The aim of this study is to identify and classify the most important factors affecting patient satisfaction in the COVID-19 pandemic crisis considering economic effects. This is an analytical study using the analytic hierarchy process (AHP) method and ANN-MLP (Artificial neural network based on multilayer perceptron model as a supervised learning algorithm) as an innovative methodology. The questionnaire was completed by 72 healthcare experts (N = 72). The inter-class correlation (ICC) coefficient value was confirmed in terms of consistency to determine sampling reliability. The findings show that interpersonal care and organizational characteristics have the greatest and least influence, respectively. Furthermore, the observations confirm that the highest and lowest effective sub-criteria, respectively, are patient safety climate and accessibility. Based on the study’s objective and general context, it can be claimed that private hospitals outperformed public hospitals in terms of patient satisfaction during the COVID-19 pandemic. Focusing on performance sensitivity analysis shows that, among the proposed criteria to achieve the study objective, the physical environment criterion had the highest difference in private and public hospitals, followed by the interpersonal care criterion. Furthermore, we used a multilayer perceptron algorithm to assess the accuracy of the model and distinguish private and public hospitals as a novelty approach. Overfitting results in finding an MLP model which is reliable, and the accuracy of the model is acceptable.
Given the mediating role of value co-creation, this paper tries to demonstrate how social network marketing (SNM) could influence consumer purchase behavior (CPB). The proposed hypotheses are empirically tested in this study using a PLS-SEM and Necessary Condition Analysis (NCA) method combination. The novel methodology adopted in this study includes the use of NCA, IPMA matrix, permutation test, CTA, and FIMIX. The assessment of the outer model, the inner model, the NCA matrix, and the IPMA matrix are the four steps that the paper takes. Instagram users with prior experience making purchases online made up the statistical population of the study. Four hundred twenty-seven questionnaires were analyzed by SmartPLS3 software. Based on the findings, SNM positively and significantly influenced economic, enjoyment, and relational values. Furthermore, these three types of values significantly and directly influenced CPB. For CPB, the model accounted for 73.8% of the variance. The model had high predictive power because it outperformed the PLS-SEM benchmark for all of the target construct’s indicators in terms of root mean square error (RMSE). According to the NCA’s findings, SNM, economic, recreational, and relational values are necessary conditions for CPB that are meaningful (d ≥ 0.1) and significant (p < 0.05). Four prerequisites must be met for CPB to reach a 50% level: relational value at no less than 8.3%, enjoyment value at no less than 16.7%, economic value at no less than 33.3%, and SNM at no less than 31.1%. The highest importance score for SNM is shown to be 0.738, which means that if Instagram channels improve their SNM performance by one unit point, their overall SNM will also improve by 0.738.
Purpose ─ This study is aimed at analyzing the main drivers of business cycle in Iran and some selected oil producing countries during the 1970:Q1-2015:Q4 period. In addition, the study evaluates causality of leading macroeconomic indicators for each different regimes of the business cycles.Methods ─ This study proposes a new methodological approach by combining Markov-Switching Vector Autoregressive (MSVAR) and MS-Granger causality approach.Findings ─ The results show that there are diverse sources of business cycle. Iran experienced higher volatility of GDP where machinery investment and export are found as main driver of its business cycle. Meanwhile, consumer price index has countercyclical effect in all countries. We also find some similarities to the US, the UK, and Canada regarding the probability of a business cycle, number of observations, and the average duration, especially in the first regime of MS-VAR models. The high level of oil price volatility relative to the GDP volatility indicates the power of oil price shock to generate cycles. In addition, the results of the traditional Granger causality test confirm the Markov-Switching Granger Causality (MS-GC) test in all countries except export from the UK.Implication ─ Identification the main driver of business cycles is very significant to formulate the steady growth path so that the government able to select the most adequate economic policy.Originality ─ The novelty of this study is the adoption of a new approach by combining stylized facts and MS-VAR and MS-Granger causality to analyze the business cycles in different regime.
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