Exploring historic changes in musical communication: Deconstructing emotional cues in preludes by Bach and Chopin

A growing body of research analyzing musical scores suggests mode’s relationship with other expressive cues has changed over time. However, to the best of our knowledge, the perceptual implications of these changes have not been formally assessed. Here, we explore how compositional choices of 17th- and 19th-century composers (J. S. Bach and F. Chopin, respectively) differentially affect emotional communication. This novel exploration builds on our team’s previous techniques using commonality analysis to decompose intercorrelated cues in unaltered excerpts of influential compositions. In doing so, we offer an important naturalistic complement to traditional experimental work—often involving tightly controlled stimuli constructed to avoid the intercorrelations inherent to naturalistic music. Our data indicate intriguing changes in cues’ effects between Bach and Chopin, consistent with score-based research suggesting mode’s “meaning” changed across historical eras. For example, mode’s unique effect accounts for the most variance in valence ratings of Chopin’s preludes, whereas its shared use with attack rate plays a more prominent role in Bach’s. We discuss the implications of these findings as part of our field’s ongoing effort to understand the complexity of musical communication—addressing issues only visible when moving beyond stimuli created for scientific, rather than artistic, goals.

The impact of social capital on entrepreneurial intention and its antecedents: Differences between social capital online and offline

Entrepreneurial intention is a key research question in entrepreneurship. Previous studies have proven the theory of planned behavior (TPB) to explain entrepreneurial intention. Scholars have thus focused on analyzing factors to develop the three antecedents of TPB, one of which is social capital. However, research has barely considered social capital online. We extend research by exploring the effect of social capital on these antecedents and on entrepreneurial intention, and by analyzing the differences in these influences between social capital online and offline. Using partial least squares and commonality analysis for 587 individuals in Spain, we find that social capital influences these antecedents and entrepreneurial intention. Furthermore, social capital online has a greater effect in attitude toward entrepreneurship, a similar effect on perceived behavioral control, and a lesser effect on social norms than social capital offline. Finally, social capital online has a greater influence on entrepreneurial intention than social capital offline. JEL CLASSIFICATION: M1 Business Administration, M13 New Firms • Startups

Commonality Analysis for Multiple Chain Integrity Failures

The aim of this paper is to disclose an alternative FA approach to handle complex cases, showing multiple chain failures with multiple candidates. Starting from a commonality layout analysis of candidates resulting from the diagnosis, it is possible to identify a common interconnection shared between the several candidates, already at schematic level. The effectiveness of such analysis has been successfully verified by means of a photo-emission microscopy (PEM) analysis, while running scan chain patterns and by physical analysis.

rdacca.hp: an R package for generalizing hierarchical and variation partitioning in multiple regression and canonical analysis

SummaryCanonical analysis, a generalization of multiple regression to multiple response variables, is widely used in ecology. Because these models often involve large amounts of parameters (one slope per response per predictor), they pose challenges to model interpretation. Currently, multi-response canonical analysis is constrained by two major challenges. Firstly, we lack quantitative frameworks for estimating the overall importance of single predictors. Secondly, although the commonly used variation partitioning framework to estimate the importance of groups of multiple predictors can be used to estimate the importance of single predictors, it is currently computationally constrained to a maximum of four predictor matrices.We established that commonality analysis and hierarchical partitioning, widely used for both estimating predictor importance and improving the interpretation of single-response regression models, are related and complementary frameworks that can be expanded for the analysis of multiple-response models.In this application, we aim at: a) demonstrating the mathematical links between commonality analysis, variation and hierarchical partitioning; b) generalizing these frameworks to allow the analysis of any number of responses, predictor variables or groups of predictor variables in the case of variation partitioning; and c) introducing and demonstrating the usage of the R package rdacca.hp that implements these generalized frameworks.

A MULTI-MODEL APPROACH TO ASSESS THE RELATIVE WEIGHTS AND SENSITIVITIES OF THE FACTORS OF REGIONAL COMPETITIVENESS

In order to prioritize the intervention to augment regional competitiveness, it is essential to assess the relative weights and sensitivities related to the factors of competitiveness. Improper assignment of relative weights is prominent in the case when multi-co-linearity exists among independent variables. The paper tests the suitability of multiple models for their capacity of assessing relative weights, and subsequently for forming a competitiveness index. The relative weights of critical components of economic infrastructure have been assessed with Zero-order correlation, Structure coefficient analysis, Beta coefficient analysis, Product measure analysis, Relative weight analysis, and Commonality analysis. Subsequently, regional competitiveness indices have been formed with relative weights as a linear combination. The most suitable technique to form an index has been identified through the Pearson correlation and Spearman rank correlation analyses. Multiple regression analysis assigns the relative weights and consecutively forms the regional competitiveness index, better than other applied techniques. Zero-order correlation and Structural coefficient analysis performed reasonably well. Commonality analysis is a very appropriate technique for the detailed investigation of unique and shared effects among variables. The result shows that the common effects of the critical components of the economic infrastructure are stronger than their unique effects. The sensitivity of competitiveness related to the variables has been assessed through Artificial Neural Network. Regional competitiveness is most sensitive to the variable of rural roads. The results indicate that better connectivity triggers capital and labor drain from the rural areas of the region.

Emotion and expertise: how listeners with formal music training use cues to perceive emotion

Although studies of musical emotion often focus on the role of the composer and performer, the communicative process is also influenced by the listener’s musical background or experience. Given the equivocal nature of evidence regarding the effects of musical training, the role of listener expertise in conveyed musical emotion remains opaque. Here we examine emotional responses of musically trained listeners across two experiments using (1) eight measure excerpts, (2) musically resolved excerpts and compare them to responses collected from untrained listeners in Battcock and Schutz (2019). In each experiment 30 participants with six or more years of music training rated perceived emotion for 48 excerpts from Bach’s Well-Tempered Clavier (WTC) using scales of valence and arousal. Models of listener ratings predict more variance in trained vs. untrained listeners across both experiments. More importantly however, we observe a shift in cue weights related to training. Using commonality analysis and Fischer Z score comparisons as well as margin of error calculations, we show that timing and mode affect untrained listeners equally, whereas mode plays a significantly stronger role than timing for trained listeners. This is not to say the emotional messages are less well recognized by untrained listeners—simply that training appears to shift the relative weight of cues used in making evaluations. These results clarify music training’s potential impact on the specific effects of cues in conveying musical emotion.

Construct Validity and Reliability of the Work Environment Assessment Instrument WE-10

The purpose of this study was to validate the construct and reliability of an instrument to assess the work environment as a single tool based on quality of life (QL), quality of work life (QWL), and organizational climate (OC). The methodology tested the construct validity through Exploratory Factor Analysis (EFA) and reliability through Cronbach’s alpha. The EFA returned a Kaiser–Meyer–Olkin (KMO) value of 0.917; which demonstrated that the data were adequate for the factor analysis; and a significant Bartlett’s test of sphericity (χ² = 7465.349; Df = 1225; p ≤ 0.000). After the EFA; the varimax rotation method was employed for a factor through commonality analysis; reducing the 14 initial factors to 10. Only question 30 presented commonality lower than 0.5; and the other questions returned values higher than 0.5 in the commonality analysis. Regarding the reliability of the instrument; all of the questions presented reliability as the values varied between 0.953 and 0.956. Thus; the instrument demonstrated construct validity and reliability