Exploring the antecedents of team performance in collaborative learning of computer software

As teamwork becomes common in computer software learning, there is an increasing need to study factors contributing to team performance. To this pursuit, the concept of collective efficacy offers great potential to researchers of information systems (IS). Drawing upon social cognitive theory (SCT), this study examines the relationships among computer collective efficacy (CCE), outcome expectations and team performance in the context of collaborative learning. Computer collective efficacy is further divided into two constructs, general CCE and specific CCE. Their causal relationships with two other constructs, performance and outcome expectations, are then investigated using longitudinal data collected from 188 groups of students of an 18-week computer software course. The results indicate that a team’s software learning performance is strongly influenced by specific CCE and outcome expectations, which in turn are influenced by prior performance and general CCE. Finally, prior performance of a group has no direct impact on its subsequent performance.     

Boosting trainees' expectations of success through knowledge of performance norms

A field study of the role of performance norms in computer training shows that norms have an influential impact on expectations and anticipated satisfaction.     

Self‐efficacy,learning method appropriation and software skills acquisition in learner‐controlled CSSTS environments

A computer‐simulated software training system (CSSTS) delivers a specific form of computer‐based training in which participants are allowed to select various training features within a simulated software environment. Given the growing use of these systems as end‐user training (EUT) aids, there is a need for greater understanding of how participants use these systems, as well as whether participant‐controlled learning environments are truly effective. The present research examines how a particular learner characteristic, software self‐efficacy, drives appropriation in a high learner control, CSSTS environment. Contrary to notions in the literature, results from spreadsheet and database software training courses reveal that pre‐training specific software self‐efficacy constitutes a significant, negative predictor of faithful appropriations of the CSSTS. This research also establishes a positive relationship between faithful appropriation and increases in software self‐efficacy (SSE). In essence, faithful appropriations lead to greater increases in SSE, which influences software skills performance. In addition, the research validates prior EUT research by extending prior findings to a database training environment. A psychometrically sound measure is put forth to capture database self‐efficacy.     

Effects of computer use during coursework on computer aversion

To test the impact of computer experience on computer aversion, 1542 students, enrolled in 32 sections of an Introductory Psychology course utilizing a computerized tutorial program were surveyed. The 32 sections were also randomly assigned to three teaching formats that involved differing levels of computer tutorial usage. Measures of computer aversion, computer attitudes, and computer anxiety were administered three times during the course and analyzed to reveal differential change patterns stemming from courseware use which were based upon gender and preexisting computer anxiety levels. These observed changes are discussed in relation to students' reinforcement, efficacy, and outcome expectations from computer use.     

Predicting individual differences in performance on computer-administered tests and tasks: Development of the computer aversion scale

This article describes the development of a theoretically-based instrument to measure aversive reactions to computers. Previous efforts to assess computer averse states are reviewed, and it is argued that a self-report instrument could be useful in detecting negative reactions to computers. The Computer Aversion Scale (CAVS) is based on three expectancy categories (efficacy, outcome, and reinforcement expectations) derived from social learning theory. Data analyses demonstrated moderate to high reliability and validity estimates for CAVS total score and three expectancy subscales. In addition, a study using a modified CAVS was shown to predict poor performance on a computer-based test of cognitive abilities and to moderate a relationship between mood and memory measures. Implications of CAVS findings for psychological assessment via computer are discussed.     

Occupational differences in computer-related anxiety: implications for the implementation of a computerized patient management information system

The present investigation was concerned with the implementation of an information system within a health care setting. A large number of staff required training on a new patient management information system (MIS). The aim of this study was to assess occupational differences on a number of psychological variables associated with MIS success. Computer anxiety was of primary concern due to its relationship to avoidance of computers. A total of 175 questionnaires were distributed, with 103 questionnaires being completed and returned for analysis (61.1%). It was found that clerical/administrative staff had significantly higher self-efficacy regarding computer use and more experience with computers. The nursing group experienced significantly more, computer anxiety, negative attitudes, and negative expectations than the clerical group. Self-efficacy was found to be the best predictor of computer related anxiety. The results have implications for MIS implementation strategies particularly in the areas of training and resource allocation.     

Survey of handheld computing among medical students

The purpose of this study was to identify trends in the utilization and acceptance of handheld computers (personal digital assistants) among medical students during preclinical and clinical training. We surveyed 366 medical students and collected information on computer expertise, current handheld computer use, predicted future use, and user acceptance. Handheld computers were primarily used for personal applications by students during their preclinical training and as drug references and clinical calculators during their clinical training. In the future, all participants predicted they would use handheld computers at significantly higher rates and on a broader range of medical applications. The adoption of handheld computing was independent of user satisfaction. Those with more clinical experience were less satisfied with handheld computers, suggesting that the expectations of the more experienced users were not met. The lack of institutional support was seen as a key limitation.     

Improved Quality Output through Computer‐Based Training: An Automotive Assembly Field Study

In this article, two field experiments, conducted in an automotive assembly plant, evaluate how computer‐based training of operational sequences and related quality information can support the assembly performance of the operators. The experiments were performed during the launch of a new vehicle. A comparison was made of learning progress and quality performance between a reference group of operators that only had regular training and a test group for which some of the regular training was replaced with individual computer‐based training. Both quantitative measures of the quality output and questionnaires and observations were used to evaluate the effects of computer‐based training. The results show a clear positive difference in learning progress and improvements in quality output for the test group compared with the reference group. This combined with positive attitudes expressed by the operators and their team leaders shows that this type of training is an effective way to train operators during launches of new vehicles in automotive production.     

Understanding the effect of e-learning on individual performance: The role of digital literacy

With the diffusion of easy-to-use Web 2.0 tools, such as podcasts, blogs and wikis, e-learning has become a popular mechanism for individual training. While individuals use these tools in the hope that their training will improve their performance, this relationship is not a given. This paper proposes that an individual's level of digital literacy affects her performance through its impact on her performance and effort expectations. To explain the influence of digital literacy on the intention of individuals to continue using e-learning and their performance, we integrate the concept of digital literacy with the Unified Theory of Acceptance and Use of Technology (UTAUT) and test our model using survey data from New Zealand accountants working in small and medium-sized enterprises (SMEs). The results indicate that these relationships were significant: digital literacy on users' performance and effort expectations, performance expectations on users' intentions to continue using Web 2.0 tools, and continuance intention on performance. These findings suggest that individual digital literacy facilitates the use of e-learning, and should be considered when examining the impact of the latter on performance.     

Assessing computer anxiety: Development and validation of the Computer Anxiety Rating Scale

Despite the growing role of computers in society, some individuals may actually avoid and resist learning about computers due to their anxiety. The present study was conducted in order to develop and validate the Computer Anxiety Rating Scale (CARS), by examining the behavioral, cognitive, and affective components of computer anxiety. The CARS was shown to be a reliable and valid measure. Higher levels of computer anxiety were related to greater math and test anxiety, and to less computer experience and mechanical interest. During an actual computer interaction, greater computer anxiety was associated with lower expectations and poorer task performance, as well as with greater state anxiety, reported physiological arousal, and debilitative thoughts. These results are consistent with a cognitive-attentional theory of computer anxiety and suggest directions for future research. Consistent differences between women and men on indices of computer anxiety were not found.     

Adequacy of training data for evolutionary mining of trading rules

A crucial issue related to data mining on time-series is that of training period duration. The training horizon used impacts the nature of rules obtained and their predictability over time. Longer training horizons are generally sought, in order to discern sustained patterns with robust training data performance that extends well into the predictive period. However, in dynamic environments patterns that persist over time may be unavailable, and shorter-term patterns may hold higher predictive ability, albeit with shorter predictive periods. Such potentially useful shorter-term patterns may be lost when the training duration covers much longer periods. Too short a training duration can, of course, be susceptible to over-fitting to noise. We conduct experiments using different training horizons with daily-data for the S&P500 index and report the sensitivity of the performance of the obtained rules with respect to the training durations. We show that while the performance of the rules in the training period is important for inducing the “best” rules, it is not indicative of their performance in the test-period and propose alternative measures that can be used to help identify the appropriate training durations.     

A study of student satisfaction in a blended e-learning system environment

This study proposes a research model that examines the determinants of student learning satisfaction in a blended e-learning system (BELS) environment, based on social cognitive theory. The research model is tested using a questionnaire survey of 212 participants. Confirmatory factor analysis (CFA) was performed to test the reliability and validity of the measurements. The partial least squares (PLS) method was used to validate the measurement and hypotheses. The empirical findings indicate that computer self-efficacy, performance expectations, system functionality, content feature, interaction, and learning climate are the primary determinants of student learning satisfaction with BELS. The results also show that learning climate and performance expectations significantly affect learning satisfaction. Computer self-efficacy, system functionality, content feature and interaction significantly affect performance expectations. Interaction has a significant effect on learning climate. The findings provide insight into those factors that are likely significant antecedents for planning and implementing a blended e-learning system to enhance student learning satisfaction.     

Factors affecting e-training adoption: an examination of perceived cost,computer self-efficacy and the technology acceptance model

The objective of this paper is to highlight the role that perceived cost, computer self-efficacy and the technology acceptance model (TAM) constructs have in e-training adoption in the Nigerian civil service. With the use of the SmartPLS 2.0 M3 software, a framework that included perceived cost, computer self-efficacy and TAM constructs (perceived usefulness (PU) and perceived ease of use (PEOU)) was tested on 450 heads of departments. Scales on perceived cost, computer self-efficacy, PU and PEOU developed by previous studies were modified accordingly and used. This paper found perceived cost to have a significant effect on e-training adoption. Computer self-efficacy was statistically insignificant through PEOU. In addition, PEOU had an indirect effect through PU. Therefore, only PU of the TAM constructs indicated strong predictive strength in e-training adoption. This paper showed that with e-training adoption, facilities such as computers can be put in place, which can improve the outlook of the civil service. In addition, the cost that is usually associated with traditional training can be reduced, increasing the opportunity to invest in other aspects of the civil service which can help in adding to the overall performance. Relationships were examined in this paper, which were shown to be favourable to e-training adoption in the Nigerian civil service.     

Prior negative experiences: their impact on computer training outcomes

This study examined how a person's negative experiences with computers prior to a training class interacted with their motivation to predict their performance in a computer class. Participants were 384 students who completed a computer course that was designed to introduce them to personal computers and a popular software package. Questionnaires were administered to the participants on the first day of the course to assess their prior negative experiences with computers, along with their computer attitudes, achievement motivation, and computer knowledge. Performance was measured objectively throughout the course with tests over the relevant material. Results indicated some evidence that the participants' pre-training motivation was moderated by their prior negative experiences when predicting test performance.     

An empirical investigation of sources of application-specific computer-self-efficacy and mediators of the efficacy—performance relationship

Integrating theory from research on training, computer self-efficacy (CSE), and motivation, this research validated a model of four factors that contribute to application-specific computer self-efficacy (AS-CSE) formation (previous experience, personality, learning goal orientation and computer anxiety) and three factors that mediate the relationship between AS-CSE and performance (goal level, goal commitment and performance goal orientation (PGO)). Using data from 313 individuals in an introductory computer skills course, the model was validated for database software. Results indicate that previous database software experience, trainee personality and learning goal orientation were positively related to AS-CSE, whereas computer anxiety was negatively related to AS-CSE. Additionally, goal level, goal commitment and PGOs were related to database test performance, and the relationship between AS-CSE and database test performance was also partially mediated by goal level and goal commitment. Implications for research and practice are discussed.     

Learning to use a word-processing system as a function of training strategy

Three training strategies were evaluated for their effectiveness in teaching naive computer users to use a word-processing system. One hundred and thirty five women ranging in age from 25 years to 70 years participated in the study. Subjects were trained using one of three techniques (instructor, manual, computer( to perform basic word-processing tasks. The effectiveness of the training strategies was assessed by examining performance on basic word-processing tasks such as typing a letter or memo and editing an existing file. Results showed that for all subjects, computer-based training was a less effective teaching method than either instructor- or manual-based training. In general, subjects who were trained using the computer-based method attempted and completed fewer tasks, took longer to perform tasks, and also made more errors. These findings demonstrate the need for directing efforts towards the development of appropriate training methods for computer tasks     

Using OVA modeling to improve classification performance for large datasets

One-Versus-All (OVA) classification is a classifier construction method where a k-class prediction task is decomposed into k 2-class sub-problems. One base model is constructed for each sub-problem and the base models are then combined into one model. Aggregate model implementation is the process of constructing several base models which are then combined into a single model for prediction. In essence, OVA classification is a method of aggregate modeling. This paper reports studies that were conducted to establish whether OVA classification can provide predictive performance gains when large volumes of data are available for modeling as is commonly the case in data mining. It is demonstrated in this paper that firstly, OVA modeling can be used to increase the amount of training data while at the same time using base model training sets whose size is much smaller than the total amount of available training data. Secondly, OVA models created from large datasets provide a higher level of predictive performance compared to single k-class models. Thirdly, the use of boosted OVA base models can provide higher predictive performance compared to un-boosted OVA base models. Fourthly, when the combination algorithm for base model predictions is able to resolve tied predictions, the resulting aggregate models provide a higher level of predictive performance.     

Computer anxiety in individuals with serious mental illness

This study was designed to compare computer anxiety in individuals with severe mental illness to a normative college sample and to examine whether computer training would be effective in reducing computer anxiety in individuals with severe mental illnesses. The Computer Anxiety Rating Scale (CARS) was used to assess the self-reported computer anxiety of 30 individuals with severe psychiatric disorders enrolled in computer training at a mental health facility's employment training center. The CARS was administered prior to training, after 1 month of training, and after 3 months. Anxiety levels before computer training began were compared to the computer anxiety levels of 35 community college students enrolled in a course providing similar computer training. The individuals with severe mental illness were found to have significantly higher initial computer anxiety scores than the comparison group. Computer anxiety was not significantly reduced by the computer training in either group.     

Human factors and usability in service quality measurement

The purpose of this study was to develop a modified measurement instrument for service quality that includes human factor considerations. Tangibles, reliability, responsiveness, assurance, and empathy were dimensions commonly used to measure service quality through a survey instrument termed SERVQUAL. A sixth dimension, usability, was added in a modified survey instrument termed SERVUSE. To examine the predictive power of both instruments, 200 patients at an on‐campus health clinic were surveyed. The survey measured subject expectations and perceptions regarding the service system. Gap scores were calculated as the difference between these two measures. Positive gap scores reflected the exceeding of customer expectations. Negative gap scores reflected a failure to meet these expectations. The three response variables were perceived quality, satisfaction, and behavioral intention. Usability was found to be a significant predictor of all response variables. It also adds significant predictive value to the regression models when measuring behavioral intention. © 2008 Wiley Periodicals, Inc.     

Age related differences in learning to use a text-editing system

The increased use of computer technology in most occupations means that many middle-aged and older workers who lack computer experience will need to acquire skills to interact with this technology. This study examined age-group differences in learning to use a text-editing system. One hundred and thirty-five females ranging in age from 25 to 70 participated in the study. They received training under one of three training conditions: instructor-based, online or manual-based. Age differences were assessed by comparing performance on criterion tasks. Results indicated significant differences among the groups in learning success, younger learners being the most successful. There were no significant age by training interactions. The findings indicate a need to develop more effective training strategies to teach older learners to use computer technologies. They also indicate the need for changes in system design because computer applications are difficult for novice users to learn.