How leaders are persuaded: An elaboration likelihood model of voice endorsement.
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ABSTRACT:
SUBMITTER: Li X
PROVIDER: S-EPMC8130952 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:With the increasingly rapid development of Web 2.0 technologies, the application of mobile health (mHealth) care in the field of health management has become popular. Accordingly, patients are able to access consulting services and effective health information online without temporal and geographical constraints. The elaboration-likelihood model (ELM) is a dual-process persuasion theory that describes the change of attitudes and behavior. OBJECTIVE:In this study, we drew on the ELM to investigate patients' continuous usage intentions regarding mHealth services. In addition, we further examined which route-central or peripheral-has a stronger impact on a patient's usage of health care management. METHODS:To meet these objectives, five hypotheses were developed and empirically validated using a field survey to test the direct and indirect effects, via attitude, of the two routes on continuous usage intention. RESULTS:We found that patients' perceived mHealth information quality and perceived mHealth system quality had a positive effect on their personal attitudes. The results revealed that social media influence had a positive effect on a patient's attitude toward mHealth services. In particular, our findings suggest that a patient's health consciousness has a positive effect on the relationship between social media influence and attitude. CONCLUSIONS:This study contributes to the mHealth services literature by introducing the ELM as a referent theory for research, as well as by specifying the moderating role of health consciousness. For practitioners, this study introduces influence processes as policy tools that managers can employ to motivate the uptake of mHealth services within their organizations.
| S-EPMC7171561 | biostudies-literature
Project description:BackgroundMessages on one's stance toward vaccination on microblogging sites may affect the reader's decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited.ObjectiveThis study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message.MethodsEnglish-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user.ResultsContent-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001).ConclusionsThe dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics.
| S-EPMC9239316 | biostudies-literature
Project description:Employees who thrive contribute to their organization's competitive advantage and sustainable performance. The aim of this study was to explore how employees' thriving is shaped by their leaders' behavior. Drawing on social learning theory, we examined the relationship between perceived leader's helping behavior and employees' thriving. Positing voice behavior as a mediator and perceived leader's role overload as a moderator, we constructed a moderated mediation model. Using 205 daily data points from 51 employees in various industries, we found that perceived leader's helping behavior had a positive effect on employees' thriving at work and that employees' voice behavior mediated this effect. With the increase of perceived leader's role overload, the positive relationship between perceived leader's helping behavior and employees' voice behavior as well as the indirect effect of perceived leader's helping behavior on employees' thriving via employees' voice behavior were increasingly strong. The findings of our study have implications for research on employees' thriving at work, leaders' helping behavior, and social learning theory. There are also practical implications for the behavior of leaders who experience role overload.
| S-EPMC7554307 | biostudies-literature
Project description:BACKGROUND:Web-based crowdsourcing promotes the goals achieved effectively by gaining solutions from public groups via the internet, and it has gained extensive attention in both business and academia. As a new mode of sourcing, crowdsourcing has been proven to improve efficiency, quality, and diversity of tasks. However, little attention has been given to crowdsourcing in the health sector. OBJECTIVE:Crowdsourced health care information websites enable patients to post their questions in the question pool, which is accessible to all doctors, and the patients wait for doctors to respond to their questions. Since the sustainable development of crowdsourced health care information websites depends on the participation of the doctors, we aimed to investigate the factors influencing doctors' participation in providing health care information in these websites from the perspective of the elaboration-likelihood model. METHODS:We collected 1524 questions with complete patient-doctor interaction processes from an online health community in China to test all the hypotheses. We divided the doctors into 2 groups based on the sequence of the answers: (1) doctor who answered the patient's question first and (2) the doctors who answered that question after the doctor who answered first. All analyses were conducted using the ordinary least squares method. RESULTS:First, the ability of the doctor who first answered the health-related question was found to positively influence the participation of the following doctors who answered after the first doctor responded to the question (?offline1=.177, P<.001; ?offline2=.063, P=.048; ?online=.418, P<.001). Second, the reward that the patient offered for the best answer showed a positive effect on doctors' participation (?=.019, P<.001). Third, the question's complexity was found to positively moderate the relationships between the ability of the first doctor who answered and the participation of the following doctors (?=.186, P=.05) and to mitigate the effect between the reward and the participation of the following doctors (?=-.003, P=.10). CONCLUSIONS:This study has both theoretical and practical contributions. Online health community managers can build effective incentive mechanisms to encourage highly competent doctors to participate in the provision of medical services in crowdsourced health care information websites and they can increase the reward incentives for each question to increase the participation of the doctors.
| S-EPMC7367514 | biostudies-literature
Project description:Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two-way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations.
| S-EPMC4838635 | biostudies-literature
Project description:ImportanceSince 2014, all hospitals in Maryland have operated under an all-payer global budget system. Hospital global budgets have gained renewed attention as a strategy for constraining cost growth, improving patient outcomes, and preserving health care access in rural and underserved communities. Lessons from the implementation of the Maryland All-Payer Model (MDAPM) may have implications for policy makers, payers, and hospitals in other settings seeking to adopt global budgets or other value-based payment models.ObjectiveTo examine perspectives on the implementation of the MDAPM among health care leaders who participated in its design and execution.Design setting and participantsThis qualitative study with semistructured telephone interviews was conducted from November 1, 2019, to February 11, 2020. The purposive sample of Maryland health care leaders represents diverse stakeholder groups, including hospitals, state government and regulatory agencies, the federal government, and payers.Main outcomes and measuresKey high-level themes were extracted from interviews using qualitative content analysis, with barriers and facilitators to implementation specified within each theme.ResultsA total of 20 interviews were conducted with hospital leaders (n = 6), state regulators (n = 4), federal regulators (n = 4), payer representatives (n = 3), and state leaders (n = 3). Key themes were labeled as (1) expectations (setting bold yet achievable goals), (2) autonomy (allowing hospitals to follow individual strategies within MDAPM parameters), (3) communication (encouraging early and ongoing communication between stakeholders), (4) actionable data (sharing useful hospital and patient-level data between stakeholders), (5) global budget calibration (anticipating technical challenges when negotiating budgets for individual hospitals), and (6) shared commitment to change (harnessing collective motivation for system change). Together, these themes suggest that implementing the payment model followed an evolving and collaborative process that requires stakeholder communication, data to guide decisions, and commitment to operating within the new payment system.Conclusions and relevanceThe implementation of hospital global budgets in the state of Maryland offers generalizable lessons that can inform the evolution and expansion of this approach to value-based payment in other states and settings.
| S-EPMC8903109 | biostudies-literature
Project description:In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined parameter estimates and predictions. Too small of a model will not be able to describe the data whereas a model which is too large tends to overfit measurement errors and does not provide precise predictions. Typically, the model is modified and tuned to fit the data, which often results in an oversized model. To restore the balance between model complexity and available measurements, either new data has to be gathered or the model has to be reduced. In this manuscript, we present a data-based method for reducing non-linear models. The profile likelihood is utilised to assess parameter identifiability and designate likely candidates for reduction. Parameter dependencies are analysed along profiles, providing context-dependent suggestions for the type of reduction. We discriminate four distinct scenarios, each associated with a specific model reduction strategy. Iterating the presented procedure eventually results in an identifiable model, which is capable of generating precise and testable predictions. Source code for all toy examples is provided within the freely available, open-source modelling environment Data2Dynamics based on MATLAB available at http://www.data2dynamics.org/, as well as the R packages dMod/cOde available at https://github.com/dkaschek/. Moreover, the concept is generally applicable and can readily be used with any software capable of calculating the profile likelihood.
| S-EPMC5010240 | biostudies-literature
Project description:The marginal likelihood is commonly used for comparing different evolutionary models in Bayesian phylogenetics and is the central quantity used in computing Bayes Factors for comparing model fit. A popular method for estimating marginal likelihoods, the harmonic mean (HM) method, can be easily computed from the output of a Markov chain Monte Carlo analysis but often greatly overestimates the marginal likelihood. The thermodynamic integration (TI) method is much more accurate than the HM method but requires more computation. In this paper, we introduce a new method, steppingstone sampling (SS), which uses importance sampling to estimate each ratio in a series (the "stepping stones") bridging the posterior and prior distributions. We compare the performance of the SS approach to the TI and HM methods in simulation and using real data. We conclude that the greatly increased accuracy of the SS and TI methods argues for their use instead of the HM method, despite the extra computation needed.
| S-EPMC3038348 | biostudies-literature
Project description:Promotive voice is essential for improving team and organization performance. Yet in the current literature, less was known regarding the psychological reasons why people engage in promotive voice. Through the lens of social exchange, we proposed that employees who received support from colleagues may develop higher level of felt obligation for constructive change which leads to promotive voice. Analyses of multi-source data from 51 cross-functional sources (51 team supervisors and 162 employees) showed that employees' felt obligation for constructive change positively mediates the relationship between colleague support and promotive voice behavior. Moreover, the impact of colleague support on felt obligation for constructive change is stronger when there is a low level of subgroup formation in the team. Theoretical and practical implications of these findings are discussed.
| S-EPMC4492547 | biostudies-literature
Project description:A likelihood function based on the multivariate probability distribution of all observed structure-factor amplitudes from a single isomorphous replacement with anomalous scattering experiment has been derived and implemented for use in substructure refinement and phasing as well as macromolecular model refinement. Efficient calculation of a multidimensional integration required for function evaluation has been achieved by approximations based on the function's properties. The use of the function in both phasing and protein model building with iterative refinement was essential for successful automated model building in the test cases presented.
| S-EPMC2748965 | biostudies-literature