Search results

Recent disruptions have sparked concern about building a resilient and sustainable manufacturing supply chain. While artificial intelligence (AI) strengthens resilience, research is needed to understand how cloud adoption can foster integration, collaboration, adaptation and sustainable manufacturing. Therefore, this study aimed to unleash the power of cloud adoption and AI in optimizing resilience and sustainable performance through collaboration and adaptive capabilities at manufacturing firms.

This research followed a deductive approach and employed a quantitative method with a survey technique to collect data from its target population. The study used stratified random sampling with a sample size of 1,279 participants working in diverse manufacturing industries across California, Texas and New York.

This research investigated how companies can make their manufacturing supply chains more resilient and sustainable. The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance (hypotheses H1-H7, except H5). Additionally, utilizing artificial intelligence helps improve adaptability, further strengthening resilience and sustainability (H8-H11). Interestingly, the study found that internal integration alone does not significantly impact collaboration (H5). This suggests that external factors are more critical in fostering collaboration within the manufacturing supply chain during disruptions.

This study dives into the complex world of interconnected factors (formative constructs in higher order) influencing manufacturing supply chains. Using advanced modeling techniques, it highlights the powerful impact of cloud-based integration. Cloud-based integration and artificial intelligence unlock significant improvements for manufacturers and decision-makers by enabling information processes and dynamic capability theory.

This study objective is twofold. This study aims to present an institutional analysis of the implications of job localization programs in Gulf Cooperation Council (GCC) countries, such as Saudi Arabia, United Arab Emirates and Qatar. Further, it highlights the impacts of these programs on the accounting profession.

This study is based primarily on the desktop research method, where data is collected from the review of previous studies, published data on Internet Websites and reports released by International organizations such as the United Nations. In addition, the study benefitted from conducting six interviews with government officials from GCC countries. Theoretically, this study draws upon insights from the institutional logics theory to discern higher-order institutions deriving job localization decisions in the GCC region.

This paper explained how job localization policies in the GCC region are informed by three central logics: economic, socio-political and professional. Despite contributing to achieving some socio-political goals for policymakers, these policies could have serious consequences for the practice of the professions and, hence, the local business environment. Besides, this paper highlighted the serious localization policies' impacts on the accounting profession, especially the quality of the workforce (accountants) and their job readiness.

This study highlights the various implications of job localization policies for locals, foreigners, public and private sector entities and governments. Besides, it has recommended some actions to mitigate the negative influences of such policies on the surrounding society.

This study contributes to the literature by following an interpretative approach in explaining the localization of the accounting profession from an institutional perspective by bringing new evidence from GCC emerging markets.

Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance.

To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance.

Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations.

This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed.

Although organizations are investing heavily in digital transformation (DT) of the sales function, implementation and exploitation at the sales force level are ongoing challenges. As sales managers serve as conduits of influence between top management and the sales force, the success of strategic initiatives, such as DT, hinges heavily on leveraging their influence to promote change adoption at the sales force level. Accordingly, this research is guided by the research question: how can sales organizations secure the buy-in of sales managers and induce their championing behaviors directed toward the sales force?. The purpose of this paper is to investigate how organizational and psychological resources influence sales managers' DT change champion through their change readiness.

Construing DT in sales as an organizational change that creates contextual job demands, the theoretical framework offers several hypotheses linking organizational and personal resources with sales managers’ change readiness and championing behaviors. The perceived impact of change is included as a moderating variable. Using data from a sample of 176 business-to-business sales managers, the hypotheses are tested using partial least squares structural equation modeling.

The authors demonstrate that two change-related organizational resources (change communication and change mobilization) and a personal psychological resource (psychological capital) facilitate sales managers’ emotional and cognitive change readiness, which, in turn, enhances their championing behaviors toward DT initiatives. Further, the authors find that perceived change impact augments the effects of organizational and psychological resources on change readiness, thus highlighting the importance of effective positioning of the outcomes of change.

This study provides practitioners with actionable guidance on securing the buy-in of sales managers for change initiatives such as DT. Specifically, communication and mobilization are critical inducements. Managers who score high on psychological capital can be targeted as change agents. Further, the impact of change needs to be framed positively, as the resultant perceptions magnify the effects of organizational resources.

While prior research has examined salespeople’s response to change, very little is known about the antecedents of change readiness and championing behavior among sales managers. Based on the results, the authors identify theoretical and managerial implications as well as future research directions.

The purpose of this paper is to test the existence of stylized facts, such as the volatility clustering, heavy tails seen on financial series, long-term dependence and multifractality on the returns of four real estate indexes using different types of indexes: conventional and Islamic by comparing pre and during COVID-19 pandemic.

Firstly, the authors examined the characteristics of the indexes. Secondly, the authors estimated the parameters of the stable distribution. Then, the long memory is detected via the estimation of the Hurst exponents. Afterwards, the authors determine the graphs of the multifractal detrended fluctuation analysis (MF-DFA). Finally, the authors apply the WTMM method.

The results suggest that the real estate indexes are far from being efficient and that the lowest level of multifractality was observed for Islamic indexes.

The inefficiency behavior of real estate indexes gives us an idea about the prediction of the behavior of future returns in these markets on the basis of past informations. Similarly, market participants would do well to reassess their investment and risk management framework to mitigate new and somewhat higher levels of risk of their exposures during the turbulent period.

To the authors’ knowledge, this is the first real estate market study employing STL decomposition before applying the MF-DFA in the context of the COVID-19 crisis. Likewise, the study is the first investigation that focuses on these four indexes.

The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.

The K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.

Comparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.

The practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.

Reviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.

This study aims to investigate the effects of smart tourism technology experience (STTE) on tourist satisfaction and happiness in Bordeaux, with a focus on understanding the mediating role of self-gratification. By examining these relationships, the study seeks to provide insights into how smart tourism technologies can enhance tourist experiences.

The study uses partial least squares-structural equation modeling (PLS-SEM) to analyze data collected from 380 tourists who visited Bordeaux. The measurement model assesses reliability and validity, while the structural model evaluates the proposed hypotheses and the mediation effects of self-gratification.

The results confirm that STTE positively impacts tourist satisfaction, with accessibility, informativeness and personalization significantly enhancing tourist satisfaction, while interactivity does not. Tourist satisfaction, in turn, positively affects tourist happiness. Furthermore, self-gratification partially mediates the relationship between tourist satisfaction and happiness, highlighting its importance in the smart tourism context.

This research extends the understanding of STTE by demonstrating its effects on tourist satisfaction and happiness. It introduces the mediating role of self-gratification, providing a novel perspective on how personalized smart tourism experiences contribute to overall tourist happiness.

In the process of building the “Belt and Road” and “Bright Road” community of interests between China and Kazakhstan, this paper proposes the construction of an inland nuclear power plant in Kazakhstan. Considering the uncertainty of investment in nuclear power generation, the authors propose the MGT (Monte-Carlo and Gaussian Radial Basis with Tensor factorization) utility evaluation model to evaluate the risk of investment in nuclear power in Kazakhstan and provide a relevant reference for decision making on inland nuclear investment in Kazakhstan.

Based on real options portfolio combined with a weighted utility function, this study takes into account the uncertainties associated with nuclear power investments through a minimum variance Monte Carlo approach, proposes a noise-enhancing process combined with geometric Brownian motion in solving complex conditions, and incorporates a measure of investment flexibility and strategic value in the investment, and then uses a deep noise reduction encoder to learn the initial values for potential features of cost and investment effectiveness. A Gaussian radial basis function used to construct a weighted utility function for each uncertainty, generate a minimization of the objective function for the tensor decomposition, and then optimize the objective loss function for the tensor decomposition, find the corresponding weights, and perform noise reduction to generalize the nonlinear problem to evaluate the effectiveness of nuclear power investment. Finally, the two dimensions of cost and risk (estimation of investment value and measurement of investment risk) are applied and simulated through actual data in Kazakhstan.

The authors assess the core indicators of Kazakhstan's nuclear power plants throughout their construction and operating cycles, based on data relating to a cluster of nuclear power plants of 10 different technologies. The authors compared it with several popular methods for evaluating the benefits of nuclear power generation and conducted subsequent sensitivity analyses of key indicators. Experimental results on the dataset show that the MGT method outperforms the other four methods and that changes in nuclear investment returns are more sensitive to changes in costs while operating cash flows from nuclear power are certainly an effective way to drive investment reform in inland nuclear power generation in Kazakhstan at current levels of investment costs.

Future research could consider exploring other excellent methods to improve the accuracy of the investment prediction further using sparseness and noise interference. Also consider collecting some expert advice and providing more appropriate specific suggestions, which will facilitate the application in practice.

The Novel Coronavirus epidemic has plunged the global economy into a deep recession, the tension between China and the US has made the energy cooperation road unusually tortuous, Kazakhstan in Central Asia has natural geographical and resource advantages, so China–Kazakhstan energy cooperation as a new era of opportunity, providing a strong guarantee for China's political and economic stability. The basic idea of building large-scale nuclear power plants in Balkhash and Aktau is put forward, considering the development strategy of building Kazakhstan into a regional international energy base. This work will be a good inspiration for the investment of nuclear generation.

This study solves the problem of increasing noise by combining Monte Carlo simulation with geometric Brownian motion under complex conditions, adds the measure of investment flexibility and strategic value, constructs the utility function of noise reduction weight based on Gaussian radial basis function and extends the nonlinear problem to the evaluation of nuclear power investment benefit.