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Frequent human-media interaction via the electronic word-of-mouth (e-wom) platform, trust is acknowledged as an ongoing challenge. This study aimed to understand users' trust in the e-wom platform based on uses and gratifications theory and stimulus-organism-response (S-O-R) paradigm. Utilitarian gratification (perceived information quality and per...
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Citations
... The UGT has been utilized to study customer behavior and gratitude in social commerce settings, focusing on specific gratifications [52,53]. This understanding of user motivations has led to the development of models capturing relationships between gratification, attitudes, learning performance, and intention to use specific applications [54]. UGT theory offers a comprehensive understanding of consumer motivations in the e-commerce industry, highlighting how social and psychological needs influence interactions with online platforms. ...
Over the years, E-commerce industry has been witnessing a phenomenal growth, thanks to rapid technological advancement in Industry 4.0. There has been a standout surge in the use of various online shopping platforms (OSP) for daily use. The recent pandemic has accelerated the growth trajectory and made a transformational change in the digital commerce landscape. As a result, there has been a proliferation of OSPs in the competitive domain. It is therefore pertinent to address the questions: How do the customers select their favorite OSP? To what extent the OSPs differ based on consumers' preferences? The present work addresses these questions by proposing a novel group decision making framework. The ongoing study provides several innovative extensions of multi criteria decision making models like Borda count, criteria importance assessment (CIMAS), modified preference selection index (MPSI), and root assessment method (RAM). In this paper, the researchers provide a novel use of the Borda count method, integrated with CIMAS for determining criteria weights utilizing ranking of the criteria. Further, a novel extension of MPSI and RAM has been made with multiple normalizations. In this paper, the authors demonstrate a rare combination of vector and non-linear normalization using the Heron mean. The present paper derives the final criteria weights by combining Borda count, CIMAS and multi-normalization based MPSI (MNMPSI) using Bayesian logic. The criteria are selected based on Uses and Gratification theory (UGT). The findings reveal that interactive app interface and features (C16), user-friendly interface and search (C13), convenience in shopping (C14), product availability and variety (C12) and discounts and offers (C8) exert significant influence in selecting the OSP. Further, it is observed that Flipkart (A2) and Amazon (A1) are the top performers in the eyes of the users. The stability and reliability of the proposed methodology are examined by conducting a sensitivity analysis and comparing with several other models. The robustness of the proposed methodology and practical relevance of the findings of the present work shall provide notable impetus to the analysts and strategic decision-makers..
This study looks at the influence of digital innovation, notably AI-driven chatbots, on e-commerce consumer satisfaction among young customers in Vietnam. It investigates key factors influencing user satisfaction employing frameworks such as the Uses and Gratifications Theory (U&G), Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT), including utilitarian, hedonic, technological, and social gratifications, privacy risk, and social influence. This research applies a quantitative method, with data collected through an online survey utilising snowball sampling, yielding responses from 1,007 individuals aged 18 to 30. SPSS and PLS-SEM tools are used in the statistical analysis. This study finds that utilitarian, hedonic, technological, and social gratifications positively and substantially impact user satisfaction. Aside from this finding, when engaging with chatbots, consumers are often affected by suggestions and endorsements from peers and their larger social context. This highlights the significance of peer validation and social dynamics in determining user satisfaction. Additionally, Privacy Risks do not substantially impact satisfaction, indicating that customers prioritise practical and emotional advantages over data security concerns when engaging with chatbots. Practical implications include strategically using digital innovation, making reasonable assumptions about privacy risks, and adding social elements to improve consumer satisfaction in Vietnam’s thriving e-commerce industry. This study provides valuable insights for companies navigating digital innovation in Vietnam’s e-commerce ecosystem and digital banking.
This study investigates role of social media user engagement metrics in predicting career success likelihoods using supervised machine learning techniques. With platforms like LinkedIn and VKontakte becoming pivotal for networking and advancement, user statistics have emerged as potential indicators of professional capability. However, research questions metric reliability considering impression management tactics and biases. While prior studies examined limited activity features, this analysis adopts a robust CatBoost model to gauge career success prediction from multifaceted social data combinations. The study utilizes user profiles of over 17,000 on a major Russian platform. Individuals are categorized by an algorithm accounting for factors like salaries, experience, and employment status. User statistics spanning engagement, content sharing, popularity, and profile completeness provide model inputs. Following comparative evaluation, CatBoost achieved superior performance in classification accuracy, precision, recall and ROC AUC score. Analysis of SHapley Additive exPlanations values provides explanatory modeling insights into influential metrics, thresholds, and patterns. Results reveal subscribers, reposts and interest pages as highly impactful, suggesting that influence and content resonance predict success better than sheer visibility indicators like multimedia volumes. Findings also point to optimal engagement ranges beyond which career prediction gains diminish. Additionally, profile completeness and regular posting are positive to a limit, while likes to have negligible effects. The study contributes more holistic, data-driven visibility into effective social media conduct for career advancement. It advocates prioritizing network cultivation, tactical self-presentation, shareable narratives and reciprocal relationships over metrics gaming. Findings largely validate strategic communication theory around impression management and relationship-building.
