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Article
Publication date: 20 March 2024

Verdiana 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.

Details

International Marketing Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-1335

Keywords

Article
Publication date: 25 July 2018

Ke Yi Zhou and Shaolin Hu

The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and…

Abstract

Purpose

The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate.

Design/methodology/approach

Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system.

Findings

The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement.

Practical implications

This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis.

Originality/value

In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 11 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 19 January 2024

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.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

Book part
Publication date: 19 September 2014

George Chondrakis and Tomas Farchi

This article explores the effect of technological similarity in acquisitions on invention quantity and quality. In doing so, we confirm previous findings in the literature…

Abstract

This article explores the effect of technological similarity in acquisitions on invention quantity and quality. In doing so, we confirm previous findings in the literature suggesting that technological similarity exhibits an inverted U-shaped relationship with innovative output and a negative relationship with average invention quality. However, we identify the nature of the technology as an important moderating factor for both relationships. We distinguish between two types of technologies, complex and discrete, and suggest that at high levels of technological similarity, invention quantity and average quality increase more in complex technology industries as compared to discrete technology industries. These effects are attributed to innovation cumulativeness and the interdependencies developed between patent rights in complex technology settings. A study of acquisition and patenting activity in two industries over a sixteen-year period provides empirical support to our claims.

Details

Advances in Mergers and Acquisitions
Type: Book
ISBN: 978-1-78350-970-6

Keywords

Book part
Publication date: 9 November 2020

Riikka Hofmann

There is an identified need in higher education research for methods which have the capacity to generate conceptual insights grounded in concrete local practice but with wider…

Abstract

There is an identified need in higher education research for methods which have the capacity to generate conceptual insights grounded in concrete local practice but with wider applicability in understanding and facilitating research-based change. This chapter outlines an intermediate approach to qualitative data analysis which can support theoretical knowledge advancement from practice-based research, which I call the difference-within-similarity approach. It involves a particular way of conducting dialogues with our data: of interanimating similarities and differences within our qualitative datasets. The approach outlined involves first identifying a similarity, then systematically examining differences within that similarity to generate theoretical explanations. Drawing on sociocultural theorising, particularly dialogic theory and cultural–historical activity theory, the approach is based on the idea that new meanings arise from a comparison of multiple perspectives on the ‘same’ phenomenon. The tensions between such perspectives are seen as a key driver for change in educational practice. Therefore, articulating and examining such tensions in our data gives an opportunity to simulate the possibility of change in our analysis and, hence, develop insights which can inform change beyond local settings. Important here is that the differences examined are bound together by an analytically productive similarity. Through multiple research examples, the chapter identifies and illustrates a range of ways of articulating productive analytical similarities for comparison in our data: through theory/literature, through forward and backwards processing of data itself and through a process termed ‘weaving’.

Book part
Publication date: 19 July 2016

Cynthia L. Gramm and John F. Schnell

We investigate the effects of management-employee similarity on mistreated employees’ propensities to engage in legal and organizational claiming, to quit, and to not seek a…

Abstract

Purpose

We investigate the effects of management-employee similarity on mistreated employees’ propensities to engage in legal and organizational claiming, to quit, and to not seek a remedy in ongoing employment relationships.

Methodology/approach

We test hypotheses generated by the similarity-attraction and similarity-betrayal paradigms using Tobit regression and data from vignette-based employee surveys.

Findings

Mistreated employees with same-sex supervisors are more likely to initiate legal claims and to quit than those with opposite-sex supervisors, but less likely to initiate legal claims and to quit when they have a same-race supervisor than when they have a different-race supervisor. The effects of management-employee similarity on mistreated employees’ remedy-seeking responses exhibit asymmetries by gender and by race. The presence of same-race supervisors or other managers appears to diminish the greater reluctance of nonwhite employees, compared to white employees, to use organizational claiming mechanisms.

Originality/value

We know of no prior published research that has investigated the determinants of employees’ propensities to engage in multiple forms of remedy seeking, as well as the propensity to not seek a remedy, in response to plausibly illegal mistreatment not involving dismissal.

Book part
Publication date: 9 November 2023

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.

Details

Modeling Economic Growth in Contemporary Poland
Type: Book
ISBN: 978-1-83753-655-9

Keywords

Book part
Publication date: 3 October 2006

Javier Gimeno, Ming-Jer Chen and Jonghoon Bae

We investigate the dynamics of competitive repositioning of firms in the deregulated U.S. airline industry (1979–1995) in terms of a firm's target market, strategic posture, and…

Abstract

We investigate the dynamics of competitive repositioning of firms in the deregulated U.S. airline industry (1979–1995) in terms of a firm's target market, strategic posture, and resource endowment relative to other firms in the industry. We suggest that, despite strong inertia in competitive positions, the direction of repositioning responds to external and internal alignment considerations. For external alignment, we examined how firms changed their competitive positioning to mimic the positions of similar, successful firms, and to differentiate themselves when experiencing intense rivalry. For internal alignment, we examined how firms changed their position in each dimension to align with the other dimensions of positioning. This internal alignment led to convergent positioning moves for firms with similar resource endowments and strategic postures, and divergent moves for firms with similar target markets and strategic postures. The evidence suggests that repositioning moves in terms of target markets and resource endowments are more sensitive to external and internal alignment considerations, but that changes in strategic posture are subject to very high inertia and do not appear to respond well to alignment considerations.

Details

Ecology and Strategy
Type: Book
ISBN: 978-1-84950-435-5

Book part
Publication date: 12 September 2003

Joel A.C Baum and Theresa K Lant

Organizations create their environments by constructing interpretations and then acting on them as if they were true. This study examines the cognitive spatial boundaries that…

Abstract

Organizations create their environments by constructing interpretations and then acting on them as if they were true. This study examines the cognitive spatial boundaries that managers of Manhattan hotels impose on their competitive environment. We derive and estimate a model that specifies how the attributes of managers’ own hotels and potential rival hotels influence their categorization of competing and non-competing hotels. We show that similarity in geographic location, price, and size are central to managers’ beliefs about the identity of their competitors, but that the weights they assign to these dimensions when categorizing competitors diverge from their influence on competitive outcomes, and indicate an overemphasis on geographic proximity. Although such categorization is commonly conceived as a rational process based on the assessment of similarities and differences, we suggest that significant distortions can occur in the categorization process and examine empirically how factors including managers’ attribution errors, cognitive limitations, and (in)experience lead them to make type I and type II competitor categorization errors and to frame competitive environments that are incomplete, erroneous, or even superstitious. Our findings suggest that understanding inter-firm competition may require greater attention being given to the cognitive foundations of competition.

Details

Geography and Strategy
Type: Book
ISBN: 978-0-76231-034-0

Book part
Publication date: 17 November 2010

Rolando Quintana and Mark T. Leung

Most setup management techniques associated with electronic assembly operations focus on component similarity in grouping boards for batch processing. These process planning…

Abstract

Most setup management techniques associated with electronic assembly operations focus on component similarity in grouping boards for batch processing. These process planning techniques often minimize setup times. On the contrary, grouping with respect to component geometry and frequency has been proved to further minimize assembly time. Thus, we propose the Placement Location Metric (PLM) algorithm to recognize and measure the similarity between printed circuit board (PCB) patterns. Grouping PCBs based on the geometric and frequency patterns of components in boards will form clusters of locations and, if these clusters are common between boards, similarity among layouts can be recognized. Hence, placement time will decrease if boards are grouped together with respect to the geometric similarity because the machine head will travel less. Given these notions, this study develops a new technique to group PCBs based on the essences of both component commonality and the PLM. The proposed pattern recognition method in conjunction with the Improved Group Setup (IGS) technique can be viewed as an extended enhancement to the existing Group Setup (GS) technique, which groups PCBs solely according to component similarity. Our analysis indicates that the IGS performs relatively well with respect to an array of existing setup management strategies. Experimental results also show that the IGS produces a better makespan than its counterparts over a low range of machine changeover times. These results are especially important to operations that need to manufacture quickly batches of relatively standardized products in moderate to larger volumes or in flexible cell environments. Moreover, the study provides justification to adopt different group management paradigms by electronic suppliers under a variety of processing conditions.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

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