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Journal ArticleDOI

Social interaction-based consumer decision-making model in social commerce

01 Jun 2017-International Journal of Information Management (Newcastle University)-Vol. 37, Iss: 3, pp 179-189
TL;DR: It is found that positive and negative valence WOM, WOM content, and observing other consumers purchases significantly affect consumers intention to buy a product, thereby increasing the likelihood of actual buying and sharing product information with others on social commerce sites.
About: This article is published in International Journal of Information Management.The article was published on 2017-06-01. It has received 252 citations till now. The article focuses on the topics: Social relation & Social media.
Citations
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Journal ArticleDOI
TL;DR: S-commerce is revealed to be a promising new area of research, showing a new paradigm of conducting commerce using social media to reach customers and their networked friends.

245 citations

Journal ArticleDOI
TL;DR: This study develops a conceptual model to determine the most significant factors influencing user's intention, perceived satisfaction and recommendation to use mobile wallet, and provides an integrated framework for academicians to measure the moderating effect of psychological, social and risk factors on technology acceptance.

240 citations

Journal ArticleDOI
TL;DR: Results from a sample of 280 followers show that the perceived influential power of digital influencers not only helps to generate engagement but also increases expected value and behavioral intention regarding the recommended brands.

200 citations

Journal ArticleDOI
TL;DR: In this paper, a socio-technical theory was used to build a model of brand co-creation with key antecedents, including social commerce information sharing, social support, and relationship quality, with privacy concerns as a moderator.

168 citations


Cites background from "Social interaction-based consumer d..."

  • ...Social commerce creates an environment where firms can harness their brand to deliver incremental value (Gensler et al., 2013; Hajli et al., 2017; Wang & Yu, 2017; Yadav et al., 2013), and turn consumers into brand ambassadors by leveraging collective, cocreation processes with other consumers…...

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Journal ArticleDOI
TL;DR: In this paper, a new eWOM engagement model was proposed with consideration on information characteristics, consumer behavior, technological and social factors for s-commerce, and the study used 218 respondents to evaluate the proposed model using SmartPLS.
Abstract: As a business paradigm, social commerce (s-commerce) has brought about a new stage of innovation, and by extension, has transmuted the power from seller to buyer. S-commerce is a combination a commercial and social activities in which individuals may spread word of mouth (WOM) about their shopping experiences and knowledge and provide information about product and services to their to their friends. This kind of social interactions among individuals has increased the potentials of eWOM communication. Given such a backdrop, this paper aims to look into the influence of eWOM engagement on consumers’ purchase intention in s-commerce, which may complement the current effort of the research community in this area.,This study used elaboration likelihood model, theory of reasoned action and social support theory to investigate the influence of eWOM engagement on consumers’ purchase intention in s-commerce. The study used 218 respondents to evaluate the proposed model using SmartPLS.,The empirical results indicate that information characteristics, consumer behavior and technological factors exert a positive influence on consumer purchase intentions. All hypotheses between attitude toward eWOM, information credibility, innovativeness, website quality and eWOM engagement are significant. Also, eWOM engagement has a significant positive influence on consumer purchase intention. However, information quality and social support does not have any significant relationship with eWOM engagement.,This study seeks to address the dearth of research in the field of s-commerce, especially as it relates to eWOM. The study proposes a new model and empirically validates the hypothesized relationships. This research can serve as a stepping stone for future research in this field.,This study is one of the early studies focusing on the influence of eWOM engagement, especially in s-commerce. The study offers comprehensive and empirically validated factors pertaining to eWOM engagement in s-commerce. The results of this study are also important to practitioners and online companies’ managers. The study’s model has demonstrated the contextualization of what makes customers engage in eWOM and its influence in s-commerce. The study will also offer insights for firms on how to encourage eWOM engagement among customers.,A new eWOM engagement model in s-commerce is proposed with consideration on information characteristics, consumer behavior, technological and social factors. The model is validated afterwards.

153 citations

References
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TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Abstract: This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...

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TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...

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TL;DR: The extent to which method biases influence behavioral research results is examined, potential sources of method biases are identified, the cognitive processes through which method bias influence responses to measures are discussed, the many different procedural and statistical techniques that can be used to control method biases is evaluated, and recommendations for how to select appropriate procedural and Statistical remedies are provided.
Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

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TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Abstract: Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation and pitfalls of structural equation modelling (SEM) in the social sciences. The book covers introductory techniques including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods such as the evaluation of non-linear effects, the analysis of means in convariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the book offers clear instructions on the preparation and screening of data, common mistakes to avoid and widely used software programs (Amos, EQS and LISREL). The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.

42,102 citations


"Social interaction-based consumer d..." refers background or methods in this paper

  • ...We chose maximum ikelihood parameter estimation over other estimation methods e.g., weighted least squares, two-stage least squares) because the ata were fairly normally distributed (Kline, 2010)....

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  • ...When the AVE is larger than the corresponding squared inter-construct correlation estimates, it is suggested that the indicators have more in common with the construct they are associated with than they do with other constructs, which again provides evidence of discriminant validity (Kline, 2010)....

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Journal ArticleDOI
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

37,124 citations

Trending Questions (1)
Does social influences have a significant impact on consumer ethnocentrism or purchase intention?

The paper does not specifically mention the impact of social influences on consumer ethnocentrism or purchase intention.