Pay-What-You-Want Pricing in the Digital Product Marketplace: A Feasible Alternative to Piracy Prevention?

In pay-what-you-want (PWYW) pricing, buyers are allowed to pay any amount they want, often including a price of zero. Standard theory predicts that buyers are driven solely by their own interest and will always choose to pay nothing, making PWYW pricing impractical to use. Nonetheless, PWYW pricing has been consistently occurring in the marketplace. We build and analyze a theoretical model to explain the presence of PWYW pricing in the marketplace and identify the situations under which businesses are better off adopting it over the traditional posted pricing. Because the digital product domain is a particularly good fit for PWYW pricing because of its constant exposure to piracy threats, we focus on digital product firms and examine PWYW pricing as an alternative to their piracy prevention efforts. We show that PWYW pricing becomes a superior pricing strategy when the pirate version is quite similar to the authentic product and it is costly for the firm to improve its product quality. Moreover, if network externalities are present, PWYW pricing can outperform posted pricing only when the network externalities are weak. The results explain why PWYW pricing is rare in the established digital product marketplace, which exhibits strong network externalities.

Lean integration of IT security and data privacy governance aspects into product development in agile organizations

This article deals with the design of a product development-specific framework to support lean and adequate governance. This framework is based on layers of product-specific standards and regulations. The layers can be merged into a specific set to address the demands of a product to fit the state-of-the-art requirements of its domain. For the product domain, specific layers are presented with examples from IT security and data privacy for the software development phase. The approach is generic and can be extended to other domains like finance services or embedded products and their life-cycle phases.

Managing Complementary Assets to Build Cross-Functional Ambidexterity: The Transformation of Huawei Mobile

ABSTRACT During technology transitions, incumbents are frequently faced with the ambidextrous challenge of exploiting existing capabilities and exploring new ones. While extant studies focus on radical changes in the product domain, we notice radical changes can happen in both product and market domains. Pioneering studies indicate that cross-functional ambidexterity addresses this challenge at the business-unit level by juxtaposing exploration and exploitation across different functional domains (particularly in product and market domains) and that complementary assets address this challenge at the organizational level. However, how efforts at two levels can be combined to build cross-functional ambidexterity and what roles complementary assets play remain unclear. Therefore, this study conducts an in-depth case study of Huawei Mobile, which managed to achieve superior performance during a technology transition that triggers radical changes in both product and market domains. We find that multi-level synergies contribute to the transition process. Specifically, cross-functional ambidexterity is constructed by prioritizing exploration in the product domain ahead of that in the market domain, and that it generates learning, brand and channel extension, matching, and brand alliance benefits at the business-unit level. Complementary assets help to reduce the uncertainty of exploration and resolve functional conflicts at the organizational level.

RELATIONS Vs FUNCTIONS

This paper aims to understand the concepts of relations and functions as well with the inclusive of ordered pairs ,Cartesian product ,domain ,codomain ,range of a function. This paper also focusing on some of the special types of functions and also facilitates better idea on when a relation can be a function. This article also provides clear picture on what magic involved in the given relation to identify whether a function using graphing functions.

Tackling challenges of neural purchase stage identification from imbalanced twitter data

Twitter and other social media platforms are often used for sharing interest in products. The identification of purchase decision stages, such as in the AIDA model (Awareness, Interest, Desire, and Action), can enable more personalized e-commerce services and a finer-grained targeting of advertisements than predicting purchase intent only. In this paper, we propose and analyze neural models for identifying the purchase stage of single tweets in a user’s tweet sequence. In particular, we identify three challenges of purchase stage identification: imbalanced label distribution with a high number of non-purchase-stage instances, limited amount of training data, and domain adaptation with no or only little target domain data. Our experiments reveal that the imbalanced label distribution is the main challenge for our models. We address it with ranking loss and perform detailed investigations of the performance of our models on the different output classes. In order to improve the generalization of the models and augment the limited amount of training data, we examine the use of sentiment analysis as a complementary, secondary task in a multitask framework. For applying our models to tweets from another product domain, we consider two scenarios: for the first scenario without any labeled data in the target product domain, we show that learning domain-invariant representations with adversarial training is most promising, while for the second scenario with a small number of labeled target examples, fine-tuning the source model weights performs best. Finally, we conduct several analyses, including extracting attention weights and representative phrases for the different purchase stages. The results suggest that the model is learning features indicative of purchase stages and that the confusion errors are sensible.

Ontology-Based Opinion Mining for Online Product Reviews

In this chapter, the authors work at the feature level opinion mining and make a user-centric selection of each feature. Then they preprocess the data using techniques like sentence splitting, stemming, and many more. Ontology plays an important role in annotating documents with metadata, improving the performance of information extraction and reasoning, and making data interoperable between different applications. In order to build ontology in the method, the authors use (product) domain ontology, ConceptNet, and word net databases. They discuss the current approaches being used for the same by an extensive literature survey. In addition, an approach used for ontology-based mining is proposed and exploited using a product as a case study. This is supported by implementation. The chapter concludes with results and discussion.

Making Space for the Psychology of Creativity in Dance Science

Creativity is commonly recognized as a complex phenomenon; one which entails a range of debates around definition, process and product, domain specificity, cross-discipline generalisability, and appropriate testing measures. The psychology of creativity appears to find a fitting home in dance science, a field concerned with understanding and enhancing dancers' health and performance. Yet dance psychology has been predominated by research which focuses on the mental processes underpinning optimal skill execution and technical performance. This paper outlines an argument for a greater focus on the creative demands of dance within dance science, highlighting some the challenges of, and barriers to, research in the psychology of creativity in dance, before making a number of recommendations to encourage the growth of this important research area.