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The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations.

The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation.

Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI.

FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

This paper aims to identify empirically the influence of learning mechanisms provided by organizations on knowledge sharing in the organizational environment.

A quantitative study was developed in which a sample of 268 individuals from civil and military organizations of the Federal Direct Administration was researched. The questionnaire used was composed of the Organizational Learning Mechanism Scale, which was adapted at the time of the present study, in addition to a scale on knowledge sharing, which was developed within the scope of this research. After performing the factorial analysis for both scales, a canonical correlation analysis was performed between the group of variables associated with the learning mechanisms (independent variables) and the group of variables on knowledge sharing (dependent variables).

The results found in the canonical correlation analysis indicate that the learning mechanisms are responsible for explaining 35 per cent of the variance (R² = 0.352) of the group of variables on knowledge sharing.

The findings of this research can help the researched organizations to increase the knowledge management actions, mainly in relation to the actions that favor social interaction among the individuals in the work environment, making possible the exchange of knowledge and experiences in the internal organizational context, and exploring in a positive way actions related to internal acquisition.

The deeper knowledge about the relationship between organizational actions promoted by top management and knowledge support decision-making in the organizational environment regarding contextual factors that influence social interaction between individuals. In relation to the sharing of knowledge, a high correlation of knowledge absorption and reproduction aspects with the knowledge sharing phenomenon was perceived, so that the possibility of organizations thinking in ways that provide the individual with formal and informal environments can be foreseen.

The main contributions of this research are to measure the intensity of the relationship between learning mechanisms and knowledge sharing; and to test the predictive effect of learning mechanisms on knowledge sharing. Regarding the methodological aspects, it was opportune to approach the phenomenon through a little used lens in the context of administration research: the analysis of canonical correlation, which represents another look at the influence of the actions of the top management and the interaction of individuals. The discussions and the data analysis carried out in this research allow us to envisage significant contributions of this work to the analysis and theoretical refinement of the study of the variables treated.

The purpose of this paper is to develop a quantitative risk assessment method for agricultural products cold chain logistics to assess the condition of the fresh agricultural products cold chain process objectively and accurately.

A risk assessment index system of agricultural products cold chain logistics is designed on the basis of the risk identification for the process of agricultural products cold chain logistics. This paper first uses catastrophe progression method and a new maximum deviation method to build an improved catastrophe progression assessment model for agricultural products cold chain logistics. In order to verify the reliability and validity of the model, two representative enterprises are selected as the case in the study.

The results in the empirical research indicate strong support for the assessment model and coincide with the reality. The risk assessment index system can also reflect the key risk factors from agricultural products cold chain logistics scientifically. In addition, the improved catastrophe progression assessment method proposed in this paper can be scientific and reasonable to predict risk.

This paper contributes to provide a new risk assessment model for agricultural products cold chain logistics. The new model overcomes the limitation of subjective empowerment and it increases the objectivity and scientificity in the process of cold chain logistics risk assessment. This paper also shows that practitioners involved in the field of products cold chain logistics can manage the potential risk by a set of scientific methods for assessing the risk before the accident.

The paper provides a practical guideline to practitioners, especially for cold chain logistics managers, relevant management departments, and cold chain logistics management consultants. It is proved that the new risk assessment method and the risk assessment index system of agricultural products cold chain logistics can help them assess the risk scientifically and reasonably.

Although the calculation is simple, the new model can overcome the limitation of subjective empowerment scientifically and reasonably, and thus has important practical value.

This paper aims to identify the importance of individual variables in the corporate real estate (CRE) decision-making process.

Nine experts received a posed scenario of a changed business strategy requiring a CRE reduction in individual interviews. Based on their suggested response, a decision network was modelled for each expert using the causal network elicitation technique, incorporating the utilities for decision variables and importance weights for attributes and benefits. The decision model offers a graphical representation of decision-benefit links for the decisions CRE managers make in such a period of decline.

Perceived facilitators of CRE dynamic alignment were identified by calculating lift ratios on their perceived importance of the attributes they mentioned during the interviews as nodes in the network that link decisions to benefits. Facilitators included CRE metrics and workplace strategy, while capital expenditure and landlords inhibit alignment processes. The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process.

The research set a specific scenario for the experts to consider. That could be regarded as small but there was clear evidence of saturation of expert knowledge. Additional face-to-face interviews with the experts may have generated further details on the thought processes of the experts.

The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process. Thereby providing CRE decision-makers with key elements for a decision model.

The research technique, causal network elicitation technique, uses semi-structured interviews to create decision networks, which is a technique that has not been widely applied to CRE research. The research provides a granular view of what are important inhibitors or facilitators of dynamic alignment of CRE to business strategy.

The purpose of the study is to evaluate the compliance of environmental risk management systems implemented by Moroccan banks with ISO 14001 certification or environmental charters.

Data were collected through the distribution of an environmental risk management questionnaire to all Moroccan banks’ branches, business centers and various central entities. The study uses regression analysis to model the relationship between environmental management system (EMS) compliance and environmental management explanatory variables to identify the most relevant indicators that can explain the effectiveness and reliability of an EMS.

Empirical evidence reveals that the evaluation of EMS compliance in Moroccan banks should consider two categories of variables: the first category is related to the culture of environmental risk management, and the second one is related to environmental management practices.

The results show that the “information” variables play a key role in the overall design of an EMS and represent essential variables in the general definition of the environmental policy and in raising awareness and providing information on the bank’s commitment to a pro-environmental approach. The “application” variables confirm that environmental management practices need to be put in place to manage the different environmental risks. The study raises some managerial implications and further research directions.

During the pandemic, two types of fear were identified that occur significantly in all groups or profiles of people. The aim of this paper is to determine which of the two types of fears exist in certain psychological groups, and which of the fears strongly influence the decision to travel.

The VALS 2 method and standardized questionnaire were used for the segmentation of the tourist market or for the determination of the psychographic profiles of the consumers, and three additional questions were joined to it as they were vital for the research of the type of fear and its impact on the decision for traveling. When the reliability of the questionnaire and the validity of the sample were determined, the data were further processed using a computer program package IMB AMOS SPSS 21.00, and then, based on the theoretical suppositions and hypotheses, the SEM structural model was created.

The paper indicates the existence of established types of fears in humans, when it comes to pandemics and similar crisis situations. People are most afraid of infection during travel, and lack of funds and job loss during the critical period of the pandemic. The research conducted confirms that all groups of people, who are determined by the psychological technique VALS 2, react with a certain dose of fear and make decisions under the pressure of fears.

The research had limitations in terms of contact with people and conducting live surveys. The measures during the pandemic, which were carried out by the state, included social distance and limited movement of people.

The work can contribute to the community, along with similar research. The results of the research will be available, and it will be possible to see the behavior of people during crisis situations, and the impact of fears on making decisions, both travel decisions and decisions related to other areas of life.

The paper provides research results on a large sample of respondents, and can serve as a basis for further research in the field of tourism, psychology and similar fields. It is crucial to consider the type of fears, and the strength of the impact of these fears on the decision to travel, during crisis situations.

This study aims to examine the impact of external debt on economic growth in Bangladesh within a broader macroeconomic scenario.

In the process of doing so, it assesses the empirical cointegration, long-run and short-run dynamics of the concerned variables for the period of 1980–2017 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration. First, debt-gross domestic product linkage explores the impact of external debt impact on economic growth using a set of macro and country risk variables, and then this linkage is also analyzed along with a newly formed macroeconomic policy (MEP) variable using principal component analysis.

The study results reveal the negative impact of external debt on GDP growth, but the larger positive impact of MEP index indicates that this adverse effect of debt can be mitigated or even nullified by sound MEP and appropriate human resource policy.

The dynamic effects of different shocks (external debt and macro policy variable) on economic growth by vector autoregression impulse response function also confirm our ARDL findings.

The objective of this study is to analyze the role that budgetary analytical capacity (BAC) plays for fiscal discipline in a sample of Eurozone countries in the postcrisis period.

Building on the policy capacity literature, an index for the BAC is constructed by including OECD budgetary data from three dimensions: reliability of projections, openness to legislative scrutiny and transparency. The proposed BAC index is validated by checking that larger values of the index are associated with the higher fiscal discipline scores across countries.

Controlling for the economic cycle, BAC index is positively associated with fiscal discipline. The association is stronger for the index as a whole than for the three separate dimensions.

The study is done on the limited sample of countries, and it is not feasible to validate results over time.

Budgetary policymakers can improve fiscal discipline by enhancing the three pillars that support the BAC.

Stronger BAC can help to improve the quality of public decision-making and overcome political opportunism.

This is the first study that introduces the concept of BAC, makes it operational and suggests its relevance for supporting fiscal discipline.

The purpose of this research is to identify major barriers to the implementation of reverse logistics (RL). Also, the study addresses best practices among reuse, remanufacture, recycling, refurbishment and repair as alternatives for RL processes.

This study targets supply chain management experts for their opinions regarding the identification of critical barriers and alternatives for RL implementation. Their opinions were extracted through a Web questionnaire based on 14 criteria with 5 alternatives. The tools of multi-criteria decision-making are used for analysis, i.e. fuzzy VIKOR and fuzzy TOPSIS.

The results indicate that lack of recognition of competitive advantage to be gained through RL practice is the most critical barrier to RL implementation. The least barrier or major facilitator for RL is “supportive initiative for end-of-life products.” The top-ranked alternative in this study is reuse followed by remanufacturing. The least important alternative is “repair” in the case of Pakistan. These alternatives are ranked based on “q values” derived through fuzzy VIKOR.

The results of this study can only be generalized for the manufacturing sector of Pakistan during the period of the study.

The findings of this study will assist managers in deploying the best practices concerning RL.

Fuzzy VIKOR and fuzzy TOPSIS have not been applied to RL alternatives in previous research.

This research paper aim at providing a new approach of calculating the destinations competitiveness index. How can these variables been aggregated in other to reflect the realities of very distinct productive environments? We assume that: The weighting of variables provides a better measure of destinations competitiveness. Base on the Neo-Technological theory, after a life cycle differentiation, we used a panel data approach to calculate the weight of each variable as the spearman correlation coefficient of its contribution to tourism inflows growth. After integrating these weights, we came to the point that by applying an appropriate weight to its components, we end up having a competitiveness index that significantly improve the correlation between competitiveness and tourism inflows growth.