This article aims to investigate the effect of market-sensing capability among knowledge creation and process and product innovation in small and medium-sized enterprises (SMEs) in Jordan. We propose that market-sensing capability will influence knowledge creation and process as well as product innovation. The study develops a model that contributes to the flourishing marketing management literature, i.e. the higher the knowledge creation, the higher the performance of SMEs. Data are collated from valid (n=304) respondents of Jordanian SMEs. The study uses structural equation modeling (SEM) to explore the analysis and the results indicate a positive effect of market-sensing capability on knowledge creation, and process and product innovation. Knowledge creation process has also maintained a positive effect on process and product innovation. Knowledge creation process mediated the link between market-sensing capability and process and product innovation. The study suggests that market sensing abilities of an enterprise are important for SME’s improvement which requires learning-positioning for firm effectiveness. In addition, managers need to harmonize their procedures concerning market sensing and learning. Future study can streamline the findings with a longitudinal design and insights can be acquired by exploring the impact of environmental factors surrounding strategic entrepreneurial orientation.

This study aims to examine the effects of electronic word of mouth communication (eWOM) among consumers on purchase intention and brand image, specifically, Generation Y and Z groups in relation to smartphone brands. The study utilizes an empirical research model using data collected from 402 valid respondents among consumers who use smartphone brands in North Cyprus. The study uses structural equation modeling (SEM) to explore and conduct the analysis. The results confirm the significant effects of eWOM on purchase intention through brand image and the moderating role of product type among eWOM, purchase intentions and brand image. The study also recommends that firms and marketers must concentrate on online communication channels to affect consumers’ intention toward purchasing brands and brand image. Moreover, the current study model suggests that future study can be extended in different context, countries (i.e. developed, emerging, developing), industries (i.e. banks, e-commerce, tourism) and different social me-dia platforms sites (i.e. Facebook, Twitter).

Nowadays digitalization is an important topic for businesses and government agencies. There are important reports publishing about digitalization or digital transformation. This study aims to meas-ure the relative efficiency of digital transformation among EU Countries based on data envelopment analysis (DEA). The necessary data are extracted from Digital Transformation Scoreboard 2018 published by European Commission. DEA is one of popular methods for measuring the relative efficiency of similar units. This study empirically proposes an alternative ranking for countries with respect to digital transformation efficiency by using “enablers and output” approach of Digital Transformation Scoreboard. Digital Infrastructure, Investment and Access to Finance, Supply and Demand of Digital Skills, E-Leadership and Entrepreneurial Culture are considered as input while ICT start-ups and Digital Transformation are considered as the output of DEA model. The results indicate that while some countries like Denmark, Italy and United Kingdom are considered relatively efficient, Netherland and Germany are not very efficient according to our results.

Radio Frequency Identification Device (RFID) has been widely recognized as a useful tool for effectively managing firms’ supply chain activities. Extant research on RFID in the supply chain domain has focused on implementing this technology in various supply chain activities, such as logistics and transportation, assembly and manufacturing. However, the application of RFID in warehousing management has received little attention. This study explores the implementation of RFID in warehousing in the construction sector. The study objectives are twofold. First, it explores the benefits of implementing RFID. Secondly, the study identifies major challenges associated with implementing RFID, which can prevent realizing the full potential of the technology in warehouse operations. The study adopts a qualitative research design by employing a case study approach to achieve these objectives. Specifically, the study undertakes three weeks of field observation and conducts interviews with 9 participants involved in warehouse operations in the construction sector. The findings show RFID implementations could bring significant values to warehouse management related to operational efficiency, service level and warehouse flexibility. However, the study also uncovered several challenges that can block the case firm from capturing the full potential of implementing RFID. The findings can contribute to a better understanding of the application of RFID technology in warehousing. Practitioners can understand the identified challenges to help them tackle them and inform their future decisions regarding further investment in this technology.

Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method.

COVID-19 has caused severe disruptions in global economic activities, and its impacts on stock markets cannot be overemphasized. The study employs market model and event study approach with four events (WHO announcement of COVID-19 as a global health emergency, confirmed infections, confirmed deaths, and vaccination) to examine the reactions of four African regional blocs’ markets to the pandemic from September 1, 2019, to August 31, 2021, to estimate the average abnormal returns of each regional bloc. On the day of the WHO announcement, we document insignificant negative average abnormal returns in the Northern bloc. We also document significant negative average abnormal returns for infections in all but the Northern bloc on the event day. The Western bloc generated the highest significant negative average abnormal return (-43 per cent) on the day COVID-19 death was confirmed on the continent. We finally document insignificant average abnormal returns from weeks 1 to 20 after the first vaccination in the Northern and Eastern blocs. The study recommends that investors, portfolio managers, and speculators not panic during similar pandemics since they can generate significant abnormal returns and diversify their investment holdings across the four regional blocs in Africa, as demonstrated by the COVID-19 pandemic.

Considering the acceleration in the development of alternative energy sources due to climate change and the net zero carbon commitments made in this direction, there are different assessments of how the capacity of the refining industry will change in the next two decades. Refinery companies are trying to adapt to altering conditions while also trying to determine their investment strategies. Project portfolio selection problem is one of the relevant issues to be considered in line with these changes. In this article, research has been undertaken to determine which criteria refinery companies take into consideration while selecting their project portfolios. Based on the identified criteria, it is also aimed to carry out a study that will guide sector practitioners in project selection. For this purpose, interviews were conducted with industry experts. The criteria were accredited by applying categorical content analysis to the data obtained and their importance weights were identified accordingly. The most deterministic criteria were abstracted from the findings and applied to a multi-criteria decision-making (MCDM) framework, namely fuzzy MULTIMOORA to suggest a decision support tool that ranks the projects against themselves. Some of the prominent outcomes of the study are also discussed, along with the previous studies and comparative results of the proposed decision support tool.

This study considers representing decision maker preferences by Choquet integral in existence of interactions among criteria. Parameters of the Choquet integral are capacities which assign weights not only to criteria but also to each subset of criteria. This property provides Choquet integral with the ability of modeling some types of interactions. Different capacity types with different degrees of complexity have been defined in the literature. After making a review on the dependence (interaction) and independence concepts used in the multiple criteria decision making literature, we study and represent structures of interactions that can be handled by different capacity types through intuitive graphical demonstrations. Afterwards, we provide guidelines for specifying the appropriate capacity type in practical applications. Such guidance has not been provided in the literature for the practitioners to the best of our knowledge.

This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique, which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters, mainly, the destruction size and temperature scale factor, adaptively during the search process. Besides IGQ algorithms, two different mathematical modeling techniques. One of these techniques is the constraint programming (CP) model, which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model, which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models, extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms.

Due to complex structure of aortic valve (AV) leaflets and its strong interaction with the blood flow field, realistic and accurate modeling of the valve deformations comes with many challenges. In this study, we aimed to investigate the effect of AV material properties on the valve deformations, by implementing different non-linear properties of the AV leaflets in three different material models. In the computations, we captured the dynamics between the leaflet deformations and blood flow field variations by using an iterative implicit fluid-structure interaction (FSI) approach. By comparison of the FSI simulation results of these three models, the effects of hyperelasticity and anisotropy on the valve deformations have been studied in detail. The simulation results reveal the fact that the material characteristics strongly affect the deformation characteristics of the leaflets in the systolic phase. The material anisotropy stabilizes the leaflet movements during the systolic phase, which helps decreasing the flutters of the leaflets during the peak jet blood flow. Similarly, it has been observed that the hyperelastic behavior yields an increase in the valve opening area during systolic phase which prevents the risk of excessive work of the heart due to high pressure difference. Furthermore, simulation results indicate that the stress levels in hyperelastic model are much lower, compared to the stress levels in linear elastic one. This suggests that the non-linear material character of the leaflets decreases the risk of calcification.