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This paper includes three analyses concerning: expert support in the selection of impact variables for scientific models relevant to environmental planning, the quality of students’ individual estimates of corresponding impacts before and after a group discussion, and the accuracy of artificially‐aggregated judgments of independent groups. Participants were students of environmental sciences at ETH Zurich. The first analysis revealed that during participation in an environmental case study, students’ individual estimates of impacts of variables which have been suggested by experts increased, as compared to the estimates of impacts of additional variables, which have been selected by the students. The remaining analyses consider group discussions on the strength of particular environmental impacts. The quality of the estimates was analyzed referring to expert estimates of the impacts.

The purpose of this paper is to identify and prioritize the positive and negative impacts of tourism on the process of tourism growth at a national scale in Iran, by taking into account the reviews of previous studies, views of experts and structural analysis.

In this investigation, structural analysis technique has been used to identify the correlation between variables by using mix method data analysis. By using cross-impact analysis (N × N integer matrix) in the form of the Micmac method, the economic, sociocultural and environmental factors have been evaluated.

The results of the distribution of factors in the coordinate axes and the graphs between them indicate their features, and for reaching a sustainable system of tourism development, at first, priority should be given to the negative influential factors, especially the environmental fields, and then the focus should be on the decrease of the dual and risk variables as they cannot be anticipated.

For the rapid growth of tourism in many countries, governments ensure that policies have been heeded in designing and preparing general plans of the country to understand how the development trend is moving on. In this respect, arisen impacts of tourism system are one of the important issues during the development path and in the field of tourism future. Because of the complexity and broadness of tourism activities, these impacts have also many interconnected dimensions that should also be considered while studying tourism impacts.

This paper aims to identify various macroeconomic variables that affect the stock market performance of developed and emerging economies. It also investigates the effect of these factors on the stock markets of both economies. The impact of these variables on broad market indices and sectoral indices is investigated and compared too.

The publications for the study were retrieved from databases such as Emerald Insight, EBSCO, ScienceDirect and JSTOR using the keywords “Macroeconomic variables” and “Stock market” or “Stock market performance.” The result demonstrated a growing corpus of scholarly work in the domain of stock market. The study was carried out separately for each macroeconomic indicator. Given a large number of articles under consideration, the authors began by reading the titles and abstracts of all publications to identify those that were relevant. The papers are evaluated in Excel and the articles for review range from 1972 to 2021.

The authors found that gross domestic product (GDP), FDI (Foreign Direct Investment) and FII (Foreign Institutional Investment) have a positive effect on both emerging and developed economies’ stock market while gold price has a negative effect. Interest rates had a negative impact on both economies except for a few developing countries. The relationship with oil prices was positive for oil exporting countries while negative for oil importing countries. Inflation, money supply and GDP are the macroeconomic variables that have the same effect on sectoral indices as they do on broad market indices. The impact was sector-specific for the remaining variables.

This paper gives an overview of relation and effect covering variety of macroeconomic variables and stock market indices. Still, there is a scope for further research to analyze the effect on thematic, strategy and sectoral indices. A longer time horizon with new variables, such as bank deposit growth rate, nonperforming assets of banks, consumer confidence index and investor sentiment, can be studied using high-frequency data. This research may help stakeholders adopt and manage their policies during a crisis or economic slump.

This study will assist investors, researchers and educators in the fields of economics and finance in understanding how macroeconomic factors affect the stock market. Furthermore, this study can guide in portfolio diversification strategy across multiple sectors by examining the impact of macroeconomic factors specific to sectoral indices. This paper provides insight into society and researchers since it integrates a number of macroeconomic variables and their interaction with the stock market. It may also help pension funds and mutual fund firms to hedge their funds and allocate equity portfolios.

With respect to India, this study looked at new macroeconomic variables and sectors. It contrasted the impact of these variables in developed and developing economies. The effect of broad and sectoral stock indexes was also investigated and compared. The authors examined how these variables responded during crisis and economic downturns by using articles from a longer time frame. This research also looked into how changing the frequency of data for the variables altered stock performance. This paper emphasized the need for more research into thematic, strategy and broad market indices, such as small-cap and mid-cap indices.

The purpose of this paper is to estimate the impact of one productive development program (PROPYME) in a developing nation like Costa Rica. This program seeks to increase the capacity of small and medium-sized firms (SMEs) to innovate.

Impacts have been estimated assuming that beneficiary firms are trying to maximize their profits and that PROPYME aims to increase these firms productivity. The impacts were measured in terms of three result variables real average wages employment demand and the probability of exporting. A combination of fixed effects and propensity score matching techniques was used in estimations to correct for any selection bias. The authors worked with panel data companies treated and untreated for the period 2001-2011.

PROPYME’s beneficiaries performed better than other firms in terms of labor demand and their probability of exporting. In addition, the dose and the duration of the effects of the treatment (timing effects) are important.

The authors study the impact in ways that go beyond the average treatment effects on the treated (ATT) usually estimated in the existing literature. Specifically, the research focusses on the identification of the timing or dynamic effects (i.e. how long should we wait to see results?) and treatment intensity (dosage effects).

Se estima el impacto de un programa de desarrollo productivo (Propyme) en un país en vías de desarrollo como Costa Rica. El Propyme busca incrementar la capacidad innovadora de las pequeñas y medidas empresas (pymes) costarricenses.

el impacto se ha estimado y evaluado asumiendo que las pymes beneficiaras buscan maximizar sus beneficios y que Propyme se enfoca en incrementar la productividad de esas empresas. El impacto se valoró en función de tres variables: salarios reales medios, empleo demandado y la probabilidad de exportar. Se utilizó una combinación de técnicas de efectos fijos y emparejamiento en las estimaciones con el fin de prevenir sesgos de selección. Se trabajó con un panel de datos, incluyendo empresas tratadas (beneficiarias de Propyme) así como no tratadas para el periodo 2001-2011.

los beneficiarios de Propyme tuvieron mejor desempeño que las restantes empresas en términos de empleo demandado y su posibilidad de exportar. Adicionalmente los efectos dinámicos (dosis y duración) de los tratamientos son importantes.

este artículo evalúa el impacto de una forma que va más allá de lo usual en la literatura por medio de los efectos promedios de los tratamientos sobre los beneficiarios. Esto por cuanto se enfoca en efectos dinámicos como la duración así como la intensidad.

Although mobile devices are ubiquitous among employees, their awareness and readiness to use mobile technologies for competence development is still not widespread and therefore requires further exploration. The purpose of this study is to propose a conceptual model based on the unified theory of acceptance and use of technology (UTAUT) to explain the determinants that affect employees’ intention to use mobile devices and software for knowledge transfer during the process of knowledge management.

A conceptual model based on the UTAUT with new variables concerning relative usability (RU) and user autonomy (UA) and new connections between the determinants was developed as a result of a subject matter literature review. A structural equation modelling approach was used to validate the model on the basis of data collected via a survey collected from 371 employees from 21 sectors, both public and private.

The UTAUT model extended by new variables like RU and UA explains employee acceptance of mobile technologies for knowledge transfer reasonably well. New proposed variables highlighted that the usability of technology compared to other solutions and user autonomy in the selection and the use of applications have the strongest impact on the employees’ intention to use mobile devices and software for knowledge transfer.

This model explains the 55 per cent behavioral intention of employees to use mobile technologies for knowledge transfer. Even though it is quite high in terms of acceptance theories, some new variables should be explored. Furthermore, study does not verify whether m-learning acceptance for knowledge transfer is sector-specific.

Mobile technologies used for knowledge transfer by employees should allow for high UA through their ability to select solutions that they find convenient, use of preferred platforms, personalize applications and utilize devices and software in various environments. They should not be simplified and should have the same functionality and efficiency of use as alternative solutions like web and desktop applications, even if additional effort to learn them would be required. Mobile technologies that take into account UA and RU support the process of employees capturing, distributing and effectively using knowledge.

The elaborated model provides a valuable solution with practical implications for increasing mobile technologies acceptance for knowledge transfer. The study results contribute both to knowledge management and technology acceptance research fields by introducing two new determinants for the acceptance of technologies in knowledge transfer, such as UA and RU with several additional connections between existing UTAUT variables.

The study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.

This study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.

The results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.

This study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.

Socially responsible investment (SRI) is a niche and upcoming investment strategy in India. Very few researches have been conducted on SRI in the Indian context. This study identifies the SRI awareness level, attitude towards the importance of environmental, social, and governance (ESG) issues, willingness to invest in SRI avenues and obstacles in SRI investment decision-making by Indian retail investors. The second objective was among the awareness, attitude, willingness, obstacle, and demographic constructs to identify the most significant variables that impact an individual investor's SRI decision in India. .

Data for the study have been collected through a self-structured questionnaire. Descriptive statistics are used to identify the importance of variables for individual investors. This paper used the theory of planned behavior (TPB) to understand the factors impacting individual investors' SRI behavior. Binary logistics regression analysis is used to recognize the variables that affect an individual investor's SRI decision.

The descriptive statistics indicate a low level of SRI awareness; the majority of the investors agreed that ESG issues are significant in investing and showed a willingness to invest in SRI avenues. However, the investors were not willing to accept lower returns from SRI. The majority of investors found, lower returns on SRIs, no tax benefit, lack of information about SRIs, and low liquidity as important obstacles in SRI investing. Binary logistics regression results indicated that awareness about SR/ESG indices, awareness about SR/ESG funds, and willingness to invest in SRI avenues significantly impact investors' SRI decisions but demographic variables have no significant impact on SRI decision-making.

This study has implications for the ethical/SR mutual funds managers, policymakers, government, and international asset management companies. The study finds an urgent need for increasing awareness about SRI among individual investors in India. The study suggests that the issuers must provide adequate information about SRI avenues and probable risk and returns involved in these, while the regulators must make efforts to educate investors in India.

The context of the present study is original because hardly any of the earlier studies conducted in India have tried to find out the individual investors' SRI awareness level, investors' willingness towards SRI, investors' attitude towards ESG issues, and obstacles faced by investors in socially responsible investing.