Organizational commitment in Chinese small- and medium-sized enterprises: the role of extrinsic, intrinsic and social rewards

This study investigates the relationship between organizational rewards and employee commitment in Chinese small- and medium-sized enterprises (SMEs). Hierarchical regression analysis was utilized to analyse survey data from 286 employees of 11 organizations. In line with what was hypothesized extrinsic rewards were found to be strongly related to both affective and continuance commitment, whereas satisfaction with supervision and role clarity positively influenced affective commitment. In contrast to previous empirical findings, autonomy and training provision were only found to influence continuance commitment. These findings have significant managerial implications regarding the utility of providing organizational rewards to enhance the commitment of Chinese employees. In order to promote employee commitment, SME managers could start by giving their employees greater autonomy and clarity regarding their role in the organization, as well as improving supervisor support. These are relatively inexpensive measures compared to the costly alternatives of improving extrinsic benefit packages and investing in employee training.

The impact of employee perceptions of training on organizational commitment and turnover intentions: a study of multinationals in the Chinese service sector

This study examines the impact of employee perceptions of training on organizational commitment, and the latter's relationship with turnover intentions. Structured equation modelling is conducted on survey data from 437 Chinese employees of five multinational enterprises operating in the Chinese service sector. The results of the survey are consistent with social exchange theory. They highlight the importance of training as a tool to enhance the affective organizational commitment of employees, and reduce turnover. The findings differ from that of previous studies in non-Chinese settings. No evidence was found to indicate that motivation to learn and the perceived benefits of training impact on the organizational commitment of employees. This may be explained by three factors: the involuntary nature of employee training, the limited career development opportunities on offer to local employees of multinational enterprises and the difficulty employees face in applying learnt skills given cultural differences. The implications for research and practice are discussed.

Organizational factors related to the confidence of workers in working with residents with dementia or depression in aged care facilities

OBJECTIVES: There has been limited research examining how organizational factors are associated with the level of confidence of residential aged care staff in managing both residents' depression and the behavioural and psychological symptoms of residents with dementia (BPSD). This study investigated this issue. METHOD: A cross-sectional study design was employed. In total, 255 aged care staff (131 senior staff, 124 junior staff) from 21 residential care facilities participated in the study. All staff completed measures of self-efficacy in managing BPSD as well as confidence in working with older people with depression. They also completed measures of organizational climate (autonomy, cohesion, trust, pressure, support, recognition, fairness and encouragement of innovation) and measures of workplace experience (job role, number of years working in aged care facilities), job stress and satisfaction, and knowledge of depression. RESULTS: The results demonstrated that autonomy, trust, support, and job stress were associated with confidence in managing BPSD, while the factors related to confidence in managing depression were autonomy, support, job stress, job satisfaction, and knowledge of depression. CONCLUSION: These findings highlight that organizational climate factors need to be addressed in order to increase staff confidence in managing BPSD and depression. In particular, the findings demonstrate the importance of fostering organizational environments in which autonomy is promoted and there is support and cooperation among aged care staff. Attention to these factors is likely to increase the confidence of staff as they carry out their carer role.

Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting

Wind farms are producing a considerable portion of the world renewable energy. Since the output power of any wind farm is highly dependent on the wind speed, the power extracted from a wind park is not always a constant value. In order to have a non-disruptive supply of electricity, it is important to have a good scheduling and forecasting system for the energy output of any wind park. In this paper, a new hybrid swarm technique (HAP) is used to forecast the energy output of a real wind farm located in Binaloud, Iran. The technique consists of the hybridization of the ant colony optimization (ACO) and particle swarm optimization (PSO) which are two meta-heuristic techniques under the category of swarm intelligence. The hybridization of the two algorithms to optimize the forecasting model leads to a higher quality result with a faster convergence profile. The empirical hourly wind power output of Binaloud Wind Farm for 364 days is collected and used to train and test the prepared model. The meteorological data consisting of wind speed and ambient temperature is used as the inputs to the mathematical model. The results indicate that the proposed technique can estimate the output wind power based on the wind speed and the ambient temperature with an MAPE of 3.513%.

The impact of socially responsible human resource management on employees' organizational citizenship behaviour: the mediating role of organizational identification

Based on insights from social exchange and social identity theories, this paper examines the influence of three dimensions of socially responsible human resource management (SR-HRM), namely legal compliance HRM, employee-oriented HRM and general CSR facilitation HRM, on employees' organizational citizenship behaviour (OCB). Structural equation modelling of dyadic data collected from Chinese employees and their direct supervisors in three phases revealed that whilst organizational identification fully mediated the relationship between employee-oriented HRM and employee OCB, general CSR facilitation HRM had a direct effect on employee OCB. In contrast, legal compliance HRM neither influenced employee OCB directly, nor indirectly through organizational identification. The findings highlight the important but complex role played by SR-HRM in eliciting positive employee work outcomes, and contribute to our knowledge of the mechanisms underlying this relationship.

Servant leadership, trust, and the organizational commitment of public sector employees in China

© 2014 John Wiley & Sons Ltd. So-called servant leaders strive selflessly and altruistically to assist others before themselves, work to develop their followers' greatest potential, and seek to benefit the wider community. This article examines the trust-based mechanisms by which servant leadership influences organizational commitment in the Chinese public sector, using data from a survey of civil servants. Quantitative analysis shows that servant leadership strongly influences affective and normative commitment, while having no impact on continuance commitment. Furthermore, we find that affective trust rather than cognitive trust is the mechanism by which servant leadership induces higher levels of commitment. Our findings suggest that in a time of decreasing confidence levels in public leaders, servant leadership behaviour may be used to re-establish trust and create legitimacy for the Chinese civil service.

Forecasting bike sharing demand using fuzzy inference mechanism

Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy.

A new fuzzy-based combined prediction interval for wind power forecasting

This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.