Ifeoma Adaji

Perceived Effectiveness, Credibility and Continuance Intention in E-commerce: A Study of Amazon

The use of persuasive strategies has been identified as one means through which e-businesses can engage their existing clients and make new ones. To contribute to ongoing research in this area, we extend our previous study where Amazon was evaluated as a persuasive system using the Persuasive System’s Design (PSD) framework. In the current study, we further investigate the factors that affect the perceived effectiveness, credibility and continuance intention for use of e-commerce systems through the prism of the PSD framework. Using Amazon as a case study and a sample size of 324 Amazon shoppers, we develop and test a research model using partial least-squares structural equation modelling (PLS-SEM) analysis. Our results show that perceived effectiveness of an e-commerce company like Amazon is a great predictor of continuance intention. In addition, social support and primary task support are strong predictors of perceived effectiveness. Furthermore, dialogue support significantly influences perceived product credibility and perceived review credibility and both constructs are strong predictors of system credibility. These findings suggest possible design guidelines in the development of successful e-commerce sites.

Persuasive Patterns in Q&A Social Networks

Social networks differ in their structure and objectives, hence, the effectiveness of persuasion principles or patterns may vary from one type of network to another. This study aims to identify the persuasive principles that make a typical Q&A network successful. Using Stack Overflow as a case study, we applied the PSD model to evaluate the persuasive principles of the network. Our results show that all but four principles of the PSD model were implemented in Stack Overflow. This study can help social network developers build persuasive Q&A social networks and improve on existing social platforms.

Recommending Programming Languages by Identifying Skill Gaps Using Analysis of Experts. A Study of Stack Overflow

The increasing variety of programming languages available to computer programmers has led to the discussion of what language(s) should be learned. A key point in the choice of a programming language is the availability of support from experienced programmers. In this paper, we explore the use of graph theory in recommending programming languages to novice and expert programmers in a question and answer collaborative learning environment, Stack Overflow. Using social network analysis techniques, we investigate the relationship between experts (using an expertise graph) in different programming languages to identify what languages can be recommended to novice and experienced programmers. In addition, we explore the use of the expertise graph in inferring the importance of a programming language to the community. Our results suggest that programming languages can be recommended within organizational borders and programming domains. In addition, a high number of experts in a programming language does not always mean that the language is popular. Furthermore, disconnected nodes in the expertise graph suggest that experts in some programming languages are primarily on Stack Overflow to support that language only and do not contribute to questions or answers in other languages. Finally, developers are comfortable with mastering a single, general purpose language. The results of our study can help educators and stakeholders in computer education to understand what programming languages can be suggested to students and what languages can be taught and learned together.

Towards Understanding User Participation in Stack Overflow Using Profile Data

When designing a Q&A social network, it is essential to know what profile elements are necessary to build a complete profile for a user. Using data from Stack Overflow, we examined the profile data of users in order to determine the relationship between a complete profile (one that has values for each profile element: website URL, location, about me, profile image and age) and their contribution to the network in terms of reputation scores and quality of question and answer posts. Our analysis shows that most users do not have a complete profile, however the average reputation earned by users with complete profiles is significantly higher than that earned by users with incomplete profiles. In addition, users with complete profiles post higher quality question and answers, hence are more useful to the network. We also determined that, of the five profile elements studied, location and about me have a higher correlation than the others. This research is a step in determining what profile elements are important in a typical Q&A social network and which of these elements should regularly be used together.

How buyers perceive the credibility of advisors in online marketplace: review balance, review count and misattribution

In an online marketplace, buyers rely heavily on reviews posted by previous buyers (referred to as advisors). The advisor’s credibility determines the persuasiveness of reviews. Much work has addressed the evaluation of advisors’ credibility based on their static profile information, but little attention has been paid to the effect of the information about the history of advisors’ reviews. We conducted three sub-studies to evaluate how the advisors’ review balance (proportion of positive reviews) affects the buyer’s judgement of advisor’s credibility (e.g., trustworthiness, expertise). The result of study 1 shows that advisors with mixed positive and negative reviews are perceived to be more trustworthy, and those with extremely positive or negative review balance are perceived to be less trustworthy. Moreover, the perceived expertise of the advisor increases as the review balance turns from positive to negative; yet buyers perceive advisors with extremely negative review balance as low in expertise. Study 2 finds that buyers might be more inclined to misattribute low trustworthiness to low expertise when they are processing high number of reviews. Finally, study 3 explains the misattribution phenomenon and suggests that perceived expertise has close relationship with affective trust. Both theoretical and practical implications are discussed.

Predicting Churn of Expert Respondents in Social Networks Using Data Mining Techniques: A Case Study of Stack Overflow

In Q&A social networks, the few respondents that answer most of the questions are an asset to that network. Being able to predict the churn of these expert respondents will enable the owners of such network put things in place in order to keep them. In this paper, we predicted the churn of expert respondents in Stack Overflow. We identified experts based on the InDegree of the respondents and the value of the incentives earned by these experts from the questions they have answered in the past. Using four data mining techniques: logistic regression, neural networks, support vector machines and random forests, we predicted user churn and evaluated our results with four evaluation metrics: percentage correctly classified, area under receiver operating characteristic curve, precision and recall. Of the four data mining algorithms, random forests performed best with PCC of 76%, ROC area of 0.82, precision of 0.76 and recall of 0.77.

Susceptibility to Persuasive Strategies: A Comparative Analysis of Nigerians vs. Canadians

Personalizing persuasive technologies (PTs) is one of the hallmarks of a successful PT intervention. However, there is a lack of understanding of how Africans and North Americans differ or are similar in the susceptibility to persuasive strategies. To bridge this gap, we conducted a cross-cultural study among 284 subjects to investigate the moderating effect of culture on the susceptibility of users to Cialdinis principles of persuasion. Specifically, using Nigeria and Canada as a case study, we investigated how both groups vary in their levels of susceptibility to Authority, Commitment, Consensus, Liking, Reciprocity and Scarcity. The results of our analysis show that Nigerians are more susceptible to Authority and Scarcity than Canadians, while Canadians are more susceptible to Reciprocity, Liking and Consensus than Nigerians. However, both groups do not differ with respect to Commitment (the most persuasive strategy). Finally, we discussed our findings and mapped the most persuasive Cialdinis principles in each group to implementable persuasive strategies in the PT domain.

The Effect of Gender and Age on the Factors That Influence Healthy Shopping Habits in E-Commerce

People typically eat what they shop for; if consumers shop for healthy foods, they will likely eat healthy foods. In order to influence healthier eating habits among consumers, it is important to identify the factors that influence them to shop for healthy foods. To contribute to ongoing research in this area, we explore the influence of commonly used e-commerce strategies: personality, persuasive strategies, social support, relative price, and perceived product quality on healthy shopping habits among e-commerce shoppers. Research has shown that personalizing these strategies makes them more effective in achieving the desired behavior change among users. Age and gender have been identified as factors that can be used for group-based personalization. We thus investigate the moderating effect of age and gender on the factors that influence healthy shopping habits in e-commerce shoppers. To achieve this, we carried out an online study of 244 e-commerce shoppers. Using partial least squares structural equation modeling (PLS-SEM), we developed a path model using the commonly used e-commerce factors: personality, persuasive strategies, social support, relative price, and perceived product quality. The result of our analysis suggests that social support, relative price and perceived product quality significantly influence healthy shopping habits in e-commerce shoppers. In addition, females are more influenced by social support to adopt healthy shopping habits compared to male e-shoppers. Furthermore, older shoppers are more influenced by social support to adopt healthy shopping habits, while the younger shoppers are more influenced by the relative price of products.

Personality Based Recipe Recommendation Using Recipe Network Graphs

There is usually a vast amount of information that people have to sift through when searching for recipes online. In addition to looking at the ingredient list, people tend to read the reviews of recipes to decide if it is appealing to them based on the feedback of others who have prepared the recipe, with some recipes having hundreds of reviews. Several researchers have proposed recipe-based recommendation systems using details such as the nutritional information of the recipe, however, such recommendations are not personalized to the characteristics of the user. To contribute to research in this area, we propose a personalized recommendation system that makes suggestions to users based on their personality. People of the same personality tend to have many similarities, and personality is a predictor of behavior, we thus propose that the use of personality types could make recommendations more personalized. In this paper, we present the result of a preliminary investigation into the use of the personality of reviewers of recipes and a recipe-based network graph in recommending recipes to users.

Consumers’ Need for Uniqueness and the Influence of Persuasive Strategies in E-commerce

We explore the use of persuasion and need for uniqueness in the continuance intention of e-commerce shoppers. In particular, we examine if Cialdini’s six influence strategies have an effect on the three dimensions of need for uniqueness and if need for uniqueness further influences continuance intention of e-commerce shoppers. To achieve this, we carried out a study of 183 e-commerce shoppers. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), we developed a hypothetical path model using the data from the study. Our results show that the three dimensions of need for uniqueness explain about 22% of the variance in continuance intention of e-commerce shoppers. In addition, scarcity had the highest influence on the three dimensions of need for uniqueness. We further carried out a multi group analysis based on gender. Our results suggest that scarcity influences the decision of females to buy products that are not only unique, but also socially acceptable, while commitment influences males to buy unique and socially acceptable products.