Nonlinear Pricing of Software with Local Demand Inelasticity

Nonlinear usage-based pricing is applied extensively in software markets. Customers of software products usually cannot vary their required usage volume, a property we label local demand inelasticity. For instance, a client firm that needs a sales force automation software either buys one user license for every salesperson or does not buy at all. It is unlikely to buy licenses for some but not all salespersons. This demand feature violates a critical assumption of the standard nonlinear pricing literature that consumers are flexible with their usage volume, and their valuation changes smoothly with usage volume. Consequently, standard nonlinear pricing solutions are inapplicable to many software products. This paper studies the optimal nonlinear usage-based pricing of software when customers' demand is locally inelastic. This unique demand feature necessitates a new approach to solve the nonlinear pricing problem. We show that under a weak ordering condition of customer types, this complex pricing problem can be decomposed into a set of much simpler subproblems with known solutions. Our pricing solution is easily implementable and applicable to a broad range of demand systems, including those described by the families of exponential and normal distributions. Moreover, local demand inelasticity has a critical impact on key efficiency results.

An Integrated Approach to Encompass Big Data Cloud Adoption Using Data Mining

Text Mining is one of the most censorious techniques for analysis of data. It processes the unstructured data which has been found to hold nearly 80% of the world’s data. In the present time a majority of industries and big firms use and store massive amount of data sets. The information gets stored in the data Warehouses, and cloud platforms respectively. The data getting stored shows an exponential growth each time since multiple amount of new data is said to get stored every minute from distinct sources. Due to heavy presence of information stored in the platforms it becomes difficult to gather and extract the most relevant part from the data. Thus, text mining and data mining techniques are applied with algorithms such as CNN and RNN supporting it. The algorithm performs deep analysis of data and presents the most relevant piece of information, removing the less important ones. The relevance of the project comes into existence when it’s seen to be integrated into industries such as software markets, social media analysis and formulating of market reports. Thus, providing an extremely comprehensive information which would enhance scope and application of the data getting stored.

Who Chooses Open Source Software?

Economists and legal scholars have debated the reasons people adopt opensource software, and accordingly whether and to what extent the open sourcemodel can scale, replacing proprietary rights as a primary means ofproduction. In this study, we use the release by a biotechnology company ofsimilar software under both proprietary and open source licenses toinvestigate who uses open source software and why. We find that academicusers are somewhat more likely to adopt open source software than privatefirms. We find only modest differences in the willingness of open sourceusers to modify or improve existing programs. And we find that users ofopen source software often make business decisions that seem indifferent tothe norms of open source distribution. Our findings cast some doubt on thepenetration of the open source ethos beyond traditional software markets.

Customer Knowledge Management (CKM)

SMEs operating in high-tech sectors are typically reliant on specialist knowledge to help them build the right product with the objective of meeting customer needs. The nature of niche software markets means that products must be closely informed by customer requirements for the software product to be a success. The importance of understanding how smaller organisations manage knowledge has become vital to their success; however, there remains a dearth of empirical research in SMEs in the area of IS research. Using a qualitative analysis approach in five Irish software SMEs, this chapter identifies how this type of organisation leverages their customers as external knowledge resources, with particular emphasis on knowledge acquisition, a Knowledge Activity (KA) in which customers play a significant part. Through the establishment of KAs a firm can develop an approach to Customer Knowledge Management (CKM) to build value-creating relationships with a reliable customer base.