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This study aims to develop and test a framework for integration between data envelopment analysis (DEA) and artificial neural networks (ANN) to predict the best financial performance concerning return on assets and return on equity for banks listed on the Egyptian Exchange, to help managers generate what-if scenarios? For performance improvement and benchmarking.

The study empirically tested the three-stage DEA-ANN framework. First, DEA was used as a preprocessor of the banks’ efficiency scores. Second, a back-propagation neural network as a multi-layer perceptron-ANN’s model was designed using expected data sets from DEA to learn optimal performance patterns. Third, the superior performance of banks was forecasted.

The results indicated that banks are not operating under their most productive operations, and there is room for potential improvements to reach outperformance. Moreover, the neural networks’ empirical test results showed high correlations between the actual and expected values, with low prediction errors in both the test and prediction phases.

Based on best performance prediction, banks can generate alternative scenarios for future performance improvement and enabling managers to develop effective strategies for performance control under uncertainty and limited data. Besides, supporting the decision-making process and proactive management of performance.

Despite the growing research stream supporting DEA-ANN integration applications, these are still limited and scarce, especially in the Middle East and North Africa region. Therefore, the study trying to fill this gap to help bank managers predict the best financial performance.

This study used data envelopment analysis (DEA) models to measure financial efficiency of twelve commercial banks listed in the Egyptian stock exchange (CBLSE), along with evaluating changes to the financial efficiency during the period 2017–2019.

The study used BCC-I, cross-efficiency, super-efficiency models, and Malmquist productivity index (MPI) to assess financial efficiency of the examined banks. The available data from both inputs and outputs were analyzed using R. studio V.I.3. 1056 software.

Out of twelve banks examined, only four banks were efficient under BCC-I model over different years of the study period; however, only one bank (CIB) appeared to be the most efficient compared to other peers in the study sample. Moreover, MPI results revealed decreased financial efficiency during the study period, due to the decreased technological innovation, except for HDB. Tobit regression results confirmed that total assets and total equity are significant factors impacted financial efficiency of CBLSE.

This study sheds light on the importance of evaluating financial efficiency of CBLSE to all stakeholders, to pinpoint weaknesses in banks' performance, and for evaluating financial policies and investment decisions.

Several studies sought to implement different models of DEA to assess banking performance in different regions of the world, but very few studies examined financial efficiency of banks. To the best of authors’ knowledge, this study is one of those few that addressed financial efficiency of banks in Egypt.

The purpose of this paper is to develop and test empirically a new holistic performance measurement model that integrates the best of current performance measurement frameworks and methodologies and builds upon the resource-based view to strategic management.

A survey collected responses from 379 top-, middle- and supervisory-level managers from 3 telecommunications service providers in Egypt. The hypothesized direct relationships were tested through multiple linear regression (using SPSS software), and the mediating effect was tested using the structural equation modeling technique (using AMOS software).

The results reveal that the proposed model is a reliable and valid instrument for measuring and managing holistic organizational performance. The results also reveal that Egyptian telecommunications companies have primarily emphasized the use of structural and relational capital to enhance their service quality (SQ) and performance outcomes (POs). Moreover, SQ was found to significantly and partially mediate the effect of organizational capitals (OCs) on POs.

The proposed model is a novel model that needs further investigation using various research designs and multiple research methods to assure its reliability and validity as a holistic performance measurement system.

The Egyptian telecommunications companies should efficiently manage and leverage all four components of OCs, especially the components of intellectual capital to improve their SQ and consequently enhance their POs.

To the best of the authors’ knowledge, this is the first research to study the relationships among OCs, SQ and POs, merge them in an integrated performance measurement model and test this model empirically in the Egyptian telecommunications setting.

This paper empirically examines how tax haven use affects classification shifting by public and private UK firms.

The authors conduct multivariate regression analyses of classification shifting on proxies of tax haven use for a broad sample of UK non-financial public and private firms from 2010 to 2018. An array of additional tests is conducted to ensure the robustness of the findings.

Firms using tax havens engage in more classification shifting relative to those that do not. The result is concentrated for public firms. While private firms’ classification shifting is generally pronounced, it appears unaffected by tax haven use. The findings suggest that the use of tax havens facilitates public firms’ classification shifting due to the lower institutional environment quality of these jurisdictions. In addition, classification shifting may be a less costly earnings management device for public firms using tax havens due to their political sensitivity.

The study highlights the need for regulatory intervention to constrain classification shifting, especially when firms use tax havens. It also calls for further scrutiny by auditors and financial analysts on the classification of income statement items.

While prior research focuses on accrual and real earnings management by public firms, this study investigates the consequences of using tax havens on classification shifting, a largely underexplored but heavily exploited earnings management strategy. Differences between public and private firms are also tested. Overall, this study offers an advanced understanding of how a firm’s institutional and political environments influence its financial reporting.

Despite entrepreneurial linguistic style gaining increased attention in entrepreneurship studies, the field for digital vlogger entrepreneurs still lacks a comprehensive understanding of how linguistic patterns enhance audiences attitude and behaviour. This study aims to propose a conceptual model of “language-mental imagery-attitude-behaviour model” that leads to the examination of rich sensory language style of food travel vlogger entrepreneurs and its persuasive effect on audiences' attitude and behavioural intention.

The present study utilises a stimulus-based survey method that involves a sensory-rich vlog script extracted from a high social media engagement authentic vlog. Data are collected through an online questionnaire distributed to a sample of 355 participants via the Amazon Turk mechanism. The study employs confirmatory factor analysis and structural equation modelling to test the proposed hypotheses, with the aim of contributing to the advancement of theories of embodied cognition in entrepreneurial language by examining the attitudes and behaviours of audiences exposed to sensory-rich language. The findings of this research provide valuable insights into the effects of sensory-rich language on audience responses and can inform future research on the role of embodied cognition in entrepreneurial communication.

The findings demonstrate that vlogger entrepreneurial sensory-rich linguistic communication style positively influence audiences' attitude, behavioural involvement with food and intention to taste. Visit intention will be enhanced via the mediating effects of attitude, behavioural involvement with food and intention to taste.

This research highlights the significance of sensory-rich language for vlogger entrepreneurs in entrepreneurial communication, digital storytelling and for destination marketing enterprises in creating a digital sensory engagement marketing strategy.

The study contributes to the literature by elucidating the theories of embodied cognition in entrepreneurial communication. By examining the relationships between vlogger communication evoked mental imagery, audiences attitude and behaviours, this study provides novel insights into the effectiveness of sensory-rich language in vlogger entrepreneurial communication and its impact on audience engagement. These findings have important implications for communication scholars and practitioners alike, shedding light on the role of embodied cognition in entrepreneurial language and the potential of sensory-rich language to enhance audience engagement.

Big data analytics (BDA) plays a crucial role in understanding customer behavior through Customer Relationship Management (CRM), especially in a rapidly changing business environment. This paper investigates the direct effect of BDA use on market performance, besides the mediating effect through Big Data-enabled CRM strategies adoption (e.g. customization and personalization). The paper also examines the moderating role of competitive intensity in these effects.

Drawing from a knowledge-based view (KBV) and Organizational Information Processing Theory (OIPT), the authors formulated the research model. Subsequently, the measurement model and hypotheses were tested through PLS-SEM on online survey data of 229 managers from 167 companies out of Egypt's top 500.

The results indicated that BDA use does not directly affect the market performance, but this effect was significant through customization and personalization strategies adoption. The results also revealed a positive association between BDA use and the adoption of these strategies. Furthermore, competitive intensity only moderates the relationship between BDA use and personalization strategy adoption.

Companies can use BDA to improve customer knowledge and experience through customization and personalization, leading to better market performance and moving towards becoming a Big Data-driven organization. This study is limited to companies in the Egyptian context, which restricts the generalizability of the results.

This study conceptually and empirically explores how BDA usage, customization and personalization strategies impact market performance under competitive intensity situations, especially in the context of emerging markets.

This study used Data Envelopment Analysis (DEA) to measure and evaluate the operational efficiency of 26 isolation hospitals in Egypt during the COVID-19 pandemic, as well as identifying the most important inputs affecting their efficiency.

To measure the operational efficiency of isolation hospitals, this paper combined three interrelated methodologies including DEA, sensitivity analysis and Tobit regression, as well as three inputs (number of physicians, number of nurses and number of beds) and three outputs (number of infections, number of recoveries and number of deaths). Available data were analyzed through R v.4.0.1 software to achieve the study purpose.

Based on DEA analysis, out of 26 isolation hospitals, only 4 were found efficient according to CCR model and 12 out of 26 hospitals achieved efficiency under the BCC model, Tobit regression results confirmed that the number of nurses and the number of beds are common factors impacted the operational efficiency of isolation hospitals, while the number of physicians had no significant effect on efficiency.

The limits of this study related to measuring the operational efficiency of isolation hospitals in Egypt considering the available data for the period from February to August 2020. DEA analysis can also be an important benchmarking tool for measuring the operational efficiency of isolation hospitals, for identifying their ability to utilize and allocate their resources in an optimal manner (Demand vs Capacity Dilemma), which in turn, encountering this pandemic and protect citizens' health.

Despite the intensity of studies that dealt with measuring hospital efficiency, this study to the best of our knowledge is one of the first attempts to measure the efficiency of hospitals in Egypt in times of health' crisis, especially, during the COVID-19 pandemic, to identify the best allocation of resources to achieve the highest level of efficiency during this pandemic.

This study aims to examine performance assessment of organizational units through psychological empowerment (PE) and employee engagement (EE) approach and whether this relationship differs among efficient and inefficient organization units.

This study drew on merging the principal component analysis (PCA), data envelopment analysis (DEA) and partial least square-multigroup analysis (PLS-MGA) to benchmark the performance of organizational units affiliated with Zagazig University in Egypt using PE dimensions as inputs and EE as output. Besides investigating whether PE inputs have the same effect among efficient and inefficient units.

Performance assessment based on independent data showed that all the investigated organizational units are not at the same efficiency level. The results revealed that there are eight efficient units versus seven inefficient ones. Moreover, PLS-MGA results demonstrated that no significant differences concerning the impact of PE inputs on EE between efficient and inefficient units groups. Nevertheless, the effect of these inputs was slightly higher in the former.

Studies on EE performance in the service sector are scarce in the literature, this study is a novel contribution of exploring EE efficiency in Egypt as a developing economy. Specifically, using the PCA-DEA-structural equation modeling approach.

To provide an overview of self‐validating sensor technology for researchers and engineers which can help them understand the concept and recent developments of this research area.

The concept of self‐validating (SEVA) sensors, including definition, output parameters and requirement of SEVA sensors are introduced. The differences between SEVA sensors and traditional sensors are given from which we can see many advantages of SEVA sensors. The principium of SEVA sensors is presented by the functional architecture. The research development of SEVA sensors is introduced in two aspects: research development of sensor fault diagnosis and signal reconstruction and research development of SEVA sensor hardware.

Summarizes the methods for sensor fault diagnosis and signal reconstruction in the research of SEVA sensors, and the development steps of SEVA sensor hardware. Indicates the shortages and problems of current research and gives our research and ideas to solve these problems.

This paper provides a detailed description and research information of self‐validating sensor technology for those who want to know and research on this area.

The purpose of this paper is to examine the effectiveness of using colors and learner’s intelligence quotient (IQ) in teaching new vocabulary in Arabic (L1) and English (L2) to children with autism spectrum disorder (henceforth, ASD).

To this end, 12 autistic children whose ages ranged between 7 and 12 were observed while they were being taught ten new words. The children were divided into two groups based on their IQ: Low (70–74) and High (76–79). The children were also divided into two groups: Group 1 studied the words written in a black font, whereas Group 2 studied the same words, but written in colors (each letter in the word in a different color), and an illustrative picture was provided with each word for both groups. The pictures were also different in color in the former group, while the picture was in black and white in the latter. The children involved in the study have a relatively slight ability to read letters based on an annual language assessment conducted by the center, and they learn a new word by learning its shape and by repetition. The experiment took place over a two-week period that involved teaching, revising and testing.

The results of the study showed that the children’s IQ played a crucial role in learning L1 and L2 vocabulary. The results also demonstrated that using colors had no significant impact on the children’s performance in the test. Finally, the results showed that teaching new words to children with ASD through repetition and drilling could be regarded as a useful technique. The study concludes with some recommendations for further studies.

The study shows that using pictures is a very useful tool in teaching L1 and L2 vocabulary to children with ASD.