Xiaoping Pu, Guanglei Zhang, Chi-Shing Tse, Jiaojiao Feng, Yipeng Tang and Wei Fan
This study aims to investigate whether and how a high turnover rate stimulates employees to engage more in learning behavior.
Abstract
Purpose
This study aims to investigate whether and how a high turnover rate stimulates employees to engage more in learning behavior.
Design/methodology/approach
Drawing on self-regulation theory, the authors suggest that the motive for employees to engage in learning behavior is to improve themselves. Such a need can be activated when they reflect on themselves and realize the discrepancy between their current selves and desired future selves. The authors argue that the employees’ perceived poor performance at daily work may induce their desire for self-improvement via making the future work selves salient, and in turn engage more in learning behavior. This is particularly so when turnover rate is high because employees may be alert of and concerned more about their own poor performance. In an experience sampling study, the authors obtained evidence for these hypotheses.
Findings
When turnover rate was high, employees’ poor performance increased salience of future work selves, which in turn facilitated their learning behavior. This relationship was not significant when turnover rate was low.
Originality/value
Contrary to the typical view that high turnover rate leads to knowledge loss for the companies, the present study findings suggest that it could also serve as a motivational factor facilitating employees’ learning behavior, which is an important way to increase knowledge pool of the companies.
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More and more work zone projects come with the needs of new construction and regular maintenance-related investments in transportation. Work zone projects can have many…
Abstract
Purpose
More and more work zone projects come with the needs of new construction and regular maintenance-related investments in transportation. Work zone projects can have many significant impacts socially, economically and environmentally. Minimizing the total impacts of work zone projects by optimizing relevant schedules is extremely important. This study aims to analyze the impacts of scheduling long-term work zone activities.
Design/methodology/approach
Optimal scheduling of the starting dates of each work zone project is determined by developing and solving using a bi-level genetic algorithm (GA)–based optimization model. The upper level sub-model is to minimize the total travel delay caused by work zone projects over the entire planning horizon, whereas the lower level sub-model is a traffic assignment problem under user equilibrium condition with elastic demand.
Findings
Sioux Falls network is used to develop and test the proposed GA-based model. The average and minimum total travel delays (TTDs) over generations of the proposed GA algorithm decrease very rapidly during the first 20 generations of the GA algorithm; after the 20th generations, the solutions gradually level off with a certain level of variations in the average TTD, showing the capability of the proposed method of solving the multiple work zone starting date optimization problem.
Originality/value
The proposed model can effectively identify the near-optimal solution to the long-term work zone scheduling problem with elastic demand. Sensitivity analysis of the impact of the elastic demand parameter is also conducted to show the importance of considering the impact of elastic demand parameter.
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Christopher Nnaemeka Osuafor, Sree Lakshmi Enduluri, Emma Travers, Anne Marie Bennett, Elena Deveney, Shabahat Ali, Frances McCarthy and Chie Wei Fan
Constipation in hospitalised older adults leads to adverse events and prolonged stay. The purpose of this paper, therefore, is to effectively prevent and manage constipation in…
Abstract
Purpose
Constipation in hospitalised older adults leads to adverse events and prolonged stay. The purpose of this paper, therefore, is to effectively prevent and manage constipation in older adults undergoing inpatient rehabilitation using a multidisciplinary war on constipation (WOC) algorithm.
Design/methodology/approach
A quality improvement project in older adults undergoing rehabilitation for prevention and constipation management was conducted. Quality improvement “plan-do-study-act” cycles included an initial constipation audit in the wards and meetings with the multidisciplinary team (MDT) to develop an algorithm for the preventing, detecting and effectively treating constipation.
Findings
The project resulted in a 14 per cent reduction in constipation incidence after the newly developed WOC algorithm was introduced. The project also improved communication between patients and the MDT around patients’ bowel habits.
Practical implications
The project shows that using quality improvement methods in rehabilitation settings, earlier detection, earlier intervention and overall reduction in constipation in older adults can be achieved.
Originality/value
The WOC algorithm has been developed and institutionalised in the current setting. This algorithm may also be applicable in other inpatient settings.
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Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in…
Abstract
Purpose
Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.
Design/methodology/approach
As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.
Findings
The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.
Originality/value
This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.
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Jing Du, Wei Fan and Jin Nam Choi
The ubiquity of smartphones has changed how people communicate, work and entertain. In view of conservation of resources theory and the positive spillover effect, this study…
Abstract
Purpose
The ubiquity of smartphones has changed how people communicate, work and entertain. In view of conservation of resources theory and the positive spillover effect, this study explores the effect of non-work-related instant messaging (IM) in the workplace on daily task performance.
Design/methodology/approach
The authors use the experience sampling method to collect day-level data from 75 employees over a period of 10 workdays. Multilevel path analysis is used to test the hypotheses.
Findings
Non-work-related IM exerts a significant negative indirect effect on daily task performance through diminished cognitive engagement. This negative indirect effect disappears when social support is high, thereby showing the function of social support as a neutralizer of the detriment of non-work-related IM on daily task performance.
Practical implications
The findings suggest that organizations can neutralize the harm of non-work-related IM in the workplace by promoting social support perceived by employees.
Originality/value
This study advances the technology and management literature by developing and testing a balanced perspective on the ambivalent effect of workplace smartphone use that considers social and cognitive resource implications.
Details
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Abstract
Purpose
This research tested the crossover effects of a leader’s resilience on followers’ outcomes via followers’ resilience.
Design/methodology/approach
Survey data from 87 leaders with 309 followers, collected in two waves in China, were used to test the multilevel mediation hypotheses.
Findings
The results indicated that (1) resilience could transfer from the leader to followers, and (2) leader’s resilience could further contribute to alleviating followers’ job burnout, and prompt citizenship behaviors by enhancing followers’ resilience.
Originality/value
This study adopted a top-down perspective to test the crossover effects of resilience, thus expanding the resilience literature illustrating its distinct influential mechanism through a vertically interpersonal perspective.
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Nikolaos Giannellis and Georgios P. Kouretas
The aim of this study is to examine whether China’s exchange rate follows an equilibrium process and consequently to answer the question of whether or not China’s international…
Abstract
Purpose
The aim of this study is to examine whether China’s exchange rate follows an equilibrium process and consequently to answer the question of whether or not China’s international competitiveness fluctuates in consistency with equilibrium.
Design/methodology/approach
The theoretical background of the paper relies on the Purchasing Power Parity (PPP) hypothesis, while the econometric methodology is mainly based on a nonlinear two-regime Threshold Autoregressive (TAR) unit root test.
Findings
The main finding is that China’s price competitiveness was not constantly following a disequilibrium process. The two-regime threshold model shows that PPP equilibrium was confirmed in periods of relatively high – compared to the estimated threshold – rate of real yuan appreciation. Moreover, it is implied that the fixed exchange rate regime cannot ensure external balance since it can neither establish equilibrium in the foreign exchange market, nor confirm that China’s international competitiveness adjustment follows an equilibrium process.
Practical implications
The results do not imply that China acts as a currency manipulator. However, a main policy implication of the paper is that China should continue appreciating the yuan to establish external balance.
Originality/value
This paper is the first which accounts for a nonlinear two-regime process toward a threshold, which is defined to be the rate of change in China’s international competitiveness. Consequently, the paper draws attention to the role of China’s international competiveness in accepting the PPP hypothesis.