Structural changes in farming present serious challenges at all spatial levels, from individual farms to the state level. The reorientation of a farm (i.e., changing from livestock production to one of horticulture or crops) represents one of these challenges. Here, a model assessing the potential for reorganizing farms to focus on horticulture is presented. The model accounts for various criteria, including: natural resources, demographic, economic, and social factors. The selection, structure, and importance of criteria and their interrelationships in the model are based on statistical data about farms, data gathered through surveys, and expert opinion groups. The model was developed using the Decision Expert method, implemented by the software DEXi, and was validated using a selection of farms. The added value of the approach is a transparent assessment of a farm’s potential, which provides vital support for deciding about its reorientation.
COBISS.SI-ID: 7944723
A complexity of business dynamics often forces decision-makers to make decisions based on subjective mental models, reflecting their experience. However, research has shown that companies perform better when they apply data-driven decision-making. This creates an incentive to introduce intelligent, data-based decision models, which are comprehensive and support the interactive evaluation of decision options necessary for the business environment. Recently, a new general explanation methodology has been proposed, which supports the explanation of state-of-the-art black-box prediction models. Uniform explanations are generated on the level of model/individual instance and support what-if analysis. We present a novel use of this methodology inside an intelligent system in a real-world case of business-to-business (B2B) sales forecasting, a complex task frequently done judgmentally. Users can validate their assumptions with the presented explanations and test their hypotheses using the presented what-if parallel graph representation. The results demonstrate effectiveness and usability of the methodology. A significant advantage of the presented method is the possibility to evaluate seller's actions and to outline general recommendations in sales strategy. This flexibility of the approach and easy-to-follow explanations are suitable for many different applications. Our well-documented real-world case shows how to solve a decision support problem, namely that the best performing black-box models are inaccessible to human interaction and analysis. This could extend the use of the intelligent systems to areas where they were so far neglected due to their insistence on comprehensible models. A separation of the machine learning model selection from model explanation is another significant benefit for expert and intelligent systems. Explanations unconnected to a particular prediction model positively influence acceptance of new and complex models in the business environment through their easy assessment and switching.
COBISS.SI-ID: 7842323
This paper addresses the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top- performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning. To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following CRISP-DM data mining methodology.The provided ML model explanations efficiently support business decision makers,reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team. The quality and quantity of available data affects the performance of models and explanations. The application in the real-world company demonstrates utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning. All used methods are available as an open-source software and can improve acceptance of ML in data- driven decision-making. The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To our knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, action design research and data mining methodology based on CRISP-DM industry standard.
COBISS.SI-ID: 7947795
This paper presents the results of research investigating the impact of business model factors on cloud computing adoption. The introduced research model consists of 40 cloud computing business model factors, grouped into eight factor groups. Their impact and importance for cloud computing adoption were investigated among enterpirses in Slovenia. Furthermore, differences in opinion according to enterprise size were investigated. Research results show no statistically significant impacts of investigated business model factor groups to cloud computing adoption. Nevertheless, based on slope coefficient directions and statistics values, some factor groups can be recognized as having moderate or strong, positive impact on cloud computing adoption; although their impact cannot be statistically confirmed with 95 % or 90 % levels of confidence. Furthermore, significant differences in opinion about the importance of business model factor groups and factors to cloud computing adoption according to enterprises size have been identified. The results represent a contribution to the theory of cloud computing adoption from the perspective of the provider%s business model. In addition, findings provide orientation for innovation of existing business models towards the creation of a customer-oriented business model for more successful exploitation of cloud computing services and new business opportunities.
COBISS.SI-ID: 7943443
This study intends to clarify the understanding of the role of the contingency factors (i.e. long-term orientation, competitiveness and uncertainty) in the relation between sustainability practices (sustainability exploitation and sustainability exploration) and organizational performance. Using empirical data based on a large-scale survey among European organizations, this paper utilizes the regression analysis to gain insight into the relationship between sustainability practices and organizational performance. In general, the results support the contingency view of the relationship between sustainability practices and performance rather than relying upon “universal” view of sustainability practices. Particularly, the results show that in moderate environmental contexts (moderate competitiveness and uncertainty) sustainability exploitation practices seem to be a predominant predictor of organizational performance.
COBISS.SI-ID: 7966739
We draw on concepts in the innovation literature namely exploration and exploitation to examine corporate sustainability practices as well as the ensuing tensions between efficiency and innovativeness in achieving organisational performance. In particular, this paper draws upon institutional theory to enhance the understanding of sustainability-related phenomena, mainly from a perspective that has not yet been widely investigated in prior empirical studies. Therefore, the paper addresses the research question of whether sustainability exploitation and sustainability exploration practices are characterized by an organisation's country of origin. . In general, the results suggest that organisations in different countries show more differences in relation to sustainability practices and organisational performance compared to organisations within the same country. Therefore, the paper contributes to the literature by providing more clarity and better understanding of how organisations may pursue sustainability practices to gain performance benefits.
COBISS.SI-ID: 7528211
Health documentation is a prerequisite for good and sustainable health and social care. It is especially important for patient involvement and their empowerment. A transition from paper to e-documentation together with the electronic patient record should be based on thorough knowledge of the current state of documentation and its usages. The main objective of this paper was to analyse which documents and work methods of documenting processes within nursing are being used within different environments. Furthermore, what are the main reasons for their discrepancies from theoretical approaches and best practices. The analysis is based on a survey carried out on all three levels of healthcare. The survey questionnaire consisted of 12 questions to which responded 286 nursing teams from community health centres, hospitals and retirement homes in Slovenia. The results point to diversity in documenting as well as lack of interoperability. This is reflected in a great number of different documents. All phases of the nursing process were being documented in only 31.8 % of cases. The main reasons for this can be attributed to work organisation, different definitions of data-set requirements and inadequate knowledge by nurses. Survey results pointed out a need for the renewal of nursing documentation towards a more uniform system based on contemporary health technologies.
COBISS.SI-ID: 5215595
This paper presents a novel approach to the adaptation of multidimensional data models to user-specific needs. The multidimensional data models used in contemporary business-intelligence systems are inherently complex. In order to reduce the complexity of these models, we propose using a qualitative multiple-criteria decision modelling method that is based on using a hierarchical tree of the criteria to decompose the larger problem into a group of smaller problems. The final value is derived by aggregating the criteria values using simple “if-then” rules, which form the knowledge-based expert rules in the hierarchical criteria tree that reflect users’ preferences. The multiple-criteria analysis of the multidimensional model structure results in a multidimensional model that exhibits a reduced complexity and is adapted to users’ needs. The model was validated using sales data from a medium-size enterprise. The qualitative (through questionnaires) and the quantitative (through usage mining) evaluation of the proposed methodology both showed that the proposed approach increases the ease-of-use of business intelligence systems and also contributes to a higher user satisfaction.
COBISS.SI-ID: 7458067
This paper investigates ERP absorption in transition and developed economies in Central and Eastern Europe (CEE). Using absorptive capacity theory and data envelopment analysis, we view organisational transformation in Enterprise Resource Planning (ERP) absorption as an economic production process. Despite converging ERP saturation levels, our data identifies gaps in absorption levels and performance. Organisations in transition face greater challenges, engage more in phased ERP absorptions and expect higher levels of external support.
COBISS.SI-ID: 6874131
The paper addresses the impact of feedback information and facilitation on a decision making process supported by a system dynamics model. Solomon Four Group Experiment was conducted under four conditions: (a1) interrupted individual determination of a strategy supported by a simulation model and a facilitator; (a2) interrupted individual determination of a strategy supported by a simulation model and a facilitator plus group information feedback (GIF); (a3) continuous individual determination of a strategy supported by a simulation model; and (a4) continuous individual determination of a strategy supported by a simulation model plus GIF. The observed variables were criteria function, frequency of simulation runs, and insight into GIF. The hypotheses that a simulation model supports individual learning and additional GIF contributes to faster learning were confirmed. The importance of the facilitator and structure of feedback information was demonstrated and a model explaining learning in the decision process was developed.
COBISS.SI-ID: 6869779