With big data growing rapidly in importance over the past few years', academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, there has been an emphasis on the technical aspects of big data with limited attention on the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies and have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a theoretical discussion leading up to a research framework that can help explain the mechanisms through which big data lead to competitive performance gains. The research framework is grounded on past empirical work on IT-business, and builds on the resource-based view (RBV) and dynamic capabilities view (DCV) of the firm. By identifying the main areas of focus for big data and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of a competitive advantage. Response to Reviewers:The authors would like to thank the two anonymous reviewers for their constructive comments and feedback. In the new version of the manuscript we have incorporated the best we could the suggestion put forward. Specifically, these include:# The manuscript has been professionally copy-edited so the clarity and meaning are more clearly conveyed # The second and third paragraph of the introduction have been revised# We have more clearly defined the significance for IT/management research

No abstract

No abstract

Purpose – Satisfaction and experience are essential ingredients for successful customer retention. This study aims to verify the moderating effect of experience on two types of relationships: the relationship of certain antecedents with satisfaction, and the relationship of satisfaction with intention to repurchase. Design/methodology/approach – This paper applies structural equation modelling (SEM) and multi-group analysis to examine the moderating role of experience in a conceptual model estimating the intention to repurchase. Responses from 393 people were used to examine the differences between high- and low-experienced users of online shopping. Findings – The research shows that experience has moderating effects on the relationships between performance expectancy and satisfaction and satisfaction and intention to repurchase. This study empirically demonstrates that prior customer experience strengthens the relationship between performance expectancy and satisfaction, while it weakens the relationship of satisfaction with intention to repurchase. Practical implications – Practitioners should differentiate the way they treat their customers based on their level of experience. Specifically, the empirical research demonstrates that the expected performance of the online shopping experience (performance expectancy) affects satisfaction only on high-experienced customers. Instead, the effort needed to use online shopping (effort expectancy) and the user's belief in own abilities to use online shopping (self-efficacy) influence satisfaction only on low-experienced customers. The effect of trust and satisfaction is significant on online shopping behaviour on both high- and low-experienced customers. Originality/value – This paper investigates how different levels of experience affect customers' satisfaction and online shopping behaviour. It is proved that experience moderates the effect of performance expectancy on satisfaction and the effect of satisfaction on intention to repurchase. It also demonstrates that certain effects (effort expectancy and performance expectancy) are valid for only one of the two examined groups, while only one effect (trust) is valid for both (high- and low-experienced).

As the fields of learning analytics and learning design mature, the convergence and synergies between these two fields became an important area for research. This paper intends to summarize the main outcomes of a systematic literature review of empirical evidence on learning analytics for learning design. Moreover, this paper presents an overview of what and how learning analytics have been used to inform learning design decisions and in what contexts. The search was performed in seven academic databases, resulting in 43 papers included in the main analysis. The results from the review depict the ongoing design patterns and learning phenomena that emerged from the synergy that learning analytics and learning design impose on the current status of learning technologies. Finally, this review stresses that future research should consider developing a framework on how to capture and systematize learning design data grounded in learning analytics and learning theory, and document what learning design choices made by educators influence subsequent learning activities and performances over time.