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Although a supportive workplace is increasingly considered important for employees' performance, much of the evidence remains speculative, for example, it lacks offsetting mechanisms. This study addresses circumstances when perceived support helps and when it hurts work performance, depending on the mediating effects of job autonomy, intrinsic motivation and job satisfaction under the boundary conditions of perceived helpfulness of social media platforms and felt stress.

This study collected data using a questionnaire protocol that was adapted and refined from the original scales in existing studies. The sample consists of 900 employees from the public healthcare industry in Vietnam. To test the hypotheses, the partial least squares (PLS) technique was used.

This study finds that job autonomy, intrinsic motivation and job satisfaction are important for the perceived support and work performance relationship in which perceived helpfulness of social media platforms plays a critical confounding role. The findings also confirm that felt stress negatively moderates the relationship between job satisfaction and work performance, weakening the effect job satisfaction has on employee work performance.

This study specifies the boundary conditions under which work performance is mostly affected while enhancing the understanding of how to reinforce intrinsic motivation and job satisfaction. The findings offer organizational and human resource management (HRM) scholars and practitioners a closer look at perceived helpfulness of social media platforms and support the suggestions that autonomy-supportive workplaces are superior.

Knowledge sharing behaviour is becoming one of the main priorities in organizations operating in emerging economies, as knowledge sharing behaviour may impact their competitiveness. The purpose of this paper is to examine the impact of: openness and trust; top management support; and the reward system on knowledge sharing behaviour. In addition, the paper investigates how knowledge sharing behaviour impacts firm’s competitiveness.

The analyses in this paper were based on data from more than 230 companies operating in five industries in an emerging economy in the Gulf area. Structural equation modelling (SEM) methodology was used to test the impact of the three independent latent variables of openness and trust, top management support and the reward system on the knowledge sharing behaviour as well as the impact of the later on firm’s competitiveness.

Exploratory factor as well as confirmatory factor analysis were used to assess the five dimensions of knowledge sharing behaviour and firm’s competitiveness. SEM was used to test the four main hypotheses in this study and to assess the structural relationships among these five latent variables. There was a moderate relationship between the knowledge sharing behaviour and the three independent latent variables of openness and trust; top management support; and the reward system. There was a strong positive association between knowledge sharing behaviour and firm’s competitiveness.

For academics, the paper offers an opportunity to further study knowledge sharing behaviour in other emerging economies. Academics who are interested in examining this issue further may extend our study in many directions. For practitioners, our findings should motivate practitioners to place emphasis on top management support, openness and trust and the reward system to create a proper culture for knowledge creation and sharing.

To the best of the authors’ knowledge, this study is the first attempt in its purpose and design to study knowledge sharing behaviour in multiple industries in an emerging economy such as that of Saudi Arabia.

A substantial number of students read significantly below grade level, and students with disabilities perform far below their non-disabled peers. Reading achievement data indicate that many students with and at-risk for reading disabilities require more intensive reading interventions. This chapter utilizes the theoretical model of the Simple View of Reading to describe the benefit of early reading instruction, targeting both word reading and word meaning. In addition, evidence is presented supporting the use of word meaning instruction to improve accurate and efficient word reading for students who have failed to respond to explicit decoding instruction.

This study aims to examine the impact of customer brand value dimensions on relationship marketing dimensions through consumer hope in the context of digital payment applications (apps) services. The study considers the role of consumer engagement using a moderated mediation, and applies customer perceived value, affect theory of social exchange and relationship marketing theories.

The study is based on data collected from 301 digital payment app users. Structural equation modeling results were analyzed using Smart PLS. The authors performed moderated mediation, with different levels of customer engagement as a moderating variable, using Model 8 of PROCESS. The authors considered customer perceived value dimensions, digital quality value, perceived value, hedonic value and social value as antecedents to consumer hope and explored the role of trust, commitment and continued usage as a consequence of consumer hope.

Three levels of perceived consumer value, digital quality, price value and social value, positively influenced consumer hope, which has positively influenced trust, commitment and continuance usage. Using moderated mediation analysis, consumer hope influenced continuance usage through trust at different levels of engagement, but consumer hope did not influence continuance usage through commitment at different levels of engagement.

The study highlights the role of consumer hope in linking customer value dimensions with relationship marketing dimensions. The study can guide managers to ensure continued usage of digital payment apps, which is a strategic objective. The results are relevant for the digital setting.

Within the labor-intensive construction industry characterized by distinctly structural shortages in the labor force worldwide, efficient and effective migration of construction workers across regions is critical for the smooth operation of construction activities. This study aims to investigate how the interregional migration patterns of construction workers are impacted by the disparities in both employment opportunities and environment amenities between the origin and destination provinces.

Drawing on the push and pull theory and the archival data on 13,728 migrant construction workers in China, descriptive analyses are first performed to characterize the interregional migration patterns of the investigated construction workers. Combining regional data in the National Bureau of Statistics of China, this study uses hierarchical regression modeling techniques to empirically test the relative importance of the employment-related and environment-related factors in driving the interregional migration of construction workers after controlling for the effects of related economic and geographic factors.

The results provide evidence that the interregional migration of construction workers is principally driven by the disparities in employment opportunities while disparities in environment amenities (including climate comfort disparity, medical service disparity and educational service disparity) generally play much fewer substantive roles. With regard to the impacts of employment opportunities, the results provide evidence that compared with the disparity in job market size, the disparities in job income and industry development level are more significantly relevant factors, which positively pull and adversely push the interregional migration flows, respectively.

This study contributes to a deepened understanding of how workers specifically balance their employment and amenity needs to make temporary migration decisions in the “laggard” labor-intensive construction industry. This study also adds to the literature on population migration by characterizing the specific characteristics of construction workers and the temporary nature of the workers' migration activities. The findings hold important practical implications for construction organizations and policymakers for effectively managing the mobility of migrant construction workers.

The extant literature on migrant construction workers has primarily focused on the consequences of international migration and the generalization of empirical findings on population migration mechanisms in other domains to the construction industry is substantially limited by the specific characteristics of construction workers and the temporary nature of their migration activities. In addressing this gap, this study represents an exploratory effort to quantitatively characterize the interregional migration patterns of construction workers in the labor-intensive construction industry and examines the roles of employment opportunity and environmental amenity in driving interregional migration.

The purpose of this study is to identify the effect of ethnic networks on firms’ location decisions using social network theory and the homophily principle.

A traditional gravity model is used on a sample composed of high-skilled and low-skilled ethnic networks and multinational enterprises (MNEs) from Latin America, North America and the European Union.

Contrary to findings of previous studies, ethnic networks do not appear to influence location decisions of Latin American firms expanding into countries from North America and the European Union. This might be because of weak ethnic connections among the players.

Managers must be aware that ethnic networks not always can be used for the strategy of the firm, at least regarding location.

Governments and entrepreneurial and immigrant associations from home and host countries could develop initiatives addressed to strengthening the links between the members of the networks.

This study extends the social network theory in the sense that ethnic networks do not always positively affect firms’ location. Moreover, it is important to consider the specific context or features of the members of a network before analyzing its effects on firms’ location.

The purpose of this paper is to examine the feasibility of committee neural network (CNN) theory for the improvement of accuracy and consistency of the neural network model on the estimation of preconsolidation pressure from the field piezocone measurements.

In this study, CNN theory is introduced to improve the initial weight dependency of the neural network model on the prediction of preconsolidation pressure of soft clay from a piezocone test result. It was found that the proposed CNN model can improve the initial weight dependency of the NN model and provide a more consistent and precise inference result than existing NN models, as well as empirical and theoretical models.

It was found that the CNN overcomes the initial weight dependency of the single neural network model. Various committees of the single multilayer perceptrons (MLPs) were tested. It was found that if eight single MLPs, which have the same structure but have been trained with a different initial weight and bias, are accumulated in the committee with the same weighting factor, any variation on the prediction of the preconsolidation pressure from the piezocone test result can be simply and successfully eliminated.

In recent years, ANN has been found to be a powerful theory for analyzing complex relationships involving a multitude of variables, on many geotechnical applications. However, single MLP, when repeatedly trained on the same patterns, tends to reach different minima of the objective function each time and hence give a different set of neuron weights, because the solution is not unique for noisy data, as in most geotechnical problems. The authors observed that a committee neural network system is able to provide improved performance compared with a single optimal neural network.

A major trend in the changing nature of work is the increasing use of temporary workers. Although common among students, older employees have joined the ranks of temporary workers as they extend their work lives. Temporary workers tend to report lower affective commitment and consequently poorer work outcomes. However, different generations of workers may conceive temporary work differently from each other. The purpose of this paper is to explore how different generations of temporary workers, respond to human resource practices (HRP), which in turn influences their affective commitment and work performance.

The sample is comprised of 3,876 temporary agency workers (TAWs) from seven temporary employment agencies in Portugal. The authors undertook multiple group SEM analyses to test a moderated mediation model that accounts for TAWs’ affective commitment (toward the agency and the client company) across three generations (Baby Boomers, Generation X and Millennials) in the relationship between human resources practices and overall perceived performance.

After controlling for gender, age and tenure, the authors find generational differences in the perceptions of HRP and perceived performance. The results support the moderator effect of generations in the direct and indirect relationships – through both affective commitments – between TAWs’ perceived HRP and perceived performance.

The cross-sectional design limits the possibility to make causal inferences.

This study contributes to a better understanding of how different generations respond to temporary employment relationships. The findings suggest important differences in the way in which the same HRP system relates (directly and indirectly thorough affective commitment toward the client) with their perceived performance across different generations.

This paper aims to propose an improved Moth Flame Optimization (I-MFO) algorithm to optimize the cost-oriented two-sided assembly line balancing (2S-ALB). Prior to the decision to assemble a new product, the manufacturer will carefully study and optimize the related cost to set up and run the assembly line. For the first time in ALB, the power cost is modeled together with the equipment, set up and labor costs.

I-MFO was proposed by introducing a global reference flame mechanism to guide the global search direction. A set of benchmark problems was used to test the I-MFO performance. Apart from the benchmark problems, a case study from a body shop assembly was also presented.

The computational experiment indicated that the I-MFO obtained promising results compared to comparison algorithms, which included the particle swarm optimization, Cuckoo Search and ant colony optimization. Meanwhile, the results from the case study showed that the proposed cost-oriented 2S-ALB model was able to assist the manufacturer in making better decisions for different planning periods.

The main contribution of this work is the global reference flame mechanism for MFO algorithm. Furthermore, this research introduced a new cost-oriented model that considered power consumption in the assembly line design.