Consumer Perceptions of Interpersonal Equity and Satisfaction in Transactions: A Field Survey Approach

1989 ◽  
Vol 53 (2) ◽  
pp. 21-35 ◽  
Author(s):  
Richard L. Oliver ◽  
John E. Swan
Keyword(s):  
Maximum Likelihood ◽  
Structural Equation ◽  
Field Survey ◽  
Likelihood Estimation ◽  
Consumer Perceptions ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Managerial Implications ◽  
The Subject ◽  
Survey Approach

Automobile purchasers were surveyed about feelings toward their inputs to and outcomes from the sales transaction, as well as their perceptions of the inputs and outcomes of the salesperson. Structural equation modeling with maximum likelihood estimation shows two concepts advanced in the equity literature, fairness and preference (advantageous inequity), to be related differentially to input and outcome judgments. No necessary symmetry is observed between the weights attached to inputs and outcomes or between those attached to self and salesperson. When framed in a larger perspective involving satisfaction with the salesperson, the fairness dimension mediates the effect of inputs and outcomes on satisfaction whereas preference does not. The fairness influence is robust against the simultaneous inclusion of disconfirmation in the satisfaction equation. Satisfaction, in turn, is related strongly to the consumer's intention cognitions. The findings suggest that the retail sales transaction may differ in substantive ways from the subject-peer and worker-coworker comparisons in other disciplines and that models of interpersonal satisfaction in the sales transaction should include the mediating effect of the fairness dimension of equity. The managerial implications of these findings are discussed.

Experience Value Cocreation on Destination Online Platforms

2017 ◽  
Vol 57 (8) ◽  
pp. 1093-1107 ◽  
Author(s):  
Hongmei Zhang ◽  
Susan Gordon ◽  
Dimitrios Buhalis ◽  
Xifen Ding
Keyword(s):  
Structural Equation ◽  
Emotional Experience ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Five Dimensions ◽  
Online Platform ◽  
Online Platforms ◽  
Modeling Analysis ◽  
Managerial Implications ◽  
Value Cocreation

Technology is critical for facilitating the experience value cocreation process in tourism. Online platforms in particular enable consumers to develop realistic expectations and to cocreate their experiences. Limited empirical research has been done to investigate the experience value cocreation process, especially in tourism. This study fills this gap by proposing a cognition–emotion–behavior model. A scenario experiment approach is used to investigate the experience value cocreation process on destination online platforms in the pretravel stage. Structural equation modeling analysis shows that online platform experience significantly affects the destination emotional experience. This, in turn, has significant effects on the five dimensions of destination engagement intention. The mediating effect of destination emotional experience on the relationship between online platform experience and destination engagement intention is supported. These findings contribute to a better understanding of the experience value cocreation process and theoretical and managerial implications are proposed.

Enlivening the Effect of Tourists' Knowledge and Involvement on Malaysian Heritage Food (MHF) Towards Food Tourism Image in Malaysia

2020 ◽  
pp. 268-290
Author(s):  
Siti Radhiah Omar ◽  
Shahrim Ab Karim ◽  
Siti Suriawati Isa ◽  
Siti Nazirah Omar
Keyword(s):  
Structural Equation ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Consumption Experience ◽  
International Tourist ◽  
Managerial Implications ◽  
Research Findings ◽  
Food Tourism ◽  
Tourism Image ◽  
Cultural Involvement

This current article aims to empirically test the relationship between international tourist knowledge and Malaysian Heritage Food (MHF) cultural involvement on Malaysia's Food Tourism Image and secondly, to analyze the mediating effect of knowledge for both relationships. A quantitative survey of 719 international tourists with previous MHF consumption experience was conducted and analyzed via a Structural Equation Modeling (SEM) approach. Results demonstrated that the following variables have significant associations with Food Tourism Image while knowledge mediated the subsequent links positively with partial mediation. Regardless of the theoretical and managerial implications and research findings, supplementary investigations are warranted to enhance the growth of food cultural tourism.

scholarly journals Sustaining Sporting Destinations through Improving Tourists’ Mental and Physical Health in the Tourism Environment: The Case of Korea

2019 ◽  
Vol 17 (1) ◽  
pp. 122 ◽  
Author(s):  
Yunduk Jeong ◽  
Suk-Kyu Kim ◽  
Jae-Gu Yu
Keyword(s):  
Structural Equation ◽  
Likelihood Estimation ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Sport Tourism ◽  
Novelty Seeking ◽  
Emotional Experiences ◽  
Destination Loyalty ◽  
Tourist Satisfaction ◽  
The Relationship

The purpose of this study was to explore structural relationships between emotional experiences, novelty seeking, tourist satisfaction, and destination loyalty in the context of active sport tourism. The study emphasizes the mediating effect tourist satisfaction has on the relationship between emotional experiences and destination loyalty. The validities and reliabilities of the measures used were examined through confirmatory factor analysis (CFA) and correlation analysis using 230 domestic and international participants who attended a marathon race as amateur athletes. Structural equation modeling analysis with maximum likelihood estimation was conducted to investigate relationships between study variables. Findings disclosed the positive impacts of (a) emotional experiences on tourist satisfaction and destination loyalty, (b) novelty seeking on tourist satisfaction, and (c) tourist satisfaction on destination loyalty, and demonstrated that (d) tourist satisfaction fully mediates the relationship between emotional experiences and destination loyalty. Based on its results, this study (a) indicates that emotional experiences play key roles in predicting tourist satisfaction and destination loyalty, (b) provides an example of the merits of the Destination Emotion Scale (DES) in a sport tourism setting, (c) implies that both emotional experiences and novelty seeking should be incorporated into tourist behavior models, and (d) contributes to tourism studies by exploring the mediating effect of tourist satisfaction on the relation between emotional experiences and destination loyalty. Thus, destination managers should manage gorgeous natural views and beautiful cityscapes, and organize various fun events, such as prize and ticket giveaway events, music performances, and charity campaigns for tourists during events.

Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data

2003 ◽  
Vol 28 (2) ◽  
pp. 111-134 ◽  
Author(s):  
Sik-Yum Lee ◽  
Xin-Yuan Song ◽  
John C. K. Lee
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Latent Variables ◽  
Structural Equation ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimates ◽  
Equation Modeling ◽  
Nonlinear Structural ◽  
Nonlinear Structural Equation

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models and it is very common to encounter missing data. In this article, an EM type algorithm is developed for maximum likelihood estimation of a general nonlinear structural equation model with ignorable missing data, which are missing at random with an ignorable mechanism. To avoid computation of the complicated multiple integrals involved in the conditional expectations, the E-step is completed by a hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm; while the M-step is completed efficiently by conditional maximization. Standard errors of the maximum likelihood estimates are obtained via Louis’s formula. The methodology is illustrated with results obtained from a simulation study and a real data set with rather complicated missing patterns and a large number of missing entries.

scholarly journals A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome

2020 ◽  
Vol 44 (5) ◽  
pp. 447-457
Author(s):  
Su-Young Kim ◽  
David Huh ◽  
Zhengyang Zhou ◽  
Eun-Young Mun
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Structural Equation ◽  
Growth Models ◽  
Likelihood Estimation ◽  
Equation Modeling ◽  
Latent Growth ◽  
Ml Estimation ◽  
Parameter Recovery ◽  
Latent Growth Models

Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a lack of research providing guidance on when Bayesian estimation may be preferable to ML or vice versa. This study compared the performance of Bayesian versus ML estimators for LGMs by evaluating their accuracy via Monte Carlo (MC) simulations. For the MC study, longitudinal data sets were generated and estimated using LGM via both ML and Bayesian estimation with three different priors, and parameter recovery across the two estimators was evaluated to determine their relative performance. The findings suggest that ML estimation is a reasonable choice for most LGMs, unless it fails to converge, which can occur with limiting data situations (i.e., just a few time points, no covariate or outcome, modest sample sizes). When models do not converge using ML, we recommend Bayesian estimation with one caveat that the influence of the priors on estimation may have to be carefully examined, per recent recommendations on Bayesian modeling for applied researchers.

scholarly journals Training Perception and Innovative Behavior of SME Employees: Examining the Mediating Effects of Firm Commitment

SAGE Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 215824402110672
Author(s):  
Muhammad Farhan Jalil ◽  
Wasim Ullah ◽  
Zeeshan Ahmed
Keyword(s):  
Structural Equation ◽  
Management Strategies ◽  
The Body ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Innovative Behavior ◽  
Mediating Effects ◽  
Calculative Commitment ◽  
Managerial Implications ◽  
The Relationship

Many critical decisions about an employee’s innovative performance are significantly based on the training results, as they are accountable for a variety of behavioral-related consequences. Training is among the most important human resource management strategies. The aim of this study is to examine the relationship between employees’ perceptions of training and their innovative behavior in the Malaysian SME sector, as well as the mediating effect of affective and calculative commitment on this relationship. Structured questionnaires were used to collect the data. A total of 635 employees from 200 SMEs were selected through a stratified random sampling method, and structural equation modeling was applied to test the relationship. The findings of the study supported the hypothesized relationships, as training in Malaysia significantly engaged SME employees in innovative behavior. Furthermore, the study discovered that affective and calculative commitment have partial mediating effects on the association between training and innovative behavior. In the context of the SME sector, theoretical and managerial implications have been addressed. The originality of the study is that it examines the relationship between employees’ perceptions of training and their innovative behavior in SMEs. The relationship was measured using a multidimensional approach in the study. The research also adds to the body of knowledge by identifying the mediating effect of affective and calculative commitment.

Enlivening the Effect of Tourists' Knowledge and Involvement on Malaysian Heritage Food (MHF) Towards Food Tourism Image in Malaysia

2019 ◽  
Vol 3 (1) ◽  
pp. 23-42 ◽  
Author(s):  
Siti Radhiah Omar ◽  
Shahrim Ab Karim ◽  
Siti Suriawati Isa ◽  
Siti Nazirah Omar
Keyword(s):  
Structural Equation ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Consumption Experience ◽  
International Tourist ◽  
Managerial Implications ◽  
Research Findings ◽  
Food Tourism ◽  
Tourism Image ◽  
Cultural Involvement

This current article aims to empirically test the relationship between international tourist knowledge and Malaysian Heritage Food (MHF) cultural involvement on Malaysia's Food Tourism Image and secondly, to analyze the mediating effect of knowledge for both relationships. A quantitative survey of 719 international tourists with previous MHF consumption experience was conducted and analyzed via a Structural Equation Modeling (SEM) approach. Results demonstrated that the following variables have significant associations with Food Tourism Image while knowledge mediated the subsequent links positively with partial mediation. Regardless of the theoretical and managerial implications and research findings, supplementary investigations are warranted to enhance the growth of food cultural tourism.

scholarly journals Multidimensional Perspective of Firms’ IT Capability Between Digital Business Strategy and Firms’ Efficiency: A Case of Chinese SMEs

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402097056
Author(s):  
Zhuquan Wang ◽  
Memon Rafait Mahmood ◽  
Hafeez Ullah ◽  
Imran Hanif ◽  
Qaiser Abbas ◽  
...  
Keyword(s):  
Business Strategy ◽  
Structural Equation ◽  
Business Models ◽  
Digital Technologies ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Survey Questionnaire ◽  
It Capability ◽  
Efficient Performance ◽  
Managerial Implications

In the contemporary and perpetually changing environment, firms have transformed their business models by integrating advanced digital technologies in which their capabilities contribute crucially. This has evoked competition and challenges especially for small and medium-sized enterprises (SMEs). Therefore, the research aimed to analyze the mediating effect of information technology (IT) capability between digital business strategy and firms’ efficiency. The case of Chinese SMEs was considered specifically. The research was quantitative; therefore, the sample comprised 351 participants accumulated using a survey questionnaire. The mediating effect was tested using structural equation modeling (SEM) where partial mediation of the IT capability was found only in terms of IT proactive stance. Therefore, the research has certain managerial implications specifically in terms of proactive stance as the managers need to initiate the transformation within for efficient performance.

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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