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This study aims at investigating the impact of total quality management (TQM) on corporate sustainability (CS) by mediating the role of corporate social responsibility (CSR) in developing countries.

A mixed research approach was adopted by conducting interviews and a survey. A survey was used as a quantitative tool for data collection from 67 respondents at different managerial levels. Partial least squares structural equation modeling (PLS-SEM) was used for analysis.

The findings demonstrate that customer focus and human resource management (HRM) were the most effective practices. Structural analysis revealed that TQM had a significant impact on CSR and CS. Furthermore, CSR partially mediates the relationship between TQM and CS.

The study has developed a conceptual framework to provide policymakers with guidelines on integrating TQM practices and CSR activities into their strategy.

This research bridges the gap between TQM and the three pillars of CS, especially in manufacturing enterprises in a developing country context. This research also supports the TQM advocate's argument that effective TQM deployment can dramatically improve organizational performance. Additionally, this study verifies the CS model based on the triple bottom line (TBL) theory and stakeholder theory by assessing the conceptual model's robustness using a mixed-method research methodology, which has never been done before from a Palestinian perspective.

The purpose of this study is to examine the influence of knowledge creation (KC) process on customer relations management (CRM) in Palestinian commercial banks, taking into consideration which factors of KC process support the CRM system.

The study uses a quantitative research design wherein questionnaires have been used to collect data from 345 respondents in the Palestinian banking sector. Research hypotheses have been tested using multiple regression analysis.

The findings unveil that socialisation and combination processes have a positive impact on CRM. In contrast, internalisation process negatively affects CRM system, but outsourcing knowledge does not significantly affect CRM.

Past studies empirically validated the success of CRM adaptation in the context of different industries. This study provides a new conceptual model which validates the influence of KC on CRM in the banking sector. It also affirms the integral role of KC in supporting CRM from an emerging country perspective like Palestine.

This study offers new insights into creating of knowledge by employees in supporting CRM. It will encourage future scholars to further explore the key dimensions of the KC process for a more detailed investigation at a workplace. This study suggests that banks’ directors and employees should behave in a social manner to support relationship with customers. This study also suggests facilitating knowledge from different resources in innovative ways, through encouraging creative thinking from experiences, using technology in sharing knowledge, focussing on appropriate training to resolve customers' problems and disseminating new knowledge among employees.

This study expands the body of knowledge on KC process in supporting CRM from an emerging country perspective. This study validates the influence of KC on CRM in the Palestinian banking sector. This sheds light on the integration of these two concepts.

Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.

An Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.

The MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R² value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.

This study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.

By providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.

Total quality management (TQM) and knowledge management (KM) are two similar and complementary management philosophies synergistic combinations that can form a cycle of improvement and development. This paper aims to investigate the impact of TQM factors on knowledge creation in the organizations of Bangladesh.

A list of TQM factors was identified through reviewing the relevant literature. Knowledge creation was analyzed through the framework proposed by Nonaka and Takeuchi (1995). Data were collected through a self-administered questionnaire survey among 450 top and mid-level managers of the organizations. A quantitative research approach, namely, structural equation modeling (SEM) was used in the study. The data were analyzed in SmartPLS 3.

From the study, it is found that a positive and significant relationship exists between leadership, employee empowerment, benchmarking, customer focus and information technology with the knowledge creation process and four knowledge conversion modes. In contrast, a negative and significant relationship has been found between employee training and continuous improvement with the knowledge creation process and three knowledge conversion modes.

Previous researches in Bangladesh empirically validated the effect of TQM on organizational performance, competitive advantage, financial performance, market performance and productivity. But, no such study was undertaken to empirically validate the effect of TQM on knowledge creation process for organizations of Bangladesh. Here, the study has a unique contribution. The empirical support for the hypotheses to explain and predict the contribution of the TQM in promoting knowledge creation.

The findings highlight the role of leadership practices. Organizational leadership needs to focus more on following issues: gathering required knowledge from the superior sources; transforming prevailing knowledge into a meaningful format to perform activities in better and innovative ways; employing opinions from experiences; facilitating mechanism for employees to update their knowledge; creating a virtual network; implementing customer relationship management system and providing tools and technologies to employees for performing KM activities more efficiently. On the other hand, negative effect of employee training and continuous improvement on knowledge creation sheds light on understanding the reasons for this kind of relationship and formulating effective strategies to resolve problems inside the organizations. In this regard, employees need to be well equipped through regular and appropriate training. Also, employees need to use problem-solving approach to identify problems inside the organizational system thereby availing opportunities for continuous improvement.

This study was conducted to identify factors that enable knowledge creation in organizations from developing countries like Bangladesh. TQM has been adopted in many organizations. But, its effect was mostly measured to determine its impact on improving organizational performance or competitiveness or identifying its critical success factors of implementation. Hence, this study sheds light on identifying the effect of TQM from a new perspective.

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.

Research on training and/or L&D effectiveness is predominantly conducted in a traditional L&D context. Little research is conducted on training and/or L&D in the context of artificial intelligence (AI)-based learning. The present study aims to investigate the relationship between the adoption of AI-based learning systems and learners’ behavior. Drawing from the theory of planned behavior, the research examines the impact of attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC) as AI-based learning intention (ALI) factors relate to changes in learners' behavior. Additionally, inspired by the self-determination theory by Deci and Ryan, the study further examines the mediating role of learner engagement between ALI and behavioral change.

Following a theoretical framework and using a systematic literature review method, the survey research has been planned by considering a sample from Indian industries. The collected data have been analyzed using SPSS-AMOS 27. While path analysis has been conducted to analyze the direct impact of ALI on learners' behavior, Hay’s PROCESS macro has been used to check the mediating impact of learner engagement between ALI and learners' behavior.

The results proved a significant and positive impact of all ALI factors such as ATT, SN and PBC on learners’ behavioral change. Further, the research found that learning engagement (LE) successfully mediates between AI learning intention and behavioral change.

In the absence of any empirical study in identifying the relationship among learning intention, LE and behavioral outcome, the result of this study may provide useful insights to researchers and practitioners.

This study aims to focus on delineating the drivers of intention to adopt mobile banking (m-banking) and its actual use among Islamic banking customers by extending the UTAUT2 model with the trust factor. The study also examined the moderating roles of age, gender and experience in the model.

An explanatory research design was used, and an online survey was conducted to collect responses from Islamic banking customers. A total of 329 completed responses were used to analyze the data. The partial least squares method was used for data analysis, and a multi-group analysis was applied for moderation-related analysis.

Trust positively and significantly influences the behavioral intention to adopt m-banking among Islamic banking customers. In addition, social influence, effort expectancy, hedonic motivation and habits significantly influence behavioral intentions among Islamic banking customers.

This study provides an extended UTAUT2 model that has never been tested in the context of Islamic m-banking. In addition, this study is expected to be the first scholarly research on Islamic banking in the Maldives.