Based on social cognitive theory and the balanced thinking–feelings model, this study proposes a research model to examine the different roles of general computer self-efficacy (CSE) and specific CSE. The research model was tested with a survey of university students in Singapore in the Facebook context. It is found that while general CSE affects continuance intention through both cognition and affection, specific CSE mainly affects continuance intention through cognition. It is also found that general computer experience affects general CSE only and specific computer experience affects specific CSE only.

Research on ontology and conceptual data modeling builds on the premise that the world could be represented in terms of concepts (also known as “things”), their properties and their (things') relationships. A persistent effort in the representation is how to distinguish things from their properties, as well as how to denote relationships that relate two things together. Following the tradition of deriving guidelines from ontology studies, this paper proposes a need to make a clear distinction between relationships among things and relationships among things' properties. The central thrust of this paper is in proposing an ontologically guided principle: For the same relationship, working with the relationship among things will lead to better user performance than working with the relationship among things' properties. This principle, called Relationship without Pointers principle, is robustly validated by re-analyzing a set of experiment data on user data modeling performance with three database models. This principle may be applicable to other contexts that study relationships.

The demand for software has increased rapidly in the global industrial environment. Open source software (OSS) has exerted significant impact on the software industry. Large amounts of resources and effort have been devoted to the development of OSS such as Linux. Based on the technology adoption model (TAM), the development of Linux as the most well-known OSS with a graphical user interface designed for ease of use and a wide range of functionalities is expected to result in high levels of Linux adoption by individual users. Linux, however, currently controls about 1% of the operating system market for personal computers. The resistance of users to switch to a new operating system remains one of the major obstacles to widespread adoption of Linux among individual users. Based on the integration of the equity implementation model and the TAM, this study examines the formation of user resistance, as well as the effects of user resistance, on the migration to Linux for personal computers. This study discusses the role and effect of user resistance based on the equity implementation model in comparison with the two main determinants in the TAM. This study contributes to the advancement of theoretical understanding of Linux migration and user resistance. The findings also offer suggestions for software communities and practitioners, of OSS in particular, to promote the use of new software by individual users.

Many education institutes are planning to use virtual worlds to offer distance education programs and to reach potential students globally. However, the success of such programs depends on students’ willingness to use virtual worlds for learning continuously. Students’ learning continuance helps to decrease the drop-out rate of the programs. Motivated thus, this study uses the lenses of the balanced thinking-feelings model and socio-constructivism to model how students’ personal learning and social learning experiences influence virtual world learning continuance. Our results show that the utilitarian and hedonic values are critical to learners’ continuous intention of virtual world learning, and they are influenced by learners’ social learning experiences. Social learning factors are also found to interact with students’ personal learning factors to influence their perceived utilitarian and hedonic values of virtual world learning. The findings provide theoretical implications by integrating information systems theories and education psychology to understand the factors influencing the virtual world learning continuance in a systematic way. The findings also provide educators empirical evidence on what influences users’ learning continuance in virtual worlds.

This paper uses cognitive fit theory to analyze the problem solving process in spreadsheet analyses. Cognitive fit theory proposes the formation of mental representation as a part of the problem solving process. However, there is little research examining mental representation, which is a key concept in cognitive fit theory. This study examines the formation of mental representation and proposes an alternative mechanism of cognitive fit between different problem representations and their corresponding mental representations when the task is invariant, but the problem representation changes. Mental representation is then empirically assessed based on the application of Hick’s law, which states that the response time of users making a choice varies with the logarithm of the number of possible choices. Therefore, this study contributes to research on cognitive fit theory by proposing an alternative fit and by demonstrating a feasible approach for identifying mental representations. It contributes to spreadsheet research by showing how problem representations affect task performance in the case of spreadsheet error correction.

This study examines how the nature of technology affects users’ intention to continue using information technologies. It proposes an extended technology acceptance model, with perceived ease of use, perceived usefulness and pleasure affecting the intention to continue using a technology. We hypothesized that these effects are moderated by the technology’s utilitarian and hedonic values. The model was validated for smartphone functions. A user survey showed that perceived ease of use significantly affected the intention to continue using only for high-utilitarian functions, whereas pleasure affected the intention to continue using only for high-hedonic functions. The effect of perceived ease of use on perceived usefulness was stronger for high-utilitarian than for low-utilitarian functions. The effect of pleasure on perceived usefulness was stronger for high-hedonic than for low-hedonic functions. The results suggest that marketing should consider the nature of the functions.

This study proposes a new methodology that facilitates organizational decision support through knowledge integration across organizational units. For this purpose, this study develops a decision support loop and explains how to organize individual knowledge related to a specific business problem and formulate and test the organized knowledge using cognitive modeling techniques for decision support. This study discusses the proposed approach in the context of an application case involving a beverage company. The application case shows the validity and usefulness of the proposed approach.

The technology acceptance model (TAM) is very widely used for studying technology acceptance. The model states that an individual's behavioral intention (BI) to use an information system is determined by his perceived usefulness (PU) and perceived ease of use (PEOU) of it. While many studies have applied the TAM, none has examined the model's behavior over its entire value range. We conducted two surveys to examine the values of BI over the two-dimensional boundary space formed by PU and PEOU. Contrary to current understanding, we find that the effects of PU and PEOU vary over the boundary space.

Abstract Research patterns could enhance understanding of the Information Systems (IS) field. Citation analysis is the methodology commonly used to determine such research patterns. In this study, the citation methodology is applied to one of the top‐ranked Information Systems conferences—International Conference on Information Systems (ICIS). Information is extracted from papers in the proceedings of ICIS 2000 to 2002. A total of 145 base articles and 4,226 citations are used. Research patterns are obtained using total citations, citations per journal or conference, and overlapping citations. We then provide the citation ranking of journals and conferences. We also examine the difference between the citation ranking in this study and the ranking of IS journals and IS conferences in other studies. Based on the comparison, we confirm that IS research is a multidisciplinary research area. We also identify the most cited papers and authors in the IS research area, and the organizations most active in producing papers in the top‐rated IS conference. We discuss the findings and implications of the study.

Abstract User acceptance models such as the technology acceptance model, the theory of reasoned action, and the theory of planned behavior have been widely used to study a specific information system, a group of systems, or even computers in general. This study examines the usage of competitive information systems. It applies the theory of planned behavior (TPB) in a comparative frame of reference model (relative model) in which relative attitude, relative subjective norm, relative intention, and relative usage are examined. The study is set in the context of two instant messaging technologies. Based on a survey from 300 instant messaging users, the effects of attitude and subjective norm on intention in each model were different (i.e., when TPB is tested once for each application). This confirms that the behavioral model can show different effects for competitive products. In addition, correct competitive answers were given by the relative model; however, these may differ from the answers found from a single application model. The authors show the importance of studying the relative model for competitive products.

This paper presents the result of an empirical study that investigates user involvement and user satisfaction in the context of information-seeking activity. This study adopts the definition of user involvement as the psychological state of the importance and personal relevance that users attach to a given system. Following Celsi and Olson, intrinsic and situational motivators were considered as two antecedents of user involvement. These two types of motivator were treated as formative exogenous variables, while user involvement and user satisfaction were treated as reflective mediating endogenous variable and final endogenous variable, respectively. Five hypotheses were proposed, and all were supported by the data from a laboratory experiment. This paper also reports two other interesting findings. First, intrinsic motivators have a stronger positive effect on user involvement than situational motivators have. Second, situational motivators have a stronger positive effect on user satisfaction than intrinsic motivators have. The discussion part explains these interesting findings.

Abstract End‐user computing has become a well‐established aspect of enterprise database systems today. End‐user computing performance depends on the user–database interface, in which the data model and query language are major components. We examined three prominent data models—the relational model, the Extended‐Entity‐Relationship (EER) model, and the Object‐Oriented (OO) model—and their query languages in a rigorous and systematic experiment to evaluate their effects on novice end‐user computing performance in the context of database design and data manipulation. In addition, relationships among the performances for different tasks (modeling, query writing, query comprehension) were postulated with the use of a cognitive model for the query process, and are tested in the experiment. Structural Equation Modeling (SEM) techniques were used to examine the multiple causal relationships simultaneously. The findings indicate that the EER and OO models overwhelmingly outperformed the relational model in terms of accuracy for both database design and data manipulation. The associations between tasks suggest that data modeling techniques would enhance query writing correctness, and query writing ability would contribute to query comprehension. This study provides a better and thorough understanding of the inter‐relationships among these data modeling and task factors. Our findings have significant implications for novice end‐user training and development.

Users see the database interface as the database system. A good interface enables them to formulate queries better. The semantics communicated through the interface can be classified according to abstraction levels, such as the conceptual and logical levels. With the conceptual interface, interaction is in terms of real-world concepts such as entities, objects and relationships. Current user-database interaction is mainly based on the logical interface, where interaction is in terms of abstract database concepts such as relations and joins. Many researchers argue that end users will perform better with the conceptual interface. This research tested this claim, as well as the effects of query complexity and learning, on the visual query performance of users. The experiment involved three tests: an initial test, a retention test and a relearning test. The results showed that, for complex queries, conceptual interface users achieved higher accuracy, were more confident in their answers, and spent less time on the queries. This is persistent across retention and relearning tests.

Data utilization is an important aspect of information systems. Over the last two decades, numerous experimental studies have been conducted on user performance involving database-related tasks with certain database models and/or languages. In this paper, we propose a two-dimensional conceptual framework aimed at classifying and systematically analyzing these studies, in order to provide a bigger picture facilitating systematic understanding of this body of research. The classification exercise shows that studies involving natural language interfaces did not have very consistent findings; correspondingly, we applied the metaanalytic technique to attempt to gain insight into these differences.Request access from your librarian to read this article's full text.

There have been many experiments comparing query languages. Their findings are difficult to combine as the experiments have used different settings and procedures. Before a meta-analysis combining the experiments, it is proposed that these differences be checked for any possible effect. The most important measure of user performance in these experiments is query accuracy, which has been determined using many different grading schemes. The different schemes are therefore checked for possible effects on hypothesis rejection. These grading schemes are applied to two sets of queries from two different experiments. The outcomes are examined to identify any effects resulting from the grading schemes. The results show that the experimental outcomes are robust and immune to the grading schemes.

There are two main approaches to improving the effectiveness of database interfaces. One is to raise the level of abstraction for the content of the user-database interaction. The relational model belonging to the logical level has replaced the hierarchical and network models that belong to the lower physical level. It is likely that the relational model will eventually be replaced by models belonging to the even higher conceptual level, such as entity relationship models and object-oriented models. The second approach is to enhance the actual interaction process. This can be done by providing better feedback to the user. Feedback can be in the form of more comprehensible error messages, and the provision of a natural language interpretation of user's query. Such a feedback system was developed, and its effectiveness tested in an experiment. The results showed that the feedback system enhanced user performance greatly. Specifically, users who used the feedback system were 12.9% more accurate than those without the feedback system. They were also 41.2% more confident of their answers, and they took 29.0% less time than those without the feedback system.

Static program analyzers (SPA) are interactive tools that enhance program understanding by answering queries about programs. An SPA extracts relevant information from input programs and stores it in a program knowledge base (PKB). In this paper, we present a hybrid PKB design model that integrates a relational database with attributed syntax trees. In the hybrid PKB, global properties of programs are stored in a relational database and detailed program structures are stored as attributed syntax trees. The hybrid PKB approach simplifies the structure of the PKB and provides a flexible mechanism for analysis of complex structured objects such as syntax trees and control/data flow graphs. The model reduces the size of the database, and hence program queries can be answered efficiently.>

To alleviate the need to know the logical database organization, the authors propose the use of a semantic data model, known as the enhanced entity-relationship model, as a front-end to the relational systems. A formal, high-level visual knowledge query language (VKQL) has also been designed for this interface. This language provides for knowledge abstraction as the user communicates only domain knowledge with the system without any implication on the storage structure or search strategies. A translation algorithm is also described to translate VKQL queries to standard SQL equivalents.>

The authors propose a novel user interface, the Graphical Knowledge Level interface, for user-database interaction. This interface addresses two of the current research problems: the semantic inadequacy of the relational model and the logical data dependence of the relational languages. The interface is more user-friendly than the SOL interface and the Universal Relation interface, as it employs the graphical approach. The semantic inadequacy problem is alleviated by using an enhanced entity-relationship model. This data model includes abstraction concepts such as aggregation, generalization, and categorization. A formal, high-level Graphical Knowledge Level Query Language, GKQL, which provides for both logical and physical data independence has also been designed.>