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This study developed and tested a moderated mediation model by examining the relationships between humble leadership (HL), emotional intelligence, employee conflict (EC), and creative performance (CP), using resource-based theory as the theoretical foundation. We conducted a cross-sectional survey of 322 employees and their immediate supervisors (n = 53) from the telecom sector in Pakistan. The data was analyzed using AMOS 21 and SPSS 26. The results demonstrate that HL has a positive effect on creative performance and a negative relationship with employee conflict. Furthermore, employee conflict has a negative impact on CP and mediates the impact of HL on CP. Moreover, a leader's emotional intelligence moderates the negative relationship between HL and EC. Finally, this study reveals that EI moderates the indirect effects of HL on CP. The conclusions and implications are discussed at the end of this paper. [ABSTRACT FROM AUTHOR]

The effective assessment of power grid emergency response capability (PERC) is the most fundamental aspect in the emergency continuity plans for urban power grid protection. Existing PERC assessment methods assign PERC indicator values under specific disaster scenarios based on one-round expert scores. However, the emergency continuity plans are often continuous and consider diverse scenarios, which makes the assessment quite time-consuming and unrealistic given too many to consider. In this paper, a case-driven method is proposed to solve the assessment problem of PERC with multiple typhoon disaster scenarios while considering the past experience as a guide for real decision making. Firstly, in terms of hierarchical PERC indicator framework, the framework structure of power grid emergency cases is proposed to make case experience modeled and reusable. Secondly, a case scenario similarity algorithm is applied to select the PERC assessment rules (by mapping relations between response indicators and assessment results) that are most similar to the real scenarios. Then the rules are integrated while considering rule variety and case weights. Next, the hierarchical assessment results are obtained by integrating the confidence coefficient values and corresponding utility values. Finally, the method is verified based on the empirical data from Shenzhen, China. The results show the proposed method not only helps in integrating case experience but also assists in quickly generating reasonable PERC assessment results. • Power grid emergency continuity plan requires considering disaster scenario diversity. • Existing capability assessment methods are limited by unsustainable expert scoring. • We use a case-driven scheme to assess emergency capability through experience reuse. • Capability assessment rules are extracted by measuring case scenario similarity. • A three-level indicator framework is constructed for emergency capability assessment. [ABSTRACT FROM AUTHOR]

Purpose: Although past research has looked into myriad consequences of workplace incivility, little attention has been paid to the effects of supervisor incivility (SI) on employees' sense of vitality and their discretionary work effort (DWE). Moreover, the mechanisms that drive the harmful effects of SI remain largely unknown. The current study seeks to address these gaps in the literature. In particular, this study aims to examine how SI culminates in decreased DWE. Design/methodology/approach: Adopting a cross-sectional survey design, data for this study were gathered from 151 employees of two large companies in the financial services sector of Pakistan. A number of analysis techniques (e.g. confirmatory factor analysis and bootstrapping) were used to analyze the data. Findings: As predicted, SI was found to be negatively associated both with subordinates' sense of vitality and DWE while vitality was found to be positively associated with DWE. Findings also indicated that one way in which SI negatively affects subordinates' DWE is by decreasing their sense of vitality. Practical implications: This study offers several useful implications for management practice in relation to preventing SI and mitigating its effects and bolstering employees' sense of vitality. Originality/value: To the best of the knowledge, this study is the first to unpack the relationship dynamics of SI, vitality and DWE, and to introduce a mechanism by which SI translates into reduced DWE. [ABSTRACT FROM AUTHOR]

The discrepancy between expected and actual food product features can be an important indicator of food quality and thus may inform consumer decisions. Using mixed method design, this paper explores whether and how expectation discrepancy affects food consumers' quality perception and purchase intentions under food safety risk situation. In the first phase, a qualitative study based on in-depth semi structured interviews (N = 15) was conducted. The interview results revealed that consumers in general responded negatively towards the existence of expectation discrepancies as it led to poor perceived food quality. It was further revealed that the discrepancy in expectation dimensions had a stronger effect on consumers' perceptions as compared to the discrepancy in expectation extent. In the second phase extensive online survey (N = 2172), it was confirmed that expectation discrepancy had significantly affected quality perceptions and purchase intentions. Moreover, perceived quality played a mediating role between expectation discrepancy and purchase intentions. In addition, expectation proportion (measured with the importance of specific food feature) was shown to strengthen (moderate) the impact of expectation discrepancy. Furthermore, the effects of expectation discrepancy were stronger for high-risk food (versus low risk food). The current paper shows that the effects of expectation discrepancy play a highly significant role in forming perceptions about food safety, therefore, precise marketing strategies can be made by clustering consumers according to the expectation proportions. • Consumers had a negative attitude towards the existence of expectation discrepancies. • The negative quality perception limited consumers' purchase intentions. • Expectation dimension discrepancy had a stronger influence than extent discrepancy. • Effects of expectation discrepancy were higher for high-risk food. • Expectation proportions strengthened the impact of expectation discrepancy. [ABSTRACT FROM AUTHOR]

The precise classification of community service cases is the most fundamental aspect of intelligent community service systems. However, data imbalance makes it challenging to achieve the desired level of precise classification. Existing transfer learning methods use open Internet knowledge for identifying case features and mining potential feature relations. However, community service cases have the characteristics such as short text length and non-public content, which restrict the transfer learning modes. In this paper, a cross-region transfer learning method is proposed to solve the classification problem of cases with small datasets in data imbalance situation while considering the perspective of regional cooperation. First, an ontology modeling method is applied to standardize the case features, reducing the effect of semantic ambiguity on transfer results. Secondly, to improve the effectiveness of source domain classification, this paper utilizes an extended marginal fisher analysis where the distance is measured by the inner product between data. Next, the mapping matrix from target domain to source domain is learned through domain adaptation. Finally, the method is verified based on the empirical data from Lanzhou and Beidaihe in China. Experimental results on classifiers show the proposed method helps regions to improve case classification rates significantly through knowledge complementation. The proposed approach can be followed to build case-based community service systems of reasonable accuracy and limited sample sizes. • We seek to classify community service cases with small datasets in data imbalance situation. • Existing transfer learning methods have high requirements both for the length and openness of case content. • We use a cross-regional transfer learning to learn the map from local weak classes to non-local strong classes. • Classifiers equipped with learned maps may use few samples for training. • Both the cross-region semantic ambiguity and source domain feature representation are considered for effective classification. [ABSTRACT FROM AUTHOR]