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The purpose of the present research is to investigate the mechanisms by which conflict with customers (i.e. customer mistreatment) contributes to employees’ work withdrawal.

This paper tests its hypotheses by means of a field study of a sample of front-line health care workers in China. Data were collected in three waves over four months; a total of 398 health care workers completed the questionnaires and represented the final sample. A hierarchical multiple regression analysis was performed to test the proposed hypotheses.

The results indicate that mistreatment by customers contributes to employees’ work withdrawal, and emotional exhaustion serves as a mediator in this linkage. In addition, social support moderates the positive relation between customer mistreatment and employees’ emotional exhaustion, whereas conscientiousness moderates the positive relation between emotional exhaustion and withdrawal behavior.

The sample used may not fully justify the generalizability of the research results. Without distinguishing different sources of social support may be another limitation. In addition, this study could be improved by using a multi-source survey design.

To help employees effectively cope with interpersonal conflict with customers, organizations should take action to promote communication between employees and their supervisors and coworkers. It is also advisable for organizations to adjust their selection strategies and hire front-line employees high in conscientiousness.

This research presents a resource-based framework to illuminate the detrimental effects of prolonged exposure to customer mistreatment on health care workers’ withdrawal behavior in Chinese context. Furthermore, this study examines factors that may serve to mitigate the harmful effects of customer mistreatment and regards workplace social support and conscientiousness as two different kinds of resources that can play dissimilar roles when employees are coping with customer mistreatment.

This meta-analytical review aims to clarify the relationships between three bundles of human resource management (HRM) practices—competency-enhancing, motivation-enhancing and opportunity-enhancing—and organizational innovation by addressing two questions: (a) Which types of HRM bundles are most strongly related to different forms of innovation (i.e. process and product innovation)? And (b) Which mechanism provides a stronger explanation for the positive effects of HRM bundles on innovation?

Based on data from 103 studies, a meta-analysis was conducted to quantitatively summarize existing HRM–innovation studies at the organizational level.

The results showed that the competency-enhancing bundle was more positively related to product innovation than the motivation-enhancing and opportunity-enhancing bundles. The opportunity-enhancing bundle was most strongly associated with process innovation. The authors further found that knowledge management capability (KMC) and employee motivation mediated the positive relationship between the three HRM bundles and innovation outcomes. In comparing the two mechanisms, this review suggests that KMC better explains both the impact of the competency-enhancing HRM bundle on product innovation and the effect of the opportunity-enhancing bundle on process innovation.

Based on behavioral and knowledge management perspectives, this study takes a sub-bundle approach to providing an integrative review by comparing the direct effects and mediating paths of HRM bundles on product and process innovation.

Since the subject matters of human resources activities on knowledge intensive firms have been changed by coronavirus disease 2019 (COVID-19) pandemic, this study aims to analyze the impact of stock options on talent retention (knowledge worker retention) and knowledge productivity (innovation) in terms of patents, which directly affect the financial performance of knowledge intensive firms.

Drawing on agency and contingency theory to design the causality model, this study analyzes the data obtained from 227 publicly traded knowledge intensive firms in information technology (IT) and healthcare sectors. Panel data analysis is used to determine the long run causal relationship between firm innovation, knowledge worker retention and financial performance, in addition to ANOVA for evaluating firm size as a lurking variable on the effect of stock options.

The results of this study demonstrate that, when firm size is taken into account, (1) stock options significantly affect knowledge worker retention and firms' financial performance, and this impact is stronger in a during-pandemic situation than in a pre-pandemic situation (2) firm innovation significantly affects firms' financial performance and this impact is stronger in a during-pandemic situation than in a pre-pandemic situation; (3) knowledge worker retention doesn't have a significant impact on firm innovation and firms' financial performance. Moreover, random effect regression analysis for long-term relationships also depicts the same results: knowledge worker retention has non-significant impact on firm innovation and financial performance, but firm innovation significantly affects financial performance.

To the best of the authors' knowledge, the authors are the first to compare the effects of stock options, knowledge worker retention and firm innovation in both pre- and during-pandemic scenarios where firm size is taken into consideration.

The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming.

The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial ratios for improved predictive performance. The financial ratios are then analyzed by five different data mining techniques. Managerial insights, using data from Chinese companies, are revealed by the methodology employed.

The prediction accuracy increases after we discretized the continuous variables of financial ratios. A better prediction performance can be achieved by including fewer, but relatively more significant variables. Random forest has the highest overall performance following closely by SVM and neural network.

The contribution of this study is fourfold. First, the authors add to the literature on defaults by showing variable discretization to be an essential pre-processing step to improve the prediction performance for classification problems. Second, the authors demonstrate that machine learning approaches can achieve better performance than traditional statistical methods in classification tasks. Third, the authors provide the evidence for the adoption of C5.0 over other methods because rules generated with C5.0 provide managerial insights for managers. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios from each category, enabling one to build better predictive models.

The purpose of this paper is to identify a subset of key financial ratios and factors that provide the best discriminating power to distinguish between creditworthy companies (CWCs) and less creditworthy companies (LCWCs) in the USA with the proposed method.

A proposed framework of Bisection Method Based on Tabu Search + Support Vector Machines (BMTS + SVM) is used to select subset of financial ratios from a pool of candidate ratios. The selected ratios and their corresponding financial factors are considered as the key financial ratios and factors that provide the best discriminating power to distinguish between CWCs and LCWCs. The authors collected financial data for the US companies and then identify the key financial ratios and factors which the selected key financial ratios belong.

It is found that the four selected financial ratios from the proposed method and eight financial ratios which are used by Standard & Poor for their credit-rating system can be attributed to the same four financial factors, namely, cash flow factor, profitability factor, solvency factor and leverage factor. This result lends support that the proposed method can be applied to identify key financial factors to differentiate CWCs and LCWCs.

This study provides a tool for managers in financial institutions to gain better understanding about the credit risk of their applicants by focusing on a parsimonious model with fewer ratios in the key financial factors. In addition, companies that attempt to borrow money from financial institutions can also use these key financial ratios and factors as reference to attain clearer vision on what are the most important factors for being considered a creditworthy company and thus develop specific strategies to improve their financial performance.

Based on data analytic techniques, this paper identifies key financial ratios and factors for examining the creditworthiness of US companies with the proposed framework using BMTS + SVM method.

This study aims to investigate the relationship between leader group prototypicality and intergroup conflict, as well as its mechanisms and contextual factors using the social identity theory.

The research model was empirically tested using multi-phase, multi-source and multilevel survey data in China. The final sample consisted of 75 group leaders and 231 group members. Multilevel structural equation modelling and a Monte Carlo simulation were used for hypothesis testing.

The results showed that leader group prototypicality would engender intergroup conflict via intergroup distinctiveness. Further, leaders’ benchmarking behaviour moderated this indirect effect. In particular, leader group prototypicality resulted in higher intergroup distinctiveness and intergroup conflict, only when the leaders’ benchmarking behaviour was higher rather than lower.

First, this study addresses the question of whether leader group prototypicality would lead to intergroup conflict to provide theoretical and empirical insights to supplement extant literature. Second, the study advances the understanding of mechanisms (intergroup distinctiveness) and the consequences (intergroup conflict) of leader group prototypicality in an intergroup context. Third, the study shows that leaders’ benchmarking behaviour moderates the effect of leader group prototypicality on intergroup conflict through intergroup distinctiveness. As such, the findings are of value to future management practice by offering precise, practical interventions to manage the intergroup conflict caused by leader group prototypicality.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

A study of the emotional responses to mistreatment by customers in a Chinese hospital showed the negative consequences of their performance at work. The authors showed how workers tended to withdraw from difficult situations that left them in a state of emotional exhaustion. Both social support from colleagues and a conscientious attitude reduced the negative impacts.

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Drawing on the self-cognitive theory, this study aims to propose a conceptual model that links customer mistreatment with different types of helping behaviors through the self-efficacy mechanism.

The empirical analysis made use of the original data of three hotels located in southern China. The authors tested the hypotheses with a three-wave survey of a sample of 430 frontline workers in 95 groups.

Customer mistreatment may reduce employees’ self-efficacy, which has both positive and negative effects depending on the type of helping. Moreover, the coworkers’ supporting climate buffered the influence of self-efficacy on autonomous and dependent helping.

The authors resolve the ambiguity surrounding customer mistreatment-helping and self-efficacy-helping relations. Thus, the authors extend the knowledge on the influence of customer mistreatment and self-efficacy on helping behaviors by establishing that both positive and negative effects may exist depending on the type of helping. Moreover, this study identifies the predictive role of self-efficacy in autonomous and dependent helping.

Drawing on the transaction theory of stress, the purpose of this paper is to conceptualize customer mistreatment as a stressor and examine how job routinization and proactive personality help employees cope with the effects of customer mistreatment on emotional exhaustion and work engagement. The interaction of job routinization and proactive personality was also tested.

In total, 128 hundred nurses were recruited to participate in the current study, which was a daily survey for two consecutive weeks (10 working days).

The results revealed that job routinization and proactive personality attenuated the effects of customer mistreatment on emotional exhaustion and work engagement. The analyses also showed that, with more proactive personality and high job routinization, the effects of customer mistreatment were minimized.

Job routinization is a type of job resources that attenuates the negative influence of customer mistreatment. Proactive personality strengthens job routinization’s function, when proactive personality and job routinization are both high, the ill effect of customer mistreatment will be minimized.

To propose a new investment-income valuation model by real options approach (ROA) for old community renewal (OCR) projects, which could help the government attract private capital's participation.

The new model is proposed by identifying the types of options private capital has in the OCR project, selecting the option model most suitable for private capital investment decisions, improving the valuation model through the triangular fuzzy numbers to take into account the uncertainty and flexibility, and demonstrating the feasibility of the calculation model through an actual OCR project case.

The new model can valuate OCR projects more accurately based on considering uncertainty and flexibility, compared with conventional methods that often underestimate the value of OCR projects.

The investment-income of OCR projects shall be re-valuated from the lens of real options, which could help reveal more real benefits beyond the capital growth of OCR projects, enable the government to attract private capital's investment in OCR, and alleviate government fiscal pressure.

The proposed OCR-oriented investment-income valuation model systematically analyzes the applicability of real option value (ROV) to OCR projects, innovatively integrates the ROV and the net present value (NPV) as expanded net present value (ENPV), and accurately evaluate real benefits in comparison with existing models. Furthermore, the newly proposed model holds the potential to be transferred to various social welfare projects as a tool to attract private capital's participation.