The Pikachu effect: Social and health gaming motivations lead to greater benefits of Pokémon GO use

Several studies provided evidence for the effectiveness of gamification of health behaviors (e.g., exercising or walking) via video games. However, little is known about whether individuals who endorse specific gaming motivations are more likely to derive health benefits from gaming. Building upon previous studies on gamification of health behaviors, we examined whether specific gaming motives (e.g., potential health benefits) influenced Pokémon GO gaming time and gaming health outcomes, a phenomenon we termed The Pikachu Effect. We introduced health motivation as a novel component among previously established gaming motives, which reflects the ongoing gameplay transformation and increased health potential of modern games. Volunteers (N = 444) completed two measurements of Pokémon GO motivations, gaming time, physical activity, and time spent outdoors within a 6 week interval. The results supported the Pikachu effect hypothesis as health motivation and social motivation were related to health outcomes. Furthermore, we replicated previous findings indicating that individuals who spent more time playing Pokémon GO were more physical active. We also evidenced a new Pokémon GO playing behavioral benefit, i.e., increased time spent outdoors among more active players. We explored meaningful individual differences; for example, men played more and derived more benefits from Pokémon GO. These findings suggest that health motivation operates in parallel with previously identified main motives for gaming. Accounting for motivation is essential for a more accurate prediction of gaming time and gaming-related health behaviors.     

A tool for help or harm? How associations between social networking use,social support,and mental health differ for sexual minority and heterosexual youth

Although use of social networking sites has been linked to both positive and negative changes in young people's mental health, it is likely that these contributions may vary based on users' motivations and social status. For sexual minority youth, for example, the sites could provide means for social support and connections with like-minded others. Accordingly, our study sought to examine the relations between sexual minority youth's social networking site use and their social support, sexual identity strength, and mental health. We conducted an online survey, sampling 146 sexual minority youth respondents (M = 21 years; SD = 2.87 years) and 477 heterosexual youth respondents (M = 20 years; SD = 2.76 years). Results indicated that although both sexual minority and heterosexual youth use social networking sites at equal rates, sexual minority youth indicated that they use sites more for identity development and social communication. Moreover, using sites for general identity expression or exploration predicted negative mental health outcomes, whereas using sites specifically for sexual identity development predicted positive mental health outcomes. These results provide greater insight into how social networking sites may impact the mental health of marginalized groups, and provide a framework for understanding differences in social networking site use by sexuality.     

A time-aware spatio-textual recommender system

Location-Based Social Networks (LBSNs) allow users to post ratings and reviews and to notify friends of these posts. Several models have been proposed for Point-of-Interest (POI) recommendation that use explicit (i.e. ratings, comments) or implicit (i.e. statistical scores, views, and user influence) information. However the models so far fail to capture sufficiently user preferences as they change spatially and temporally. We argue that time is a crucial factor because user check-in behavior might be periodic and time dependent, e.g. check-in near work in the mornings and check-in close to home in the evenings. In this paper, we present two novel unified models that provide review and POI recommendations and consider simultaneously the spatial, textual and temporal factors. In particular, the first model provides review recommendations by incorporating into the same unified framework the spatial influence of the users’ reviews and the textual influence of the reviews. The second model provides POI recommendations by combining the spatial influence of the users’ check-in history and the social influence of the users’ reviews into another unified framework. Furthermore, for both models we consider the temporal dimension and measure the impact of time on various time intervals. We evaluate the performance of our models against 10 other methods in terms of precision and recall. The results indicate that our models outperform the other methods.     

Social presence in relation to students' satisfaction and learning in the online environment: A meta-analysis

Social presence, the ability to perceive others in an online environment, has been shown to impact student motivation and participation, actual and perceived learning, course and instructor satisfaction, and retention in online courses; yet very few researchers have attempted to look across contexts, disciplinary areas, or measures of social presence. This meta-analysis allowed us to look across these variables of the primary studies and identify the pattern of student outcomes (e.g., perceived learning and satisfaction) in relation to social presence through scrutiny of differences between the studies. The results showed a moderately large positive average correlation between social presence and satisfaction (r = 0.56, k = 26) and social presence and perceived learning (r = 0.51, k = 26). Large variation among correlations (86.7% for satisfaction and 92.8% for perceived learning, respectively) also indicated systematic differences among these correlations due to online course settings. We found that (a) the strength of the relationship between social presence and satisfaction was moderated by the course length, discipline area, and scale used to measure social presence; and (b) the relationship between social presence and perceived learning was moderated by the course length, discipline area, and target audience of the course. Implications and future research are discussed.     

Effects of measurement errors in predictor selection of linear regression model

Measurement errors may affect the predictor selection of the linear regression model. These effects are studied using a measurement framework, where the variances of the measurement errors can be estimated without setting too restrictive assumptions about the measurement model. In this approach, the problem of measurement is solved in a reduced true score space, where the latent true score is multidimensional, but its dimension is smaller than the number of the measurable variables. Various measurement scales are then created to be used as predictors in the regression model. The stability of the predictor selection as well as the estimated predicted validity and the reliability of the prediction scales is examined by Monte Carlo simulations. Varying the magnitude of the measurement error variance four sets of predictors are compared: all variables, a stepwise selection, factor sums, and factor scores. The results indicate that the factor scores offer a stable method for predictor selection, whereas the other alternatives tend to give biased results leading more or less to capitalizing on chance.     

A Measurement and Structural Model for Usability Evaluation of Shared Workspace Groupware

This study presents the development process of a set of questionnaire items to establish a measurement model for the usability of shared workspace groupware systems, which is suggested as a usability scale called SWUS, the Shared Workspace Usability Scale. Manifest variables and latent variables are based on the various dimensions of teamwork collated through the literature. A structural model was built on the measurement model. Models were evaluated through PLS-SEM methods. Data acquired on candidate questionnaire items from 398 international respondents who are users of five different online collaborative word processors was used for the model analysis. Of 37 candidate manifest variables, 22 were retained, which were measuring seven latent constructs: “3C Mechanisms,” “Grounding,” “Team Integration,” “Communication,” “Shared Access,” “Awareness,” and “Usability.” The data provided empirical evidence for the structural model based on these latent variables. The responses of the participants were not sensitive to differences between users in terms of gender and native language but showed sensitivity to age, experience with the evaluated software, and different shared workspace groupware evaluated in the study. Our structural model attempts to integrate several frameworks and models of usability for CSCW environments and provides empirical evidence for its reliability and validity based on subjective responses from users of shared workspace groupware.     

Effect of remote sensing spatial resolution on interpreting tower-based flux observations

Validation comparisons between satellite-based surface energy balance models and tower-based flux measurements over heterogeneous landscapes can be strongly influenced by the spatial resolution of the remote sensing inputs. In this paper, a two-source energy balance model developed to use thermal and visible /near-infrared remotely sensed data is applied to Landsat imagery collected during the 2004 Soil Moisture Experiment (SMEX04) conducted in southern Arizona. Using a two dimensional flux-footprint algorithm, modeled surface fluxes are compared to tower measurements at three locations in the SMEX04 study area: two upland sites, and one riparian site. The effect of pixel resolution on evaluating the performance of the land surface model and interpreting spatial variations of land surface fluxes over these heterogeneous areas is evaluated. Three Landsat scenes were examined, one representing the dry season and the other two representing the relatively wet monsoon season. The model was run at three resolution scales: namely the Landsat visible/near-infrared band resolution (30 m), the Landsat 5 thermal band resolution (120 m), and 960 m, which is nominally the MODIS thermal resolution at near-nadir. Comparisons between modeled and measured fluxes at the three tower sites showed good agreement at the 30 m and 120 m resolutions — pixel scales at which the source area influencing the tower measurement (∼ 100 m) is reasonably resolved. At 960 m, the agreement is relatively poor, especially for the latent heat flux, due to sub-pixel heterogeneity in land surface conditions at scales exceeding the tower footprint. Therefore in this particular landscape, thermal data at 1-km resolution are not useful in assessing the intrinsic accuracy of the land-surface model in comparison with tower fluxes. Furthermore, important spatial patterns in the landscape are lost at this resolution. Currently, there are no definite plans supporting high resolution thermal data with regular global coverage below ∼ 700 m after Landsat 5 and ASTER fail. This will be a serious problem for the application and validation of thermal-based land-surface models over heterogeneous landscapes.     

The effect of mobile messaging during a conversation on impression formation and interaction quality

This paper presents two experimental studies investigating the impact of mobile messaging during an offline conversation on relational outcomes. A first study examined the impact on impression formation. A 3 × 1 experiment revealed that phone users were perceived as significantly less polite and attentive, and that self-initiated messaging behavior led to more negative impression formation than messaging behavior in response to a notification. A second study examined the impact on perceived conversation quality and social attraction. A 2 × 2 experiment revealed that perceived conversation quality was negatively affected by co-present mobile messaging behavior, while social attraction was not. Whether persons were acquainted or not with the phone user did not moderate this relationship.     

Measurement equivalence and latent mean differences of personality scores across different media and proctoring administration conditions

Despite substantial interest and research in measuring personality, little is known about the measurement equivalence and mean differences in scores on personality measures across different administration conditions. The aim of the present study was to assess measurement equivalence and latent and observed mean differences of scores on the Big Five factor markers from the International Personality Item Pool across three conditions: paper-and-pencil proctored, computer-based proctored, and computer-based non-proctored. Undergraduate students (N = 401) from a Midwestern university responded to the personality questionnaire in one of the three conditions. Results indicated configural, metric, scalar, and invariant uniqueness equivalence for four of the five scales across the three conditions; Conscientiousness scores showed partial metric equivalence across computer-based proctored and computer-based non-proctored conditions. Apart from latent and observed mean differences for Emotional Stability scores in paper-and-pencil proctored vs. computer-based non-proctored conditions, no significant differences were found for the other four personality scales. These findings justify both collection and comparison of personality data using the Big Five factor markers and similar personality assessments across the three conditions. Future research should attempt to replicate the findings of the current study in high-stakes environments.     

Investigating the reliability of second-order formative measurement in information systems research

Recently, a few studies empirically explored the stability of first-order formative measurement, and raised concerns with its estimation reliability. Interpretational confounding, the disparity in the nominal and empirical meaning of a formatively measured construct, is at the center stage of the concern. Our study examines the issue in the context of the higher-order abstraction, focusing on the formatively defined relationship between the second-order construct and its indicators (i.e., first-order latent variables). Although the second-order formative abstraction is a widely accepted practice in structural equation modeling, the estimation results have been given a blind faith with no attempt to evaluate their integrity. Our empirical test, therefore, constitutes an attempt to fill the void. This study observed moderations of the theoretical relationship between reflectively designed first-order constructs and formatively defined second-order constructs when there is a change of endogenous variables. For this, two different formatively defined second-order constructs (i.e., IT management capabilities and IT personnel expertise) are utilized for the empirical testing. The estimation reveals that, while there was a considerable moderation of weights between IT management capabilities and its first-order constructs, those between IT personnel expertise and its first-order constructs remained relatively stable. These results demonstrate that the formatively defined relationship between the first- and second-order constructs can be precarious depending on the choice of the dependent variables. The analysis, therefore, revealed a significant presence of interpretational confounding and a higher chance of Type 1 error in model estimation. This implies that it becomes difficult to retain the construct validity and external validity of a formatively defined second-order construct. Thus, researchers are encouraged to exercise caution in mobilizing the formatively defined second-order measurement.     

Spatially aware feature selection and weighting for object retrieval

Many recent image retrieval methods are based on the “bag-of-words” (BoW) model with some additional spatial consistency checking. This paper proposes a more accurate similarity measurement that takes into account spatial layout of visual words in an offline manner. The similarity measurement is embedded in the standard pipeline of the BoW model, and improves two features of the model: i) latent visual words are added to a query based on spatial co-occurrence, to improve query recall; and ii) weights of reliable visual words are increased to improve the precision. The combination of these methods leads to a more accurate measurement of image similarity. This is similar in concept to the combination of query expansion and spatial verification, but does not require query time processing, which is too expensive to apply to full list of ranked results. Experimental results demonstrate the effectiveness of our proposed method on three public datasets.     

Approximate Bayesian inference in spatial GLMM with skew normal latent variables

Spatial generalized linear mixed models are common in applied statistics. Most users are satisfied using a Gaussian distribution for the spatial latent variables in this model, but it is unclear whether the Gaussian assumption holds. Wrong Gaussian assumptions cause bias in the parameter estimates and affect the accuracy of spatial predictions. Thus, there is a need for more flexible priors for the latent variables, and to perform efficient inference and spatial prediction in the resulting models. In this paper we use a skew normal prior distribution for the spatial latent variables. We propose new approximate Bayesian methods for the inference and spatial prediction in this model. A key ingredient in our approximations is using the closed skew normal distribution to approximate the full conditional for the latent variables. Our approximate inference and spatial prediction methods are fast and deterministic, using no sampling based strategies. The results indicate that the skew normal prior model can give better predictions than the normal model, while avoiding overfitting.     

Local influence analysis of nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models

Nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models are proposed to identify the relationship between manifest variables and latent variables in modern educational, medical, social and psychological studies. The nonignorable missing mechanism is specified by a logistic regression model. An EM algorithm is developed to obtain the maximum likelihood estimates of the structural parameters and parameters in the logistic regression model. Assessment of local influence is investigated in nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models on the basis of the conditional expectation of the complete-data log-likelihood function. Some local influence diagnostics are obtained via observations of missing data and latent variables that are generated by the Gibbs sampler and Metropolis-Hastings algorithm on the basis of the conformal normal curvature. A simulation study and a real example are used to illustrate the application of the proposed methodologies.     

Data augmentation strategies for the Bayesian spatial probit regression model

The well known latent variable representation of the Bayesian probit regression model due to Albert and Chib (1993) allows model fitting to be performed using a simple Gibbs sampler. In addition, various types of dependence among categorical outcomes not explained by covariate information can be accommodated in a straightforward manner as a result of this latent variable representation of the model. One example of this is the spatial probit regression model for spatially-referenced categorical outcomes. In this setting, commonly used covariance structures for describing residual spatial dependence in the normal linear model setting can be imbedded into the probit regression model. Capturing spatial dependence in this way, however, can negatively impact the performance of MCMC model-fitting algorithms, particularly in terms of mixing and sensitivity to starting values. To address these computational issues, we demonstrate how the non-identifiable spatial variance parameter can be used to create data augmentation MCMC algorithms. We compare the performance of several non-collapsed and partially collapsed data augmentation MCMC algorithms through a simulation study and an analysis of land cover data.     

Two-way analysis of high-dimensional collinear data

We present a Bayesian model for two-way ANOVA-type analysis of high-dimensional, small sample-size datasets with highly correlated groups of variables. Modern cellular measurement methods are a main application area; typically the task is differential analysis between diseased and healthy samples, complicated by additional covariates requiring a multi-way analysis. The main complication is the combination of high dimensionality and low sample size, which renders classical multivariate techniques useless. We introduce a hierarchical model which does dimensionality reduction by assuming that the input variables come in similarly-behaving groups, and performs an ANOVA-type decomposition for the set of reduced-dimensional latent variables. We apply the methods to study lipidomic profiles of a recent large-cohort human diabetes study.     

Bayesian modelling strategies for spatially varying regression coefficients: A multivariate perspective for multiple outcomes

The modelling of spatially varying regression effects for multivariate mortality count outcomes is investigated. Alternative approaches to spatial regression heterogeneity are considered: the multivariate normal conditional autoregressive (MCAR) model is contrasted with a flexible set of priors based on the multiple membership approach. These include spatial factor priors and a non-parametric approach based on the Dirichlet process. A case study considers varying regression effects for a bivariate suicide outcome, namely male and female suicides in 354 English local authorities with social deprivation, social fragmentation and rurality as predictors.     

Attention to news and its dissemination on Twitter: A survey

In recent years, news media have been hugely disrupted by news promotion, commentary and sharing in online, social media (e.g., Twitter, Facebook, and Reddit). This disruption has been the subject of a significant literature that has largely used AI techniques – machine learning, text analytics and network models – to both (i) understand the factors underlying audience attention and news dissemination on social media (e.g., effects of popularity, type of day) and (ii) provide new tools/guidelines for journalists to better disseminate their news via these social media. This paper provides an integrative review of the literature on the professional reporting of news on Twitter; focusing on how journalists and news outlets use Twitter as a platform to disseminate news, and on the factors that impact readers’ attention and engagement with that news on Twitter. Using the precise definition of a news-tweet (i.e., divided into user, content and context features), the survey structures the literature to reveal the main findings on features affecting audience attention to news and its dissemination on Twitter. From this analysis, it then considers the most effective guidelines for digital journalists to better disseminate news in the future.     

Quantification of interacting runtime qualities in software architectures: Insights from transaction processing in client–server architectures

ContextArchitecture is fundamental for fulfilling requirements related to the non-functional behavior of a software system such as the quality requirement that response time does not degrade to a point where it is noticeable. Approaches like the Architecture Tradeoff Analysis Method (ATAM) combine qualitative analysis heuristics (e.g. scenarios) for one or more quality metrics with quantitative analyses. A quantitative analysis evaluates a single metric such as response time. However, since quality metrics interact with each other, a change in the architecture can affect unpredictably multiple quality metrics.ObjectiveThis paper introduces a quantitative method that determines the impact of a design change on multiple metrics, thus reducing the risks in architecture design. As a proof of concept, the method is applied on a simulation model of transaction processing in client server architecture.MethodFactor analysis is used to unveil latent (i.e. not directly measurable) quality features represented by new variables that reflect architecture-specific correlations between metrics. Separate Analyses of Variance (ANOVA) are then applied to these variables, for interpreting the tradeoffs detected by factor analysis in terms of the quantified metrics.ResultsThe results for the examined transaction processing architecture show three latent quality features, the corresponding groups of strongly correlated quality metrics and the impact of architecture characteristics on the latent quality features.ConclusionThe proposed method is a systematic way for relating the variability of quality metrics and the implied tradeoffs to specific architecture characteristics.     

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to a loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Previously, a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been implemented to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However this approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. The focus is on improved inference by marginalising over latent variables to create the likelihood. More specifically, the emphasis is on how this marginalisation can improve the RJMCMC mixing and that alternative approaches that utilise the likelihood (e.g. DIC) can be investigated. For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. A tractable and accurate approximation for this quantity is provided and also other approximations based on Monte Carlo estimates that can be incorporated into RJMCMC are investigated.     

A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications

A downscaling tool was developed to provide sub-daily high spatial resolution surfaces of weather variables for distributed hydrologic modeling from NASA Modern Era Retrospective-Analysis for Research and Applications reanalysis products. The tool uses spatial interpolation and physically based relationships between the weather variables and elevation to provide inputs at the scale of a gridded hydrologic model, typically smaller (∼100 m) than the scale of weather reanalysis data (∼20–200 km). Nash-Sutcliffe efficiency (NSE) measures greater than 0.70 were obtained for direct tests of downscaled daily temperature and monthly precipitation at 173 SNOTEL sites. In an integrated test driving the Utah Energy Balance (UEB) snowmelt model, 80% of these sites gave NSE > 0.6 for snow water equivalent. These findings motivate use of this tool in data sparse regions where ground based observations are not available and downscaled global reanalysis products may be the only option for model inputs.