A comparative study on the impact of business model design & lean startup approach versus traditional business plan on mobile startups performance

usiness Model Design (BMD) & Lean Startup (LSA) approach are two widespread practices among entrepreneurs, where many Mobile startups declare to adopt them. However, neither of the two frameworks are well rooted in the academic literature; and few studies address the issue of whether they actually outperform traditional approaches to new Mobile Startups creation. This study's aim is to assesses the contribution to performance of the combined use of BMD and LSA for two startups operating in the highly dynamic Mobile Applications Industry; performances are then compared to those achieved by two Mobile Star-ups adopting the traditional Business Plan approach. Findings reveal how the combined use of BMD and LSA outperforms the traditional BP in the cases analyzed, thus constituting a promising methodology to support Strategic Entrepreneurship

The Relationship between Open Innovation and Strategy: Data-Driven Analysis of the Mobile Value Services Industry

As academic and practitioner studies on the subject amassed in the last decade 2003 - 2013, Open Innovation (OI) has gained growing importance in the broad field of Management and Information Systems. However, existing literature lacks a comprehensive understanding of the relationship existing between OI and a firm's Strategy. Employing a data-driven research approach, based on forty-five qualitative interviews on firms operating in the Mobile Value Services Industry involved in OI undertakings, this study originally highlights six cross-themes the OI-Strategy relationship revolves around: 1) OI and Competitive Advantage, 2) OI and Strategic Positioning, 3) OI and Business Models, 4) OI in Networks, 5) OI and Co-opetition.; 6) OI and Resilient Business Advantages. For each theme, insights are provided concerning: sub themes, findings, criticalities, and areas of development. This reorganization of the real-world OI initiatives constitutes a comprehensive research agenda or roadmap, with value for both academics and practitioners

Voriconazole treatment of Candida tropicalis meningitis: persistence of (1,3)-b-D-glucan in the cerebrospinal fluid is a marker of clinical and microbiological failure

Introduction: Infections are still the most common complications of cerebral shunt procedures. Even though fungal etiologies are considered to be rare, they are associated with significant morbidity and mortality. Due to their uncommonness, diagnostic procedures and optimal therapy are poorly defined. We report a case of Candida tropicalis infection of ventriculo-peritoneal cerebrospinal fluid (CSF) shunt in a 49-year-old immune competent male treated with voriconazole (VOR). Methods: Microbiological and CSF markers (1,3-b-D-glucan-BDG) of fungal infection, biofilm production capacity, sensitivity of serial isolates of the pathogen, and the concentration of the antifungal drug have been monitored and related to the clinical course of this infection. Results: Despite appropriate treatment with VOR, in terms of adequate achieved CSF drug concentrations and initial effective therapeutic response, loss of VOR susceptibility of the C tropicalis and treatment failure were observed. Conclusion: Biofilm production of the C. tropicalis isolate might have had a significant role in treatment failure. Of interest, clinical and microbiological unfavorable outcome was anticipated by persistence of BDG in CSF. Rising titers of this marker were associated with relapse of fungal infection

Parameter Estimation of Linear Dynamical Systems with Gaussian Noise

We present a novel optimization-based method for parameter estimation of a time-varying dynamic linear system. This method optimizes the likelihood of the parameters given measured data using an optimization algorithm tailored to the structure of this maximum likelihood estimation problem. Some parameters of the covariance of process and measurement noise can also be estimated. This is particularly useful when offset-free Model Predictive Control with a linear disturbance model is performed. To reduce the complexity of the maximum likelihood estimation problem we also propose an approximate formulation and show how it is related to the actual problem. We present the advantages of the proposed approach over commonly used methods in the framework of Moving Horizon Estimation. We also present how to use Sequential Quadratic Programming efficiently for the optimization of our formulations. Finally, we show the performance of the proposed methods through numerical simulations. First, on a minimal example with only one parameter to be estimated, and second, on a system with heat and mass transfer. Both methods can successfully estimate the model parameters in these examples.Comment: Submitted to IEEE European Control Conference 2023 (ECC23). Contains 8 pages including 6 figure

Students’ Entrepreneurial Orientation in Italy: do digital and coding skills matter?

The literature presents several papers regarding students’ entrepreneurial intention. However, only a few papers have recently analyzed student entrepreneurship. This paper aims at improving our understanding on this by testing if digital and coding skills matter for entrepreneurial orientation and student entrepreneurship. Adopting a Human Capital and Social Capital Theory perspective, we hypnotized that these individual skills may have a statistically and positive impact on entrepreneurial orientation and student entrepreneurship. Based on Logit and Probit regression analyses on more than 2000 Italian university students, we confirmed our hypotheses

Using Graph Transformation Systems to Specify and Verify Data Abstractions

This paper proposes an approach for the specification of the behavior of software components that implement data abstractions. By generalizing the approach of behavior models using graph transformation, we provide a concise specification for data abstractions that describes the relationship between the internal state, represented in a canonical form, and the observers of the component. Graph transformation also supports the generation of behavior models that are amenable to verification. To this end, we provide a translation approach into an LTL model on which we can express useful properties that can be model-checked with a SAT solver

A strategic Analysis of the European Companies in the ICT Sales Channel

The strategic role of Information and Communication Technologies (ICT) is growing in various companies. Small and Medium Enterprises (SMEs) adopt ICT solutions to support their processes and to improve their products and services. Because of SMEs’ scarce resources and inadequate ICT competencies, they need support from ICT suppliers in the ICT adoption process. Little attention has been paid to the business models and strategies of ICT suppliers in the academic and professional literature, and SMEs find it difficult to determine the characteristics of available ICT suppliers and to choose the supplier that best responds to their needs and aims. The goal of this paper is to provide a detailed picture of the ICT sales channel and its players in the European market. A classification framework is proposed and eleven different business models are identified. The paper is based on a case study methodology that included 53 semi‐standardized interviews with CEOs (Chief Executive Officers) and marketing and communications managers at leading European ICT suppliers coupled with the literature review

Compressed sensing in fluorescence microscopy.

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy