Factors influence the choice of Word-of-Mouth recommendation sources in online purchase decisions: with special reference to the tourism and hospitality industry in Sri Lanka

Purpose: Word-of-Mouth is a strong communication tool which is perceived as a credible source by consumers.  With the internet penetration now WOM is occurring in e-platforms such as blogs, social network sites etc. This has given birth to electronic Word-of-Mouth or e-WOM. Though WOM may occur from various recommendation sources based on various factors the choice of the WOM recommendation source may vary. The research focuses on three objectives. They are to identify the relationship between the perceived task difficulty and the tie-strength of the recommendation source when making an online purchase decision, to identify the relationship between consumer knowledge and perceived task difficulty and to identify the relationship between internet experience and perceived task difficulty. Design/methodology/approach: Data was collected through a questionnaire. A sample of 140 respondents was used to collect information and convenience sampling method was used. Findings: Results revealed that consumers tend to reach out to both strong ties and weak ties irrespective of the perceived task difficulty. Further, as consumer subjective knowledge and internet experience increases perceived task difficulty reduces. Originality: Although studies have been conducted regarding WOM, no studies have been conducted to understand the role of recommendation sources when making an online purchase decision in the hospitality and tourism industry in the Sri Lankan context. Implications: Thus word -of -mouth communication should be considered as a part of the communication mix and organizations in the tourism and hospitality industry should focus on using opinion leaders to promote products. Keywords: WOM, eWOM, Perceived Task Difficulty, Internet Experience, Recommendation Source

A RECOMMENDER MODEL USING SOCIAL TIE STRENGTH FOR THE CHUNK LEARNING SYSTEM

With the onset of COVID-19, rising tuition costs, and technological advancements, online courses have become a pervasive medium through which education is conducted. Currently, several online educational services tailor education to students through various methods of recommender models. One such system, the Curated Heuristic Using a Network of Knowledge (CHUNK) Learning, developed at the Naval Postgraduate School, uses a recommender system that relies on user profile attributes. We propose a complementary recommendation system to expand upon CHUNK's current recommender method by incorporating implicit recommendations from a user's social network based on tie strength between learners. In this work, we create a synthetic social network of learners and calculate the Jaccard Index and Pearson Correlation Coefficient similarity values to distinguish between strong and weak social ties. These tie classifications are then used to personalize content recommendations and expose users to greater breadth or depth of applicable knowledge based on current interests or job goals. We simulate recommendations for a user under different circumstances and show that our recommender system promotes the algorithmic formation of communities of learners on similar educational tracks. This promotes the social-emotional support for online learners that they may not currently receive and improves socialization within distance learning.Ensign, United States NavyApproved for public release. Distribution is unlimited

A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation

Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that affects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical influence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. The experimental results demonstrate the effectiveness of fusing both geographical influence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach attains a 37% improvement in MRR over a recently proposed approach that identifies negative venues only from social links

Network Triads: Transitivity, Referral and Venture Capital Decisions in China and Russia

This article examines effects of dyadic ties and interpersonal trust on referrals and investment decisions of venture capitalists in the Chinese and Russian contexts. The study uses the postulate of transitivity of social network theory as a conceptual framework. The findings reveal that referee-venture capitalist tie, referee-entrepreneur tie, and interpersonal trust between referee and venture capitalist have positive effects on referrals and investment decisions of venture capitalists. The institutional, social and cultural differences between China and Russia have minimal effects on referrals. Interpersonal trust has positive effects on investment decisions in Russia.http://deepblue.lib.umich.edu/bitstream/2027.42/40138/3/wp752.pd

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)

Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of preventing and reducing substance abuse. While effective, unfortunately, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. This paper investigates the problem of optimizing the social influence related to the deviant behavior via careful construction of the intervention groups. We propose a Mixed Integer Optimization formulation that decides on the intervention groups, captures the impact of the groups on the structure of the social network, and models the impact of these changes on behavior propagation. In addition, we propose a scalable hybrid meta-heuristic algorithm that combines Mixed Integer Programming and Large Neighborhood Search to find near-optimal network partitions. Our algorithm is packaged in the form of GUIDE, an AI-based decision aid that recommends intervention groups. Being the first quantitative decision aid of this kind, GUIDE is able to assist practitioners, in particular social workers, in three key areas: (a) GUIDE proposes near-optimal solutions that are shown, via extensive simulations, to significantly improve over the traditional qualitative practices for forming intervention groups; (b) GUIDE is able to identify circumstances when an intervention will lead to deviancy training, thus saving time, money, and effort; (c) GUIDE can evaluate current strategies of group formation and discard strategies that will lead to deviancy training. In developing GUIDE, we are primarily interested in substance use interventions among homeless youth as a high risk and vulnerable population. GUIDE is developed in collaboration with Urban Peak, a homeless-youth serving organization in Denver, CO, and is under preparation for deployment