Julián Chaparro-Peláez

Barriers and drivers for non-shoppers in B2C e-commerce: A latent class exploratory analysis

The barriers and drivers of e-shopping, as well as segmentation and behavior of e-shoppers, have been long studied in the last two decades, but the behavior of non-shoppers in business-to-consumer (B2C) e-commerce is still an open field for research which has seldom been dealt with. Our exploratory study has as its main objective the classification of non-shoppers in B2C e-commerce based on the barriers which keep deterring them from purchasing on the Internet and the drivers which might lead them to engage in e-shopping. In order to achieve this goal, data was gathered from 1499 Spanish respondents from a nationwide household panel survey. The responses were analyzed using a latent class analysis (LCA) approach and the results show four different types of non-shoppers based on the barriers for online shopping, while six different groups were identified based on the drivers to start shopping on the Internet. Implications for research and practice from the findings of the study are discussed in the final section.

Perceived Compatibility and the Adoption of B2C E-Commerce by Non-buyers

The purpose of this article is to study the factors which affect the intention to buy online for users who have never made any prior purchase using the electronic channel, with special focus to the role of perceived compatibility. To do so, an adoption model for e-commerce was proposed on the basis of the Technology Adoption Model (TAM) and the Innovations Diffusion Theory (IDT). In order to validate the model, data gathered amongst Spanish consumers who had no experience using e-commerce, were analyzed using the partial least squares (PLS) method. The results have demonstrated the importance of perceived compatibility as a significant factor to foster non-buyers’ adoption of electronic business-to-consumer e-commerce (eB2C).

The role of emotions and trust in service recovery in business-to-consumer electronic commerce

This study proposes a service recovery model to describe how cumulative satisfaction, loyalty and word-of-mouth are affected by complaints. The model is based on the role of positive and negative emotions in satisfaction with service recovery processes, with trust acting as a mediator of the relationship between satisfaction with service recovery and cumulative satisfaction, and between positive and negative emotions, satisfaction with service recovery and loyalty. The sample for this study consists of 303 business-to-consumer e-commerce users who made a complaint after an electronic transaction. The results show that positive emotions are a key factor in satisfaction with service recovery processes; this is in contrast to the major role that negative emotions have traditionally played in these models. Furthermore, trust mediates the relationship between satisfaction with service recovery and cumulative satisfaction, and between positive emotions and loyalty. Trust has an important influence on loyalty, and cumulative satisfaction is a strong predictor of word-of-mouth. While prior satisfaction with service recovery studies usually investigated only negative emotions and satisfaction with a specific transaction, this research considers both positive and negative emotions, as well as the mediating effect of trust on the relationship between satisfaction with a specific transaction and cumulative satisfaction.

A Web Services-Based Application for LMS Data Extraction and Processing for Social Network Analysis

The emergence of learning analytics as a discipline of its own has given way to a diverse subset of research fields offering very different approximations to the topic. One of the most recent and active approaches is social learning analytics, which focuses primarily on the application of social network analysis (SNA) techniques and visualizations to study and help understanding interactions in online courses as a key pillar of social construction of learning. However, and despite this interest, current tools for analysis and visualization are very limited for advanced social learning analytics, and SNA applications cannot directly process data from learning management systems. This paper presents a technical view of the design and implementation of a web services-based application that aims to overcome these limitations by extracting and processing educational data about forum interactions in online courses to generate the corresponding social graphs and enable advanced social network analysis on SNA software.

Revisiting the SEC Classification Framework in Traditional and Electronic Commerce

The recent growth of e-commerce sales has its cause not only on the increase of products sold, but also on the incorporation of new products which were previously not selling well on the online channel. An outstanding example of these products in Spain is clothing, which has rocketed from a marginal position in online sales to the fifth place of the top selling products over the Internet. This is an interesting case because clothing has conventionally been classified as an Experience product within the SEC (Search, Experience, Credence) classification framework, which proposes product segmentation based on the ability of consumers to identify the characteristics and attributes of the products before and after their purchase and use. This situation raises the question whether the SEC classification is still valid today and if there are changes in consumer perceptions about which segment the different products are categorized into. In order to answer these questions, a selection of 26 products was made by e-commerce experts; then, 204 undergraduate and graduate students were asked to classify those products within the SEC framework, and to declare their purchase intentions and actual shopping behavior for each product, both in the online and traditional channel. The findings from this study suggest that the SEC classification is still valid in electronic commerce but not in traditional retail stores. Moreover, the study detected actual changes in the customer perceptions of the nature of some products within the SEC classification.

A Comparative Study on the Influence between Interaction and Performance in Postgraduate In-Class and Distance Learning Courses Based on the Analysis of LMS Logs

Learning Management Systems’ use has been rapidly increasing during the last ten years, mainly in online distance learning courses but also in in-class courses. In parallel, technological advances have made it possible to track and store all the activity taking place in the LMS, and therefore to register the participation and interaction of students. This paper addresses two key questions: a) Is student interaction in the LMS an indicator of the final academic performance in a course?; and b) Is this interaction carried out in a different way in distance and in-class education, with different final results?. In order to answer this question, different types of interaction have been classified and extracted from Moodle LMS activity record logs during two years in one master program with online distance learning and in-class learning modalities at the Universidad Politecnica de Madrid. The results show partial or no evidence of influence between interaction indicators and academic performance. The last section of this study covers a discussion of results and implications.

Predicting teamwork group assessment using log data-based learning analytics

Abstract The application of learning analytics techniques to log data from Learning Management Systems (LMS) has raised increasing interest in the past years. Advances in this field include the selection of adequate indicators and development of research frameworks. However, most research has focused on individual students, which has hampered the development of learning analytics for team assessment in collaborative learning contexts. From a four-dimensional view of teamwork, this study proposes a set of log data-based indicators to facilitate group assessment in project-based learning courses, and identify relevant predictors of final project results.

An Analysis of the Impact of 2.0 Tools and Technologies on Organizational Processes

In this paper, the authors present results from a study on the usage rate of 2.0 tools and technologies among Spanish enterprises. The main objective of the study is to analyze, from the perceptions of executives, the influence of social software tools on a set of business processes. This analysis has been made using two graphic tools: the “2.0 Success Matrix†and the “Tool’s Footprint†. Both the review of literature and the empirical work have lead to important findings and conclusions.

An Analysis of the Use of Social Software and Its Impact on Organizational Processes

This article proposes a study on the implementation rate of the most relevant 2.0 tools and technologies in Spanish enterprises, and their impact on 12 important aspects of business processes. In order to characterize the grade of implementation and the perceived improvements on the processes two indexes, Implementation Index and Impact Rate, have been created and displayed in a matrix called ”2.0 Success Matrix”. Data has been analyzed from a survey taken to directors and executives of large companies and small and medium businesses.