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This study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.

We utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.

The results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.

This research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.

To the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

The purpose of this paper is to examine the effect of trust, privacy, service quality and customer satisfaction on the success of electronic customer relationship management (E-CRM) systems.

In this paper, a new model for determining the critical factors in the success of E-CRM systems is presented. The suggested model is verified using partial least squares with structural equation modeling. A questionnaire is designed and collected from 378 taxpayers in East Azerbaijan province of Iran.

The outcomes reveal that customer satisfaction is significantly influenced by the perceived quality of service. Customer satisfaction, in turn, is significantly impacted by the trust. Therefore, the quality of service, trust and privacy, through customer satisfaction, significantly affects the success of E-CRM systems.

The main limitation can be referred to the clients that would not cooperate well as they were avoiding to give much information about the financial issue. Also, the study was conducted only in the context of Iran and a limit sample was utilized.

The research results help service providers improve E-CRM.

This study sheds light on identifying the antecedents of trust, privacy and quality of service that affect customer satisfaction; it may contribute to the theoretical framework for customer satisfaction in the context of the E-CRM. The results of the research offer practical implications for marketing managers and practitioners who prepare strategic plans and implement tools to improve the productivity or performance of the E-CRM systems. Moreover, customer satisfaction is related to the success of E-CRM systems as a result of trust, privacy and service quality. This research offers new insights into E-CRM intentions from a taxpayer in Iran.

This paper aims to examine diversity management (DM) practices in leading private-sector organizations in Iran.

The study draws on in-depth interviews with 23 human resource management (HRM) executives and supervisors in nine private sector companies in Iran, and presents the analysis conducted using MAXQDA software.

The results categorize DM practices into four subsystems of HRM, i.e. recruitment and selection, training, performance management, and reward management. These practices indicate the inclusion of diversity-sensitive criteria and consideration of equal opportunity in the HRM subsystems.

The findings advance a contextual understanding of DM in a developing country. Considering DM practices in HRM subsystems may provide an effective way to help managers address workforce diversity in organizations.

The purpose of this paper is to look at human resource management (HRM) as a key element in the implementation of total quality management (TQM). This paper empirically tests the HRM practices that best fit this philosophy.

The results of an empirical study of 706 companies are analyzed using structural equation methodology. The practices that are used to construct the framework for analysing approaches to HRM are based on a literature review.

The results show that the alignment of the orientation towards quality and the approach to HRM is statistically significant for the utilization of the HRM system. The results also support the hypothesis that both TQM and HRM practices have a positive effect on performance.

A cross‐sectional analysis is applied, so it is necessary to be cautious in conclusions regarding causality. Single informants are used as the source of information. Although the use of single informants remains the primary research design in most studies, multiple informants would enhance the validity of the research findings.

Practitioners must bear in mind the fundamental role of human resource management in the pursuit of long‐term total quality management. Companies should look for a set of HRM practices congruent with TQM, rather than using individual practices. A strategic perspective to HRM supports these results.

There is little empirical evidence to support the effect that HRM can have on TQM implementation and most papers focus only on distinct HRM practices. This paper provides an insight into the issues involved in the development of HRM practices oriented to TQM. It examines the relationships among HRM practices, TQM and organizational performance.

There is extensive research documenting the physical outcomes of childbirth, but significantly less on socio-psychological outcomes. Investigating women’s perception of dignified treatment during birth contributes to a salient, under-examined aspect of women’s childbirth experiences.

We use a two-part conceptualization of dignity, respect and autonomy, to understand how birth experiences and interactions either facilitate or undermine women’s perceived dignity. Data came from the Listening-to-Mothers I survey, the first nationally representative study of postpartum women in the United States (n = 1,406). Through linear regression analysis, we separately modeled women’s perception of respectful treatment and women’s perception of medical autonomy during birth.

Overall women reported high scores for both autonomy and respect. Differences between the models emerged related primarily to the role of interventions and provider support. While women’s perceived dignity is related to elements that she brings in to the delivery room (e.g., birth knowledge, health status), much variation was explained by the medical encounter itself (e.g., type of medical interventions, pain management, nurse support, and number of staff present).

This study is cross-sectional, and required either a telephone or internet access, thus limiting the full generalizability of findings. Two findings have direct practical relevance for promoting women’s dignity in childbirth. First, the number of staff persons present during labor and birth was negatively associated with both respect and autonomy. Second, that women with high levels of knowledge about their legal rights during childbirth were more likely to report high scores on the dignity scale. Limiting staff in the delivery room and including knowledge of legal rights in childbirth education or during prenatal visits may be two mechanisms to promote dignity in birth.

These findings address an important, under-examined aspect of women’s childbirth experiences. This study investigates how different birth experiences and interactions either promote or violate childbearing women’s perception of dignity, and has significant implications for the provision of maternal healthcare. The results reinforce the importance of focusing on the socio-psychological dimensions of childbirth.

To propose and adaptive nonlinear controller for adjustable speed sensorless induction motor drive, using a novel adaptive rotor flux observer. The adaptive flux observer scheme in this paper provides the simultaneous estimation of the rotor speed, rotor resistance and stator resistance.

The IM rotor speed and rotor flux controllers are designed based on combination of input‐output feedback linearizing, linear optimal feedback control and sliding‐mode (SM) control methods. In addition a novel adaptive rotor flux observer is designed based on Lyapunov theory. The proposed control method is tested by simulation and experimental results.

The composite rotor speed and rotor flux observer in combination with adaptive rotor flux scheme guarantees a perfect speed, torque and flux tracking control for the IM sensorless drive.

The proposed control method has a drawback in the IM low speed operating region. Additional research may be able to solve this problem as well as should analyze the sensitivity of the IM drive system performance with respect to variation of the system controller and adaptive flux observer gains. In addition, this research should also analyze the influence of sampling rate, truncation errors, measurement noise, simplifying model assumption and magnetic saturation.

The proposed control method can be used for adaptive and robust control of the IM drive where an optimal efficiency is desired subject to the variable load torque demand.

Based on Lyapunov theory, a novel adaptive rotor flux observer is introduced in which the rotor speed, rotor resistance and stator resistance are treated as the unknown constant parameters.

This study aims to identify SHRM (strategic human resource management) essential practices for the TQM (total quality management) program regarding an Indian tire manufacturing company and formulate an inclusive interrelationship to prioritize them.

Semistructured interview with ten experts from the company was made to give SHRM practices scores. The SLR (systematic literature review) and TOPSIS (technique for order of preference by similarity to ideal solution) techniques are used to establish the model for 12 key practices and rank them afterward.

The findings clearly show that strategic planning and staffing, teamwork and leadership development have appeared as the top three essential practices. Simultaneously, performance measurement and evaluation, work design and analysis and promotion are identified as the bottom three practices. These essential practices are identified as contributing attributes.

The findings prioritize the SHRM practices as contributing attributes that help other tire manufacturing industries identify their key practices. Moreover, it provides the necessary inputs comprised of ten experts' decisions to become more active and well prepared.

The novelty of this study is to identify the key practices by using SLR and measured by the TOPSIS method to rank and consider a tire manufacturing company as a case-based approach to gain high productivity and competitive advantage.

Nowadays, communications, products, services and costs are customized through the internet technology. The main theory to continue competitiveness in the organizations is customer relationship management (CRM). CRM enables organizations to efficiently interact with customers and gather, store and examine their data for providing a complete view of them. On the other hand, the subject of cloud computing has increasingly become the bridge for the success of the CRM implementation. Therefore, this study aims to investigate the impact of cloud computing (new cloud facility, knowledge of information technology (IT), cloud security and cost) on the success of CRM systems.

The model and the questioners-based data are analyzed using the Smart PLS 3.0. The data were gathered based on 80 employees of three main agricultural companies in Iran.

The obtained results have indicated that all of the considered factors, new cloud facilities, knowledge of IT, cloud security and cost, play an important role in CRM systems’ success. Also, the evaluation and examination of the consistency and validity of the model are performed through the structural equation model.

First, the authors have conducted a study in a single region. It cannot be guaranteed that the results can be generalized to other regions. Second, for this cross-sectional study, the research design was conducted that showed constant relationships between variables. The research done for this study is cross-sectional. Third, because of time and financial restrictions, the authors have gathered data using a sample from a single location.

Proposing a new model for investigating of the impact of cloud computing (new cloud facility, knowledge of Information Technology (IT), cloud security and cost) on the success of CRM systems is the main originality of this paper.

Organizations are social entities comprising multiple people that are goal-directed and have coordinated activities that are also linked to the external environment. As information technology improves, the organizational performance is also improved and it results in positive changes and development in the organizations. Near field communication (NFC) is one of such technologies that can be implemented and utilized in an organization. The purpose of this paper is to investigate the important variables impacting the adoption of NFC in organizations and propose an applicable model for it.

In this paper, to have a successful NFC implementation in organizations and analyze main factors impacting the NFC technology adoption in organizations, a technology acceptance model-based approach is used.

The findings show that the main variables impacting NFC adoption are ease of use, potential risk, usefulness and cost. The obtained results indicate that the model has adequate and sufficient reliability, convergent validity and discriminant validity.

In this paper, the factors impacting the NFC adoption in organizations are pointed out, and the proposed model is tested on samples gathered from Azerbaijan railway employees and for statistical analysis of questionnaires, the SMART-PLS 2.0 software package is used.

This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war.

Researchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model.

The findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis.

This study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.