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Since the arrival of mass production, commodification has been plaguing markets – none more so than that for music. By separating production and consumption in space and time, commodification challenges the very conditions underlying economic exchange. This chapter explores authenticity as the institutional response to the commodification of music, rekindling the relationship between isolated market participants in the increasingly digitized world of music. Building upon the “Production of Culture” perspective, we unpack the commodification of music across five different institutional realms – (1) production, (2) consumption, (3) selection, (4) appropriation, and (5) classification – and provide a thoroughly relational account of authenticity as an institutional practice.

This Chapter is all about communication and the ways we are now able to reach out to others around the world from our personal computers or mobile devices, which were never available before. One might initially consider this section more in line with productivity tools instead of those impacting the digital humanities. I will, however, demonstrate that it is through these tools that the field is expanding, offering interesting ways in which scholars can communicate ideas with one another, share thoughts, research, and collaborate. Additionally, it is through the use of these tools that our ideas are being shared with students and interestingly how students are, in turn, reciprocating our efforts. The chapter focuses on video broadcasting tools, audio conferencing, audiocasting, and collaboration applications, offering examples of how they can be used in a classroom setting.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Information and Communication Technology offers powerful Web 2.0 tools that can benefit learners with different learning preferences. The rise of video streaming, the increased proliferation of ‘on demand’ televisual media and new smartphone streaming opportunities have generated a range of web-based media that may usefully support teachers and learners in accommodating these varied learning styles. At the same time, media streaming technologies such as YouTube have distinct drawbacks for students, teachers and their institutions, particularly in relation to appropriate content and the ethical issues around the uploading of student materials to a public repository.

Two studies are reported. In Study 1, two case studies of how teachers engaged students with a media-streaming system called Box of Broadcasts (BoB) are discussed using principles of design-based research. The result from the first case study indicated that BoB provided an improved efficiency for teachers who filmed students’ presentations in a second language. The second case study illustrated how the integration of BoB into their classroom teaching led a psychology teacher to think differently about students and the design and delivery of teaching and learning resources. In Study 2, the use of a qualitative semi-structured interview approach with eight teachers indicated that staff felt that BoB was beneficial in supporting pedagogic practice. Furthermore, staff highlighted the opportunities for dialogue about theory, reality and practice that video materials offered to students as added value. Key limitations for some staff in their use of BoB as a support for video-enriched pedagogic practice were the restricted level of available content on BoB, some difficulties relating to the skills required for creating and using clips and technical stability when using clips.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

This paper deals with the organizing of interactive product development. Developing products in interaction between firms may provide benefits in terms of specialization, increased innovation, and possibilities to perform development activities in parallel. However, the differentiation of product development among a number of firms also implies that various dependencies need to be dealt with across firm boundaries. How dependencies may be dealt with across firms is related to how product development is organized. The purpose of the paper is to explore dependencies and how interactive product development may be organized with regard to these dependencies.

The analytical framework is based on the industrial network approach, and deals with the development of products in terms of adaptation and combination of heterogeneous resources. There are dependencies between resources, that is, they are embedded, implying that no resource can be developed in isolation. The characteristics of and dependencies related to four main categories of resources (products, production facilities, business units and business relationships) provide a basis for analyzing the organizing of interactive product development.

Three in-depth case studies are used to explore the organizing of interactive product development with regard to dependencies. The first two cases are based on the development of the electrical system and the seats for Volvo’s large car platform (P2), performed in interaction with Delphi and Lear respectively. The third case is based on the interaction between Scania and Dayco/DFC Tech for the development of various pipes and hoses for a new truck model.

The analysis is focused on what different dependencies the firms considered and dealt with, and how product development was organized with regard to these dependencies. It is concluded that there is a complex and dynamic pattern of dependencies that reaches far beyond the developed product as well as beyond individual business units. To deal with these dependencies, development may be organized in teams where several business units are represented. This enables interaction between different business units’ resource collections, which is important for resource adaptation as well as for innovation. The delimiting and relating functions of the team boundary are elaborated upon and it is argued that also teams may be regarded as actors. It is also concluded that a modular product structure may entail a modular organization with regard to the teams, though, interaction between business units and teams is needed. A strong connection between the technical structure and the organizational structure is identified and it is concluded that policies regarding the technical structure (e.g. concerning “carry-over”) cannot be separated from the management of the organizational structure (e.g. the supplier structure). The organizing of product development is in itself a complex and dynamic task that needs to be subject to interaction between business units.