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.

Background and Purpose: Business model innovation (BMI) has become increasingly important, especially in the fast changing business environment. While large enterprises approach these changes systematically, small and medium sized enterprises (SMEs) are left to their own resourcefulness. For the purpose of developing dedicated methods and tools to support different SMEs in addressing these challenges, we have conducted a multiple case study to gain insights into factors that drive SMEs to innovate their BM, how they approach BMI and what changes they made to their BM. Design/Methodology/Approach: First the framework of analysis was developed based on BMI research frameworks identified in literature review. Then the multiple case studies were conducted following the case study protocol developed by Envision project. Results: Based on the proposed framework the results of four Slovene SME cases revealed differences between enterprises regarding the drivers behind BMI and changes in usage of the different BMI elements. Conclusion: Overall, the results suggest all four SMEs, coming from different sectors, are facing BMI challenges without systematically addressing it and without using any dedicated BM ontologies or tools.

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Autonomous vehicles (AV) have the potential to disrupt the entire transport industry. AV may bring many opportunities as for example reduction of road accidents, less congestion on the roads, and a lower number of vehicles that are better utilized. Full AV also brings new social element as they enable mobility for all. In addition, the use of digital technologies in combination with AV introduces new business models in transportation, where the lines between car ownership, rental, and lease modes are more and more blurred. To explore the potential of AV in a smart city context, the AV Living Lab was created on the premises of BTC City in Ljubljana, Slovenia, in 2017. The AV Living lab was created to test and to learn about real-life solutions for implementation of AV. The underlying concept is BTC City as a Living lab innovation ecosystem, where the latest advanced technologies, business models, and services are tested with real users, real cars, on real roads over the real interactions in a cross-industry environment. In this paper, we describe the AV Living Lab concept and provide details of a specific use case-a large-scale pilot demonstration of AV and future mobility solutions. During the event, users participated in a survey and expressed their attitudes towards autonomous mobility. The results offer the first insights into the readiness of citizens for AV implementation and directs future actions needed for faster adoption of AV and future mobility solutions.Sustainability 2019, 11, 4095 2 of 21 AV by 2030. By the 2030, over 95% of new car sales are expected to be at least partially electrified and 55% fully electrified [15]. Automaker and technology giants are making huge investments in self-driving initiatives. AV have different levels of automation and there are multiple definitions for them. The Society of Automotive Engineers (SAE) provides classification that defines five levels of automation. The cars are fully autonomous when they reach Level 5 (full automation) [16]. In Europe, vehicles are currently classified predominantly as Level 0 or Level 1, with only rare exceptions of Level 2 (Mercedes E-Class) and Level 3 (Audi A8). It is predicted that in 2030, the majority of vehicles will be classified as Level 2 and Level 4 [15].Despite fast technological development, which drives innovation in the field of AV, user acceptance of automated and autonomous mobility solutions will play a significant role in transportation transformation in the future. Several studies investigated different factors influencing user acceptance of AV from different perspectives [2,4,[17][18][19][20][21][22][23][24][25][26][27]. However, only a few studies have assessed the user perception of AV after the real life experiences [25,27]. Existing surveys [2,20,21,28] show positive attitudes towards AV. However, (potential) users have exposed also different concerns, primarily related to safety, cybersecurity and availability (high costs).Self-driving cars are considered as disruptive innovation and as such fit the idea of Schu...