The Impact Of Booth Recommendation System On Exhibition Attendees\u27 Unplanned Visit Behavior: An Extrinsic-Intrinsic Dichotomy Perspective

Our study on unplanned behaviour theory have examined the effect of booth recommendation system (BRS) on exhibition arise from either an extrinsic or intrinsic motivation. Previous studies, however, ignored the importance of the unplanned behavioural effectiveness through BRS that bonds extrinsic and intrinsic motivation together to deliver unexpected outcomes in exhibition. In this paper, we propose a model of the impact of BRS where perception of usefulness and threat to freedom of choice mediates the effect of both extrinsic and intrinsic motivation on unplanned booth visit behavior. We collected data from 101 visitors of exhibition and analyzed it using the Partial Lease Square (PLS) method. Our findings, interestingly, show that only intrinsic motivations (escape, attraction) significantly influence both perceived usefulness of BRS and threat to freedom of choice, however extrinsic motivation (information) does not significantly influences. Perceived usefulness of BRS mediates directly the effect of escape and attraction on unplanned booth visit behavior. The results and implications are further discussed

Hybrid user perception model: comparing users’ perceptions toward collaborative, content-based, and hybrid recommender systems

This study examines users’ perceptions toward three types of recommender systems by employing a hybrid user perception model combining with Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) in order to specifically explain a message-attitude-use process. Recommender systems, as an innovation applying big data ideas and algorithmic power, have been widely applied to multiple Internet industries. In order to further investigate how users perceived the use of recommender systems and the differences among users’ perceptions toward the use of different recommender systems (collaborative filtering, content-based filtering, and hybrid filtering), three perception variables (perceived usefulness, perceived behavioral control, and perceived enjoyment) were specifically assessed by using an online survey of college students. Overall, the results indicated that there were some statistically significant differences among the user perceptions towards different types of recommender systems. In addition, users generally feel positive about the use of these recommender systems, and users’ perceptions toward hybrid-filtering system were rated higher than perceptions toward collaborative filtering and content-based filtering

The impact of coronavirus pandemic (COVID-19) on education: the role of virtual and remote laboratories in education

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners’ benefits

Impacto de la publicidad en internet en la decisión de compra

La publicidad tiene el propósito de mantener la atención de la audiencia para lograr metas específicas -- Las nuevas plataformas digitales, como redes sociales, correo electrónico, blogs y páginas web, se han convertido en medios eficaces para informar, persuadir o recordar diferentes aspectos de un producto -- Este artículo presenta un estudio realizado con el objetivo de analizar y evaluar el impacto de la publicidad en internet para determinar su influencia en la decisión de compra de artículos de la categoría cocina del hogar -- Se realizó un estudio descriptivo orientado hacia hombres y mujeres de 23 a 60 años de edad de todos los estratos -- Los resultados sugieren que la publicidad en los canales digitales es un factor preponderante sobre el proceso de estímulo-respuesta de los consumidores en aspectos como el precio, la confiabilidad y la comodidad para realizar transacciones comerciale

Sharing Political Content in Online Social Media: A Planned and Unplanned Behaviour Approach

Human’s decision making is not necessarily always planned; their unplanned behaviour—determined by natural personality traits—also contributes to the decision making process. In this study, we investigate factors related to planned and unplanned behaviour to understand why people share political content in online social media. Based on an online survey of 257 social media users, our results demonstrate that the factors representing both planned (i.e., perceived social recognition and altruistic motivation) and unplanned behaviour (i.e., extroversion and impulsiveness) affect people’s political content sharing behaviour. Our study understands that sharing political content is not like sharing other forms of content such as tourist attractions—the former can provoke serious punishment in some countries. Accordingly, trait impulsiveness is negatively associated with political content sharing behaviour. We also found that collective opinion moderates people’s planned behaviour, but not their unplanned behaviour. In other words, personality traits are unaffected by others’ opinions, but traits that humans can control can be shaped by others’

Design of Review Systems – A Strategic Instrument to shape Online Reviewing Behavior and Economic Outcomes

As online reviews play a decisive role in consumers’ purchase decisions, e-commerce platforms are using review systems strategically to obtain a competitive advantage. However, the strategic potential can only be leveraged if the review system is designed appropriately. Research on the design of review systems and the effects of design choices has not yet been summarized or synthesized in a review article. We aim to close this gap by providing a scoping review. In our synthesis we posit that the design of review systems moderates the impact of online reviews on economic outcomes and the factors that drive the formation of reviews. After reviewing current research findings, we identify gaps and provide a research agenda covering three key themes: Design features, environments, and devices

Online impulse buying behavior model for social commerce in Malaysian context

The rapid development in Web 2.0 technologies and social media has given rise to a new business model—social commerce (S-commerce)—that emphasizes social aspects. Consequently, the consumer‘s online impulse buying behavior (IBB) increases and contributes significantly to S-commerce revenues. In line with this, an understanding of such behavior is critical for E-retailers to strengthen competitiveness, enhance revenue, and ensure business sustainability in today‘s challenging business environment. Most of the previous studies have utilized urge to buy impulsively (UBI) as a proxy to examine the consumer‘s online IBB instead of the actual IBB. As a result, the number of research that distinguishes between UBI and IBB is minimal. These limited studies have only focused on the impact of consumer characteristics, websites-related factors, and marketing factors. However, they neglected the important role of social-related factors on IBB. Furthermore, because the majority of the previous studies adopted the stimulus-organism-response (S-O-R) framework, building a model based on a theoretical foundation to investigate online IBB in the context of S-commerce is another challenge. The main aim of this study is to examine the impact of social-related factors on triggering online IBB in S-commerce websites. To investigate the social elements influencing online IBB, this study integrated the Social Influence Theory (SIT) and the Uses and Gratifications Theory (UGT). A quantitative approach was applied to test fifteen hypotheses of the proposed model by surveying 365 consumers from four Malaysian S-commerce websites. The Partial Least Squares-Structural Equation modeling (PLS-SEM) technique was employed to analyze the survey data. The findings indicate that compliance, identification, internalization, entertainment, purposive value, and maintaining interpersonal connectivity significantly influence IBB. It was also discovered that UBI has significant moderating effects on the relationships between compliance, identification, internalization, and IBB. Moreover, it was also identified that impulsiveness has served as a moderator in the relationships between compliance, internalization, and UBI. Theoretically, this study has enriched the understanding of online IBB in S-commerce. It also has practical implications on S-commerce designers, marketers, and managers in terms of regulating online IBB

Customers’ loyalty model in the design of e-commerce recommender systems

Recommender systems have been adopted in most modern online platforms to guide users in finding more suitable items that match their interests. Previous studies showed that recommender systems impact the buying behavior of e-commerce customers. However, service providers are more concerned about the continuing behavior of their customers, specifically customers’ loyalty, which is an important factor to increase service providers’ share of wallet. Therefore, this study aimed to investigate the customers’ loyalty factors in online shopping towards e-commerce recommender systems. To address the research objectives, a new research model was proposed based on the Cognition-Affect-Behavior model. To validate the research model, a quantitative methodology was utilized to gather the relevant data. Using a survey method, a total of 310 responses were gathered to examine the impacts of the identified factors on customers’ loyalty towards Amazon’s recommender system. Data was analysed using Partial Least Square Structural Equation Modelling. The results of the analysis indicated that Usability (P=0.467, t=5.139, p<0.001), Service Interaction (P=0.304, t=4.42, p<0.001), Website Quality (P=0.625, t=15.304, p<0.001), Accuracy (P=0.397, t=6.144, p<0.001), Novelty (P=0.289, t=4.406, p<0.001), Diversity (P=0.142, t=2.503, p<0.001), Recommendation Quality (P=0.423, t=7.719, p<0.001), Explanation (P=0.629, t=15.408, p<0.001), Transparency (P=0.279, t=5.859, p<0.001), Satisfaction (P=0.152, t=3.045, p<0.001) and Trust (P=0.706, t=14.14, p<0.001) have significant impacts on customers’ loyalty towards the recommender systems in online shopping. Information quality, however, did not affect the quality of the website that hosted the recommender system. The findings demonstrated that accuracy-oriented measures were insufficient in understanding customer behavior, and other quality factors, such as diversity, novelty, and transparency could improve customers’ loyalty towards recommender systems. The outcomes of the study indicated the significant impact of the website quality on customers’ loyalty. The developed model would be practical in helping the service providers in understanding the impacts of the identified factors in the proposed customers’ loyalty model. The outcomes of the study could also be used in the design of recommender systems and the deployed algorithm