Software metrics programs are an important part of a software organization's productivity and quality initiatives as precursors to process-based improvement programs. Like other innovative practices, the implementation of metrics programs is prone to influences from the greater institutional environment the organization exists in. In this paper, we study the influence of both external and internal institutional forces on the assimilation of metrics programs in software organizations. We use previous case-based research in software metrics programs as well as prior work in institutional theory in proposing a model of metrics implementation. The theoretical model is tested on data collected through a survey from 214 metrics managers in defense-related and commercial software organizations. Our results show that external institutions, such as customers and competitors, and internal institutions, such as managers, directly influence the extent to which organizations change their internal work-processes around metrics programs. Additionally, the adaptation of work-processes leads to increased use of metrics programs in decision-making within the organization. Our research informs managers about the importance of management support and institutions in metrics programs adaptation. In addition, managers may note that the continued use of metrics information in decision-making is contingent on adapting the organization's work-processes around the metrics program. Without these investments in metrics program adaptation, the true business value in implementing metrics and software process improvement is not realized.

Internet referral services, hosted either by independent third-party infomediaries or by manufacturers serve as lead-generators in electronic marketplaces, directing consumer traffic to particular retailers. In a model of price dispersion, we investigate the competitive implications of these institutions on retailer prices and their impact on channel structures and distribution of profits. Offline, retailers face a higher customer acquisition cost. In return, they obtain full information about consumer valuations. Online, they save on the acquisition costs, but lose the ability to charge prices based on consumer valuation. This critical tradeoff drives equilibrium profits. The establishment of a referral service is a strategic decision by the manufacturer, in response to a third-party infomediary. It leads to an increase in channel profits and a reallocation of the increased surplus to the manufacturer, via the franchise fees. The impetus to increased profits comes from both retailers' ability to price discriminate between informed and uninformed consumers, and by the lowering of acquisition costs due to diversion of traffic from the offline to the online channel. Our model highlights that online prices are lower than offline prices for the retailer enrolled with the infomediary. We point out that in equilibrium, the retailer associated with the infomediary, has higher expected sales and gross profits. We also show that some of the online cost savings are transferred to the consumer, leading to an overall increase in consumer welfare.

Measurement programs in software organizations are an important source of control over quality and cost in software development. The findings of this research presented here are based on an industry-wide survey conducted to examine the factors that influence success in software metrics programs. Our approach is to go beyond the anecdotal information on metrics programs that exists in the literature and use the industry-wide survey data to rigorously test for the effects of various factors that affect metrics programs success. We measure success in metrics programs using two variables-use of metrics information in decision-making and improved organizational performance. The various determinants of metrics program success are divided into two sets-organizational variables and technical variables. The influence of these variables on metrics programs success is tested using regression analysis. Our results support some of the factors discussed in the anecdotal literature such as management support, goal alignment, and communication and feedback. Certain other factors such as metrics quality and the ease of data collection are not as strongly influential on success. We conclude the paper with a detailed discussion of our results and suggestions for future work.

Article Communication and information: alternative uses of the Internet in households Share on Authors: Robert Kraut Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PAView Profile , Tridas Mukhopadhyay Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PAView Profile , Janusz Szczypula Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PAView Profile , Sara Kiesler Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PAView Profile , William Scherlis Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims CHI '98: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsJanuary 1998 Pages 368–375https://doi.org/10.1145/274644.274695Online:01 January 1998Publication History 32citation2,409DownloadsMetricsTotal Citations32Total Downloads2,409Last 12 Months35Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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Article Free Access Share on HomeNet: a field trial of residential Internet services Authors: Robert Kraut Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PAView Profile , William Scherlis Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PAView Profile , Tridas Mukhopadhyay Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PAView Profile , Jane Manning Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PAView Profile , Sara Kiesler Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims CHI '96: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsApril 1996 Pages 284–291https://doi.org/10.1145/238386.238531Published:13 April 1996Publication History 30citation1,769DownloadsMetricsTotal Citations30Total Downloads1,769Last 12 Months132Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Software effort estimation is an important but difficult task. Existing algorithmic models often fail to predict effort accurately and consistently. To address this, we developed a computational approach to software effort estimation. cEstor is a case-based reasoning engine developed from an analysis of expert reasoning. cEstor's architecture explicitly separates case-independent productivity adaptation knowledge (rules) from case-specific representations of prior projects encountered (cases). Using new data from actual projects, uncalibrated cEstor generated estimates which compare favorably to those of the referent expert, calibrated Function Points and calibrated COCOMO. The estimates were better than those produced by uncalibrated Basic COCOMO and Intermediate COCOMO. The roles of specific knowledge components in cEstor (cases, adaptation rules, and retrieval heuristics) were also examined. The results indicate that case-independent productivity adaptation rules affect the consistency of estimates and appropriate case selection affects the accuracy of estimates, but the combination of an adaptation rule set and unrestricted case base can yield the best estimates. Retrieval heuristics based on source lines of code and a Function Count heuristic based on summing over differences in parameter values, were found to be equivalent in accuracy and consistency, and both performed better than a heuristic based on Function Count totals.

Models are developed to estimate lines of code and function counts directly from user application features of process control systems early in the software development lifecycle. Since the application features are known with reasonable degree of confidence during early stages of development, it is possible to extend the use of the constructive cost model (COCOMO) and function-points-based approach for early software cost estimation. Alternative feature-based models that estimate size and effort using application features and productivity factors are developed. The feature-based models have been shown to estimate software effort with the least error.>