Call for Papers - Cognitive Biases and Heuristics in the New Product Development Process: A Call for More Empirical Evidence

The New Product Development (NPD) process is a complex phenomenon involving decision-making under incomplete information and uncertain outcomes. Cognitive biases such as overconfidence, anchoring, planning fallacy, and sunk-cost fallacy can significantly impact decision-making in NPD processes, leading to suboptimal outcomes. Also, the reliance on heuristics can assist decision-makers, enabling quick and efficient decisions without extensive analysis. Identifying and mitigating cognitive biases and studying heuristics in the NPD process are key factors that can significantly impact the success of the NPD process. Further empirical research is necessary to better understand how cognitive biases and heuristics influence decision-making in the NPD process to foster a climate of innovation, favor the emergence of serendipity, and improve firm performance. Thus, authors are encouraged to submit original research papers (quantitative, qualitative, experimental), addressing missing empirical evidence on the topic of this call. Review papers are not encouraged for the present special issue

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

Purpose – Designing knowledge management systems capable of transforming big data into information characterised by strategic value is a major challenge faced nowadays by firms in almost all industries. However, in the managerial field, big data is now mainly used to support operational activities, while its strategic potential is still largely unexploited. Based on these considerations, this study proposes an overview of the literature regarding the relationship between big data and business strategy. Design/methodology/approach – A bibliographic coupling method is applied over a dataset of 128 peerreviewed articles, published from 2013 (first year when articles regarding the big data-business strategy relationship were published) to 2019. Thereafter, a systematic literature review is presented on 116 papers, which were found to be interconnected based on the VOSviewer algorithm. Findings – This study discovers the existence of four thematic clusters. Three of the clusters relate to the following topics: big data and supply chain strategy; big data, personalisation and co-creation strategies; big data, strategic planning and strategic value creation. The fourth cluster concerns the relationship between big data and knowledge management and represents a ‘bridge’ between the other three clusters. Research implications – Based on the bibliometric analysis and the systematic literature review, this study identifies relevant understudied topics and research gaps, which are suggested as future research directions. Originality/value – This is the first study to systematise and discuss the literature concerning the relationship between big data and firm strategy

Editorial: Interview With the Translational Apparatus: Stories of Intriguing Circuits and Mechanisms to Regulate Translation in Bacteria

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Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we measured the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage

Terrestrial Laser Scanning and settled techniques: a support to detect pathologies and safety conditions of timber structures

Nowadays, there is an increasing demand for detailed geometrical representation of the existing cultural heritage, in particular to improve the comprehension of interactions between different phenomena and to allow a better decisional and planning process. The LiDAR technology (Light Detection and Ranging) can be adopted in different fields, ranging from aerial applications to mobile and terrestrial mapping systems. One of the main target of this study is to propose an integration of innovative and settled inquiring techniques, ranging from the reading of the technological system, to non-destructive tools for diagnosis and 3D metric modeling of buildings heritage. Many inquiring techniques, including Terrestrial Laser Scanner (TLS) method, have been exploited to study the main room of the Valentino Castle in Torino. The so-called “Salone delle Feste” (Hall of Honor), conceived in the XVIIth century under the guidance of Carlo di Castellamonte, has been selected as a test area. The beautiful frescos and stuccoes of the domical vault are sustained by a typical Delorme carpentry, whose span is among the largest of their kind. The dome suffered from degradation during the years, and a series of interventions were put into place. A survey has revealed that the suspender cables above the vault in the region close to the abutments have lost their tension. This may indicate an increase of the vault deformation; therefore a structural assessment of the dome is mandatory. The high detailed metric survey, carried out with integrated laser scanning and digital close range photogrammetry, reinforced the structural hypothesis of damages and revealed the deformation effects. In addition, the correlation between the survey-model of the intrados and of the extrados allowed a non-destructive and extensive determination of the dome thickness. The photogram-metrical survey of frescos, with the re-projection of images on vault surface model (texture mapping), is purposed to exactly localize former restorations and their signs on fresco continuity. The present paper illustrates the generation of the 3D high-resolution model and its relations with the results of the structural survey; both of them support the Finite Element numerical simulation of the dome

Strengthening Chvátal-Gomory cuts for the stable set problem

The stable set problem is a well-known NP-hard combinatorial optimization problem. As well as being hard to solve (or even approximate) in theory, it is often hard to solve in practice. The main difficulty is that upper bounds based on linear programming (LP) tend to be weak, whereas upper bounds based on semidefinite programming (SDP) take a long time to compute. We propose a new method to strengthen the LP-based upper bounds. The key idea is to take violated Chvátal-Gomory cuts and then strengthen their right-hand sides. Although the strengthening problem is itself NP-hard, it can be solved reasonably quickly in practice. As a result, the overall procedure proves to be capable of yielding competitive upper bounds in reasonable computing times

Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation

Big Data Analytics Capabilities (BDAC) represent critical tools for business competitiveness in highly dynamic markets. In this connection, by leveraging on the Dynamic Capabilities View (DCV) this study analyses the relationship between BDAC and Business Model Innovation (BMI). It argues that the impact of BDAC (a lower-order dynamic capability) on BMI is mediated by Entrepreneurial Orientation (EO; a higher-order dynamic capability). The proposed model is assessed by PLS-SEM (symmetric) and fuzzy-set Qualitative Comparative Analysis (asymmetric) methods using survey data from 253 UK firms. Our findings demonstrate that BDAC have both direct and indirect positive effects on BMI, with the latter being mediated by EO. These results enrich the innovation management literature on Big Data (BD) by showing that BDAC influence company strategic logics and objectives, rather than depending on them, thus playing a significant role in creating value for companies and their stakeholders

Fractal dimension of cerebral white matter : A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T1-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age \ub1 standard deviation, 74.6 \ub1 6.9, education 7.9 \ub1 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age \ub1 standard deviation, 72.3 \ub1 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value =.039), Symbol Digit Modalities Test scores (p-value =.039), and Trail Making Test Part A scores (p-value =.025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS