Business model innovation (BMI) is one of the key activities, which has to be continuously undertaken in every enterprise to sustain competitiveness in today’s digital economy. Although the interest in business models (BM) has risen significantly since the late 1990s, little is known about BMI practices in small- and medium-sized enterprises (SMEs), representing a majority of enterprises in the European market. Our study addresses this research gap and focuses on the investigation of drivers and outcomes of BMI in SMEs. A partial least squares path modeling (PLS-PM) method was used to empirically test the model using data collected in 2017 from 71 SMEs in Slovenia. Results have shown that the innovativeness of enterprises and their business environment has a positive impact on the level of BMI activities in the enterprises. However, this is not the case of fast developing and rapidly changing information technology. In addition, the results show that the level of BMI has a positive impact on BMI outcomes and further on overall business performance. The results contribute to the body of knowledge in the field of BMI activities in SMEs and offer useful insights to SMEs that intend to innovate their business model(s) and government to create a supportive environment.

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.

Purpose: The purpose of this paper is to address 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. Design/methodology/approach: 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 cross-industry standard process for data mining (CRISP-DM) methodology. Findings: 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. Research limitations/implications: The quality and quantity of available data affect the performance of models and explanations. Practical implications: The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning. Social implications: All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making. Originality/value: The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors’ knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.

This paper aims to explore the patterns in micro, small-, and medium-sized enterprises’ (MSMEs) digital transformation journey during recent years. Using an emergent concurrent mixed-methods approach, we combined insights from six in-depth interviews with MSME managers and owners with survey data from 66 MSMEs. The findings reveal major inhibitors and accelerators of MSMEs’ digital transformation and demonstrate how they cope with them by engaging in digitalisation actions. This study also provides insights into how inhibitors, accelerators, and digitalisation actions vary across MSME sizes. While an increased adoption of digital technology was observed among the participating MSMEs, this study identifies three distinct digital transformation paths: necessary, experimental, and committed. Each path is shaped by a unique combination of inhibitors and accelerators. From the practical perspective, this research provides insights for MSME managers and owners on how to tailor their digital transformation efforts to their unique inhibitors and accelerators. In addition, our insights can help policy makers to promote the digital transformation of MSMEs through appropriate measures and support mechanisms tailored to the specific needs of smaller enterprises.

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.