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The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The present study aims to investigate the psychometric properties of the picture story exercise (PSE), a tool for measuring implicit motivations in Iran.

The research method is descriptive correlation and was conducted in two studies. In the first study 24 psychology master’s students were selected by convenience sampling method and tested them to check retest and inter-coder reliability. The second study investigated the correlation between self-reports and the PSE test on a sample of managers. Its statistical population comprised all the managers of a refinery company, 50 people were selected by convenience sampling method. To check the concurrent validity of the PSE test, the participants of the second study, completing the PSE, also completed the needs questionnaire of Steers and Porter (1979).

The findings showed that the PSE test in the Iranian sample had high retest reliability (0.62 on mean) and inter-coder reliability (0.87 on mean), and, following previous research, it does not show a significant relationship with self-reported motives.

PSE can be used in future research as a tool that has demonstrated its reliability and validity in the Iranian sample.

Measurement of implicit motives is a practical factor for predicting people’s behavior, the necessity of using tools that can accurately evaluate implicit motives is strongly felt. Taking into account the fact that so far, in Iranian samples, implicit motivations have not been measured, therefore, the current research is trying to answer this question, whether one of the most important and prominent tools that were made for this purpose and used in various studies can also be used in Iranian samples.

The incidence of the COVID-19 pandemic has come as a big blow to every dimension of any economy. It disrupts the usual constructs and functioning of economies across the globe. Different macroeconomic variables such as income, employment, etc. have been in shambles because of the unprecedented pandemic. Such a situation needs an alternative thought to turn around everything, and circular economic approach indeed is a potent tool to weather this tumultuous situation. Circular economic framework facilitates recycling and reusing of wastes discarded. Because of various activities, wastes are generated, and if products could be generated out of wastes through a system, it would be beneficial to economies as this ensures the judicious utilisation of resources. Recycling, refurbishing and reducing are three important pillars of the circular economic framework, where product lifecycle gets extended and prudent use of existing resources can be possible. The usage of resources even out of wastes stands out to be a game changer in terms of sustainability of environment. The alternative usage of resources in this way creates alternative employment opportunities. The extension of product lifecycle through recycling and reusing creates alternative rooms for employment, giving ample opportunities for sustainable development. It leads to market creation for waste products. Such a circular economy approach, paving the way for alternative employment generation, leads to sustainable development. The present work tries to re-examine the efficacy of the circular economic framework and its resilience in the context of the unprecedented pandemic in terms of providing alternative employment opportunities. It attempts to underscore how circular economic construct positively impacts prudent use of resources through alternative employment generation in pandemic.

This paper examines and empirically validates the artificial intelligence-enabled human resource management (AI-enabled HRM) dimensions and sustainable organisational performance (SOP) relationship. It also examines the mediation and moderation of employee engagement (EE) and fusion skills (FS).

The indirect effects of AI-enabled HRM dimensions on SOP were found using structural equation modelling (SEM), bootstrapping and FS’s moderation effect by AMOS 22.

Results showed that AI-enabled HRM dimensions indirectly affected SOP through EE as a full and partial mediator with no moderation effects of FS.

This is the first study to link AI-enabled HRM dimensions, EE and SOP and determine how FS moderates EE and SOP.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

The library is one of the critical foundations of informing and educating the public, and it should fulfil its function in the community, which is disseminating knowledge effectively. The library must have a dynamic space and an appropriate atmosphere to do this. Also, librarians should assist clients outside of stressful work environments. This study aimed to examine the association between organizational climate and job burnout in the hospital and college libraries of Shahid Beheshti University of Medical Sciences.

This study is a cross-sectional survey. The population includes all librarians working in the college and hospital libraries of Shahid Beheshti University of Medical Sciences. Two questionnaires, including organizational climate “ClimateQUAL” and job burnout “Maslach and Jackson”, were applied to collect data. It was hypothesized that there is a correlation between the organizational climate and the burnout of librarians.

The findings revealed that the organizational climate and job burnout are not in a favourable situation, with average scores of 4.70 and 2.50, respectively. Furthermore, there is a significant statistical relationship between organizational climate and job burnout (p-value<0.001).

Knowledge of the significant relationship between the organizational climate and job burnout in medical libraries may encourage policymakers to make more efforts to provide their staff with a better climate and less burnout. Regarding that it is crucial to enhance the existing condition in medical libraries, the findings of the present study suggest that policymakers should be conscious of improving the current condition, which could lead to promoting patrons’ satisfaction and productivity in the mentioned libraries.

The purpose of this study was to gain a deeper understanding of the experiences and challenges of Middle Eastern refugee mothers in using technology for language learning in regional Australian context.

This study used a qualitative research design and used a narrative enquiry technique to understand participants’ experiences of language acquisition with limited access to technology in new resettlement setting.

The findings presented in this paper were derived from a qualitative investigation conducted on a sample of 21 refugee mothers from Middle Eastern countries. The research aimed to explore the role of a language education programme on their integration within the regional Australian context. Participants discussed a variety of aspects of their everyday lives within their new resettlement context while offering insights on the language education programme and its impacts on their educational progress. The theme “lack of access/use of technology in English language learning” emerged from the narratives. This paper draws on selected interview data from the participants.

This study fills the gap in the literature on Middle Eastern refugees in regional Australia by exploring how lack/limited technology access can impact language acquisition of women from under-represented contexts.