organizations, and institutions. In other words, the variety is then retained in the structural characteristics of the environment, commonly referred to as organizational routines and technological paradigms. Finally, these structural characteristics subsequently provide the context in/from which new mutations (or variations) are created. From there, the cycle can be repeated. Because, nowadays, technology is mostly developed in an organizational context, the appropriate place to study technology and technological change is in the context of organization science, which is an academic discipline that studies all facets of organization. Even though technology deserves a central role in any organization theory, technology has not yet penetrated fully the domain of organization science. The only domain in which technology has a central role is within evolutionary economics, a school of economic thought that was influenced by evolutionary biology. Even though evolutionary economics has surely added much to our understanding of the process of technological change, in our view, this school of thought mainly concentrates its attention on idiosyncratic accounts of variety creation and their subsequent selection by the environment. Much less attention has been attributed to how the selection environment (or the structural characteristics thereof) determines the variety creation. Consequently, insights from organizational ecology, which has its center of gravity at the selection environment, can add value over and above the ones originating from evolutionary economics. The key source of inspiration of organizational ecology is bioecology, which makes it evolutionary economics’ counterpart in sociology. In this study, we therefore seek to close the evolutionary circle by developing a structural or ecological perspective of technological change. After all, holding both links between variety and selection in focus at the same time (i.e., how variety is selected by the environment, and how the selection environment facilitates and constrains the creation of variety) provides for a truly evolutionary model of technological change. Accordingly, we define our research objective as follows: Research objective: To develop an ecology of technology in organization science. Because this objective is rather vague and abstract, we formulate several research questions to provide more direction in our quest to fulfill our objective. We formulate our first research question as follows. Research question 1: What is the importance of biotechnology? Providing an answer to this research question is the subject of Chapter 2. As a means of introducing biotechnology, we first describe biotechnology’s central dogma (i.e., DNA as the building block of life). Moreover, we provide a timeline to get a certain feel of the history and evolution of biotechnology, and list numerous socio-economic trends to get an idea of the importance of biotechnology in society. These trends clearly illustrate that biotechnology drives important social and economic events. Next, we evaluate biotechnology’s position in the overall technological landscape. Our main finding is that, despite its sharply increasing societal and economic importance, biotechnology still has not yet conquered a place in the technological core of our society. Reviewing the developments within synthetic biology (in this domain, complex systems are designed by (re-)combining DNA into biological parts that represent biological functions and, as such, is the domain where all aspects of biotechnology come together), it becomes clear that biotechnology as a whole is not yet in the growth stage of technological convergence that is characterized by a stable configuration of component technologies (i.e., a dominant design). Moreover, on the basis of the future expectations of experts, we conclude that biotechnology is a strategic technology that is nowhere near its peak influence, and that we can expect the importance to increase even further over the coming years. Obviously, whether biotechnology can deliver on its promise and materialize the expectations of insiders is not certain. Even when biotechnology delivers on only a small part of the promise, though, its impact will already be gigantic. For example, consider the fact that, in a 2007 interview, Craig Venter – who is one of the most well-renowned biotechnologists today – said that, in 20 years time, synthetic genomics is going to become the standard for making anything (Aldhous, 2007). So, in conclusion, biotechnology is a technology that is still emerging and does yet not display a stable and predictable pattern of growth that characterizes mature (i.e., non-emerging) technologies. Our next research question thus is as follows. Research question 2: How to study the growth of an emerging technology? In Chapter 3, on the basis of ecological insights and principles, we develop a structural or systemic view towards technology, and hereby take into explicit account the embedded nature of technology. That is, we propose that it adds value to view technology as a system composed of a set of interdependent components (or subsystems). More specifically, by relying on density dependence theory from organizational ecology, we effectively develop a multilevel framework that can be used to empirically study emerging technologies. Moreover, we employ the concept of the technological niche from organizational ecology, with its associated dimensions of crowding (associated with processes of competition) and status (associated with processes of legitimation), and add diversity as a key dimension. Through sophisticated multivariate analysis of biotechnology patents from the United States Patent and Trademark Office (USPTO), we validate this model, which we label the ‘ecology of technology’. However, we also discover some anomalies, which point to the limitations of our model, the most important being its rather static nature. Because emerging technologies are characterized by fluid patterns of growth, a static model is a severe misrepresentation of the evolution of emerging technologies. Our next research question naturally follows from this. Research question 3: How to study the evolution of an emerging technology? On the basis of insights from evolutionary economics, Chapter 4 distinguishes between two stages of technological development, namely the stages of divergence and convergence (that connect nicely with the seed and growth stage of life cycle theory). The focal element is what is generally referred to as the deep structure (in the context of technology also commonly referred to as a dominant design) that facilitates cumulative changes by reducing uncertainty and enabling specialization and integration through standardization. The stage of divergence is characterized by the absence of a deep structure, while the stage of convergence is characterized by its presence. So, in the latter stage, there is a relatively stable configuration of the system’s component technologies that results in relatively stable and predictable patterns of growth. On the basis of these insights, we adapt our multi-level model to identify these different stages of development at the component level. More specifically, if there is a mutualistic relationship between a component and the system (i.e., if system density contributes positively to component entry), the component is argued to have a dominant design. As we are dealing with an emerging technology, our main interest lies in the transition from the initial seed stage of technological divergence (i.e., the absence of a deep structure) to a growth stage of technological convergence (i.e., the existence of a deep structure), or the creation of a deep structure. This means that we do not take into account the revolutionary transition from a stage of convergence into divergence (i.e., the maturity and decline stage in life cycle theory). Not only do we refine our predictions regarding the effects of our existing dimensions (i.e., multilevel density dependence, crowding, status, and focal diversity), but, by further taking into account the lineage of technology, we refine our dimension of diversity by adding antecedent and descendant diversity as additional dimensions to the technological niche. This results in an intricate model that can be used to study the growth and evolution of an emerging technology. We demonstrate this by an empirical investigation of biotechnology patents from the USPTO and hereby provide further support for our ‘ecology of technology’. In the light of our research objective, before we answer the question of what the precise consequences are for organizations, we ask ourselves how we can effectively integrate our findings at the organizational level of analysis. We thus formulate our next research question accordingly. Research question 4: How can we integrate technology into the theory of the organization specifictechnological niche? In Chapter 5, we use a process of logical formalization to represent the theory of the organization-specific technological niche in a formal logical language. The reason for doing so is threefold. First, this forces us to explicate all underlying assumptions and to remove any inconsistencies to make the argument logically sound. Second, this requires us to supplement the theory so that it is complete, without missing elements. Third and finally, it results in a logically sound and complete theory fragment ready for extension by integrating the insights from the study of the evolution of technology. We choose nonmonotonic logic as the language in which we represent our arguments because nonmonotonic logic is better suited for theory building, and this connects better to the current wave of formalization in non-monotonic logic in organizational ecology. On the basis of this analysis, we already make two important theoretical extensions. First, by distinguishing between crowding in technological and market space, we tie technological crowding to both competition and legitimation. To be precise, technological crowding results in competition mainly if the crowding organization is a competitor of the focal organization. Second, uncertainty mediates the relationship between the perceived and actual technological quality of the organization. More specifically, under uncertainty, the actual quality of an organization’s technology cannot be readily observed so that resource controllers have to rely on status (i.e., historic technological quality) instead. With this formalized, logically sound and complete theory fragment in hand, we can turn to the question of the organizational consequences. We thus pose our next research question as follows. Research question 5: What are the consequences of integrating several technological insights into thetheory of the organization-specific technological niche? In Chapter 6, we integrate four technological insights from Chapters 3 and 4 into our formalized theory fragment from the previous chapter. These insights are: (1) multiple technological domains exist that have (2) different stages of development, (3) different levels of uncertainty, and (4) different growth rates. On the basis of these four insights, we extend the theory of the organization-specific technological niche considerably. For crowding, we demonstrate that the effect of crowding is not only conditional upon the identity of the other organization, but also on the stage of technological development. We also add non-crowding to the mix. Regarding the effect of (non-)crowding, in the stage of divergence, multiple competing design configurations exist, and crowding (non-crowding) increases (decreases) the competitiveness of the supported design configuration, having a legitimating (competition) effect. In contrast, in the stage of convergence, crowding (non-crowding) loses its legitimating (competition) function and results in competitive (legitimation) pressure. For status, the most important consequences are that: (1) status is domain dependent, and (2) its effect is dependent upon the stage of technological development (i.e., the effect of status is higher in the stage of divergence). We also add two additional dimensions, which are (1) technological opportunities (that can be represented by the growth rate of the domain), and (2) technological diversity (measured by the distribution of activities over alternative domains). By operationalizing performance as a two-dimensional vector, we suggest that the dimensions of the technological niche are related to different performance measures in distinct temporal relationships. However, even though this theoretical extension is certainly valuable, the subsequent question is whether these extensions hold when subjected to advanced empirical tests. We therefore formulate our next research question as follows. Research question 6: Can we find proof for our extended theory of the organization-specific technological niche? In Chapter 7, we empirically test several of our theoretical extensions of the organization-specific technological niche. Our dependent variable is biotechnology innovation (i.e., the number of biotechnology patents). Through a sophisticated empirical analysis, we find strong support for our extended theory. However, we also encounter some inconsistencies and anomalies. This seems to connect to the fact that processes of competition and legitimation are more appropriately defined at lower levels of analysis (i.e., at the component instead of at the system level). Moreover, due to the dual role of a direct technological tie (i.e., it can have both a competing and a legitimating function) that forms the basis for our measure of status, status is better defined at the component level of analysis. In contrast, biotechnological quality can be aggregated to the system level without losing significance. We thus find strong support for this dimension. Furthermore, we also clearly demonstrate the importance of taking into account the different dimensions of technological diversity (i.e., antecedent, focal, and descendant), with a vital role for antecedent diversity, which logically connects with the notion of absorptive capacity. The subsequent question is what this means for the broader academic debate regarding the (co-)evolution of technology and organization. We formulate our next research question accordingly. Research question 7: What are the implications for the study of the (co-)evolution of technology and organization? In the final chapter of this dissertation, we start by stating the main contribution of this dissertation, which is that we develop a dynamic multilevel model that can be used to empirically study the evolution of an emerging technology. As this model is based on the assumption that technology can effectively be studied as a system composed of an interacting set of components, we pay explicit attention to the embedded nature of technology. Hence, when studying the evolution of technology, it is inappropriate to focus on a single level of analysis and using a multilevel perspective adds value over and above any single level study. That is, technology (e.g., biotechnology) is composed of a set of technological components (e.g., biotechnology’s component technologies) while, at the same time, being embedded in a larger technological system (i.e., technological landscape). It is precisely this multilevel nature of technology that gives it the potential to close part of the chasm in the debate between organizational adaptation (i.e., the dominant perspective in evolutionary economics) and environmental selection (i.e., the dominant perspective in organizational ecology). More specifically, by defining technology at different levels of analysis (e.g., invention, component, system, and landscape), it is possible to tie the evolution of technology to the evolution of organization at different levels of analysis (i.e., individual organization, population of organizations, community, and society). This enables studying the evolution of technology and organization in unison, and thus provides the basis for a co-evolutionary model of technology and organization. Employing a multilevel perspective to both technology and organization at the same time, and defining technology and organization as nested hierarchies tied together at multiple levels of analysis, effectively allows an analyzes of how stable configurations travels upwards in this hierarchy. After all, "it is the information about stable configurations […] that guides the process of evolution" (Simon, 1952: 473)

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    Abstracts : policy research working paper series - numbers 2680 - 2753

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    This paper contains abstracts of Policy Research Working Paper series, numbers 2680 - 2753.Environmental Economics&Policies,Economic Theory&Research,Banks&Banking Reform,Health Monitoring&Evaluation,Macroeconomic Management
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