Research proposal: Industry convergence - Driving forces, factors and consequences

Industry convergence – the merger of hitherto separate industries – is a phenomenon that has had a profound effect on several industries and received considerable interest among practitioners and business press over the past decades. Despite this, industry con- vergence has only received limited attention from the academic management field, al- though an emergent discussion on convergence can be identified. Prior research is limited by a lack of coherent theoretical definitions of convergence, and a tendency to focus on technological aspects rather than on consequences for industry structure and individ- ual firms. Moreover, there is lack of empirical work in actual convergent industry set- tings. This research proposal reviews some of the literature on convergence to date, in order to develop a theoretical framework of industry convergence that takes drivers, types and consequences on industry and firm level into account. The preliminary frame- work positions industry convergence as being conceptually and causally distinct from technology convergence, although the two are often intrinsically linked. Industry con- vergence is defined as a process whereby two or more industries – made up of producers of substitute products – converge over time, and where the outcome is uncertain with many alternatives. Two main types of industry convergence are proposed, convergence in substitutes and convergence in complements. With a view to increase the understanding of industry convergence, the preliminary theoretical framework will be applied in a longitudinal case study of the electronic security industry. This sector is currently converging with the IT industry, a process mainly driven by the pervasiveness of Internet Protocol (IP) networking technology, that allows the integration of a number of previously separate security and information systems

ACUTA Journal of Telecommunications in Higher Education

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Network Convergence: Where is the Value?

The entire telecommunications industry is going through very difficult times. Rapidly changing technology, lack of good business models, and lack of visibility in the near-term for renewed growth all create an uncertain environment. Yet, the global Internet is becoming a multi-service network infrastructure that can potentially replace existing disparate voice and data networks. Although it is widely believed in the telecommunications industry that network convergence of voice, data, video, and images is an industry driver, not much attention has yet been paid to a key proposition: what value does network convergence bring to business and residential customers? This paper explains how different industries are converging; the technological, economic and regulatory forces that are at play and how the various customer segments can benefit from network convergence. While technological advancement is transforming industry and business models rapidly, one question keeps coming back to haunt managers: Where is the business value? We illustrate the value proposition of convergence (for various players) by first explaining the paradigm shifts happening across industries and then highlighting the high velocity spiral of knowledge dissemination theory that is fueled by convergence

Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Disagreeable Privacy Policies: Mismatches between Meaning and Users’ Understanding

Privacy policies are verbose, difficult to understand, take too long to read, and may be the least-read items on most websites even as users express growing concerns about information collection practices. For all their faults, though, privacy policies remain the single most important source of information for users to attempt to learn how companies collect, use, and share data. Likewise, these policies form the basis for the self-regulatory notice and choice framework that is designed and promoted as a replacement for regulation. The underlying value and legitimacy of notice and choice depends, however, on the ability of users to understand privacy policies. This paper investigates the differences in interpretation among expert, knowledgeable, and typical users and explores whether those groups can understand the practices described in privacy policies at a level sufficient to support rational decision-making. The paper seeks to fill an important gap in the understanding of privacy policies through primary research on user interpretation and to inform the development of technologies combining natural language processing, machine learning and crowdsourcing for policy interpretation and summarization. For this research, we recruited a group of law and public policy graduate students at Fordham University, Carnegie Mellon University, and the University of Pittsburgh (“knowledgeable users”) and presented these law and policy researchers with a set of privacy policies from companies in the e-commerce and news & entertainment industries. We asked them nine basic questions about the policies’ statements regarding data collection, data use, and retention. We then presented the same set of policies to a group of privacy experts and to a group of non-expert users. The findings show areas of common understanding across all groups for certain data collection and deletion practices, but also demonstrate very important discrepancies in the interpretation of privacy policy language, particularly with respect to data sharing. The discordant interpretations arose both within groups and between the experts and the two other groups. The presence of these significant discrepancies has critical implications. First, the common understandings of some attributes of described data practices mean that semi-automated extraction of meaning from website privacy policies may be able to assist typical users and improve the effectiveness of notice by conveying the true meaning to users. However, the disagreements among experts and disagreement between experts and the other groups reflect that ambiguous wording in typical privacy policies undermines the ability of privacy policies to effectively convey notice of data practices to the general public. The results of this research will, consequently, have significant policy implications for the construction of the notice and choice framework and for the US reliance on this approach. The gap in interpretation indicates that privacy policies may be misleading the general public and that those policies could be considered legally unfair and deceptive. And, where websites are not effectively conveying privacy policies to consumers in a way that a “reasonable person” could, in fact, understand the policies, “notice and choice” fails as a framework. Such a failure has broad international implications since websites extend their reach beyond the United States