The Effects of Information Load on Decision Making In a Decision Support Environment

The conflicting results of previous studies examining DSS effectiveness suggest that other factors may be affecting a user’s ability to process information. Several research studies in the marketing, accounting and psychology disciplines have examined the effects information load has on decision quality involving manual decision making tasks. Their results strongly indicate that decision-makers working under increased loads of information beyond an optimal point perform poorly or render poorer decisions. This study examines the relationship between information load and decision quality in a DSS (computer-aided problem solving) environment. The results suggest that in spite of information technology’ s support, information load can affect a user’s decisions

A Comparative Analysis of Manual and Computer-Aided Ranking Tasks for Curriculum Development

Inconsistencies in judgement during a manual ranking task can prevent the clear identification of underlying (ranking) policy. AHP (analytical hierarchy process) provides an alternative to overcoming this problem. This study examines these methods in the context of IS curriculum development for their ability to accurately capture the policies of twenty-eight judges. A cluster analysis based on the rankings identifies their underlying policies, and thereby suggests the core courses for the curriculum. The results demonstrate the AHP’s ability to capture more consistent ranking policies, and thereby produce clusters of higher predictive quality

Classifying Network Intrusions: A Comparison of Data Mining Methods

Network intrusion is an increasingly serious problem experienced by many organizations. In this increasingly hostile environment, networks must be able to detect whether a connection attempt is legitimate or not. The ever-changing nature of these attacks makes them difficult to detect. One solution is to use various data mining methods to determine if the network is being attacked. This paper compares the performance of two data mining methods— i.e., a standard artificial neural network (ANN) and an ANN guided by genetic algorithm (GA)— in classifying network connections as normal or attack. Using connection data drawn from a simulated US Air Force local area network each method was used to construct a predictive model. The models were then applied to validation data and the results were compared. The ANN guided by GA (90.67% correct classification) outperformed the standard ANN (81.75% correct classification) significantly, indicating the superiority of GAbased ANN

Integrating the IT/IS Professional Community with IT/IS Academic Programs

Developing a successful IT/IS curriculum requires departments to understand the needs of their constituents, organizations that hire their graduates. As many recent studies have revealed, the success of an IT/IS graduate rests on the possession of both non-technical and technical skills. Furthermore, a greater understanding of how IT can be applied to solving organizational problems is sought. This study presents the findings of a recent national survey that asked respondents to rank the importance of certain skills and academic/profession community involvement. The results suggest IT/IS curricula should emphasize developing professional skills, such as work ethic, problem solving, and oral and team communication skills in students. By the same token, ways should be sought to integrate professional experiences into curricula for developing these skills

Selecting the number of factors in principal component analysis by permutation testing Numerical and practical aspects

[EN] Selecting the correct number of factors in principal component analysis (PCA) is a critical step to achieve a reasonable datamodelling,where the optimal strategy strictly depends on the objective PCA is applied for. In the last decades, much work has been devoted to methods like Kaiser's eigenvalue greater than 1 rule, Velicer's minimum average partial rule, Cattell's scree test, Bartlett's chi-square test, Horn's parallel analysis, and cross-validation. However, limited attention has been paid to the possibility of assessing the significance of the calculated components via permutation testing. That may represent a feasible approach in case the focus of the study is discriminating relevant fromnonsystematic sources of variation and/or the aforementioned methodologies cannot be resorted to (eg, when the analysed matrices do not fulfill specific properties or statistical assumptions). The main aim of this article is to provide practical insights for an improved understanding of permutation testing, highlighting its pros and cons,mathematically formalising the numerical procedure to be abided bywhen applying it for PCA factor selection by the description of a novel algorithm developed to this end, and proposing ad hoc solutions for optimising computational time and efficiency.Spanish Ministry of Economy and Competitiveness, Grant/Award Number: DPI2014-55276-C5-1R; Shell Global Solutions International B.V.Vitale, R.; Westerhuis, JA.; Naes, T.; Smilde, AK.; De Noord, OE.; Ferrer, A. (2017). Selecting the number of factors in principal component analysis by permutation testing Numerical and practical aspects. Journal of Chemometrics. 31(12):1-15. doi:10.1002/cem.2937S115311

Implicit theories of online trolling: evidence that attention-seeking conceptions are associated with increased psychological resilience.

Three studies were conducted to investigate people’s conceptions of online trolls, particularly conceptions associated with psychological resilience to trolling. In Study 1, factor analytic analysis of participants’ ratings of characteristics of online trolls found a replicable bifactor model of conceptions of online trolls, with both a general factor of general conceptions towards online trolls being identified, but five group factors (attention-conflict seeking, low selfconfidence, viciousness, uneducated, amusement) as most salient. In Study 2, participants evaluated hypothetical profiles of online trolling messages to establish the validity of the five factors. Three constructs (attention-conflict seeking, viciousness, and uneducated) were actively employed when people considered profiles of online trolling scenarios. Study 3 introduced a 20-item ‘Conceptions of Online Trolls scale’ to examine the extent to which the five group factors were associated with resilience to trolling. Results indicated that viewing online trolls as seeking conflict or attention was associated with a decrease in individuals' negative affect around previous trolling incidents. Overall, the findings suggest that adopting an implicit theories approach can further our understanding and measurement of conceptions towards trolling through the identification of five salient factors, of which at least one factor may act as a resilience strategy

The Number of Factors Problem

This chapter focuses on formal criteria to assess the dimensionality for exploratory factor modelling with the aim to facilitate the selection of a proper criterion in empirical practice. It introduces the different foundations that underlie the various criteria and provides an overview of currently available formal criteria, which we selected on the basis of their popularity in empirical practice and/or proven effectiveness. The chapter successively reviews principal component analysis (PCA)‐based methods and common factor analysis (CFA)‐based methods to assess the number of common factors. To assess the number of factors underlying an empirical data set, the chapter suggests some strategies. It explains the finding in many studies that the Kaiser criterion clearly yields inaccurate indications of the number of PCs and common factors, mostly indicating too many factors. Minimum average partial (MAP) performances in indicating the number of major factors deteriorated when the unique variances increased, with no clear tendency to over‐ or underindicate the number of factors

Revisiting the five-facet structure of mindfulness

The current study aimed to replicate the development of the Five-Facet Mindfulness Questionnaire (FFMQ) in a sample of 399 undergraduate students. We factor analyzed the Mindful Attention and Awareness Questionnaire (MAAS), the Freiburg Mindfulness Scale, the Southampton Mindfulness Questionnaire (SMQ), the Cognitive Affective Mindfulness Scale Revised (CAMS-R), and the Kentucky Inventory of Mindfulness Skills (KIMS), but also extended the analysis by including a conceptually related measure, the Philadelphia Mindfulness Scale (PHLMS), and a conceptually unrelated measure, the Langer Mindfulness Scale (LMS). Overall, we found a partial replication of the five-factor structure, with the exception of non-reacting and non-judging which formed a single factor. The PHLMS items loaded as expected with theoretically related factors, whereas the LMS items emerged as separate factor. Finally, we found a new factor that was mostly defined by negatively worded items indicating possible item wording artifacts within the FFMQ. Our conceptual validation study indicates that some facets of the FFMQ can be recovered, but item wording factors may threaten the stability of these facets. Additionally, measures such as the LMS appear to measure not only theoretically, but also empirically different constructs

The development and validation of the Addiction-like Eating Behaviour Scale

Background: Overeating and obesity are frequently attributed to an addiction to food. However, there is currently a lack of evidence to support the idea that certain foods contain any specific addictive substance. An alternative approach is to focus on dimensions of observable behaviour, which may underpin a behavioural addiction to eating. To facilitate this, it is necessary to develop a tool to quantify addiction-like eating behaviour, which is not based on the clinical criteria for substance dependence. The current study provides initial validation of the Addiction-like Eating Behaviour Scale (AEBS). Methods: English speaking male and female participants (N=511) from a community sample completed the AEBS, alongside a range of other health- and eating-related questionnaires including the Yale Food Addiction Scale (YFAS) and Binge Eating Scale (BES). Participants also provided their height and weight to enable calculation of body mass index (BMI). Finally, to assess test–retest reliability, an additional 70 participants completed the AEBS twice, 2 weeks apart. Results: Principle components analysis revealed that a two-factor structure best accounted for the data. Factor 1 consisted of items that referred to appetitive drive, whereas factor two consisted of items that referred to dietary control practices. Both subscales demonstrated good internal reliability and test–retest reliability, and a confirmatory factor analysis confirmed the two-factor scale structure. AEBS scores correlated positively with body mass index (BMI) (P<0.001) and other self-report measures of overeating. Importantly, the AEBS significantly predicted variance in BMI above that accounted for by both the YFAS and BES (P=0.027). Conclusions: The AEBS provides a valid and reliable tool to quantify the behavioural features of a potential ‘eating addiction’. In doing so, the AEBS overcomes many limitations associated with applying substance-dependence criteria to eating