Perceived fatigue related to work has often been measured in one dimension. The main purpose of the present study was to validate a proposed five-factor model of perceived fatigue in a new sample. 597 persons, employed in five occupations with different types of work loads, rated their fatigue after work. The ratings were subjected to analyses of linear structural equation models. The results suggest a slightly revised model for perceived fatigue, still with the five dimensions: Lack of energy, Physical exertion, Physical discomfort, Lack of motivation and Sleepiness. As expected, the rating profiles describing fatigue states differed between the five occupations. On the basis of these results, a revised version of the Swedish Occupational Fatigue Inventory (SOFI) is presented.

With the shift to an information-based society and to the de-centralisation of information, information overload has attracted a growing interest in the computer and information science research communities. However, there is no clear understanding of the meaning of the term, and while there have been many proposed definitions, there is no consensus. The goal of this work was to define the concept of “information overload”. In order to do so, a concept analysis using Rodgers' approach was performed.

Social media are often criticized for being a conduit for misinformation on global health issues, but may also serve as a corrective to false information. To investigate this possibility, an experiment was conducted exposing users to a simulated Facebook News Feed featuring misinformation and different correction mechanisms (one in which news stories featuring correct information were produced by an algorithm and another where the corrective news stories were posted by other Facebook users) about the Zika virus, a current global health threat. Results show that algorithmic and social corrections are equally effective in limiting misperceptions, and correction occurs for both high and low conspiracy belief individuals. Recommendations for social media campaigns to correct global health misinformation, including encouraging users to refute false or misleading health information, and providing them appropriate sources to accompany their refutation, are discussed.

Cognitive Fatigue (CF) is an important confound impacting cognitive performance. How CF is triggered and what are the features that make a cognitive effort perceived as exhausting remain unclear. In the theoretical framework of the Time-based Resource-sharing (TBRS) model (Barrouillet et al., 2004), we hypothesized that CF is an outcome of increased cognitive load due to constrained time to process ongoing cognitive demands. We tested this cognitive load-related CF hypothesis across 2 experiments manipulating both task complexity and cognitive load induced by the processing time interval. To do so, we used the TloadDback paradigm, a working memory dual task in which high and low cognitive load levels can be individually adjusted. In Experiment 1, participants were administered a high cognitive load (HCL, short processing time interval) and a low cognitive load (LCL, large processing time interval) conditions while complexity of the task was kept constant (1-back dual task). In Experiment 2, two tasks featuring different levels of complexity were both administered at the individual's maximal processing speed capacity for each task (i.e., short processing time interval). Results disclosed higher CF in the HCL than in the LCL condition in Experiment 1. On the contrary, in Experiment 2 similar levels of CF were obtained for different levels of task complexity when processing time interval was individually adjusted to induce a HCL condition. Altogether, our results indicate that processing time-related cognitive load eventually leads to the subjective feeling of CF, and to a decrease in alertness. In this framework, we propose that the development of CF can be envisioned as the result of sustained cognitive demands irrespective of task complexity.Copyright © 2017 Elsevier Ltd. All rights reserved.

Cancer-related affect and cognition, such as cancer fear, cancer worry, and cancer risk perception, are important predictors of cancer prevention and communication behaviors. However, they have not been clearly conceptualized in cancer communication literature, and in particular, the role of affect (i.e., cancer fear) in cancer prevention and communication has not been fully investigated. The present study developed a 3-factor cancer-related mental condition model encompassing affective (cancer fear), cognitive (cancer risk perception), and affective-cognitive (cancer worry) conditions. Two studies were conducted. Study 1 developed the model with Sample 1 (U.S. undergraduates, N = 309), and subsequently validated the model with Sample 2 (Korean general population, ages 40 years or older, N = 1,130). Study 2, using Sample 2, tested the model's relationship with cancer information use, cancer information avoidance, and screening intention. While Sample 1 participants were asked about cancer in general, Sample 2 participants were asked specifically about stomach cancer. Thus, the model derived from the specific sample in a general context was confirmed via the general sample in a specific context. The results showed that both cancer worry and cancer risk perception are positively associated with cancer information use and screening intention, but they are negatively associated with cancer information avoidance. Cancer fear was positively associated with cancer information use, but it was also positively related to cancer information avoidance. Moreover, cancer fear was negatively associated with screening intention. Although the three components of the model are positively related to one another, they function differently in the cancer context.

Low health literacy remains an extremely common and problematic issue, given that individuals with lower health literacy are more likely to experience health challenges and negative health outcomes. In this study, we use the first three stages of the innovation-decision process found in the theory of diffusion of innovations (Rogers, 2003). We incorporate health literacy into a model explaining how perceived health knowledge, information sharing, attitudes, and behavior are related. Results show that health information sharing explains 33% of the variance in behavioral intentions, indicating that the communicative practice of sharing information can positively impact health outcomes. Further, individuals with high health literacy tend to share less information about heart health than those with lower health literacy. Findings also reveal that perceived heart-health knowledge operates differently than health literacy to predict health outcomes.

Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or statistical analysis. The authors argue that the choice of interpretative statistic must be based on the research objective. They demonstrate that when this is done the Fornell-Larcker testing system is internally consistent and that it conforms to the rules of correspondence for relating data to abstract variables.

Commonly used discrete choice model analyses (e.g., probit, logit and multinomial logit models) draw on the estimation of importance weights that apply to different attribute levels. But directly estimating the importance weights of the attribute as a whole, rather than of distinct attribute levels, is challenging. This article substantiates the usefulness of partial least squares structural equation modeling (PLS-SEM) for the analysis of stated preference data generated through choice experiments in discrete choice modeling. This ability of PLS-SEM to directly estimate the importance weights for attributes as a whole, rather than for the attribute’s levels, and to compute determinant respondent-specific latent variable scores applicable to attributes, can more effectively model and distinguish between rational (i.e., optimizing) decisions and pragmatic (i.e., heuristic) ones, when parameter estimations for attributes as a whole are crucial to understanding choice decisions.\n

This article addresses Rönkkö and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

The aim of this study was to assess motivation as a factor in mental fatigue using subjective, performance, and physiological measures.Sustained performance on a mentally demanding task can decrease over time. This decrement has two possible causes: a decline in available resources, meaning that performance cannot be sustained, and decrement in motivation, meaning a decline in willingness to sustain performance. However, so far, few experimental paradigms have effectively and continuously manipulated motivation, which is essential to understand its effect on mental fatigue.Twenty participants performed a working memory task with 14 blocks, which alternated between reward and nonreward for 2.5 hr. In the reward blocks, monetary rewards could be gained for good performance. Besides reaction time and accuracy, we used physiological measures (heart rate variability, pupil diameter, eyeblink, eye movements with a video distractor) and subjective measures of fatigue and mental effort.Participants reported becoming fatigued over time and invested more mental effort in the reward blocks. Even though they reported fatigue, their accuracy in the reward blocks remained constant but declined in the nonreward blocks. Furthermore, in the nonreward blocks, participants became more distractable, invested less cognitive effort, blinked more often, and made fewer saccades. These results showed an effect of motivation on mental fatigue.The evidence suggests that motivation is an important factor in explaining the effects of mental fatigue.

People differ in their openness to different types of information and some information may evoke greater avoidance than does other information. We developed an 8-item measure of people's tendency to avoid learning information. The flexible instrument can function as both a predictor and outcome measure. The results from 4 studies involving 7 samples and 4,393 participants reveal that scores on the measure are generally internally consistent, remain relatively stable across time, and correlate modestly with measures of similar constructs and with avoidance behavior. The measure is adaptable to a variety of types of information (e.g., health outcomes, attractiveness feedback) and is internally consistent in several distinct populations (e.g., high school students, college students, U.S. adults, low-socioeconomic-status adults). Discussion centers on potential uses for the scale and an online supplement discusses a 2-item version of the scale. (PsycINFO Database Record(c) 2016 APA, all rights reserved).

Research on executive functioning and on self-regulation have each identified a critical resource that is central to that domain and is susceptible to depletion. In addition, studies have shown that self-regulation tasks and executive-functioning tasks interact with each other, suggesting that they may share resources. Other research has focused specifically on restoring what we propose is the shared resource between self-regulation and executive functioning. Utilizing a theory-based natural environment intervention, these studies have found improvements in executive-functioning performance and self-regulation effectiveness, suggesting that the natural environment intervention restores this shared resource. © The Author(s) 2010.

To examine and identify the scope of research addressing health information overload in consumers.In accordance with a published protocol, six electronic databases (PubMed, CINAHL, ERIC, PsycINFO, Embase, and Scopus), reference lists of included articles, and grey literature (Google Advanced Search and WorldCat) were searched. Articles in English were included, without any limit on the date of publication.Of the 69 records included for final analysis, 22 studies specifically examined health information overload, whereas the remainder peripherally discussed the concept alongside other concepts. The 22 studies focused on one or more of the following: 1) ways to measure health information overload (multi-item/single-item scales); 2) predictors of health information overload - these included low education level, health literacy, and socioeconomic status; and 3) interventions to address information overload, such as videotaped consultations or written materials. Cancer information overload was a popular topic amongst studies that focused on information overload.Based on the identified studies, there is a clear need for future studies that investigate health information overload in consumers with chronic medical conditions other than cancer.This review is the initial step in facilitating future efforts to create health information that do not overload consumers.Copyright © 2019 Elsevier B.V. All rights reserved.

Message fatigue refers to a state of being exhausted and tired of prolonged exposure to similarly-themed messages (e.g., anti-obesity messages; So, Kim, & Cohen, 2017). This study tests a mediational model that accounts for how one's preexisting fatigue toward anti-obesity messages may contribute to two different types of resistance-reactance and disengagement-toward an incoming anti-obesity message, which, in turn, reduce intention to adopt weight-management behaviors advocated in the message. The proposed model was tested in an experimental study (N = 312) involving a sample of overweight or obese adults in the United States. In the meditational model, reactance significantly mediated the negative effects of message fatigue on intention to adopt only one of four weight-management behaviors promoted in the message. However, inattention, which was an operationalization of disengagement, significantly mediated the negative effects of message fatigue on behavioral intention to adopt all four weight-management behaviors. This study urges future research on message fatigue and resistance to persuasion to consider disengagement with a message as a significant barrier to effective health communication and to devise ways to increase engagement with messages communicating "overtaught" health issues.

This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review. The selected studies covered mainly substance addiction (N = 14, 82.4%), including smoking (N = 4), alcohol drinking (N = 3), as well as uses of cocaine (N = 4), opioids (N = 1), and multiple substances (N = 2). Other studies were non-substance addiction (N = 3, 17.6%), including gambling (N = 2) and internet gaming (N = 1). There were eight cross-sectional, seven cohort, one non-randomized controlled, and one crossover trial studies. Majority of the studies employed supervised learning (N = 13), and others employed unsupervised learning (N = 2) and reinforcement learning (N = 2). Among the supervised learning studies, five studies used ensemble learning methods or multiple algorithm comparisons, six used regression, and two used classification. The two included reinforcement learning studies used the direct methods. These results suggest that machine learning methods, particularly supervised learning are increasingly used in addiction psychiatry for informing medical decisions.Copyright © 2019 Elsevier B.V. All rights reserved.

The global outbreak of COVID-19 in 2020 has led to the dominance of COVID-19 prevention information on all media channels. Drawing on the ability–motivation model of information processing, this study examined how such an information overabundance hampered individuals’ ability and motivation to process in the era of COVID-19. With a survey conducted from 493 participants, we found that less message elaboration of COVID-19 prevention information was predicted by greater message fatigue, a state of low motivation due to information overabundance. In addition, greater message fatigue was accompanied by greater information overload, a state of low ability due to information overabundance. Moreover, certain motivation-related (i.e. health status, trait reactance and frequency of information seeking) and ability–related factors (i.e. health literacy, health status, trait anxiety and information quality) were found to be associated with message fatigue and information overload, respectively. The theoretical and practical implications are discussed.

Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

Social media platforms like Facebook, YouTube, and Twitter have become major objects of criticism for reasons such as privacy violations, anticompetitive practices, and interference in public elections. Some of these problems have been associated with algorithms, but the roles that algorithms play in the emergence of different harms have not yet been systematically explored. This article contributes to closing this research gap with an investigation of the link between algorithms and harms on social media platforms. Evidence of harms involving social media algorithms was collected from media reports and academic papers within a two-year timeframe from 2018 to 2019, covering Facebook, YouTube, Instagram, and Twitter. Harms with similar casual mechanisms were grouped together to inductively develop a typology of algorithmic harm based on the mechanisms involved in their emergence: (1) algorithmic errors, undesirable, or disturbing selections; (2) manipulation by users to achieve algorithmic outputs to harass other users or disrupt public discourse; (3) algorithmic reinforcement of pre-existing harms and inequalities in society; (4) enablement of harmful practices that are opaque and discriminatory; and (5) strengthening of platform power over users, markets, and society. Although the analysis emphasizes the role of algorithms as a cause of online harms, it also demonstrates that harms do not arise from the application of algorithms alone. Instead, harms can be best conceived of as socio-technical assemblages, composed of the use and design of algorithms, platform design, commercial interests, social practices, and context. The article concludes with reflections on possible governance interventions in response to identified socio-technical mechanisms of harm. Notably, while algorithmic errors may be fixed by platforms themselves, growing platform power calls for external oversight.

Using the backdrop of the COVID-19 pandemic, this three-wave experiment ( N = 1,830) examined whether a public health crisis motivates people to engage with complicated information about the virus in the form of jargon. Results revealed that although the presence of jargon negatively impacted message acceptance for topics that were not particularly urgent (flood risk and federal risk policy), the presence of jargon within the COVID-19 topic condition did not affect message perceptions—at first. In subsequent waves of data collection, however, it was found that the influence of jargon strengthened over time within the COVID-19 topic condition. Specifically, jargon began to exert a stronger influence on processing fluency despite the continued urgency of the topic. This finding suggests that motivation to process COVID-19 related information declined over time. Theoretical contributions for language, processing fluency, and persuasion are offered and practical implications for health, risk, science, and crisis communicators are advanced.

Branding and product differentiation should lead to distinct product positioning in the minds of consumers, which helps them to choose between products. However, the increasing numbers of similar products make it more difficult for consumers to distinguish between brands, which can lead to a loss of utility through mistaken and misinformed purchases. Despite this risk, little research has addressed the perceived product similarity construct, and there is no accepted measure or any idea of which consumers are vulnerable to it. This research develops a parsimonious scale for measuring consumers’ orientation for inferring that all products within a category are similar and identifies those vulnerable to it. Support was found for a one-dimensional six-item measure, and its reliability and validity were assessed. Cluster analysis identified three similarity groups of which one appeared highly vulnerable to seeing products as similar. Implications for consumer policy and marketplace trust are discussed.