Search results

The present study contributes to our understanding of how to curb burnout among hospital staff over time. The authors extend existing research by examining the mediating role of mission valence in the link between transformational leadership and burnout.

Self-administered questionnaire data from employees in a Canadian general hospital (N = 185) were analyzed using a time-lagged research design to examine whether transformational leaders can increase employees' attraction to the organization's mission (i.e. mission valence) and in turn alleviate long-term burnout.

Structural equation modeling analysis demonstrated that transformational leadership (time 1) was negatively related to the burnout components of emotional exhaustion and depersonalization (time 2). Further, the results showed that mission valence mediated these relationships.

The study findings are important for managers and professionals as they identify transformational leadership as a potent strategy to alleviate employee burnout and clarify the process through which this is achieved, namely, by increasing mission valence.

To date, surprisingly little research has explored how transformational leadership influences followers' burnout. To address this issue, the present study examined the role of transformational leadership on staff burnout through the mechanism of increasing mission valence. Understanding how to mitigate burnout is particularly critical in health care organizations given that burnout not only negatively impacts employee wellbeing but also the wellbeing and quality of care provided to patients.

This study aims to investigate the merits of the unified model of task-specific motivation (UMTM) in predicting transfer of training and to investigate (relationships between) changes in UMTM components over time. In doing so, this study takes the multidimensionality of transfer motivation into account.

The authors collected data among 514 employees of the judiciary who filled in the UMTM questionnaire directly after the training and after three weeks. The data were analyzed by means of structural equation modelling.

The outcomes show that transfer motivation predicts transfer intention and transfer of training over time. Moreover, the study shows that (change in) transfer motivation is predicted by (change in) personal and contextual factors identified by the UMTM as antecedents of motivation.

This study describes the first longitudinal evaluation of the UMTM in the literature and shows its applicability for predicting transfer of training. It is also one of the few studies that investigate transfer motivation multidimensionally and the role it plays for transfer of training. As such, this study informs other transfer of training models about the nature of transfer motivation and how transfer of training could be predicted.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

Firms employ various forms of disclosure to demonstrate commitment to and involvement in sustainable supply chain management (SSCM) practices. This research provides guidance to firms employing framing strategies when communicating their SSCM with external stakeholders like consumers as part of their supply chain transparency efforts.

The authors employed a middle-range theorizing approach to understand the context of SSCM practices and mechanisms of variously framed communication methods to disclose sustainability information to consumers. The authors conducted two experiments in an e-waste recycling context, studying how sustainable information disclosed to consumers using attribute framing and goal framing can affect consumers' attitudes. The authors also examined the moderating role of consumers' environmental involvement.

Results suggest that when attribute framing is used, firms should avoid framing the attribute from a negative valence. When goal framing is used, messages with consequences stated as “avoid loss” yield the most substantial effect. Additionally, framing effects are more significant for consumers with higher-than-average environmental involvement.

The authors’ results contribute to the ongoing theorization of SSCM by providing contextual understanding of how to communicate sustainability information. Corroborating evidence from marketing, framing effects are found to be context specific, thereby elucidating the framing literature more fully to the SSCM context. The authors extend this literature by studying attribute framing and comparing the effectiveness of all possible goal framing combinations of valence and gain/loss perspective in the SSCM communication context.

The purpose of this study is to operationalise and measure the effects of negative customer engagement (CE) in conjunction with positive CE. Both valences are explored through affective, cognitive and behaviour dimensions, and, in relation to the antecedent of involvement and outcome of word-of-mouth (WOM). It also explores the moderating influence of service context by examining engagement within a social service versus a social networking site (SNS). Engagement with the dual focal objects of a service brand and a service community are also examined.

Structural equation modelling is used to analyse 625 survey responses.

Involvement is a strong driver of positive CE, and positive CE has a strong effect on WOM. These findings are consistent across the “brand” and “community” object, suggesting positive CE is mutually reinforced by different objects in a relationship. Positive CE is also found to operate consistently across the service types. Involvement is a moderately negative driver of negative CE, and negative CE is a positive driver of WOM. These relationships operate differently across the objects and service types. Involvement has a stronger inverse effect on negative CE for the social service, diverging from assumptions that negative CE is reflective of highly involved customers. Interestingly, negative CE has a stronger effect on WOM in the social service, highlighting the active and vocal nature of customers within this service context.

To the best of the authors’ knowledge, this is the first paper to quantitatively measure positive and negative valences of engagement concurrently, and examine the moderating effect of dual objects across contrasting service types.

Considering the relevance of understanding what influences environmentally sustainable consumer choices, the present study aims to examine and synthesize the key determinants factors from literature and outline a new conceptual framework for explaining green purchasing behaviors (GPBs).

A bibliometric analysis was conducted on 161 articles extracted from Web of Science and Scopus databases, which were systematically evaluated and reviewed, and represent the current GPB knowledge base. Content analysis, science mapping and bibliometric analysis techniques were applied to uncover the major theories and constructs from the state-of-the-art.

The evolving debate between altruistic and self-interest consumer motivations reveals challenges for rational-based theories, as most empirical applications are not focused on buying behaviors, but instead either on pro-environmental (non-buying) activities or on buying intentions. From the subset of leading contributions and emerging topics, nine thematic clusters are unveiled in this investigation, which were combined to create the new PSICHE framework with the purpose of predicting GPB: (P)roduct-related factors, (S)ocial influences, (I)ndividual factors, (C)oncerns about the environment, (H)abits and (E)motions.

By uncovering the multiple intervening factors in GPB decision processes, this study will assist practitioners and academics to move forward on how to foster more sustainable consumer behaviors.

The present study provides readers a summary of an unprecedentedly broad collection of papers, from which the key themes are categorized, the domain's intellectual structure is captured and an actionable framework for enhancing the understanding GPB is proposed. Four new thrust areas and a set of future research questions are included.

Drawing on the dual process theory and the cultural dimension of power distance, the current research investigates the impact of a specific service clue—the linguistic style of address forms (salutation) in hotel manager letters to guests—on customer satisfaction in a hotel context in Ecuador.

Following an experimental design research approach, this research conducted a series of two studies to examine how customers' cultural values (high vs low power distance), linguistic style of address forms (formal vs casual) and service valence (service success vs service failure) together influenced customer satisfaction. Specifically, Study 1 examined the service success condition, and Study 2 investigated the service failure condition.

The research results show that, in the service success condition, customers follow their distinct cultural orientations (high vs low power distance) when responding to the different linguistic styles (formal vs casual). On the other hand, in the service failure situation, as customers desire for expressions of respect that can be reflected in a formal address form, the level of satisfaction is lower when the casual address form is used in guest communications, regardless of customers' cultural orientations in power distance.

This research adds to existing cross-cultural service research, particularly in terms of service valence, and provides practical implications for enhancing service providers' cultural awareness and sociolinguistic competence to effectively communicate with customers from diverse cultural backgrounds.

This study aims to examine how characteristics of an online review and a consumer reading the review influence the probability that the consumer will assess the review as authentic (real) or inauthentic (fake). This study further examines the specific factors that increase or decrease a consumer’s ability to detect a review’s authenticity and reasons a consumer makes these authenticity assessments.

Hypothesized relationships were tested using an online experiment of over 400 respondents who collectively provided 3,224 authenticity assessments along with 3,181 written self-report reasons for assessing a review as authentic or inauthentic.

The findings indicate that specific combinations of factors including review valence, length, readability, type of content and consumer personality traits and demographics lead to systematic bias in assessing review authenticity. Using qualitative analysis, this paper provided further insight into why consumers are deceived.

This research showed there are important differences in the way the authenticity assessment process works for positive versus negative reviews and identified factors that can make a fake review hard to spot or a real review hard to believe.

This research has implications for both consumers and businesses by emphasizing areas of vulnerability for fake information and providing guidance for how to design review systems for improved veracity.

This research is one of the few works that explicates how people assess information authenticity and their consequent assessment accuracy in the context of online reviews.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.