16,607 research outputs found

    The uses of qualitative data in multimethodology:Developing causal loop diagrams during the coding process

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    In this research note we describe a method for exploring the creation of causal loop diagrams (CLDs) from the coding trees developed through a grounded theory approach and using computer aided qualitative data analysis software (CAQDAS). The theoretical background to the approach is multimethodology, in line with Minger’s description of paradigm crossing and is appropriately situated within the Appreciate and Analyse phases of PSM intervention. The practical use of this method has been explored and three case studies are presented from the domains of organisational change and entrepreneurial studies. The value of this method is twofold; (i) it has the potential to improve dynamic sensibility in the process of qualitative data analysis, and (ii) it can provide a more rigorous approach to developing CLDs in the formation stage of system dynamics modelling. We propose that the further development of this method requires its implementation within CAQDAS packages so that CLD creation, as a precursor to full system dynamics modelling, is contemporaneous with coding and consistent with a bridging strategy of paradigm crossing

    Towards the Framing of Venture Capital Policies: a Systems-Evolutionary Perspective with Particular Reference to the UK/Scotland and Israeli Experiences

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    We compare some of the policies that have been attempted in Europe (UK/Scotland) and Israel over the past fifteen years to elaborate a new Systems Evolutionary (SE) framework for rethinking VC policy and related ITP. We argue that this perspective is useful for both real world (‘positive’) analysis and policy (‘normative’) analys is. Our SE framework is shaped by (i) a multidimensional view of VC; (ii) strong between VC, VC policy and the development of EHTCs; and (iii) a strategic approach to policy. In contrast, many VC policies in Europe up to and including the 1990s took a ‘static’ financial view of VC that focused on ‘bridging existng early phase finance gaps of innovative companies’ rather than creating of a new mechanism to assure the timely growth of EHTCs. We aim to present the new framework rather than to provide specific recommendations. The main conclusion is that the success of VC policies depend on factors such as the phase of evolution of (i) VC or related innovation finance organizations; (ii) the underlying segment of start up companies and of high tech industries; (iii) the specific country/region institutional setting. While in some contexts it may be worth considering the targeting of a new VC industry/market (and associated EHTC) in others the focus of policy should center in improving pre-emergence conditions. More specifically it may be, given that VC searches for ‘investment ready opportunities’, that ITP should, in many contexts, precede VC policies. Another key conclusion is that implementing this perspective necessitates the creation of a strategic level of policy, with a view of specifying a set of strategic priorities for Scie nce, Technology, and Innovation, priorities that should precede rather than follow policy design and implementation. A major challenge is to extend the present framework that was initially based on VCs oriented towards ICT to LS.

    On Developing Sustainable Digital Ecosystems and their Spatial-temporal Knowledge Management

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    The research aims to assess the sustainment of multiple ecosystems with viable and adaptable models. We propose an Information System (IS) modelling approach and examine the sustainment between ecosystems through connectable multidimensional IS artefacts. For example, humans survive in healthy and hassle-free environments for long-term economic benefits. We conceptualize human, healthcare, and environmental ecosystems are connectable, and the interconnectivity depends on how the ecologies are supportive together and with each other. The ecosystems emerge and grow with data heterogeneity challenges, which can disorganize ecological connectivity, impeding the implementation of resilient digital ecosystems. The development of multidimensional repositories is added motivation to explore connectivity, for which Attribute Journey Mapping and Modelling (AJMM) method is sought. Map views are computed to successfully interpret and establish connectivity, including coherency between attributes of multiple digital ecosystems. Besides, Big Data has changed the ecological research direction with which the coexistence between human-healthcare-environment ecosystems is assessed

    THE ROLE OF ISLAMIC BANKING IN AGRICULTURE FINANCING (CASE STUDY OF INDONESIAN AGRICULTURE SECTOR)

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    Purpose: This paper aims to analyze the role of Islamic banking in improving the agricultural sector as well as providing alternative solutions such as financing scheme for agricultural financing. Methodology: The research method is qualitative. A review of the extant literature was carried out for collecting primary and secondary data. In-depth interviews with key informants such the farmer and Islamic bank financing manager were also conducted. Data analysis was performed by adopting data reduction, data display with SWOT matrix, verification, and conclusion. Main findings: This paper finds that Islamic banking allocates financing for agricultural sector which is less than 10 percent of total financing. The finding is in line with the problem faced by the farmer. Based on the interview, it is known that the main problem of agriculture industry is limited access to the source of capital. The second finding pertains to the lack of Islamic banking’s role in agricultural financing caused by high risk perception and minimum competence of human resources to maintain the agricultural financing. Applications: This paper suggests the implementation of Ba'I As Salam scheme as an alternative for agricultural financing. Salam Financing Scheme is more suitable for agricultural financing than the murabaha financing that are commonly used today. Because the salam financing scheme intends to finance the sale and purchase of new commodities that are to be processed or produced and the delivery of their goods in the future, as well as allow for irregular payment schemes made in the harvest. Some of these advantages can be agricultural financing solution that is more in line with the characteristics of agricultural sector cash flow. Novelty/Originality of this study: Previous Studies related to the agricultural sector only deals with the impact of agricultural finance without offering low cost financing models as the solution to the main problem in the agricultural sector. This study provides solutions to these problems

    Developing Strategy Maps for the Formulation of Digital Divides Strategies

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    Prior investigations commented that almost no country is completely ready to bridge digital divide due to the absence of the balance between strategizing, coordination and action. In the e-government sector, the links among strategic objectives, action plans, and performance measures related to strategies for reducing digital divides had been constantly overlooked. This paper aims at adopting and combining the concepts of strategy map and the balanced scorecard to fill up the absences. A generic model of digital divide strategy maps is presented and the steps of developing strategy maps are illustrated in detail as well

    Decision Support in Information Systems Security

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    As the structure of modern organizations shifts, so correspondingly must the methodologies which underlie the evaluation and development of the security posture of their information systems. We have witnessed an ever-growing gap between organizational policy and technology. We have also witnessed an ever increasing complexity of decisions regarding the planning and design of IS security. Within this paper, we propose a decision support framework consistent with security and decision theory and develop a model of the decision analysis space suitable for multiple criteria decision making (MCDM). The adoption of MCDM techniques within the context of this model can show inherent trade-offs between alternatives in a security decision, encapsulate qualitative as well as quantitative elements within the analysis space, and facilitate group-decision making thereby dealing with conflicting perspectives of multiple stakeholders. The paper concludes with a demonstration of the proposed model through a case study conducted with a major financial services provider

    Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

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    Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

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    In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations
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