Search results

The purpose of this study is to investigate how smart tourism technologies and memorable tourism experiences affect tourist satisfaction and tourist destination loyalty.

A total of 600 questionnaires were distributed, 360 were returned (60% response rate) and a covariance-based structural equation modeling technique was used to test the hypotheses.

The results of this study explain that smart tourism technologies and memorable tourism experiences play essential roles in enhancing tourist satisfaction and tourist destination loyalty.

This study specifies that tourists have pleasant memories and are satisfied at a tourist destination; as a result, they are more likely to revisit and recommend a tourist destination to their friends, family and other tourists. If a tourist has a negative experience with smart city info-structure facilities, a tourist might reach an overall conclusion to not revisit or recommend the location to other tourists.

This study provides empirical evidence to support the importance of smart tourism technologies and memorable tourism experiences in enhancing tourist satisfaction and tourist destination loyalty.

本论文旨在研究智慧旅游科技和难忘的旅游体验如何影响游客满意度和游客对目的地的忠诚。

共600份问卷发放, 并收回360份数据（回应率为60%）, 本论文采样基于协方差的结构方程模型以测验假设。

研究结果表明智慧旅游科技和难忘的旅游体验对增强游客满意度和游客对目的地的忠诚度起到至关重要的作用。

本论文证实游客有着美好的旅游回忆与对旅游目的地满意, 因此, 他们更愿意重游以及向他们的朋友、家人、以及其他游客推荐这个目的地。如果游客对智慧城市信息基础建设有不好的体验, 那么他们则会决定不会重游或者推荐这个城市给其他游客。

本论文为证实智慧旅游科技和难忘的旅游体验对加强游客满意度和游客对目的地忠诚度的至关重要的作用方面, 提供了强有力的实践证实。

Modeling represents the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides insight, answers and guidance useful for the originating application. The purpose of this paper is to determine the causal causes of schedule overrun and cost escalation of highway projects in Egypt in order to be used as independents variables in mathematical models for predicting the percentages of schedule overrun and cost escalation of such projects in Egypt.

A survey of a randomly selected samples yielded responses from 40 owners, 15 consultants and 56 contractors. The survey includes 38 schedule overrun factors and 26 cost escalation factors. The effectiveness degree of the identified factors has been identified by the triangle fuzzy approach.

The results of the survey show that “contractor’s technical staff is insufficient and ineligible to accomplish the project” is the most important cause of schedule overrun, while the major cause of cost escalation is inadequate preparation of the project concerning planning and execution.

The main contribution of this study is predicting the percentages of schedule overrun and cost escalation of highway projects in Egypt. Through the application of the linear regression analysis method and statistical fuzzy theory, four predictive models have been developed and it has been noted that the linear regression-based model shows prediction accuracy better than statistical fuzzy-based model in predicting percentages of schedule overrun and cost escalation.

This study aims to identify the negotiation parameters of needs for Halal talents in Malaysia and proffer solutions on the sustainability of halal talents in Malaysia.

A focus group discussion was performed among seven Halal talents from the industries of various schemes. Using the theory of inventive problem solving (TRIZ) analysis approach, a functional model, as well as causes and effect analysis, is generated by the participants. The model is translated into a contradiction matrix to obtain the necessary solutions in developing a sustainable framework for Halal Talents in Malaysia.

The cause-and-effect analysis reveals that poor career path, lack of training and poor salary structure are experienced by the participants.

Having had vast categories of halal industries, this research focuses on nine schemes under halal certification JAKIM which excludes banking and tourism.

It is suggested that the implementation of mutual benefits should be in place to enhance positivism among Halal talents in the near future.

Unlike the social science approach, the use of TRIZ analysis provides a substantial framework of sustainability among halal talents in Malaysia.

Previous research has little specific guidance on how to improve large infrastructures’ risk analysis. This paper aims to propose a practical risk analysis framework across the project lifecycle with Bayesian Networks (BNs).

The framework includes three phases. In the qualitative phase, primary risks were identified by literature reviews and interviews; questionnaires were used to determine key risks at each project stage and causal relationships between stage-related risks. In the quantitation, brainstorming and questionnaires, and techniques of ranked nodes/paths, risk map and Bayesian truth serum were adopted. Then, a BN-based risk assessment model was developed, and risk analysis was conducted with AgenaRisk software.

Twenty key risks across the lifecycle were determined: some risks were recurring and different risks emerged at various stages with the construction and feasibility most risky. Results showed that previous stages’ risks significantly amplified subsequent stages’ risks. Based on the causality of stage-related risks, a qualitative model was easily constructed. Ranked nodes/paths facilitated the quantification by requiring less statistical knowledge and fewer parameters than traditional BNs. As articulated by a case, this model yielded very simple and easy-to-understand representations of risks and risk propagation pathways.

Rare research has developed a BN risk assessment model from the perspective of project stages. A structured model, a propagation network among individual risks, stage-related risks, and the final adverse consequence, has been designed. This research provides practitioners with a realistic risk assessment approach and further understanding of dynamic and stage-related risks throughout large infrastructures’ lifecycle. The framework can be modified and used in other real-world risk analysis where risks are complex and develop in stages.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

The lightning phenomenon is one of the main threats in photovoltaic (PV) applications. Suitable protection systems avoid major damages from direct strikes but also nearby strikes may induce overvoltage transients in the module itself and in the power conditioning circuitry, which can permanently damage the system. The effects on the PV system sensibly depend on the converter topology and on the adopted power switch. In the present study, a comparative analysis of the transient response due to a nearby lightning strike (LS) is carried out for three PV systems, each equipped with a different converter, namely, boost, buck and buck–boost, based on either silicon carbide metal oxide semiconductor field effect transistors (SiC MOSFET) or insulated gate bipolar transistors controlled power switch devices, allowing in this way an analysis at different switching frequencies. The purpose of this paper is to present the results of the numerical analysis to help the design of suited protection systems.

Using a recently introduced three-dimensional semi-analytical method to simulate the electromagnetic transients caused in PV modules by nearby LSs, we investigate numerically the effect of a LS on the electronic circuits connecting the module to the alternate current (AC) power systems. This study adopts numerical simulations because experimental analyses are not easy to perform and does not grant a sufficient coverage of all statistically relevant aspects. The approach was validated in a previous paper against available experimental data.

It is found that the load voltage is not severely interested by the strike effects, thanks to the low pass filters present at the converter output, whereas a relatively high overvoltage develops between the negative pin of the inner circuitry and the “ground” voltage reference. The overcurrent present in the active switches is hardly comparable because of the different topologies and working frequencies; however, the highest overcurrent is observed in the buck converter topology, with SiC MOSFET technology, although it shows the fastest decay.

This research proposes, to the best of the authors’ knowledge, a comprehensive comparison of the indirect lighting strike effects on the converter connected to PV panels. A proper design of the lightning and surge protection system should take into account such aspects to reduce the risk of induced overvoltage and overcurrent transients.

This study examines the relationship between employees' perceptions of informational fairness and project performance. Furthermore, it examines if this relationship is sequentially mediated by (1) knowledge sharing and role clarity and (2) communication openness and role clarity.

Data are collected from 302 full-time employees of seven project-based construction organizations in Pakistan. Data are analyzed through variance-based structural equation modeling technique and the Preacher and Hayes' bootstrapping procedure.

Results indicate that project employees' perceptions of informational fairness positively predict project performance. Moreover, this relationship is sequentially mediated by (1) communication openness and role clarity and (2) knowledge sharing and role clarity.

This study provides further insights on the informational fairness and project performance relationship by examining their underlying mechanisms. It draws on the much ignored context of Pakistan, and offers some implications for managers and researchers with regard to how behavioral factors may further enhance project performance.

This study aims to evaluate the influence of corporate governance index (CGI), ownership concentration (OC) and other features on the dividends of listed Indian pharmaceutical companies. The other features included are leverage, excess return over cost of equity and stock-market return. This study thus helps to provide more insights on the dividend distribution issues for a shareholder in the challenging and demanding pharma industry, especially when stakes are high.

The data for all 26 pharmaceutical companies which form part of the NSE NIFTY-500 index for six years (2014–2019) is procured using Centre for Monitoring Indian Economy’s (CMIEs) Prowess database. An eight-pointer scale (unweighted scale) is used to develop the CGI. For OC, this paper considers the proportion of promoters’ shareholding, domestic institutional investors’ shareholding and foreign owners’ shareholding. Both static and dynamic panel data models are used to evaluate the effect of CGI and OC on dividends.

The panel data analysis depicts that CGI significantly positively influences the dividends of pharmaceutical companies in India. Thus, the authors find support for La Porta et al.’s outcome agency model. The results also reveal that only promoters’ holdings are significantly inversely related to dividends out of the three OC variables used for this study. This discussion implies that family-run pharmaceutical companies in India tend to retain profits instead of distributing dividends.

This study provides two direct insights for policymakers and stakeholders. First, because this study shows that CGI significantly positively influences dividends, corporate governance (CG) is an essential factor for determining dividends. Second, because the results also reveal that OC in the hands of promoters hurts dividends, it implies that the higher the promoter holding, lesser is the dividend distributed by the company. Both these results can be used as a quantitative tool by investors to assess Indian pharmaceutical companies better. However, a similar study could be directed to assess the impact of CGI and OC on dividends of other industries. Moreover, additional variables of CG and OC can also be evaluated in further detail. There is also a need to empirically validate the impact of CG and OC on a company’s performance.

The results are robust and reveal that variation in CGI does impact dividend policy. This aids in confirming that CG is a crucial aspect influencing dividends. The findings also add to the increasing studies across the globe evaluating the influence of CG and OC on dividends.