Structural equation modeling reveals complex relationships in mixed forage swards

2015 ◽  
Vol 78 ◽  
pp. 106-113 ◽  
Author(s):  
Erin M. McLeod ◽  
Samiran Banerjee ◽  
Edward W. Bork ◽  
Linda M. Hall ◽  
Donald D. Hare
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Complex Relationships

scholarly journals EFFECTS OF E-COMMERCE ON THE SHARIA HOTELS BRAND AWARENESS IN BANDUNG

2018 ◽  
Vol 5 (10) ◽  
Author(s):  
Muchyidin Natakusuma ◽  
Ria Arifianti ◽  
Iwan Sukoco
Keyword(s):  
Structural Equation Modeling ◽  
Significant Influence ◽  
Structural Equation ◽  
Internet Technology ◽  
Brand Awareness ◽  
Equation Modeling ◽  
The Internet ◽  
Boutique Hotel ◽  
Complex Relationships ◽  
A Company

Sharia has become global, including the development of Sharia Hotels. In addition, the development of internet technology in Indonesia can be seen with the existence of e-commerce which benefits in the form of business convenience because the internet is seen as an advertising media that is cheaper, more efficient and effective. Tight business competition has made brand as an important aspect that can be a benchmark for a company. Sharia hotel owners use e-commerce to build strong brands and make consumers acknowledge that their brands are well received by consumers. This study aims to find out how e-commerce influences brand awareness in Narapati Indah Sharia Boutique Hotel and Convention Bandung. Respondents in this study were sampled using the Structural Equation Modeling (SEM) method that allows testing a series of relatively complex relationships simultaneously. The results showed that respondents' responses regarding e-commerce and brand awareness at Narapati Indah Sharia Hotel were in the good category. In addition, there is a significant influence between e-commerce on brand awareness in Narapati Indah Sharia Hotel especially through their own website so that to depend on other parties are no longer needed.

scholarly journals Pathways to Social Inequality

2019 ◽  
Author(s):  
Hannah J. Haynie ◽  
Patrick H. Kavanagh ◽  
Fiona Jordan ◽  
Carol Ember ◽  
Russell Gray ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Social Inequality ◽  
Environmental Conditions ◽  
Structural Equation ◽  
Global Analysis ◽  
Modern Human ◽  
Equation Modeling ◽  
Modern World ◽  
Small Scale ◽  
Complex Relationships

Social inequality is now pervasive in human societies, despite the fact that humans lived in relatively egalitarian, small-scale societies across most of our history. Prior literature highlights the importance of environmental conditions, economic defensibility, and wealth transmission for shaping early Holocene origins of social inequality. However, it remains untested whether the mechanisms that drive the evolution of inequality in recent human societies follow a similar trajectory. We conduct the first global analysis of pathways to inequality within modern human societies using structural equation modeling. Our analytical approach demonstrates that environmental conditions, resource intensification, and wealth transmission mechanisms impact various forms of social inequality via a complex web of causality. We further find that subsistence practices have a direct impact on some institutionalized forms of inequality. This work identifies drivers of social inequality in the modern world and demonstrates the application of structural equation modeling methods to investigate complex relationships between elements of human culture.

scholarly journals An Introduction to Structural Equation Modeling

2021 ◽  
pp. 1-29
Author(s):  
Joseph F. Hair ◽  
G. Tomas M. Hult ◽  
Christian M. Ringle ◽  
Marko Sarstedt ◽  
Nicholas P. Danks ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Multivariate Data Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Equation Modeling ◽  
Causal Explanations ◽  
Measurement Models ◽  
Key Characteristics ◽  
Predictive Approach ◽  
Complex Relationships

AbstractStructural equation modeling is a multivariate data analysis method for analyzing complex relationships among constructs and indicators. To estimate structural equation models, researchers generally draw on two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM). Whereas CB-SEM is primarily used to confirm theories, PLS represents a causal–predictive approach to SEM that emphasizes prediction in estimating models, whose structures are designed to provide causal explanations. PLS-SEM is also useful for confirming measurement models. This chapter offers a concise overview of PLS-SEM’s key characteristics and discusses the main differences compared to CB-SEM. The chapter also describes considerations when using PLS-SEM and highlights situations that favor its use compared to CB-SEM.

Perceived robotic server qualities and functions explaining customer loyalty in the theme park context

2020 ◽  
Vol 13 (12) ◽  
pp. 3895-3923
Author(s):  
Ady Milman ◽  
Asli Tasci ◽  
Tingting(Christina) Zhang
Keyword(s):  
Structural Equation Modeling ◽  
Customer Loyalty ◽  
Structural Equation ◽  
Online Survey ◽  
Positive Influence ◽  
Equation Modeling ◽  
Theme Park ◽  
Content Type ◽  
Human Orientation ◽  
Complex Relationships

Purpose This paper aims to explore theme park visitors’ attitudes toward interacting with robots and investigated the qualities and functions of robotic servers and their influence on customers’ loyalty. A structural equation modeling approach was used to identify the complex relationships among variables in the entire network. Design/methodology/approach An online survey randomly assigned respondents to four different robotic server scenarios with robots that look like humans, animals, cartoon characters and anime features. The influence of robot types was investigated by manipulating robot type with four different pictures; however, the data were analyzed with a structural equation modeling model to identify the complex relationships rather than one-way analysis of variance to identify influences of robot types on different variables in separate analyzes. Findings The data collected from the 385 experienced theme park visitors revealed that perception of robots with human orientation and safety qualities had the strongest effect on the perceived robotic functionality, while emotions and co-creation qualities hardly had any effect on the perceived functionality, which included utilitarian rather than experiential functions such as excitement. Human orientation qualities, regardless of the specific robotic design, had a significant impact on perceived robotic functionality. The study also revealed a strong positive influence of perceived robotic functionality on customer loyalty. Originality/value The debate of whether or not to introduce and blend the growing robotic technology into the theme park experience is in its infancy. The study contributes to the theory of how robotics qualities and functions can augment customer loyalty.

Testing the Assumption of Uncorrelated Errors for Short Scales by Means of Structural Equation Modeling

2014 ◽  
Vol 35 (4) ◽  
pp. 201-211 ◽  
Author(s):  
André Beauducel ◽  
Anja Leue
Keyword(s):  
Structural Equation Modeling ◽  
Negative Correlation ◽  
Structural Equation ◽  
Error Score ◽  
Classical Test Theory ◽  
Test Theory ◽  
Equation Modeling ◽  
Eysenck Personality Questionnaire ◽  
Personality Questionnaire ◽  
True Score

It is shown that a minimal assumption should be added to the assumptions of Classical Test Theory (CTT) in order to have positive inter-item correlations, which are regarded as a basis for the aggregation of items. Moreover, it is shown that the assumption of zero correlations between the error score estimates is substantially violated in the population of individuals when the number of items is small. Instead, a negative correlation between error score estimates occurs. The reason for the negative correlation is that the error score estimates for different items of a scale are based on insufficient true score estimates when the number of items is small. A test of the assumption of uncorrelated error score estimates by means of structural equation modeling (SEM) is proposed that takes this effect into account. The SEM-based procedure is demonstrated by means of empirical examples based on the Edinburgh Handedness Inventory and the Eysenck Personality Questionnaire-Revised.

The Measurement Invariance of the Short Dark Triad

2020 ◽  
Vol 41 (4) ◽  
pp. 207-218
Author(s):  
Mihaela Grigoraș ◽  
Andreea Butucescu ◽  
Amalia Miulescu ◽  
Cristian Opariuc-Dan ◽  
Dragoș Iliescu
Keyword(s):  
Structural Equation Modeling ◽  
Item Response ◽  
Structural Equation ◽  
Dark Triad ◽  
Theory And Practice ◽  
Equation Modeling ◽  
Scale Level ◽  
High Stake ◽  
Differential Functioning ◽  
Item Functioning

Abstract. Given the fact that most of the dark personality measures are developed based on data collected in low-stake settings, the present study addresses the appropriateness of their use in high-stake contexts. Specifically, we examined item- and scale-level differential functioning of the Short Dark Triad (SD3; Paulhus & Jones, 2011 ) measure across testing contexts. The Short Dark Triad was administered to applicant ( N = 457) and non-applicant ( N = 592) samples. Item- and scale-level invariances were tested using an Item Response Theory (IRT)-based approach and a Structural Equation Modeling (SEM) approach, respectively. Results show that more than half of the SD3 items were flagged for Differential Item Functioning (DIF), and Exploratory Structural Equation Modeling (ESEM) results supported configural, but not metric invariance. Implications for theory and practice are discussed.

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

Celebrity Worship Among University Students in Malaysia

2011 ◽  
Vol 16 (4) ◽  
pp. 334-342 ◽  
Author(s):  
Viren Swami ◽  
Tomas Chamorro-Premuzic ◽  
Khairul Mastor ◽  
Fatin Hazwani Siran ◽  
Mohammad Mohsein Mohammad Said ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Attitude Scale ◽  
Equation Modeling ◽  
Future Research ◽  
The West ◽  
Factor Solution ◽  
Movie Stars ◽  
Three Factor

The present study examined conceptual issues surrounding celebrity worship in a Malay-speaking population. In total, 512 Malay and 269 Chinese participants from Malaysia indicated who their favorite celebrity was and completed the Celebrity Attitude Scale (CAS) as well as a range of demographic items. Results showed that the majority of Malay and Chinese participants selected pop stars and movie stars as their favourite celebrities, mirroring findings in Western settings. In addition, exploratory factor analysis revealed a three-factor solution of the CAS that was consistent with previous studies conducted in the West. Structural equation modeling further revealed that participant’s age was negatively associated with celebrity worship and that self-rated attractiveness was positively associated with celebrity worship. Overall, the present results suggest that celebrity worship in Malaysia may be driven by market and media forces, and future research may well be guided by use of the CAS.

Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Interoceptive Awareness ◽  
Multidimensional Assessment ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Complex Models ◽  
Approximate Zero ◽  
Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

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