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The purpose of this paper is to analyze and compare the effects of conventional and unconventional FDI on the host country in a more comprehensive and systematic way.

Both the OLI paradigm and the imbalance theory are linked to the diamond model in order to compare the effects of conventional and unconventional FDI on the host country. This methodology is then applied to the real world as a case study, FDI toward the Korean automobile industry.

Conventional FDI is often said to be more beneficial to the host country than the unconventional type. However, the actual effect of unconventional FDI is shown to be more positive with better management and is often larger than perceived. Therefore, unconventional FDI emerges as important as conventional FDI for sustainable economic development.

In general, unconventional FDI has often been criticized severely because of misperceptions derived from the dominance of conventional FDI on theoretical aspects, incomprehensive perspectives toward assessing the effects of FDI, and negative political views. Therefore, rigorous and holistic case study analyses based on solid analytical tools are needed in order to better understand the effects of unconventional FDI and to draw up effective and proper FDI promotion policies.

This paper provides a way to better understand the effect of unconventional FDI on the host country comprehensively and systematically by expanding and deepening existing theories. Based on this, the effects of conventional and unconventional FDI on the host country are compared theoretically and empirically, particularly with the case of the Korean automobile industry.

This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.

This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.

The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.

This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.

The aim of this paper is to examine dynamic linkages between price and rent and between property types. Intuition suggests that housing market segments experience different market cycles in response to macroeconomic shocks. However, they may be dynamically interlinked in urban areas because of substitutability. The linkage may even change, if preference weakens for multiple occupancies. A sudden reduction in apartment demand may create repercussions to other housing segments. Past analyses, despite their contributions, are static and do not consider possible linkages between property types. To fill this void, this paper investigates the price-rent dynamics for urban homes by adopting the case of Singapore.

This paper applies a methodology from Phillips et al. (2015) to Singaporean housing (price and rent) data. Phillips et al. (2015) recently proposed a test for an explosive root in time series data and has spurred several empirical applications in the bubble literature.

This paper finds for Singapore that the markets were subjected to explosive growth (where rents grew at a higher rate than prices did) during the Global Financial Crisis. Also, the results suggest that rent drives price and that non-landed housing (offices in central areas) leads to other residential housing (non-residential housing) in both price and rent.

Overall, the present findings suggest that rent drives price, while property types are interlinked. Non-landed homes and offices in central areas are the sources of repercussions. Under normal circumstances, rental shocks may be propagated positively from nonlanded housing (central offices) to the other residential (non-residential) property types as the present findings suggest, which enables us to infer that a decrease in non-landed housing (central offices) rent may lead to an increase in rent on other property types because pandemic shocks only shift demand fromone property type to another, unlike typical macroeconomic shocks.

Urban homes are faced with uncertainty arising from the COVID-19 outbreak for which city residents have a stronger incentive to exile to suburbs. Urban life may no longer be attractive because of social distancing and work from home policy. This has implications for urban home demands that are closely linked to urban house price and rent. In the present study, the paper set out to investigate the price-rent and property-type dynamics for urban homes in Singapore.

Various approaches have emerged to assist practitioners in making more informed decisions in highway construction projects. However, industry practitioners are still using subjective ways to make decisions. Also, researchers have developed tools and techniques with similar objectives. Lack of information on what has been developed might lead to those issues. Therefore, this paper aims to review trends of evolution, pinpoint strengths and gaps in the literature and identifies potential future directions for decision-making research in highway construction projects.

A systematic review was conducted on published articles on decision-making in highway construction projects using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique.

The analysis of 101 articles revealed that existing decision-making research in highway construction projects targets improvements in four areas: feasibility, conceptual, detailed scope and detailed design. The four areas consist of sixteen subthemes that are detailed in this study. In addition, most research involved developing decision support tools and systems as well as decision-making models, techniques and frameworks. Lastly, several research areas have emerged, such as adding more decision criteria including those with uncertainties, expanding existing decision-making models into decision support systems, benchmarking decision criteria between different sample populations and exploring inter-and intra-relationships between decision criteria.

This paper provides an overview of existing research on decision-making in highway construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research. Finally, industry practitioners can use the findings to develop strategies for effective decision-making processes.

The purpose of this paper is to investigate the effects of irregular unsettling on the smoking model in form of the stochastic model as in the deterministic model these effects are neglected for simplicity.

In this research, the authors investigate a stochastic smoking system in which the contact rate is perturbed by Lévy noise to control the trend of smoking. First, present the formulation of the stochastic model and study the dynamics of the deterministic model. Then the global positive solution of the stochastic system is discussed. Further, extinction and the persistence of the proposed system are presented on the base of the reproductive number.

The authors discuss the dynamics of the deterministic smoking model form and further present the existence and uniqueness of non-negative global solutions for the stochastic system. Some previous study’s mentioned in the Introduction can be improved with the help of obtaining results, graphically present in this manuscript. In this regard, the authors present the sufficient conditions for the extinction of smoking for reproductive number is less than 1.

In this work, the authors investigated the dynamic stochastic smoking model with non-Gaussian noise. The authors discussed the dynamics of the deterministic smoking model form and further showed for the stochastic system the existence and uniqueness of the non-negative global solution. Some previous study’s mentioned in the Introduction can be improved with the help of obtained results, clearly shown graphically in this manuscript. In this regard, the authors presented the sufficient conditions for the extinction of smoking, if <1, which can help in the control of smoking. Motivated from this research soon, the authors will extent the results to propose new mathematical models for the smoking epidemic in the form of fractional stochastic modeling. Especially, will investigate the effective strategies for control smoking throughout the world.

This study is helpful in the control of smoking throughout the world.

This paper aims to consider a problem of assembly sensitivity in a multi-station assembly process. The authors focus on the assembly process of aircrafts, which includes cabins and inertial navigation system (INSs), and establish the assembly process state space model for their assembly sensitivity research.

To date, the process-related errors that cause large variations in key product characteristics remains one of the most critical research topics in assembly sensitivity analysis. This paper focuses on the unique challenges brought about by the multi-station system: a system-level model for characterizing the variation propagation in the entire process, and the necessity of describing the system response to variation inputs at both station-level and single fixture-level scales. State space representation is used to describe the propagation of variation in such a multi-station process, incorporating assembly process parameters such as fixture-locating layout at individual stations and station-to-station locating layout change.

Following the sensitivity analysis in control theory, a group of hierarchical sensitivity indices is defined and expressed in terms of the system matrices in the state space model, which are determined by the given assembly process parameters.

A case study of assembly sensitivity for a multi-station assembly process illustrates and validates the proposed methodology.

Visual merchandising has a direct impact on shopping experience, making it pertinent for the retailers to ensure that their store environment has an effective design. This study blends fuzzy numbers and Analytical Hierarchy Process to create a fuzzy evaluation model prioritizing the relative weights of visual merchandising dimensions of online fashion apparel store. This study will help the fashion retailers in creating more engaging and informative online stores leading to subsequent increase in online retail sales.

Visual merchandising dimensions and sub dimensions used by retailers for online stores were identified through review of literature and discussion with experts. Twenty experts were interviewed and their responses captured. The responses were analyzed using Fuzzy AHP technique resulting in prioritizing the dimensions according to the weight. For testing the stability of the results, sensitivity analysis was conducted.

Four key dimensions and sixteen sub dimensions were extracted. Weights were calculated using FAHP. “Pictorial Presentation” has the highest weight followed closely by “Product Information” dimension. Amongst the sub-dimensions “Number of Product Images” scored highest followed by “Product Presentation mode”. “Alterable Background” scored the least weight.

Previous studies have identified Online Visual Merchandising dimensions and have also studied the impact of some of these dimensions on consumer buying behavior, but the relative significance has not been determined yet. This study fills the gap.

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

This study aims to investigate the abusive supervision climate as an antecedent abusive supervision and attempts to uncover underlying mechanisms that affects employees' behavioural outcomes in terms of their performance.

A framework embedded in social learning theory is developed and empirically tested using a time-lag research design. Data have been collected from 330 functional dyads of supervisors and followers using judgement sampling (from public hospitals in Pakistan) that have been identified and matched for drawing analysis and inferences.

The results indicate that abusive supervision does occur because of the hypothesized precursor (abusive supervision climate) and that the underlying mechanisms (fear) delineated in this research positively and significantly affect performance of employees. The added significance of the study is its relevance for practitioners with opportunities to manage the factors affecting supervision and its relationship with employees' outcomes through appropriate interventions to improve the behavioural outcomes.

The current study holds not only managerial and academic implications but also has economical and social implications. The findings of the study will help the supervisors and organisations understand how they become a source of their abusive behaviour. With the understanding of the root causes, they can encourage employees in developing mindfulness in recommendations which helps them build an internal capability to face external adversities. Policymakers will get insights into the underlying mechanisms of abusive supervision as well as problems they are facing with the employees. This understanding will help employers and employees in building internal control of employees improving their performance as well as mental health ultimately.

The study opens new avenues for further research with regard to the study of contextual, organisational and personal factors to mitigate abusive supervision as well as exploring additional moderators to lessen the relationship between abusive supervision and behavioural outcomes.

The purpose of this paper is to investigate the effect of obese models vs normal weight models on fashion brands’ attractiveness.

An experiment was carried out in which 1,225 university students in Sweden and Brazil rated the attractiveness of a fashion brand worn by a normal weight model and an obese model.

The overall effect of obese models’ effect on fashion brand attractiveness was insignificant. Furthermore, neither culture nor the consumer’s own weight had a significant effect. There was, however, a significant effect of the participant’s own gender; women rate fashion brands worn by obese models significantly higher on attractiveness than they did fashion brands worn by normal weight models. Men displayed the inverse response.

The effect of the model’s ethnicity was beyond the scope of the experiment, and the brand attractiveness scale captured only one aspect of brand character, leaving other potential brand effects for future studies.

Companies can use obese models with no overall brand attractiveness penalty across markets and for marketing to women of all sizes. Given men’s negative reactions, such models might however be unsuitable for the male-to-female gift market.

The results support the use of obese models, which can lead to greater representation of larger women in the media, and consequently, reduced fat stigma.

The study validates the theory of user imagery, and it extends the theory by examining how different target consumers react to user imagery traits and thus provides evidence for gender bias toward obese models.