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1 – 10 of over 6000Verdiana Giannetti, Jieke Chen and Xingjie Wei
Anecdotal evidence suggests that casting actors with similar facial features in a movie can pose challenges in foreign markets, hindering the audience's ability to recognize and…
Abstract
Purpose
Anecdotal evidence suggests that casting actors with similar facial features in a movie can pose challenges in foreign markets, hindering the audience's ability to recognize and remember characters. Extending developments in the literature on the cross-race effect, we hypothesize that facial similarity – the extent to which the actors starring in a movie share similar facial features – will reduce the country-level box-office performance of US movies in East and South-East Asia (ESEA) countries.
Design/methodology/approach
We assembled data from various secondary data sources on US non-animation movies (2012–2021) and their releases in ESEA countries. Combining the data resulted in a cross-section of 2,616 movie-country observations.
Findings
Actors' facial similarity in a US movie's cast reduces its box-office performance in ESEA countries. This effect is weakened as immigration in the country, internet penetration in the country and star power increase and strengthened as cast size increases.
Originality/value
This first study on the effects of cast's facial similarity on box-office performance represents a novel extension to the growing literature on the antecedents of movies' box-office performance by being at the intersection of the two literature streams on (1) the box-office effects of cast characteristics and (2) the antecedents, in general, of box-office performance in the ESEA region.
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Ping Huang, Haitao Ding, Hong Chen, Jianwei Zhang and Zhenjia Sun
The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs…
Abstract
Purpose
The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.
Design/methodology/approach
According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.
Findings
The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.
Originality/value
This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.
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Michał Bernardelli and Mariusz Próchniak
The comparison between economic growth and the character of monetary policy is one of the most frequently studied issues in policymaking. However, the number of studies…
Abstract
Research Background
The comparison between economic growth and the character of monetary policy is one of the most frequently studied issues in policymaking. However, the number of studies incorporating a dynamic time warping approach to analyse the similarity of macroeconomic variables is relatively small.
The Purpose of the Chapter
The study aims at assessing the mutual similarity among various variables representing the financial sector (including the monetary policy by the central bank) and the real sector (e.g. economic growth, industrial production, household consumption expenditure), as well as cross-similarity between both sectors.
Methodology
The analysis is based on the dynamic time warping (DTW) method, which allows for capturing various dimensions of changes of considered variables. This method is almost non-existent in the literature to compare financial and economic time series. The application of this method constitutes the main area of value added of the research. The analysis includes five variables representing the financial sector and five from the real sector. The study covers four countries: Czechia, Hungary, Poland and Romania and the 2010–2022 period (quarterly data).
Findings
The results show that variables representing the financial sector, including those reflecting monetary policy, are weakly correlated with each other, whereas the variables representing the real economy have a solid mutual similarity. As regards individual variables, for example, GDP fluctuations show relatively substantial similarity to ROE fluctuations – especially in Czechia and Hungary. In the case of Hungary and Romania, CAR fluctuations are consistent with GDP fluctuations. In the case of Poland and Hungary, there is a relatively strong similarity between the economy's monetisation and economic growth. Comparing the individual countries, two clusters of countries can be identified. One cluster includes Poland and Czechia, while another covers Hungary and Romania.
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Through the lens of self-perception theory, this paper investigates how avatar design (i.e. avatar user similarity) affects users' self-awareness and shapes their task engagement…
Abstract
Purpose
Through the lens of self-perception theory, this paper investigates how avatar design (i.e. avatar user similarity) affects users' self-awareness and shapes their task engagement and performance in the Metaverse.
Design/methodology/approach
The authors conducted a 2 (avatar user similarity: high vs low) × 2 (task type: procedural vs creative) lab experiment and collected data from questionnaires, the recording of users' behavior during tasks and their actual task performance.
Findings
The results show that higher avatar user similarity leads to higher task engagement in general. Furthermore, while a similar avatar promotes users to regulate their behaviors and achieve better performance in a procedural task, high similarity also inhibits users' creativity by invoking habitual thinking, resulting in worse performance in generating original ideas in a creative task.
Originality/value
This study is expected to contribute to the information systems literature by revealing the value of avatar design and providing new perspectives on improving users' experiences in the Metaverse.
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Xavier Parent-Rocheleau, Kathleen Bentein, Gilles Simard and Michel Tremblay
This study sought to test two competing sets of hypotheses derived from two different theoretical perspectives regarding (1) the effects of leader–follower similarity and…
Abstract
Purpose
This study sought to test two competing sets of hypotheses derived from two different theoretical perspectives regarding (1) the effects of leader–follower similarity and dissimilarity in psychological resilience on the follower's absenteeism in times of organizational crisis and (2) the moderating effect of relational demography (gender and age similarity) in these relationships.
Design/methodology/approach
Polynomial regression and response surface analysis were performed using data from 510 followers and 149 supervisors in a financial firm in Canada.
Findings
The results overall support the similarity–attraction perspective, but not the resource complementarity perspective. Dissimilarity in resilience was predictive of followers' absenteeism, and similarity in surface-level conditions (gender and age) attenuates the relational burdens triggered by resilience discrepancy.
Practical implications
The findings reiterate the importance of developing employees' resilience, while shedding light on the importance for managers of being aware of their potential misalignment with subordinates resilience.
Originality/value
The results (1) suggest that it is the actual (di)similarity with the leader, rather than leader's degree of resilience, that shapes followers' absenteeism and (2) add nuance to the resilience literature.
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Jandre J. van Rensburg, Catarina M. Santos and Simon B. de Jong
An underlying assumption in the shared mental model (SMM) literature is that SMMs improve whilst team members work together for longer. However, whether dyad members indeed have…
Abstract
Purpose
An underlying assumption in the shared mental model (SMM) literature is that SMMs improve whilst team members work together for longer. However, whether dyad members indeed have higher perceived SMMs with higher shared tenure has not been explored. This study aims to, therefore, firstly, investigate this idea, and we do so by focusing on perceived SMMs at the dyadic level. Secondly, because in today’s fast-paced world perceived SMMs often need to be built quickly for dyads to perform, we assess if goal interdependence can reduce the dyadic tenure required for higher perceived SMM similarity. Thirdly, we analyse if these processes are related to dyadic performance.
Design/methodology/approach
We collected a dual-source sample of 88 leader–member dyads across various industries. We conducted PROCESS analyses to test their first-stage moderated mediation model.
Findings
Results showed that dyadic tenure was positively related to perceived SMM similarity, and that goal interdependence moderated this relationship. Additionally, perceived SMM similarity mediated the relationship between dyadic tenure and dyadic performance. Lastly, the overall moderated mediation model was supported.
Originality/value
We contribute to the perceived SMM literature by: investigating perceived SMMs in dyads, testing a key idea regarding the influence of dyadic tenure on perceived SMMs and investigating how goal interdependence may prompt perceived SMM similarity earlier in dyadic tenure and, ultimately, improve dyadic performance.
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Atefeh Momeni, Mitra Pashootanizadeh and Marjan Kaedi
This study aims to determine the most similar set of recommendation books to the user selections in LibraryThing.
Abstract
Purpose
This study aims to determine the most similar set of recommendation books to the user selections in LibraryThing.
Design/methodology/approach
For this purpose, 30,000 tags related to History on the LibraryThing have been selected. Their tags and the tags of the related recommended books were extracted from three different recommendations sections on LibraryThing. Then, four similarity criteria of Jaccard coefficient, Cosine similarity, Dice coefficient and Pearson correlation coefficient were used to calculate the similarity between the tags. To determine the most similar recommended section, the best similarity criterion had to be determined first. So, a researcher-made questionnaire was provided to History experts.
Findings
The results showed that the Jaccard coefficient, with a frequency of 32.81, is the best similarity criterion from the point of view of History experts. Besides, the degree of similarity in LibraryThing recommendations section according to this criterion is equal to 0.256, in the section of books with similar library subjects and classifications is 0.163 and in the Member recommendations section is 0.152. Based on the findings of this study, the LibraryThing recommendations section has succeeded in introducing the most similar books to the selected book compared to the other two sections.
Originality/value
To the best of the authors’ knowledge, itis for the first time, three sections of LibraryThing recommendations are compared by four different similarity criteria to show which sections would be more beneficial for the user browsing. The results showed that machine recommendations work better than humans.
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V. Senthil Kumaran and R. Latha
The purpose of this paper is to provide adaptive access to learning resources in the digital library.
Abstract
Purpose
The purpose of this paper is to provide adaptive access to learning resources in the digital library.
Design/methodology/approach
A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.
Findings
This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.
Research limitations/implications
The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.
Practical implications
The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.
Originality/value
This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.
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Meike Huber, Dhruv Agarwal and Robert H. Schmitt
The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid…
Abstract
Purpose
The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.
Design/methodology/approach
This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration
Findings
The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.
Originality/value
The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.
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Meijuan Li, Jiarong Zhang and Zijie Shen
Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making…
Abstract
Purpose
Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.
Design/methodology/approach
First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.
Findings
To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.
Originality/value
The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.
Details