This page has only limited features, please log in for full access.
Small and medium-sized enterprises (SMEs) need to keep pace with large enterprises, thus they need to digitally transform. Since they usually lack resources (budget, knowledge, and time) many countries have their support environment to help SMEs in this endeavor. To be able to ensure the right kinds of support, it is crucial to assess the digital maturity of an enterprise. There are many models and assessment tools for digital maturity, however, they are either theoretical models, partial, vendor oriented, or suited for large enterprises. In this paper, we address the problem of assessing digital maturity for SMEs. For this purpose, we developed a multi-attribute model for assessment of the digital maturity of an SME. We followed the design science research approach, where the multi-attribute model is considered as an IT artifact. Within the design cycle, the decision expert (DEX) methodology of a broader multi-attribute decision making methodologies was applied. The developed model was validated by a group of experts and upgraded according to their feedback and finally evaluated on seven real-life cases. Results show that the model can be used in real business situations.
Mirjana Kljajić Borštnar; Andreja Pucihar. Multi-Attribute Assessment of Digital Maturity of SMEs. Electronics 2021, 10, 885 .
AMA StyleMirjana Kljajić Borštnar, Andreja Pucihar. Multi-Attribute Assessment of Digital Maturity of SMEs. Electronics. 2021; 10 (8):885.
Chicago/Turabian StyleMirjana Kljajić Borštnar; Andreja Pucihar. 2021. "Multi-Attribute Assessment of Digital Maturity of SMEs." Electronics 10, no. 8: 885.
Purpose The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods. Design/methodology/approach The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model. Findings Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts. Research limitations/implications The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians. Practical implications The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases. Social implications The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable. Originality/value These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.
Matjaž Kragelj; Mirjana Kljajić Borštnar. Automatic classification of older electronic texts into the Universal Decimal Classification–UDC. Journal of Documentation 2020, 77, 755 -776.
AMA StyleMatjaž Kragelj, Mirjana Kljajić Borštnar. Automatic classification of older electronic texts into the Universal Decimal Classification–UDC. Journal of Documentation. 2020; 77 (3):755-776.
Chicago/Turabian StyleMatjaž Kragelj; Mirjana Kljajić Borštnar. 2020. "Automatic classification of older electronic texts into the Universal Decimal Classification–UDC." Journal of Documentation 77, no. 3: 755-776.
Background: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models. Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate. Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance. Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results. Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data.
Aljaž Ferencek; Davorin Kofjač; Andrej Škraba; Blaž Sašek; Mirjana Kljajić Borštnar. Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period. Business Systems Research Journal 2020, 11, 36 -50.
AMA StyleAljaž Ferencek, Davorin Kofjač, Andrej Škraba, Blaž Sašek, Mirjana Kljajić Borštnar. Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period. Business Systems Research Journal. 2020; 11 (2):36-50.
Chicago/Turabian StyleAljaž Ferencek; Davorin Kofjač; Andrej Škraba; Blaž Sašek; Mirjana Kljajić Borštnar. 2020. "Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period." Business Systems Research Journal 11, no. 2: 36-50.
A problem of product failure prediction within the warranty period is presented in a case of a household appliance manufacturer. Predicting product failure within a warranty period is necessary for optimal resources planning. When based on scarce information and intuition, companies reserve non-optimal amount of funds. To address this problem we developed a machine learning model to decrease the prediction error and make the process of warranty claims more transparent. Following the CRISP-DM process, in this paper, we are focusing on data preparation phases. Results show that among 33 attributes, we have identified seven that hold some prediction value, suggesting that the value of the collected data is small compared to its cost. In the future effort should be invested in collecting quality standardised data across markets.
Aljaž Ferencek; Mirjana Kljajić Borštnar. Data quality assessment in product failure prediction models. Journal of Decision Systems 2020, 1 -8.
AMA StyleAljaž Ferencek, Mirjana Kljajić Borštnar. Data quality assessment in product failure prediction models. Journal of Decision Systems. 2020; ():1-8.
Chicago/Turabian StyleAljaž Ferencek; Mirjana Kljajić Borštnar. 2020. "Data quality assessment in product failure prediction models." Journal of Decision Systems , no. : 1-8.
Mirjana Kljajić Borštnar; Tomi Ilijaš; Polona Šprajc; Iztok Podbregar; Damjan Maletič; Mirjana Radovanović. Preliminarna analiza pripravljenosti malih in srednje velikih podjetij na storitve zelo zmogljivega računalništva. 38. mednarodna konferenca o razvoju organizacijskih znanosti: Ekosistem organizacij v dobi digitalizacije: konferenčni zbornik 2019, 1 .
AMA StyleMirjana Kljajić Borštnar, Tomi Ilijaš, Polona Šprajc, Iztok Podbregar, Damjan Maletič, Mirjana Radovanović. Preliminarna analiza pripravljenosti malih in srednje velikih podjetij na storitve zelo zmogljivega računalništva. 38. mednarodna konferenca o razvoju organizacijskih znanosti: Ekosistem organizacij v dobi digitalizacije: konferenčni zbornik. 2019; ():1.
Chicago/Turabian StyleMirjana Kljajić Borštnar; Tomi Ilijaš; Polona Šprajc; Iztok Podbregar; Damjan Maletič; Mirjana Radovanović. 2019. "Preliminarna analiza pripravljenosti malih in srednje velikih podjetij na storitve zelo zmogljivega računalništva." 38. mednarodna konferenca o razvoju organizacijskih znanosti: Ekosistem organizacij v dobi digitalizacije: konferenčni zbornik , no. : 1.