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The purpose of this paper is to evaluate the effect of CRM performance and technological innovation on performance of media entrepreneurs considering firm size.

This is an analytical study used to empirically test the hypotheses proposed for SEM techniques using PLS and R packages. It used two steps in this way: the assessment of the outer model and the assessment of the inner model. Moreover, a bootstrapping method was employed to test indirect effects. Data were collected by distributing 127 questionnaires between the managers and deputies of active firms across Rasht, Iran.

The effect of CRM performance on SMEs performance development is partially mediated by media entrepreneurship. Moreover, the effect of technological innovation on SMEs performance development is mediated by media entrepreneurship. Furthermore, permutation test results indicated that there is no significant difference between small- and medium-sized firms.

This study used cross-sectional sampling method that can seriously limit result generalization. Therefore, conducting longitudinal studies is strongly recommended.

The results of IPMA matrix indicated the serious importance of technological innovation, as a variable with the highest importance for SMEs performance development. Nevertheless, this variable has received the lowest importance in the studied population. Therefore, SMEs’ managers should pay sufficient attention to the concepts of “product innovations” and “process innovations.”

This study is of high importance in that it has adopted new and effective indices for statistical analysis. IPMA matrix, permutation test, CTA and FIMIX are examples. In addition, plspm and Matrixpls packages in R were used for the first time in this study.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

The purpose of this study is to assess the effect of process quality management (PQM) activities on firm's operational performance (OP) through the mediation of firm's absorptive capacity (AC).

This research builds on the theory of knowledge-based view to conduct a survey of 294 manufacturing companies in India. With the use of Hayes' PROCESS Macro in SPSS, the collected data were used to analyze the proposed mediating effect of firm's AC and moderating effects of leadership commitment (LC).

Study results suggest that both PQM and firm's AC contribute to improved OP and should be promoted. The firm's AC was found to partially mediate the impact of PQM on the firm's OP. Results also show that improved firm's AC can have a substantial effect on improvement in OP by stronger support of LC.

The results may lack generalizability due to the selected cross-sectional nature of the current study. Researchers are also encouraged to further test the proposed ideas using a longitudinal design approach.

To translate PQM initiatives into core strategic competencies, manufacturing firms need to develop their AC. Senior managers in the manufacturing sector should concentrate strongly on developing a knowledge-driven working culture to enhance operational efficiency and manufacturing productivity.

Current research study can be considered as one of the very few empirical analyses that investigated the mediating impact of the firm's AC on the PQM-firm's OP relationship. In the operations management (OM) literature, the investigation of the moderating effect of LC on the mediation of the firm's AC between the PQM and OP metrics can be considered to be a noteworthy theoretical addition.

This study aims to develop a price policy for fossil fuel consumption, as it is an effective instrument to manage the demand-side of energy economics.

This research estimates the demand elasticities of diesel, gasoline, fuel oil and kerosene by using static, dynamic and error-correction models in log-linear form.

The findings show that fossil fuel demand responds to price changes less than income changes, as fuel price is inelastic, but income is elastic. In that respect, the impact of price change decreases constantly with increasing energy price, followed by subsidy reform. Subsidy removal and price policy reformation is the UN recommendation for subsidizing countries, including Iran, to reduce fossil fuel consumption, whose intensity depends on the price elasticities.

As a result of this price policy, diesel, gasoline and liquefied petroleum gas prices should increase at least 1.8%–7.3%, 4.4%–6.4% and 7%–8.6%, respectively, and gradually within 2018–2030. The price policy improves all the pillars of sustainable development, including economy, environment and social (health). Overall, such a target can potentially save 3%–29% of diesel, 34%–56% of gasoline and 15%–20% of liquefied petroleum gas, as well as reduce 15%–40% of CO₂ emissions annually, and can save potentially more than 510,000 lives annually. Thus, the energy price policy can fundamentally improve sustainability.

The estimated elasticities outline the required prices to decrease the fossil fuels, according to the UN mitigation targets, as price policy recommendation.

The paper provides a step-by-step guide in the guise of a roadmap for service improvement initiatives using importance performance map analysis (IPMA).

To empirically illustrate how IPMA can be applied to any service industry, three sectors are considered; sports and fitness (study A), hospitality (study B) and higher education (study C). Following the proper selection of measuring instruments and their evaluation using structural equation modeling-partial least squares (Smart-PLS), IPMA is applied to identify those attributes having strong total effects (high importance) over the targeted construct (satisfaction) but which also have low average latent variable scores (low performance).

For sports and fitness (study A), the physical aspects and programme quality require managerial attention. For the hospitability sector (study B), it is service commitment, interaction quality and internal sense of happiness. Whereas for higher education (study C), it is administrative quality as well as the element of transformative quality, namely the university’s role in adding to its students’ emotional stability, which needs the attention of the top management.

This study provides researchers and practitioners with a roadmap for applying PLS-SEM and IPMA for continuous service quality improvement. The roadmap extends upon Ringle and Sarstedt’s (2016) work. It highlights critical decisions that need to be considered in the pre-analytical stages of the IPMA application, i.e. at the research design phase in selecting the most appropriate service quality measurement model specifications. It not only contributes to the existing body of knowledge by providing empirical evidence to advance theory development in the quality management field but also has implications for the practitioners in any service sector on where to focus their attention for an effective service improvement.

The purpose of the article is to examine the concepts of knowledge management strategies, innovation and service quality in information technology companies that have research and development (R&D) departments in the technoparks of research universities.

The research was carried out in information technology companies with R&D departments in the technoparks of universities. Due to the “innovation” focus of the research, 302 engineers were selected by random sampling from engineers working in information technology companies in technoparks, and the prepared scale was sent to them via e-mail. In total, 302 units of data were subjected to path analysis and mediation effect analysis using the SmartPLS program.

In the research, it is supported by hypotheses that both knowledge management strategies and organizational innovation have a positive effect on the success of service quality and product innovation in information technology companies with R&D departments. At the same time, it can be explained as a result of analysis that innovation capability has both an independent and an intermediary variable effect.

Considering the limitations of the research, it is not correct to generalize the results of the analysis because the research was conducted only in information technology companies located in technoparks, and the data were collected from engineers working in these companies. For this reason, it is recommended that similar studies that are planned to be conducted in the future should do their research by taking this situation into account. At the same time, it is recommended to carry out future studies in different sectors and to bring the results obtained to the literature by comparing them.

The importance of information is increasing in technology-oriented companies where competition is increasing. Companies that cannot go beyond imitation or offer similar products and/or services cannot compete with their competitors in a competitive environment. The fact that companies can be successful in a competitive environment is supported by hypotheses as a result of the analysis that they need to develop organizational innovation and knowledge, as well as develop innovation capability at the same time.

The research is an original study in terms of examining the R&D departments of information technology companies operating in the technoparks of universities. Innovation and knowledge management strategies are examined within the scope of the research model by collecting data from information technology companies with R&D departments.

The aim of the current study is to recommend and compare the estimates of finite element model (FEM), analytical model, and artificial neural networks (ANN) model for capturing the LCC of FCSC members. A database comprising 325 FCSC columns was constructed from previous studies to propose FEM and ANN models while the analytical model was proposed based on a database of 712 samples and encasing mechanics of steel tube and FRP wraps. The concrete damage plastic model was used for concrete along with bilinear and linear elastic models for steel tube and FRP wraps, respectively. Analytical and ANN models effectively considered the lateral encasing mechanism of FCSC columns for accurate predictions.

The study aimed to compare the prediction accuracy of finite element (FEM), analytical, and artificial neural network (ANN) models for the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC). A database of 325 FCSC columns was developed for FEM and ANN models, while the analytical model was based on 712 samples, utilizing encasing mechanics of steel tube and FRP wraps. FEM used a concrete damage plastic model, bilinear steel tube, and linear elastic FRP models. Statistical accuracy was evaluated using MAE, MAPE, R², RMSE, and a 20-index across all models.

Based on the experimental database, the FEM presented the accuracies in the form of statistical parameters MAE = 223.76, MAPE = 285.32, R² = 0.94, RMSE = 210.43 and a20-index = 0.83. The analytical model showed the statistics of MAE = 427.229, MAPE = 283.649, R2 = 0.8149, RMSE = 275.428 and a20-index = 0.73 while ANN models portrayed the predictions with MAE = 195, MAPE = 229.67, R² = 0.981, RMSE = 174 and a20-index = 0.89 for the LCC of FCSC columns.

Although various investigations have already been performed on the prediction of the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC) using small and noisy data, none of them compared the accuracy of prediction of different modeling techniques based on a refined large database.