Learning analytics beyond the LMS : the Connected Learning Analytics toolkit

We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. Core to the toolkit is the notion of learner access to their own data. A number of implementational issues are discussed, and an ontology of xAPI verb/object/activity statements as they might be unified across 7 different social media and online environments is introduced. After considering some of the analytics that learners might be interested in discovering about their own processes (the delivery of which is prioritised for the toolkit) we propose a set of learning activities that could be easily implemented, and their data tracked by anyone using the toolkit and a LRS.

Analysing reflective text for learning analytics : an approach using anomaly recontextualisation

Reflective writing is an important learning task to help foster reflective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.

Not just what they want, but why they want it: Traditional market research to deep customer insights

Purpose This paper explores advantages and disadvantages of both traditional market research and deep customer insight methods in order to lay the platform for revealing how a relationship between these two domains could be optimised during firm-based innovation. Design/methodology/approach The paper reports on an empirical research study conducted with thirteen Australian based firms engaged in a design-led approach to innovation. Firms were facilitated through a design-led approach where the process of gathering deep customer insights was isolated and investigated further in comparison to traditional market research methods. Findings Results show that deep customer insight methods are able to provide fresh, non-obvious ways of understanding customer needs, problems and behaviours that can become the foundation of new business opportunities. Findings concluded that deep customer insights methods provide the critical layer to understand why customers do and don’t engage with businesses. Revealing why was not accessible in traditional market research methods. Research limitations/implications The theoretical outcome of this study is a complementary methods matrix, providing guidance on appropriate implementation of research methods in accordance with a project’s timeline to optimise the complementation of traditional market research methods with design-led customer engagement methods. Practical implications Deep customer insight methods provide fresh, non-obvious ways of understanding customer needs, problems and behaviours that can become the foundation of new business opportunities. It is hoped that those in a position of data collection are encouraged to experiment and use deep customer insight methods to connect with their customers on a meaningful level and translate these insights into value. Originality/value This paper provides original value to a new understanding how design techniques can be applied to compliment and strengthen existing market research strategies. This is crucial in an era where business competition hinges on a subtle and often intimate understanding of customer needs and behaviours.

High-performance computing for data analytics

One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques.

Improved residential electricity demand management through analysis of the customer perspective

Peak electricity demand requires substantial investment to update transmission, distribution and generation infrastructure. A successful community peak demand reduction project was examined to identify residential consumer motivational and contextual factors involved in their decision to adopt/not adopt interventions. Energy professionals actively worked to achieve community 'peer' membership and by becoming a trusted information source, facilitated voluntary home energy assessment requests from over 80% of the residential community. By combining and tailoring interventions to the specific needs and motivations of individual householders and the community, interventions promoting energy conservation and efficiency can be effective in achieving sustained reduction in peak demand.

Re-examining online customer experience to include purchase frequency and perceived risk

This research examines the important emerging area of online customer experience (OCE) using data collected from an online survey of frequent and infrequent online shoppers. The study examines a model of antecedents for cognitive and affective experiential states and their influence on outcomes, such as online shopping satisfaction and repurchase intentions. The model also examines the relationships between perceived risk, trust, satisfaction and repurchase intentions. Theoretically, the study provides a broader understanding of OCE, through insights into two shopper segments identified as being important in e-retailing. For managers, the study highlights areas of OCE and their implications for ongoing management of the online channel.

Customer-driven innovation: Digital channels and community

This study investigates the role of digital channels in community-led businesses by exploring two case study companies, Uber and Airbnb. At present, these community-led businesses are disrupting traditional industries by connecting with customers via digital channels and facilitating transactions between two parties. A deductive structured qualitative content analysis approach utilising a predetermined categorization matrix was implemented to decipher the digital channels used by both companies. The results discovered that both company’s digital channels push the customer to their core channel, allowing, customers to create their own physical, largely self-governed communities. However, little research exists which explores and analyses the role of digital channels in forming community-led businesses. Therefore, this paper aims to instigate future research and discussion in this emerging area by concluding with future research agendas.

Customer agility, smart shopping apps and their implications: Customer relationship management systems in the digital age

This thesis examines the complementarities and vulnerabilities of customer connectivity that contemporary firms achieved through ubiquitous digital technologies. Taking the example of deployment of smart shopping apps to connect with consumers in the context of Australian retail, the study examines how such customer connectivity positively influences firm performances through firm's customer agility whilst creating implications for firms' digital business strategy through altered customer cognitions. Employing Oliver's (1977) Expectation Confirmation Theory, this study empirically tests a conceptual model involving digital connectivity, digital expectations, experiences and satisfaction of the customers who uses smart shopping apps in Australian consumer retail.

Integration of Opinion Mining into customer analysis model

This research proposes a multi-dimensional model for Opinion Mining, which integrates customers' characteristics and their opinions about products (or services). Customer opinions are valuable for companies to deliver right products or services to their customers. This research presents a comprehensive framework to evaluate opinions' orientation based on products' hierarchy attributes. It also provides an alternative way to obtain opinion summaries for different groups of customers and different categories of produces.

A service-oriented architecture for text analytics enabled business applications

Experiences showed that developing business applications that base on text analysis normally requires a lot of time and expertise in the field of computer linguistics. Several approaches of integrating text analysis systems with business applications have been proposed, but so far there has been no coordinated approach which would enable building scalable and flexible applications of text analysis in enterprise scenarios. In this paper, a service-oriented architecture for text processing applications in the business domain is introduced. It comprises various groups of processing components and knowledge resources. The architecture, created as a result of our experiences with building natural language processing applications in business scenarios, allows for the reuse of text analysis and other components, and facilitates the development of business applications. We verify our approach by showing how the proposed architecture can be applied to create a text analytics enabled business application that addresses a concrete business scenario. © 2010 IEEE.

The marketing blog competition: Integrating educational blogging and analytics in the classroom

How should marketing educators teach today’s technologically savvy college students the latest knowledge as well as relevant soft and hardskills for employment in a world of Web 2.0? The changing environment requires the development of innovative pedagogical approaches to enhance students’ experiential learning. Recent research has focused on the idea of implementing technology and the adoption of educational blogging in the marketing curriculum. This paper outlines a semesterlong marketing blog competition, in which students had to (1) create and maintain a marketing blog and (2) apply web analytics to analyze, manage and improve their blog performance based on key performance indicators. This article offers a detailed discussion of the design and implementation as well as the outcomes based on quantitative and qualitative student feedback.

From ‘free’ to fee: Acceptability of airline ancillary fees and the effects on customer behavior

Businesses in various consumer service industries have begun to unbundle their service offerings by introducing numerous fees for products and services that were previously provided as “free.” Anecdotal evidence in the media indicates that these fees cause widespread public displeasure, frustration, and outrage. This paper develops a framework of fee acceptability, negative emotions, and dysfunctional customer behavior, which is tested using data from the airline industry. Findings identify the strongest effects on betrayal in the case of baggage fees, followed by charges for comfort. Also, betrayal has a direct effect on complaining, whereas anger mediates the relationship between betrayal and negative word of mouth.

Quality in group service encounters: A theoretical exploration of the concept of a simultaneous multi‐customer co‐creation process

Purpose The purpose of this paper is to explore the concept of service quality for settings where several customers are involved in the joint creation and consumption of a service. The approach is to provide first insights into the implications of a simultaneous multi‐customer integration on service quality. Design/methodology/approach This conceptual paper undertakes a thorough review of the relevant literature before developing a conceptual model regarding service co‐creation and service quality in customer groups. Findings Group service encounters must be set up carefully to account for the dynamics (social activity) in a customer group and skill set and capabilities (task activity) of each of the individual participants involved in a group service experience. Research limitations/implications Future research should undertake empirical studies to validate and/or modify the suggested model presented in this contribution. Practical implications Managers of service firms should be made aware of the implications and the underlying factors of group services in order to create and manage a group experience successfully. Particular attention should be given to those factors that can be influenced by service providers in managing encounters with multiple customers. Originality/value This article introduces a new conceptual approach for service encounters with groups of customers in a proposed service quality model. In particular, the paper focuses on integrating the impact of customers' co‐creation activities on service quality in a multiple‐actor model.

The link between marketing instruments and customer perceptions

Measuring marketing effectiveness is a major challenge for marketing academics and practitioners. An investigation that uses dependence analysis to examine the link between objective marketing instruments and subjective customer perceptions of them is missing from existing approaches. In examining this link, the present article redresses this deficit. A conceptual model is developed in order to structure and explain the effects of instruments on perceptions for 3 marketing instrument categories (value, relationship, and brand). The links are analyzed in an empirical study with bank customers. Instruments are measured by using objective internal data. Perceptions are measured based on a customer survey. The relationships between instruments and perceptions are examined using regression analysis. Most of the effects of instruments on perceptions are significant, signaling that the activities conducted are perceived as intended. In particular, some interesting results become evident, such as the negative effect on relationship perceptions of follow-up calls subsequent to direct mail services.