Giving too much social support: social overload on social networking sites

As the number of messages and social relationships embedded in social networking sites (SNS) increases, the amount of social information demanding a reaction from individuals increases as well. We observe that, as a consequence, SNS users feel they are giving too much social support to other SNS users. Drawing on social support theory (SST), we call this negative association with SNS usage ‘social overload’ and develop a latent variable to measure it. We then identify the theoretical antecedents and consequences of social overload and evaluate the social overload model empirically using interviews with 12 and a survey of 571 Facebook users. The results show that extent of usage, number of friends, subjective social support norms, and type of relationship (online-only vs offline friends) are factors that directly contribute to social overload while age has only an indirect effect. The psychological and behavioral consequences of social overload include feelings of SNS exhaustion by users, low levels of user satisfaction, and a high intention to reduce or even stop using SNS. The resulting theoretical implications for SST and SNS acceptance research are discussed and practical implications for organizations, SNS providers, and SNS users are drawn.     

Product fit uncertainty and its effects on vendor choice: an experimental study

Recommender systems and other Internet-enabled technologies have changed the surrounding conditions of pre-purchase evaluations on the Internet. In some cases consumers can now sample entire products prior to a purchase – hereby removing all uncertainty about whether a product fits their taste. While previous research has mainly focused on vendor and product quality uncertainty, it is still not clear how declining product fit uncertainty affects consumers. To close this gap, we conducted a laboratory experiment to analyze the effects on consumers’ vendor selection. We find that full elimination of product fit uncertainty is beneficial for vendors, as it increases both the number of purchases and consumer loyalty. Interestingly, if product fit uncertainty is only partially eliminated, consumers do not necessarily show differential behavior for different levels of remaining product fit uncertainty. This has important implications for online vendors that consider the implementation of additional means to reduce product fit uncertainty.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

A Python extension for the massively parallel multiphysics simulation framework waLBerla

We present a Python extension to the massively parallel HPC simulation toolkit waLBerla. waLBerla is a framework for stencil based algorithms operating on block-structured grids, with the main application field being fluid simulations in complex geometries using the lattice Boltzmann method. Careful performance engineering results in excellent node performance and good scalability to over 400,000 cores. To increase the usability and flexibility of the framework, a Python interface was developed. Python extensions are used at all stages of the simulation pipeline: they simplify and automate scenario setup, evaluation, and plotting. We show how our Python interface outperforms the existing text-file-based configuration mechanism, providing features like automatic nondimensionalization of physical quantities and handling of complex parameter dependencies. Furthermore, Python is used to process and evaluate results while the simulation is running, leading to smaller output files and the possibility to adjust parameters dependent on the current simulation state. C++ data structures are exported such that a seamless interfacing to other numerical Python libraries is possible. The expressive power of Python and the performance of C++ make development of efficient code with low time effort possible.     

Discriminating Conduct: Deploying Systems to Support Awareness in Organizations

Alongside the emergence of the use of fieldwork studies for design there has been a discussion on how best these studies can inform system development. Concerns have been expressed as to whether their most appropriate contribution is a list of requirements or design recommendations. This article explores a recurrent issue that has emerged from fieldwork studies in Computer-Supported Cooperative Work, awareness, and with respect to a particular system development project discusses some of the implications for the development and deployment of one particular kind of technology—image recognition systems—in particular, organizational settings. In the setting in question—surveillance centers or operations rooms—staff utilize a range of practices to maintain awareness. Rather than extending field studies so that they can better assist design, it may be considered how workplace studies can contribute to a respecification of key concepts, like awareness, that are critical to an understanding of how technologies are used and deployed in everyday environments.     

Enterprise Social Networks as Digital Infrastructures - Understanding the Utilitarian Value of Social Media at the Workplace

ABSTRACT

In this study, we first show that while both the perceived usefulness and perceived enjoyment of enterprise social networks impact employees’ intentions for continuous participation, the utilitarian value significantly outpaces its hedonic value. Second, we prove that the network’s utilitarian value is constituted by its digital infrastructure characteristics: versatility, adaptability, interconnectedness and invisibility-in-use. The study is set within a software engineering company and bases on quantitative survey research, applying partial least squares structural equation modeling.     

OXPath: A language for scalable data extraction, automation, and crawling on the deep web

The evolution of the web has outpaced itself: A growing wealth of information and increasingly sophisticated interfaces necessitate automated processing, yet existing automation and data extraction technologies have been overwhelmed by this very growth. To address this trend, we identify four key requirements for web data extraction, automation, and (focused) web crawling: (1) interact with sophisticated web application interfaces, (2) precisely capture the relevant data to be extracted, (3) scale with the number of visited pages, and (4) readily embed into existing web technologies. We introduce OXPath as an extension of XPath for interacting with web applications and extracting data thus revealed—matching all the above requirements. OXPath’s page-at-a-time evaluation guarantees memory use independent of the number of visited pages, yet remains polynomial in time. We experimentally validate the theoretical complexity and demonstrate that OXPath’s resource consumption is dominated by page rendering in the underlying browser. With an extensive study of sublanguages and properties of OXPath, we pinpoint the effect of specific features on evaluation performance. Our experiments show that OXPath outperforms existing commercial and academic data extraction tools by a wide margin.