Dataset Information

How leaders are persuaded: An elaboration likelihood model of voice endorsement.

ABSTRACT: Organizations need both employee voice and managerial endorsement to ensure high-quality decision-making and achieve organizational effectiveness. However, a preponderance of voice research focuses on employee voice with little attention paid to voice endorsement. Building on the social persuasion theory of the elaboration likelihood model, we systematically examine the sender and receiver determinants of voice endorsement and how the interplay of those determinants affects voice endorsement. By empirically analyzing 168 paired samples, we find that issue-relevant information, i.e., voicer credibility, has a positive effect on voice endorsement and matters most when leaders have high felt obligation. The results also show that the peripheral cue used in the study, i.e., positive mood, has a positive effect on voice endorsement and matters most when leaders have low felt obligation or low cognitive flexibility. We discuss the contributions of these findings and highlight limitations and directions for future research.

SUBMITTER: Li X

PROVIDER: S-EPMC8130952 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

How leaders are persuaded: An elaboration likelihood model of voice endorsement.

Li Xiaobo X Wu Ting T Ma Jianhong J

PloS one 20210518 5

Organizations need both employee voice and managerial endorsement to ensure high-quality decision-making and achieve organizational effectiveness. However, a preponderance of voice research focuses on employee voice with little attention paid to voice endorsement. Building on the social persuasion theory of the elaboration likelihood model, we systematically examine the sender and receiver determinants of voice endorsement and how the interplay of those determinants affects voice endorsement. By ...[more]

PMID: 34003846

Similar Datasets

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.

Project description:Interpersonal communication through vocal information is very important for human society. During verbal interactions, our vocal cord vibrations convey important information regarding voice identity, which allows us to decide how to respond to speakers (e.g., neither greeting a stranger too warmly or speaking too coldly to a friend). Numerous neural studies have shown that identifying familiar and unfamiliar voices may rely on different neural bases. However, the mechanism underlying voice identification of individuals of varying familiarity has not been determined due to vague definitions, confusion of terms, and differences in task design. To address this issue, the present study first categorized three kinds of voice identity processing (perception, recognition and identification) from speakers with different degrees of familiarity. We defined voice identity perception as passively listening to a voice or determining if the voice was human, voice identity recognition as determining if the sound heard was acoustically familiar, and voice identity identification as ascertaining whether a voice is associated with a name or face. Of these, voice identity perception involves processing unfamiliar voices, and voice identity recognition and identification involves processing familiar voices. According to these three definitions, we performed activation likelihood estimation (ALE) on 32 studies and revealed different brain mechanisms underlying processing of unfamiliar and familiar voice identities. The results were as follows: (1) familiar voice recognition/identification was supported by a network involving most regions in the temporal lobe, some regions in the frontal lobe, subcortical structures and regions around the marginal lobes; (2) the bilateral superior temporal gyrus was recruited for voice identity perception of an unfamiliar voice; (3) voice identity recognition/identification of familiar voices was more likely to activate the right frontal lobe than voice identity perception of unfamiliar voices, while voice identity perception of an unfamiliar voice was more likely to activate the bilateral temporal lobe and left frontal lobe; and (4) the bilateral superior temporal gyrus served as a shared neural basis of unfamiliar voice identity perception and familiar voice identity recognition/identification. In general, the results of the current study address gaps in the literature, provide clear definitions of concepts, and indicate brain mechanisms for subsequent investigations.

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.

Project description:IntroductionWe investigated the effect of time spent at home on employee voice behavior and leadership openness during Covid 19. According to DeRue's adaptive leadership theory which offers an interactionist perspective to explain adaptive organizational behavior during an environmental crisis, we proposed that in the WFH's (work from home) reduced and limited communication space, leaders, who need more feedback, will encourage employees to express their opinions and will show more willingness to listen to them. Meanwhile, employees will ask more questions and make more suggestions to alleviate uncertainty and misunderstanding.MethodsUsing an online questionnaire, a cross-sectional study (N = 424) has been carried out with employees working from home for a different amount of their working time during the pandemic. Data were analyzed using structural equation models (SEM) in which the effect of leadership openness on employee voice behavior was assessed through the mediation of affective commitment, psychological safety, and intrinsic motivation.ResultsThe results showed that in the WFH situation, time spent in home office had a low but significant direct negative effect on promotive voice behavior. At the same time, leadership openness was growing with the amount of time spent at home. Leadership openness counteracted the negative effect of WFH on voice behavior: although leadership openness did not have a direct significant effect on voice behavior, it had a positive effect on psychological safety and work motivation which, in turn, influenced positively both promotive and prohibitive voice behavior. Employee's voice, for its part, further augmented leadership openness.DiscussionIn our research we could demonstrate the contingent nature and the mutual influence patterns and feedback loops of leaders-employees exchange. In the WFH situation the openness of the leader is growing with the amount of time spent at home and with the amount of promotive voice manifested by the employee. In consent with DeRue social interactionist adaptive leadership theory, a mutually reinforcing process of leadership openness and employee voice could be demonstrated. We argue that leadership openness is a key factor to motivate employee voice behavior during WFH.

Dataset Information

How leaders are persuaded: An elaboration likelihood model of voice endorsement.

Publications

How leaders are persuaded: An elaboration likelihood model of voice endorsement.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets