The unique characteristics of online social communities call for a reexamination and adaptation of established behavioral theories of trust decision-making. Guided by relevant social science and computational graph theories, we propose a conceptual model of trust decision-making in online social networks. This is the first study that integrates the existing graph-based view of trust decision-making in social networks into socio-psychological theories of trust to provide a richer understanding of trusting decisions in online social networks. We introduce new behavioral antecedents of trusting decisions, and redefine and integrate existing graph-based concepts to develop our proposed conceptual model. We introduce new behavioral antecedents of trusting decisions that have not been identified in previous research. We also identify novel operationalization methods to measure behavioral trust-inducing factors for online social networks. Our empirical findings indicate that both behavioral and network-specific trust decision-making factors should be considered in studying trusting decisions in online social networks.

Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.

Data warehousing (DW) has emerged as one of the most powerful technology innovations in recent years to support organization-wide decision making and has become a key component in the information technology (IT) infrastructure. Proponents of DW claim that its infusion can dramatically enhance the ability of businesses to improve the access, distribution, and sharing of information and provide managerial decision support for complex business questions. DW is also an enabling technology for data mining, customer-relationship management, and other business-intelligence applications. Although data warehouses have been around for quite some time, they have been plagued by high failure rates and limited spread or use. Drawing upon past research on the adoption and diffusion of innovations and on the implementation of information systems (IS), we examine the key organizational and innovation factors that influence the infusion (diffusion) of DW within organizations and also examine if more extensive infusion leads to improved organizational outcomes. In this paper, we conducted a field study, where two senior managers (one from IS and the other from a line function) from 117 companies participated, and developed a structural model to test the research hypotheses. The results indicate that four of the seven variables examined in this paper-organizational support, quality of the project management process, compatibility, and complexity-significantly influence the degree of infusion of DW and that the infusion, in turn, significantly influences organization-level benefits and stakeholder satisfaction. The findings of this paper have interesting implications for both research and practice in IT and DW infusion, as well as in the organization-level impact of the infusion of enterprise-wide infrastructural and decision support technologies such as DW.

A data warehouse is developed using a data warehousing process (DWP) methodology. Currently, there are a large number of methodologies available in the data warehousing market. The reason for this is the lack of any centralized attempts at creating platform-independent DWP standards. For the development of such standards, it is very important that we first examine the current practices being followed by the data warehousing industry. In this study, we review 30 commercial data warehousing methodologies and analyze the standard practices they have adopted with respect to DWP. To perform the analysis, we first develop an ontological model of DWP based on a thorough review of the literature and inputs from experts in the data warehousing field. The ontological model consists of two hierarchies: a composition hierarchy which shows the decomposition of DWP tasks such as system development, extract, transform, and load (ETL), and end-user application design; and a classification hierarchy which specifies the alternative methods or techniques available for performing the tasks. We next apply hierarchical cluster analysis to group the methodologies that share a common set of standards. Our study provides valuable insights into the prevailing standard practices for different DWP tasks-system development, requirements analysis, architecture design, data modeling, ETL, data extraction, and end-user application design-and identifies important directions for future research on DWP standardization

Several methods have been proposed for cascading other classification algorithms with decision tree learners to alleviate the representational bias of decision trees and, potentially, to improve classification accuracy. Such cascade generalization of decision trees increases the flexibility of the decision boundaries between classes and promotes better fitting of the training data. However, more flexible models may not necessarily lead to more predictive power. Because of potential overfitting problems, the true classification accuracy on test data may not increase. Recently, a generic method for cascade generalization has been proposed. The method uses a parameter — the maximum cascading depth — to constrain the degree that other classification algorithms are cascaded with decision tree learners. A method for efficiently learning a collection (i.e., a forest) of generalized decision trees, each with other classification algorithms cascaded to a particular depth, also has been developed. In this article, we propose several new strategies, including elitist and ensemble (weighted or unweighted), for using the various decision trees in such a collection in the prediction phase. Our empirical evaluation using 32 data sets in the UCI machine learning repository shows that, on average, the elitist strategy outperforms the weighted full ensemble strategy, which, in turn, outperforms the unweighted full ensemble strategy. However, no strategy is universally superior across all applications. Since the same training process can be used to evaluate the various strategies, we recommend that several promising strategies be evaluated and compared before selecting the one to use for a given application.

A shortcoming of univariate decision tree learners is that they do not learn intermediate concepts and select only one of the input features in the branching decision at each intermediate tree node. It has been empirically demonstrated that cascading other classification methods, which learn intermediate concepts, with decision tree learners can alleviate such representational bias of decision trees and potentially improve classification performance. However, a more complex model that fits training data better may not necessarily perform better on unseen data, commonly referred to as the overfitting problem. To find the most appropriate degree of such cascade generalization, a decision forest (i.e., a set of decision trees with other classification models cascaded to different degrees) needs to be generated, from which the best decision tree can then be identified. In this paper, the authors propose an efficient algorithm for generating such decision forests. The algorithm uses an extended decision tree data structure and constructs any node that is common to multiple decision trees only once. The authors have empirically evaluated the algorithm using 32 data sets for classification problems from the University of California, Irvine (UCI) machine learning repository and report on results demonstrating the efficiency of the algorithm in this paper.

We report the results of an empirical study comparing user comprehension of object oriented (OO) and process oriented (PO) models. The fundamental difference is that while OO models tend to focus on structure, PO models tend to emphasize behaviour or processes. Proponents of the OO modeling approach argue that it lends itself naturally to the way humans think. However, evidence from research in cognitive psychology and human factors suggests that human problem solving is innately procedural. Given these conflicting viewpoints, we investigate empirically if OO models are in fact easier to understand than PO models. But, as suggested by the theory of cognitive fit, model comprehension may be influenced by task-specific characteristics. We therefore compare OO and PO models based on whether the comprehension activity involves: 1) only structural aspects, 2) only behavioral aspects, or 3) a combination of structural and behavioral aspects. We measure comprehension through subjects' responses to questions designed along these three dimensions. Results show that for most of the simple questions, no significant difference was observed insofar as model comprehension is concerned. For most of the complex questions, however, the PO model was found to be easier to understand than the OO model. In addition to describing the process and the outcomes of the experiments, we present the experimental method employed as a viable approach for conducting research into various phenomena related to the efficacy of alternative systems analysis and design methods. We also identify areas where future research is necessary, along with a recommendation of appropriate research methods for empirical examination.

The object-oriented methodology for systems analysis and design has generated considerable interest recently. Object-orientation represents a fundamental shift in focus from the traditional process-oriented approaches that have dominated software development for over two decades. Although there is anecdotal evidence to suggest that systems analysts experienced in process-oriented modeling approaches will find it difficult to apply objective-oriented methodologies, there is no empirical work investigating the relationship between a procedural mindset and an ability to learn and apply object-oriented concepts. Prior research in human problem solving, however, suggests that the efficacy of a systems analysis and design methodology should be judged in the context of the task to which it is applied. To explore the effects of prior experience and task characteristics on performance in systems analysis and design, we conducted an experiment in which two groups of subjects applied the object-oriented methodology to two types of tasks, one process-oriented and the other object-oriented. One group had significant prior experience in process-oriented methodologies, while the other group had no formal experience. Both groups were provided identical training in object-oriented analysis and design prior to the experiment. The results of the study suggest that both prior experience and task characteristics play a role in determining performance. The implications that follow for research and practice are discussed.

A laboratory experiment was conducted to assess the basic theory and extensions to the theory for recursive tasks across programming languages. The experiment used 34 LISP and 48 PASCAL computer science students in two repeated measures designs. Findings of the study are reported and analyzed. The results strongly suggest that investigation of programming constructs should take place in the context of specific programming languages. Since a number of languages provide similar kinds of programming constructs, it is difficult for programmers to choose those implementations that best suit their needs. One way of encouraging the use of desirable constructs would be to develop languages adapted to certain types of tasks. Such an approach would inherently lead to cognitive fit and the attendant performance benefits would be realized.>